Development and validation a machine learning model based on clinical factors to predict short-term prognosis of ICU intracerebral hemorrhage patients: a retrospective study

Development and validation a machine learning model based on clinical factors to predict short-term prognosis of ICU intracerebral hemorrhage patients: a retrospective study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development and validation a machine learning model based on clinical factors to predict short-term prognosis of ICU intracerebral hemorrhage patients: a retrospective study Hanbo Liu, Weigao Liu, Ping Xue This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8866295/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Objective This study aims to construct and validate a short-term prognostic nomogram model for Intracerebral hemorrhage (ICH) based on information immediately upon admission, providing support for early risk identification and clinical decision-making in the intensive care unit (ICU). Methods This study retrospectively analysised 135 patients with intracerebral hemorrhage (ICH) admitted to the ICU of Neijiang Hospital of Traditional Chinese Medicine in Sichuan Province from January 2021 to December 2025, and divided them sequentially into a training set (n = 97) and a validation set (n = 38). Univariate and multivariate Cox regression analyses were employed to identify independent predictive factors, and nomogram were constructed to predict survival probabilities. Model discrimination, calibration, clinical utility, and stratification capability were evaluated using receiver operating characteristic curves, calibration curves, decision curves and Kaplan-Meier survival curves. Results Multivariate analysis showed that age ≥ 65 years, history of heart disease, admission pulse ≥ 82 beats/min, admission GCS ≥ 9 points, decreased pupil reflex to light, abnormal muscle tone, physiological reflex loss, and white blood cell count ≥ 10 × 10 ⁹/L were independent predictive factors for ICU admission mortality. The column chart constructed based on the above indicators had AUCs of 0.878, 0.824, and 0.863 on the 3/5/7 days in the training set, and AUCs of 0.727, 0.772, and 0.761 in the validation set. The Kaplan Meier curve for risk stratification clearly distinguishes between high, intermediate, and low-risk groups ( P < 0.0001). Conclusion The short-term survival prediction nomogram for ICU intracerebral hemorrhage patients constructed in this study has good predictive performance, robust validation results, and high clinical usability. This model is based on readily available clinical information at the bedside and can achieve early risk stratification for severe ICH patients, providing reliable basis for clinical treatment decision-making, resource allocation, and communication with family members. In the future, multi center prospective studies are needed to further validate its generalization ability. Intracerebral hemorrhage ICU nomogram short-term survival prognostic Model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Firstly, from an epidemiological perspective, the global burden of ICH remains extremely heavy. According to the Institute for Health Metrics and Evaluation global disease burden study in 2021, there will be about 3.444 million new cases of ICH in the world in 2021, with age standardized incidence rate (ASIR) of about 40.8/100000, mortality (ASDR) of about 39.1/100000, and disability adjusted life years (DALYs) of about 92.4/100000. Furthermore, among 204 countries and regions, the ICH ASIR, ASDR, and DALYs in low SDI regions were significantly higher than those in high SDI countries 1 .Although the age standardized ratio has shown a downward trend in recent years, the absolute number of cases has not significantly decreased due to population aging, structural changes, and accumulated risk factors 2 . In addition, from a prognostic perspective, the risk of death and disability in ICH patients is extremely high. Multiple systematic reviews indicate that the one month mortality rate of ICH patients can reach 30% -40%, and there are many deaths within one year. The proportion of those who can recover to independent living in the long term is only 12% -39% 3,4 . In the ICU environment, due to the critical condition, multiple comorbidities, and complex treatment measures, prognosis assessment and clinical decision-making become increasingly difficult 5 . Specifically, for ICH patients in the ICU, doctors face a series of important decisions in the early stages of admission, such as whether to perform invasive treatment, antihypertensive management, monitoring upgrades, and prognosis communication (such as whether to set treatment restrictions), which often rely on judgments about the patient's future course of illness and the possibility of functional recovery 6 . It can be seen that establishing a reliable prognostic model for ICU patients with cerebral hemorrhage not only helps optimize resource allocation, guide treatment intensity, and promote early rehabilitation planning, but also enhances doctor-patient communication and assists family decision-making, thus having strong clinical significance. At present, various prognostic scoring models and tools have been proposed for the evaluation of ICH prognosis. For example, the classic ICH Score (including age, Glasgow Coma Scale [GCS], bleeding volume, presence of intraventricular hemorrhage, and bleeding site) is widely used for early mortality risk assessment 7 . In addition, functional/death prognosis models such as ICH GS and MAX ICH are constantly being studied and validated 8 . However, although these models have shown good discriminative ability in research (such as a c-index of over 0.80), their application and accuracy in clinical decision support still have many limitations in the actual ICU cerebral hemorrhage patient population. Previous studies have shown that common issues with these prognostic tools in model development include a lack of sufficient internal/external validation, variable selection based on expert experience rather than full data-driven, failure to consider ICU specific factors (such as ventilator dependence, renal failure, infection complications, etc.), and neglect of withdrawal of care (WOC) bias 9 . Similarly, in a systematic review of 72 prognostic tools including 48133 ICH patients, although the average c-index of the death prediction model was 0.88 and the average functional prognosis was 0.87, the authors pointed out that the reported "event/variable ratio" was low, there was a lack of blinding, uneven follow-up time, and insufficient data processing, suggesting that there are risks in the practical clinical transfer application 10 . In addition, another study on ICU patients compared the general ICU severity scores such as SAPS III and APACHE IV with the ICH specific score. The results showed that although the general ICU score included more physiological variables, it lacked imaging parameters, while the ICH specific score, although designed concisely, often overlooked common organ dysfunction and complications in the ICU, limiting its predictive ability for severe ICH patients in the ICU 11 . In addition, there are many difficulties in clinical practice.Firstly, the dynamic changes in early bleeding volume (such as hematoma expansion) are difficult to quantify in real time 12 . Secondly, severe patients with multiple organ failure, mechanical ventilation, severe infections, and other factors make it difficult to accurately reflect the overall prognosis based solely on admission imaging or neurological status scores 13 . Finally, most existing models are derived from general neurology or stroke ward patients, and rarely focus on ICU cerebral hemorrhage patients (such as those with multiple critical complications or those undergoing mechanical ventilation) 14 . Therefore, the applicability and decision support effectiveness of current clinical evaluation methods in the high-risk group of ICU cerebral hemorrhage patients are still significantly insufficient, and research in this field is still insufficient. Therefore, this study aims to construct and validate a clinical prognostic model for patients with cerebral hemorrhage admitted to the ICU. Material and Methods Patients This study retrospectively collected clinical data of patients with cerebral hemorrhage admitted to the Intensive Care Medicine Department of Neijiang Traditional Chinese Medicine Hospital in Sichuan Province from January 2021 to December 2025. All methods comply with relevant guidelines and regulations. Inclusion criteria: (1) age > 18; (2) all patients were diagnosed with cerebral hemorrhage through imaging testing; (3) all individuals received relevant treatment in accordance with current clinical guidelines. Exclusion criteria: (1) incomplete patient information; (2) the patient did not suffer from any other life-threatening diseases except for cerebral hemorrhage. This study has been approved by the Ethics Committee of Neijiang Traditional Chinese Medicine Hospital in Sichuan Province. Due to the retrospective nature of this study, the ethics committee waived informed consent for this study. Research Endpoint The endpoint of this study is the survival period of all patients during their ICU stay, defined as the time from the first day of admission to the ICU until the occurrence of a death event or improvement in their condition during hospitalization and their transfer from the ICU. This study predicted the probability of 3-day, 5-day, and 7-day survival for all patients after admission to the ICU. Predictive Factors This study collected all the information that patients can provide to medical staff through preliminary examinations after admission, including personal basic information, medical history information, admission physical examination report, admission imaging report, admission blood routine examination report, admission coagulation four item examination report, and admission liver and kidney function examination report. The variables included in the predictive model include age, cardiac history, admission pulse, admission consciousness state GCS score, pupil light reflex, muscle tone, physiological reflex, and white blood cells. Age = 65 years old is classified as 1; no history of heart disease is classified as 0, and history of heart disease is classified as 1; if the admission pulse is less than 82, it is classified as 0; if the admission pulse is greater than or equal to 82, it is classified as 1; a GCS score of less than 9 is classified as 0, while a GCS score of greater than or equal to 9 is classified as 1; Pupil's slow or absent light reflex is classified as 0, while pupil's sensitive or slightly slow light reflex is classified as 1; weakened muscle tone is classified as 0, normal muscle tone is classified as 1, and increased muscle tone is classified as 2; physiological reflex weakened or not induced is classified as 0, while physiological reflex present or normal is classified as 1; white blood cell count = 10 * 10 ^ 9/L is classified as 1. Age and consciousness status GCS scores are classified according to clinical guidelines 15 . Muscle tone retains the original classification, and other variables are classified using the median of the total data as the boundary. Model construction and validation Based on previous research 9 , this study collected basic clinical information of patients (age, gender, etc.), admission physical examination reports, hematological tests (blood routine, liver and kidney function, etc.), and imaging reports. Divide the included patients into a training set and a validation set in chronological order, with data from 2021 to 2023 as the training set and data from 2024 to 2025 as the validation set. Subsequently, in the training set, the included clinical information was subjected to univariate Cox regression analysis. Variables with differences (P < 0.05) in the univariate analysis were included in the multivariate analysis, and variables with statistical differences (P < 0.05) in the multivariate analysis were visualized to establish a predictive model.Subsequently, the predictive performance of the model was validated using receiver operating characteristic (ROC), calibration curves, and decision curves in both the training and validation sets. Finally, all patients were scored according to the column chart and divided into low-risk, medium risk, and high-risk groups based on their quartiles. The differentiation effect of the model was validated through survival analysis. Data Analysis For continuous variables, we compared them using t-test and sum of squares test; We use chi square test and Fisher's test to compare categorical variables. Single factor and multi factor Cox regression analysis were used to screen independent predictive factors. Survival analysis was validated through Kalplan-Meier survival curves and log analysis. All comparisons were statistically significant with P < 0.05. The data organization and statistical analysis are completed based on the Storm Statistics Platform or Zstats software ( www.zstats . net) and R version 4.3.3. Result Baseline characteristics of patients This study included a total of 135 patients, with 97 (71.85%) in the training set, and 38 (28.15%) in the validation set. Among them, there were 42 (43.3%) males in the training set, and 55 (56.3%) females. There were 61 (62.8%) patients aged > 65 in the training set, and 36 (37.2%) patients aged ≤ 65. There were 15 (15.5%) patients who died within 3 days in the training set, 20 (20.6%) patients who died within 5 days, and 26 (26.8%.) patients who died within 7 days In the validation set, there were 23 ( 60.5%) males, and 15 (39.5%) females. There were 61 (62.8%) patients aged > 65 in the validation set, and 36 (37.2%) patients aged ≤ 65. There were 15 (15.5%) patients who died within 3 days in the validation set, 20 (20.6%) patients who died within 5 days, and 26 (26.8%) patients who died within 7 days. The specific baseline characteristics of the two groups of patients are shown in Table 1 . Table 1 Baseline table for comparison between training set and validation set Variables Total 0 1 P (n = 135) (n = 97) (n = 38) Gender, n(%) 0.686 Female 57 (42.22) 42 (43.30) 15 (39.47) Male 78 (57.78) 55 (56.70) 23 (60.53) Age, n(%) 0.977 < 65 50 (37.04) 36 (37.11) 14 (36.84) ≥ 65 85 (62.96) 61 (62.89) 24 (63.16) Prognosis, n(%) 0.51 Survive 91 (67.41) 67 (69.07) 24 (63.16) Dead 44 (32.59) 30 (30.93) 14 (36.84) Smoking, n(%) 0.368 No 93 (68.89) 69 (71.13) 24 (63.16) Yes 42 (31.11) 28 (28.87) 14 (36.84) Alcohol, n(%) 0.202 No 96 (71.11) 72 (74.23) 24 (63.16) Yes 39 (28.89) 25 (25.77) 14 (36.84) History of Hypertension, n(%) 0.948 No 61 (45.19) 44 (45.36) 17 (44.74) Yes 74 (54.81) 53 (54.64) 21 (55.26) History of Heart Disease, n(%) 0.68 No 118 (87.41) 86 (88.66) 32 (84.21) Yes 17 (12.59) 11 (11.34) 6 (15.79) History of Diabetes, n(%) 1 No 123 (91.11) 88 (90.72) 35 (92.11) Yes 12 (8.89) 9 (9.28) 3 (7.89) History of Brain Disease, n(%) 0.918 No 122 (90.37) 87 (89.69) 35 (92.11) Yes 13 (9.63) 10 (10.31) 3 (7.89) Pulse, n(%) 0.324 < 82/min 66 (48.89) 50 (51.55) 16 (42.11) ≥ 82/min 69 (51.11) 47 (48.45) 22 (57.89) Systolic Pressure, n(%) 0.546 < 170mmHg 66 (48.89) 49 (50.52) 17 (44.74) ≥ 170mmHg 69 (51.11) 48 (49.48) 21 (55.26) Diastolic Pressure, n(%) < .001 < 96mmHg 102 (75.56) 81 (83.51) 21 (55.26) ≥ 96mmHg 33 (24.44) 16 (16.49) 17 (44.74) GCS, n(%) 0.714 < 9 50 (37.04) 35 (36.08) 15 (39.47) ≥ 9 85 (62.96) 62 (63.92) 23 (60.53) Pupil Size, n(%) 0.021 Unequal 23 (17.04) 12 (12.37) 11 (28.95) Equal 112 (82.96) 85 (87.63) 27 (71.05) Pupil reflex to light, n(%) 0.344 Significantly Weakened 95 (70.37) 66 (68.04) 29 (76.32) Not Significantly Weakened 40 (29.63) 31 (31.96) 9 (23.68) Muscle Tension, n(%) 0.671 Low 16 (11.85) 11 (11.34) 5 (13.16) Normal 99 (73.33) 70 (72.16) 29 (76.32) High 20 (14.81) 16 (16.49) 4 (10.53) Physiological Reflex, n(%) 0.017 No 19 (14.07) 18 (18.56) 1 (2.63) Yes 116 (85.93) 79 (81.44) 37 (97.37) Core Functional Area Bleeding, n(%) 0.552 No 73 (54.07) 54 (55.67) 19 (50.00) Yes 62 (45.93) 43 (44.33) 19 (50.00) Amount of Bleeding, n(%) 0.9 < 15.18mL 77 (57.04) 55 (56.70) 22 (57.89) ≥ 15.18mL 58 (42.96) 42 (43.30) 16 (42.11) Midline Shift, n(%) 0.21 No 72 (53.33) 55 (56.70) 17 (44.74) Yes 63 (46.67) 42 (43.30) 21 (55.26) Ventilator Support, n(%) 0.506 No 72 (53.33) 50 (51.55) 22 (57.89) Yes 63 (46.67) 47 (48.45) 16 (42.11) WBC, n(%) 0.546 < 10*10^9/L 69 (51.11) 48 (49.48) 21 (55.26) ≥ 10*10^9/L 66 (48.89) 49 (50.52) 17 (44.74) Ab, n(%) 0.995 < 132*10^9/L 64 (47.41) 46 (47.42) 18 (47.37) ≥ 132*10^9/L 71 (52.59) 51 (52.58) 20 (52.63) Plt, n(%) 0.957 10mg/L 19 (14.07) 14 (14.43) 5 (13.16) hs-CRP, n(%) 0.012 ≤ 1mg/L 55 (40.74) 46 (47.42) 9 (23.68) > 1mg/L 80 (59.26) 51 (52.58) 29 (76.32) PT, n(%) 0.872 ≤ 11.2s 66 (48.89) 47 (48.45) 19 (50.00) > 11.2s 69 (51.11) 50 (51.55) 19 (50.00) TT, n(%) 17.4s 70 (51.85) 38 (39.18) 32 (84.21) APTT, n(%) 0.413 ≤ 24.5s 68 (50.37) 51 (52.58) 17 (44.74) > 24.5s 67 (49.63) 46 (47.42) 21 (55.26) FIB, n(%) 0.546 ≤ 2.685g/L 69 (51.11) 48 (49.48) 21 (55.26) > 2.685g/L 66 (48.89) 49 (50.52) 17 (44.74) AST/ALT, n(%) 0.706 ≤ 1.26 64 (47.41) 45 (46.39) 19 (50.00) > 1.26 71 (52.59) 52 (53.61) 19 (50.00) GGT, n(%) 0.413 ≤ 26.5U/L 68 (50.37) 51 (52.58) 17 (44.74) > 26.5U/L 67 (49.63) 46 (47.42) 21 (55.26) Alb, n(%) 0.354 ≤ 41.2g/L 66 (48.89) 45 (46.39) 21 (55.26) > 41.2g/L 69 (51.11) 52 (53.61) 17 (44.74) Crea, n(%) 0.019 ≤ 64umol/L 68 (50.37) 55 (56.70) 13 (34.21) > 64umol/L 67 (49.63) 42 (43.30) 25 (65.79) GFR, n(%) 0.957 ≤ 78.5ml/min 68 (50.37) 49 (50.52) 19 (50.00) > 78.5ml/min 67 (49.63) 48 (49.48) 19 (50.00) GLU, n(%) 0.476 ≤ 7.85mmol/L 86 (63.70) 60 (61.86) 26 (68.42) > 7.85mmol/L 49 (36.30) 37 (38.14) 12 (31.58) K, n(%) 0.546 ≤ 3.61mmol/L 69 (51.11) 48 (49.48) 21 (55.26) > 3.61mmol/L 66 (48.89) 49 (50.52) 17 (44.74) Na, n(%) 0.91 ≤ 139.78mmol/L 70 (51.85) 50 (51.55) 20 (52.63) > 139.78mmol/L 65 (48.15) 47 (48.45) 18 (47.37) Screening of independent predictive factors This study included all variables collected from the training set in univariate and multivariate Cox regression analysis, and included variables with p 65 years (HR: 2.74, 95% CI: 1.04 ~ 7.02), history of heart disease (HR: 3.31, 95% CI: 1.32 ~ 8.28), history of cardiovascular medication (HR: 3.68, 95% CI: 1.10 ~ 12.28), and admission pulse > 82 times/ min (HR: 2.58, 95%CI༚1.17–5.67),GCS > 9 (HR༚0.16, 95%CI༚0.07 ~ 0.38). Pupil size is equal (HR: 0.28, 95% CI: 0.12 ~ 0.63), pupil reflex is not weakened (HR: 0.32, 95% CI: 0.11 ~ 0.94), muscle tone is normal (HR: 0.32, 95% CI: 0.12 ~ 0.84), muscle strength is reduced (HR: 0.23, 95% CI: 0.06 ~ 0.98), physiological reflex is present (HR: 0.20, 95% CI: 0.10 ~ 0.42), bleeding volume > 15.1835mL (HR: 2.49, 95% CI: 1.15 ~ 5.37), midline shift (HR: 2.27, 95% CI: 1.05 ~ 4.89), use of ventilator (HR: 12.90, 95% CI: 3.0) 5 ~ 54.54), white blood cells > 10 * 10 ^ 9/L (HR: 3.34, 95% CI: 1.35 ~ 8.23), monocytes > 0.57 * 10 ^ 9/L (HR: 3.97, 95% CI: 1.38 ~ 11.46), total bile acids > 4.2umol/L (HR: 0.45, 95% CI: 0.21 ~ 0.96), and monoamine oxidase > 6.6U/L (HR: 3.62, 95% CI: 1.53 ~ 8.55) are correlated with mortality events during hospitalization (Table 2 ). By incorporating the variables with differences in univariate analysis into a two-way multivariate analysis, the study found that age > 65 years old (HR: 4.28, 95% CI: 1.25 ~ 14.61, P = 0.020), history of heart disease (HR: 6.16, 95% CI: 1.68 ~ 22.55, P = 0.006), and admission pulse > 82 times/ min(HR༚3.39, 95%CI: 1.27 ~ 9.02, P = 0.014), GCS > 9 (HR: 0.22, 95%CI: 0.06 ~ 0.83, P = 0.025). Weak pupil reflex to light (HR: 3.97, 95% CI: 6.84 ~ 44.84, P = 0.045), normal muscle tone (HR: 0.14, 95% CI: 0.04 ~ 0.47, P = 0.001), presence of physiological reflex (HR: 0.29, 95% CI: 0.11 ~ 0.76, P = 0.012), and white blood cell count > 10 * 10 ^ 9/L (HR: 3.22, 95% CI: 1.09 ~ 9.56, P = 0.035) are independent predictive factors for patient survival during hospitalization (Table 2 ). Table 2 Single factor and multi factor analysis Variables Single Factor Multi-factor β S.E Z P HR (95%CI) β S.E Z P HR (95%CI) Gender Male vs. Female -0.05 0.37 -0.15 0.884 0.95 (0.45 ~ 1.97) Age ≤ 65 vs. >65 1.01 0.49 2.04 0.041 2.74 (1.04 ~ 7.20) 1.45 0.63 2.32 0.02 4.28 (1.25 ~ 14.61) Smoking No vs. Yes 0.02 0.42 0.05 0.963 1.02 (0.45 ~ 2.31) Alcohol No vs. Yes -0.48 0.49 -0.98 0.325 0.62 (0.23 ~ 1.62) History of Hypertension No vs. Yes -0.49 0.37 -1.31 0.189 0.61 (0.29 ~ 1.27) History of Heart Disease No vs. Yes 1.2 0.47 2.55 0.011 3.31 (1.32 ~ 8.28) 1.82 0.66 2.74 0.006 6.16 (1.68 ~ 22.55) History of Diabetes No vs. Yes -0.16 0.74 -0.22 0.826 0.85 (0.20 ~ 3.62) History of Brain Disease No vs. Yes 0.21 0.61 0.34 0.731 1.23 (0.37 ~ 4.12) Pulse < 82/min vs. ≥82/min 0.95 0.4 2.36 0.018 2.58 (1.17 ~ 5.67) 1.22 0.5 2.45 0.014 3.39 (1.27 ~ 9.02) Systolic Pressure < 170mmHg vs. ≥170mmHg 0.08 0.38 0.22 0.824 1.09 (0.52 ~ 2.27) Diastolic Pressure < 96mmHg vs. ≥96mmHg -0.11 0.54 -0.21 0.833 0.89 (0.31 ~ 2.58) GCS < 9 vs. ≥9 -1.83 0.43 -4.21 < .001 0.16 (0.07 ~ 0.38) -1.51 0.67 -2.24 0.025 0.22 (0.06 ~ 0.83) Pupil Size Unequal vs. Equal -1.29 0.42 -3.05 0.002 0.28 (0.12 ~ 0.63) Pupil reflex to light Significantly Weakened vs. Not Significantly Weakened -1.14 0.55 -2.07 0.038 0.32 (0.11 ~ 0.94) 1.92 0.96 2 0.045 6.84 (1.04 ~ 44.84) Muscle Tension Low vs. Normal -1.15 0.5 -2.32 0.021 0.32 (0.12 ~ 0.84) -1.98 0.62 -3.18 0.001 0.14 (0.04 ~ 0.47) Low vs. High 0.01 0.52 0.02 0.982 1.01 (0.36 ~ 2.81) -0.55 0.56 -0.99 0.323 0.58 (0.19 ~ 1.72) Physiological Reflex No vs. Yes -1.61 0.37 -4.3 < .001 0.20 (0.10 ~ 0.42) -1.25 0.49 -2.52 0.012 0.29 (0.11 ~ 0.76) Core Functional Area Bleeding No vs. Yes -0.16 0.38 -0.42 0.672 0.85 (0.41 ~ 1.78) Amount of Bleeding < 15.18mL vs. ≥15.18mL 0.91 0.39 2.32 0.02 2.49 (1.15 ~ 5.37) Midline Shift No vs. Yes 0.82 0.39 2.08 0.037 2.27 (1.05 ~ 4.89) Ventilator Support No vs. Yes 2.56 0.74 3.48 < .001 12.90 (3.05 ~ 54.54) 1.51 0.88 1.72 0.086 4.53 (0.81 ~ 25.43) WBC < 10*10^9/L vs. ≥10*10^9/L 1.2 0.46 2.61 0.009 3.34 (1.35 ~ 8.23) 1.17 0.55 2.11 0.035 3.22 (1.09 ~ 9.56) Ab < 132*10^9/L vs. ≥132*10^9/L 0.12 0.38 0.32 0.75 1.13 (0.54 ~ 2.37) Plt 10mg/L -0.66 0.62 -1.07 0.285 0.52 (0.15 ~ 1.73) hs-CRP ≤ 1mg/L vs. >1mg/L -0.1 0.38 -0.27 0.785 0.90 (0.43 ~ 1.90) PT ≤ 11.2s vs. >11.2s -0.2 0.37 -0.53 0.595 0.82 (0.40 ~ 1.70) TT ≤ 17.4s vs. >17.4s -0.24 0.4 -0.6 0.551 0.79 (0.36 ~ 1.73) APTT ≤ 24.5s vs. >24.5s 0.29 0.38 0.75 0.453 1.33 (0.63 ~ 2.82) FIB ≤ 2.685g/L vs. >2.685g/L -0.08 0.37 -0.22 0.828 0.92 (0.44 ~ 1.92) AST/ALT ≤ 1.26 vs. >1.26 -0.57 0.38 -1.51 0.132 0.57 (0.27 ~ 1.19) GGT ≤ 26.5U/L vs. >26.5U/L 0.47 0.38 1.26 0.207 1.61 (0.77 ~ 3.35) Alb ≤ 41.2g/L vs. >41.2g/L -0.75 0.38 -1.96 0.05 0.47 (0.22 ~ 0.99) Crea ≤ 64umol/L vs. >64umol/L 0.58 0.37 1.54 0.123 1.78 (0.86 ~ 3.72) GFR 0 1.00 (Reference) ≤ 78.5ml/min vs. >78.5ml/min -0.07 0.37 -0.2 0.845 0.93 (0.45 ~ 1.93) GLU ≤ 7.85mmol/L vs. >7.85mmol/L 0.51 0.38 1.36 0.173 1.67 (0.80 ~ 3.50) K ≤ 3.61mmol/L vs. >3.61mmol/L -0.25 0.38 -0.66 0.511 0.78 (0.37 ~ 1.65) Na ≤ 139.78mmol/L vs. >139.78mmol/L 0.17 0.37 0.45 0.655 1.18 (0.57 ~ 2.45) Abbreviation: HR,Hazard Ratio; CI, Confidence Interval. Model establishment and validation Incorporating the independent predictive factors into the prediction model yields the nomogram shown in Fig. 1 . Each variable corresponds to a specific point value, and a higher total score indicates a greater risk of adverse events during hospitalization. For example, consider a patient aged 67 years with a history of heart disease, an admission pulse rate of 83 beats/min, a Glasgow Coma Scale (GCS) score of 10, sluggish pupillary light reflex, reduced muscle tone, preserved physiological reflexes, and a leukocyte count > 10×10^9/L. The corresponding predictive variables for this patient are as follows: Age = 1, HHD = 1, Pulse = 1, GCS = 1, PRL = 0, TM = 0, PhyReflex = 1, and Leu = 1. The total score, calculated as 70 + 17.5 + 29 + 0 + 0 + 38 + 0 + 38, is 192.5. Based on this score, the estimated probabilities of a favorable prognosis at 3, 5, and 7 days after admission are approximately 75%, 70%, and 60%, respectively. ROC for predicting outcomes at 3, 5, and 7 days were constructed in both the training and validation sets using the independent predictive factors identified. In the training cohort, the AUC for the 3-day prediction was 0.8786 (95% CI: 0.7717 ~ 0.9660); for the 5-day prediction was 0.8235 (95% CI: 0.7091 ~ 0.9380); and for the 7-day prediction was 0.8631 (95% CI: 0.7458 ~ 0.9803). In the validation cohort, the AUC for the 3-day prediction was 0.7273 (95% CI: 0.5208 ~ 0.9338); for the 5-day prediction was 0.7724 (95% CI: 0.5932 ~ 0.9516); and for the 7-day prediction was 0.7614 (95% CI: 0.5632 ~ 0.9596) (Fig. 2 ). These results indicate that the model demonstrates good discriminative ability in both the training and validation cohorts, with relatively stable generalizability. The calibration curves for the 3-day, 5-day, and 7-day predictions in both the training and validation cohorts closely adhered to the 45° reference line, indicating good agreement between the predicted and observed risks and demonstrating a high degree of calibration (Fig. 3 ). Clinical practicality verification of the model This study evaluated the net benefit of the "pr_24" model, "Treat All" strategy, and "Treat None" strategy at different threshold probabilities through decision curve analysis. The results are shown in Fig. 4 . From the three decision curves, the net benefit of the “Treat None” strategy remains consistently at zero, indicating no additional net gain associated with this approach. The net benefit of the “Treat All” strategy declines rapidly as the threshold probability increases; although it exceeds that of “Treat None” at very low threshold probabilities (approximately < 20%), it quickly drops below zero thereafter. This suggests that treating all patients provides some benefit only when the threshold probability is extremely low, but beyond this range, its net benefit becomes negative.In contrast, the net benefit curve of the “pr_24” model remains above both the “Treat All” and “Treat None” curves across a broad range of threshold probabilities (from nearly 0% to approximately 70%–80%). This indicates that the “pr_24” model yields greater net benefit for patients within this interval. These findings imply that, compared with the extreme strategies of treating all or treating none, clinical decisions guided by the “pr_24” model can provide substantially greater patient benefit across a wider range of threshold probabilities, demonstrating superior clinical utility. Model validity verification All patients were scored using the nomogram, and based on the tertiles of the total score, they were categorized into high-, intermediate-, and low-risk groups. Kaplan–Meier survival analyses for these groups are shown in Fig. 5 . The results indicate that the survival curve of the low-risk group shows almost no decline, with survival probability remaining close to 100%. The intermediate-risk group exhibits a slight decline, yet survival remains at a relatively high level (> 70%). In contrast, the high-risk group demonstrates a marked decline, with survival dropping below 50% at approximately 10 days. The three curves are well separated with virtually no overlap.The log-rank test yielded p < 0.0001, indicating that the model has strong discriminative performance, effectively differentiating survival probabilities among risk groups. These results suggest that the model possesses robust and stable predictive capability. Discussion This study identified age, history of heart disease, admission pulse rate, GCS score, pupillary light reflex, muscle tone, physiological reflexes, and leukocyte count as independent predictors of in-ICU mortality among patients with intracerebral hemorrhage. Based on these factors, a nomogram model was successfully constructed to predict 3-, 5-, and 7-day survival probabilities in ICU patients with intracerebral hemorrhage. The model demonstrated good discrimination (AUC > 0.7) and calibration in both the training and validation cohorts. Furthermore, decision curve analysis in the validation cohort confirmed that the model provides substantial clinical net benefit. Compared with existing studies, several of the independent predictors identified in this research—specifically age, GCS score, and leukocyte count—have also been reported in previous work. Age, in particular, is included as a key predictor in nearly all prognostic models for ICH. For example, in a dynamic nomogram study published by S. Li et al. in 2025, age was incorporated as an essential variable in their model for predicting 90-day mortality16. Age reflects the progressive decline in the body’s tolerance to hemorrhage, brain injury, and systemic stress responses (such as inflammation and immune activation). Older patients are more prone to concomitant organ dysfunction (e.g., cardiac or renal impairment), which limits their capacity for recovery. In addition, advanced age is associated with cerebral atrophy, reduced cerebrovascular elasticity, poorer control of hematoma expansion, and increased risks of rebleeding and complications such as infections or chronic comorbidities.The GCS score is also a highly consistent and reliable predictor in ICH prognostic models. Numerous nomogram-based studies have incorporated the GCS score and identified it as an independent prognostic factor 17 – 19 . The GCS score reflects the severity of neurological impairment, including the level of consciousness and potential involvement of brainstem function. A reduced level of consciousness often indicates larger hematoma volume, mass effect, brain herniation, or more extensive neurological damage, all of which directly contribute to early mortality. Moreover, patients with low GCS scores are more susceptible to multisystem complications such as respiratory instability, circulatory dysfunction, and infections.In addition, although leukocyte count is not included as an independent predictor in all nomogram models, it has been identified as such in several studies. For example, in the 30-day mortality prediction model published by J. Zou et al. in 2022, leukocyte count was incorporated as one of the independent prognostic factors 20 .An elevated leukocyte count can be regarded as an indicator of inflammatory activation and systemic stress response. Following intracerebral hemorrhage, tissue destruction, disruption of the blood–brain barrier, extracellular matrix remodeling, and infiltration of inflammatory cells (including leukocytes) collectively trigger systemic inflammation. This inflammatory cascade further exacerbates brain injury, promotes cerebral edema and neuronal apoptosis, and ultimately contributes to poor clinical outcomes.In addition, previous studies have reported that inflammatory markers—such as the neutrophil-to-lymphocyte ratio and neutrophil-to-albumin ratio—are associated with unfavorable prognosis following intraventricular hemorrhage 21 . Compared with existing studies, the identification of a history of heart disease, admission pulse rate, pupillary light reflex, and physiological reflexes as independent predictors has rarely been reported; however, these variables enrich prognostic dimensions that are often overlooked in traditional ICH mortality prediction models. Pre-existing cardiovascular disease (such as coronary artery disease, arrhythmias, or cardiomyopathy) may reflect impaired cardiovascular function, including reduced cardiac output, increased thrombotic risk, rhythm instability, and inadequate perfusion. These conditions can exacerbate circulatory instability, compromise cerebral perfusion, and increase the likelihood of secondary injuries in ICH patients, such as rebleeding or ischemia. Moreover, many patients with cardiac disease have a history of using medications such as anticoagulants or antiplatelet agents, which may influence hematoma expansion and recovery. Pulse rate (heart rate) is a key indicator of circulatory status. An elevated heart rate (tachycardia) upon admission in patients with acute ICH may reflect sympathetic overactivation, pain, hemodynamic instability caused by hemorrhage, compensatory responses to hypotension, dehydration, or blood loss. Persistent tachycardia may signal sustained circulatory instability, increased cardiac workload, and impaired cerebral and systemic perfusion, all of which are associated with heightened early mortality risk. As a dynamic physiological marker of sympathetic activation and circulatory stress, heart rate has been underestimated in its prognostic value. The pupillary light reflex is an essential neurological sign reflecting brainstem function, particularly midbrain and oculomotor nerve integrity. In ICH, sluggish or absent pupillary light reflex often suggests brainstem compression, impending or ongoing herniation (especially transtentorial), or disruption of neural pathways, all of which are directly linked to life-threatening deterioration and mortality. Traditional prognostic models often omit this clinical neurological examination finding, resulting in incomplete integration of early neurological signs. Muscle tone abnormalities and primitive reflexes (such as grasp reflex, Babinski sign, or other brainstem/cortical reflexes) serve as sensitive indicators of dysfunction in the central nervous system, particularly upper motor neuron pathways and corticobulbar circuits. In ICH patients, alterations in muscle tone—either hypertonia or hypotonia—may reflect structural disruption involving the cortex, basal ganglia, or brainstem. The presence or loss of primitive reflexes indicates impaired higher-level cortical regulation. These signs may reflect poor neurological recovery potential and extensive neural pathway involvement, thereby functioning as proxy markers for elevated mortality risk. Their inclusion provides valuable supplementary information in comprehensive neurological assessment. The newly identified independent predictors in this study fill an important gap in current nomogram research by incorporating circulatory stress markers and detailed neurological physical examination findings. These results offer meaningful insights for improving risk stratification and guiding critical care management in patients with intracerebral hemorrhage. Compared with existing prognostic models that focus on long-term or fixed time-point outcomes in intracranial hemorrhage, the prediction model developed in this study for in-hospital survival offers several notable advantages. First, this model is specifically designed to predict short-term (in-hospital/early) outcomes in the ICU setting, whereas many previously published nomograms or scoring systems use 30-day, 90-day, or long-term functional outcomes as their primary endpoints (for example, several recent nomogram studies focus mainly on 90-day or long-term prognosis). In contrast, our model targets the clinically urgent endpoint of “in-hospital adverse events,” enabling more direct support for early risk identification and timely intervention in the ICU. This focus addresses an important gap in early dynamic risk assessment for critically ill ICH patients 16 , 22 . Second, the selection of variables in this study balances readily obtainable bedside neurological signs (such as GCS score, pupillary light reflex, and muscle tone/physiological reflexes) with routine laboratory indicators (e.g., leukocyte count) and relevant medical history (such as a history of heart disease). This design ensures that the model can be rapidly implemented in most tertiary hospitals and ICU settings. Existing research has demonstrated that inflammation- and leukocyte-related markers have prognostic relevance in ICH. By incorporating leukocyte count > 10×10^9/L into the multivariable model—and identifying it as an independent predictor—this study further confirms the value of such easily accessible indicators in short-term risk assessment 23 . Third, the model’s time window and dynamic evaluation framework are more closely aligned with real-world clinical decision-making. By providing predicted probabilities for days 3, 5, and 7 after admission, the model enables healthcare teams to reassess patient status at multiple time points and make informed decisions regarding the continuation of invasive interventions, allocation of intensive care resources, or communication of prognosis with families. Compared with models that focus on a single long-term endpoint, this multi–time point prediction approach better supports a “decision–reassessment” feedback loop, thereby enhancing the practicality and responsiveness of ICU management 22 . Fourth, in terms of methodology and interpretability, this study employed Cox regression for variable selection and presented the predictive tool in the form of a nomogram—achieving a balance among statistical robustness, readability, and interpretability. Although recent studies increasingly adopt machine learning techniques to enhance predictive performance, such models often function as “black boxes” and require large sample sizes and extensive feature engineering, limiting their clinical applicability. In contrast, the present model maintains transparency while achieving strong discrimination (high AUC in the training cohort and consistently good AUC in the validation cohort) and good calibration. This transparency facilitates clinical acceptance, practical implementation, and secondary validation. Comparisons with recent machine learning–based prognostic studies further indicate that, although machine learning can improve predictive accuracy in certain tasks, it still falls short in interpretability and real-world clinical operability 22 , 24 . Fifth, model validation was conducted using a temporally split training/validation design (training cohort: March 2021–December 2023; validation cohort: January 2024–July 2025), which partially simulates temporal extrapolation and more closely reflects real-world external generalizability. This approach provides a more realistic assessment of the model’s stability in future clinical cohorts compared with internal validation based solely on random splitting within the same time window. Moreover, decision curve analysis demonstrated that the model provides net clinical benefit across a wide range of threshold probabilities, a criterion increasingly recognized in both domestic and international nomogram studies as an important indicator of clinical feasibility 22 . In summary, this model combines four key advantages: focus on early/in-hospital endpoints, use of readily obtainable bedside variables, strong interpretability, and support from temporal validation and decision curve analysis. These features collectively enhance its practical utility and potential for implementation in ICU clinical decision-making. This model can serve multiple roles in clinical practice, specifically including: Early risk stratification and prioritized resource allocation In the ICU setting, resources such as beds, ventilators, continuous neuro-monitoring, and surgical interventions are limited and costly. This model provides each patient with a quantified risk of experiencing adverse events at 3, 5, and 7 days after admission, enabling healthcare teams to prioritize limited resources for high-risk patients. For example, high-risk individuals may receive more intensive neuroimaging follow-up, early multidisciplinary consultations, or proactive management of potential complications. Existing literature emphasizes the importance of early dynamic risk identification for improving short-term outcomes, and the short-term predictive capability of this model aligns directly with this clinical need 16 , 22 . Individualized treatment decisions and family communication support By providing intuitive risk scores and corresponding prognostic probabilities through the nomogram, clinicians can engage in more quantitatively informed discussions with patients’ families—for example, when considering invasive interventions, transfer to higher-level monitoring, or expected outcomes. This approach enhances decision-making transparency and supports shared decision-making. Compared with a simple “high/low risk” binary classification, probability-based outputs facilitate a more nuanced assessment of the balance between potential treatment benefits and associated burdens 22 . Early warning of complications and targeted interventions Variables in the model—such as elevated leukocyte count, GCS score, pupillary reflex, and muscle tone—not only reflect disease severity but also indicate potentially modifiable factors, including infection, brain herniation/increased intracranial pressure, or neurological dysfunction. Identifying high-risk individuals allows for prioritized interventions, such as early infection screening and antimicrobial therapy, intensified intracranial pressure management, or prompt rehabilitation measures, with the goal of mitigating reversible risk factors and reducing the incidence of adverse in-hospital outcomes. Previous studies have also highlighted the association of inflammation-related markers (e.g., leukocyte count, PIV, NLR) with ICH prognosis. This model confirms the clinical utility of leukocyte count as an independent predictor and demonstrates its potential role in early complication warning 23 , 25 . Baseline tool for clinical pathways and quality improvement This model can serve as a baseline risk assessment tool for designing hospital clinical pathways, early warning systems (EWS), or ICU quality improvement initiatives, enabling evaluation of intervention effects. For example, before and after implementing new infection control measures, rapid imaging protocols, or early brain herniation detection workflows, the model can track changes in the proportion of high-risk patients and the actual incidence of adverse events, providing an objective measure of improvement effectiveness. Framework for future external multicenter validation and dynamic optimization Although this study conducted temporal validation and demonstrated good discrimination and calibration, external validation across multiple centers and diverse populations is still necessary to assess generalizability. The model’s clear variable structure and ease of data collection facilitate rapid replication and data acquisition in different institutions. In the future, it could be expanded by incorporating additional biomarkers, quantitative imaging metrics, or dynamic physiological signals to evolve into a hybrid model or an online tool with continuous updating. Recent studies have explored the integration of nomograms with dynamic or machine learning approaches, and this model could serve as a clinically interpretable baseline for comparison and integration within such combined strategies 24 , 26 . Nevertheless, this study has several limitations. First, it is a single-center retrospective study, and its generalizability remains uncertain. The relatively small sample size of 136 patients may lead to chance findings. Moreover, validation was performed using an internal dataset rather than external cohorts, raising the possibility of model overfitting. Second, the study variables were limited to patient admission information and related examination results, which may have excluded other potentially relevant predictors. Therefore, future research should focus on multicenter prospective studies to enhance the model’s generalizability. Finally, expanding the sample size would improve the representativeness and applicability of the data. Incorporating additional biomarkers from emerging research could further enrich the model, making it more multidimensional and enabling more accurate prognostic assessment of patients. Conclusion The clinical nomogram developed in this study for predicting the risk of adverse events in ICU patients with intracerebral hemorrhage demonstrates strong predictive performance and practical clinical utility. It effectively discriminates among high-, intermediate-, and low-risk patients, making it a valuable tool for clinicians to assess the likelihood of adverse in-hospital outcomes. Abbreviations GCS:Glasgow Coma Scale HHD:History of Heart Disease Leu:Leukocyte OS:Overall Survival PhyReflex:Physiological Reflex PRL:Pupils Reflect to Light Declarations Confirmation of Compliance with Instructions to Authors The authors confirm that this manuscript has been prepared in full compliance with all the instructions to authors provided by BMC Medical Informatics and Decision Making , including but not limited to formatting, ethical guidelines, reference style, and submission requirements. All authors have reviewed and approved the final version of the manuscript for submission. Detailed description of individual author contributions: Hanbo Liu, Propose project design and research ideas, conduct data collection, cleaning, validation, and management. Using statistical methods for data analysis, designing charts, visualizing and presenting research data and results. The first writing and initial draft of the core content of the paper have been completed. Weigao Liu, Conduct data collection, cleaning, validation, and management. Develop research methods, experimental plans, and analytical models; Provide guidance, supervision, and oversight for the overall research; Methodological validation and result verification of research findings Ping Xue,Methodological validation and result verification of research findings; Repeated revisions, polishing, proofreading, and finalization of the paper Confirmation that authorship requirements have been met and the final manuscript was approved by all authors The authorship requirements have been met and the final manuscript was approved by all authors Publish This manuscript has not been published elsewhere and is not under consideration by another journal. Ethics This study was conducted in accordance with the ethical standards of the institutional research committee and with the 1964 Declaration of Helsinki and its later amendments.This study has been approved by the Ethics Committee of Neijiang Traditional Chinese Medicine Hospital in Sichuan Province. Due to the retrospective nature of this study, the ethics committee waived informed consent for this study. Conflicts of Interest NA Confirmation of the use of a reporting checklist NA Funding NA Acknowledgement NA Author Contributions Hanbo Liu,Complete the thesis topic design, collect data and conduct data analysis, draw the thesis charts, write the initial draft of the thesis and participate in its revision. Weigao Liu,Provide data sources, assist in refining research ideas, offer research methods, and participate in paper revisions. Ping Xue,Guide paper writing and participate in paper revision. Data availability statement: The data used in this study is temporarily not publicly available due to ethical approval requirements of Neijiang Traditional Chinese Medicine Hospital. The use of data requires special approval from the Ethics Committee of Neijiang Traditional Chinese Medicine Hospital. Interested researchers can contact the corresponding author [email protected] to consult application process and related requirements. References Xu L, Wang Z, Wu W, Li M, Li Q. Global, regional, and national burden of intracerebral hemorrhage and its attributable risk factors from 1990 to 2021: results from the 2021 Global Burden of Disease Study. BMC Public Health. 2024;24(1):2426. Sun T, Yuan Y, Wu K, et al. Trends and patterns in the global burden of intracerebral hemorrhage: a comprehensive analysis from 1990 to 2019. Front Neurol. 2023;14:1241158. An SJ, Kim TJ, Yoon B, Epidemiology. Risk Factors, and Clinical Features of Intracerebral Hemorrhage: An Update. J Stroke. 2017;19(1):3–10. Magid-Bernstein J, Girard R, Polster S, et al. Cerebral Hemorrhage: Pathophysiology, Treatment, and Future Directions. Circ Res. 2022;130(8):1204–29. James FR, Power N, Laha S. Decision-making in intensive care medicine – A review. J Intensive Care Soc. 2018;19(3):247–58. Greenberg SM, Ziai WC, Cordonnier C, et al. 2022 Guideline for the Management of Patients With Spontaneous Intracerebral Hemorrhage: A Guideline From the American Heart Association/American Stroke Association. Stroke. 2022;53(7):e282–361. The ICH, Score. Ruiz-Sandoval JL, Chiquete E, Romero-Vargas S, Padilla-Martínez JJ, González-Cornejo S. Grading Scale for Prediction of Outcome in Primary Intracerebral Hemorrhages. Stroke. 2007;38(5):1641–4. Gregório T, Pipa S, Cavaleiro P, et al. Prognostic models for intracerebral hemorrhage: systematic review and meta-analysis. BMC Med Res Methodol. 2018;18(1):145. Witsch J, Siegerink B, Nolte CH, et al. Prognostication after intracerebral hemorrhage: a review. Neurol Res Pract. 2021;3(1):22. Aytuluk HG, Basaran S, Dogan NO, Demir N. Comparison of Conventional Intensive Care Scoring Systems and Prognostic Scores Specific for Intracerebral Hemorrhage in Predicting One-Year Mortality. Neurocrit Care. 2021;34(1):92–101. Kim E, Wee JH, Choi YH, et al. Clinical and Radiological Predictors for Early Hematoma Expansion After Spontaneous Intracerebral Hemorrhage: A Retrospective Study. Neurol Int. 2025;17(10):170. Lehmann F, Schenk LM, Ilic I, et al. Prolonged Mechanical Ventilation in Patients with Deep-Seated Intracerebral Hemorrhage: Risk Factors and Clinical Implications. J Clin Med. 2021;10(5):1015. Rodríguez-Fernández S, Castillo-Lorente E, Guerrero-Lopez F, et al. Validation of the ICH score in patients with spontaneous intracerebral haemorrhage admitted to the intensive care unit in Southern Spain. BMJ Open. 2018;8(8):e021719. Clinical. Update – 2022 Guideline for the Management of Patients With Spontaneous Intracerebral Hemorrhage. Li S, Li H, Chen J, et al. Development and external validation of a dynamic nomogram for predicting the risk of functional outcome after 90 days in patients with acute intracerebral hemorrhage. Front Neurol. 2025;16:1519091. Liu M, Wang Z, Meng X, et al. Predictive Nomogram for Unfavorable Outcome of Spontaneous Intracerebral Hemorrhage. World Neurosurg. 2022;164:e1111–22. Han Q, Zuo Z, Su D et al. Identification and Validation of a Novel Nomogram Predicting 7-day Death in Patients with Intracerebral Hemorrhage. Balkan Med J. 2022. Li Y, Liu X, Wang J, Pan C, Tang Z. A Nomogram Model for Predicting Prognosis in Spontaneous Intracerebral Hemorrhage Patients. J Integr Neurosci. 2023;22(2):42. Zou J, Chen H, Liu C, et al. Development and validation of a nomogram to predict the 30-day mortality risk of patients with intracerebral hemorrhage. Front Neurosci. 2022;16:942100. Wang H, Li Y, Ye F, et al. Clinical value of inflammatory indices in predicting poor prognosis and post-hemorrhagic hydrocephalus in patients with intraventricular hemorrhage. BMC Neurol. 2025;25(1):115. Lu G, Zhang G, Zhang J, Wang L, Du B. Prognostic value of nomogram model based on clinical risk factors and CT radiohistological features in hypertensive intracerebral hemorrhage. Front Neurol. 2024;15:1502133. Yu Z, Zheng J, Guo R, et al. Prognostic impact of leukocytosis in intracerebral hemorrhage. Med (Baltim). 2019;98(28):e16281. Matsumoto K, Ishihara K, Matsuda K, et al. Machine Learning–Based Prediction for In-Hospital Mortality After Acute Intracerebral Hemorrhage Using Real‐World Clinical and Image Data. J Am Heart Assoc. 2024;13(24):e036447. Wang S, Zhang W, Li J, Yang X, Wang Y. Nomogram based on pan-immune-inflammation value to predict short-term prognosis in spontaneous intracerebral hemorrhage. Front Neurol. 2025;16:1606436. Wang H, Li S, Nie Y, et al. Online Dynamic Nomogram for Predicting 90-Day Prognosis of Patients With Primary Basal Ganglia Cerebral Hemorrhage After Microscopic Keyhole Craniotomy for Hematoma Removal. Brain Behav. 2025;15(2):e70344. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 01 Apr, 2026 Reviewers agreed at journal 27 Mar, 2026 Reviewers invited by journal 20 Mar, 2026 Editor invited by journal 25 Feb, 2026 Editor assigned by journal 16 Feb, 2026 Submission checks completed at journal 16 Feb, 2026 First submitted to journal 12 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8866295","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":610612578,"identity":"a45c2cda-2dd5-476a-af7b-31884e842daa","order_by":0,"name":"Hanbo Liu","email":"","orcid":"","institution":"Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hanbo","middleName":"","lastName":"Liu","suffix":""},{"id":610612579,"identity":"d028ed63-78db-4f30-820a-9dc8e9ddea28","order_by":1,"name":"Weigao Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYHACxgcfDGzk+NkbGx9+IEY9DwMDs+GMgjRjyZ7DzcYSRGphE+b5cChxw430NgEeYrTYSzc/Y+YxOMA4c+bDNgYJBjs53QZCtsgcM3s4x+AOM790YtuDAoZkY7MDhLRI5LAbvDF4xiY5O7HdQILhQOI2IrSwSfAYHOYxuHmwTYKHWC2SQC0SBjcYidVyI83YcIZBmoFkTyIwkA2I8Av7jOSHDz78sanvZz/+8OGHCjs5glrQgAFpykfBKBgFo2AU4AAAX39CeKnqeq0AAAAASUVORK5CYII=","orcid":"","institution":"Neijiang Traditional Medicine Hospital","correspondingAuthor":true,"prefix":"","firstName":"Weigao","middleName":"","lastName":"Liu","suffix":""},{"id":610612580,"identity":"427c9a7f-d3d5-4234-9826-26863f9605ad","order_by":2,"name":"Ping Xue","email":"","orcid":"","institution":"Neijiang Maternal and Child Health Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ping","middleName":"","lastName":"Xue","suffix":""}],"badges":[],"createdAt":"2026-02-13 01:08:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8866295/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8866295/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105353175,"identity":"9c487761-7231-4828-a4ce-2f42b05dbca3","added_by":"auto","created_at":"2026-03-25 06:15:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":380547,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for predicting short-term mortality in ICU patients with cerebral hemorrhage.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8866295/v1/29b715dc3001fb63d68016a3.png"},{"id":105353177,"identity":"80b2dfc2-7194-47c7-877c-3bc45ae36dbf","added_by":"auto","created_at":"2026-03-25 06:15:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":307787,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve. (A) ROC of the training set. (B) ROC of the validation set.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8866295/v1/6a3c4d66d83d48d51431f3e7.png"},{"id":105353180,"identity":"ce6a8a5f-0477-4b6d-8948-b894c8ab33b5","added_by":"auto","created_at":"2026-03-25 06:15:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":306180,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curve. (A) Calibration curve for 3-day survival of training set. (B) Calibration curve for 5-day survival of training set. (C) Calibration curve for 7-day survival of training set. (D) Calibration curve for 3-day survival of validation set. (E) Calibration curve for 5-day survival of validation set. (F) Calibration curve for 7-day survival of validation set.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8866295/v1/28be305171ef425ad7e4ff30.png"},{"id":105353176,"identity":"da2027cd-d088-406f-b11a-80cfe64b663b","added_by":"auto","created_at":"2026-03-25 06:15:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":269629,"visible":true,"origin":"","legend":"\u003cp\u003eClinical decision curve of validation set. (A) Clinical decision curve for 3-day survival of validation set. (B) Clinical decision curve for 5-day survival of validation set. (C) Clinical decision curve for 7-day survival of validation set.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8866295/v1/4482e4853505226297e62722.png"},{"id":105353178,"identity":"bf1b0bda-d66b-440f-8ba3-e10c865b7159","added_by":"auto","created_at":"2026-03-25 06:15:12","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":266790,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival analysis curve based on nomogram.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-8866295/v1/04a0fe2bd8e854edff9fa541.png"},{"id":105565336,"identity":"15658a21-96e6-4c00-8b74-4d1d708badbe","added_by":"auto","created_at":"2026-03-27 12:52:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3386408,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8866295/v1/ed792cca-e97c-44cd-8bcf-533c5660c3d6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and validation a machine learning model based on clinical factors to predict short-term prognosis of ICU intracerebral hemorrhage patients: a retrospective study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFirstly, from an epidemiological perspective, the global burden of ICH remains extremely heavy. According to the Institute for Health Metrics and Evaluation global disease burden study in 2021, there will be about 3.444\u0026nbsp;million new cases of ICH in the world in 2021, with age standardized incidence rate (ASIR) of about 40.8/100000, mortality (ASDR) of about 39.1/100000, and disability adjusted life years (DALYs) of about 92.4/100000. Furthermore, among 204 countries and regions, the ICH ASIR, ASDR, and DALYs in low SDI regions were significantly higher than those in high SDI countries\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e.Although the age standardized ratio has shown a downward trend in recent years, the absolute number of cases has not significantly decreased due to population aging, structural changes, and accumulated risk factors\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. In addition, from a prognostic perspective, the risk of death and disability in ICH patients is extremely high. Multiple systematic reviews indicate that the one month mortality rate of ICH patients can reach 30% -40%, and there are many deaths within one year. The proportion of those who can recover to independent living in the long term is only 12% -39%\u003csup\u003e3,4\u003c/sup\u003e. In the ICU environment, due to the critical condition, multiple comorbidities, and complex treatment measures, prognosis assessment and clinical decision-making become increasingly difficult\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Specifically, for ICH patients in the ICU, doctors face a series of important decisions in the early stages of admission, such as whether to perform invasive treatment, antihypertensive management, monitoring upgrades, and prognosis communication (such as whether to set treatment restrictions), which often rely on judgments about the patient's future course of illness and the possibility of functional recovery\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. It can be seen that establishing a reliable prognostic model for ICU patients with cerebral hemorrhage not only helps optimize resource allocation, guide treatment intensity, and promote early rehabilitation planning, but also enhances doctor-patient communication and assists family decision-making, thus having strong clinical significance.\u003c/p\u003e \u003cp\u003eAt present, various prognostic scoring models and tools have been proposed for the evaluation of ICH prognosis. For example, the classic ICH Score (including age, Glasgow Coma Scale [GCS], bleeding volume, presence of intraventricular hemorrhage, and bleeding site) is widely used for early mortality risk assessment\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. In addition, functional/death prognosis models such as ICH GS and MAX ICH are constantly being studied and validated\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. However, although these models have shown good discriminative ability in research (such as a c-index of over 0.80), their application and accuracy in clinical decision support still have many limitations in the actual ICU cerebral hemorrhage patient population. Previous studies have shown that common issues with these prognostic tools in model development include a lack of sufficient internal/external validation, variable selection based on expert experience rather than full data-driven, failure to consider ICU specific factors (such as ventilator dependence, renal failure, infection complications, etc.), and neglect of withdrawal of care (WOC) bias\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Similarly, in a systematic review of 72 prognostic tools including 48133 ICH patients, although the average c-index of the death prediction model was 0.88 and the average functional prognosis was 0.87, the authors pointed out that the reported \"event/variable ratio\" was low, there was a lack of blinding, uneven follow-up time, and insufficient data processing, suggesting that there are risks in the practical clinical transfer application\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. In addition, another study on ICU patients compared the general ICU severity scores such as SAPS III and APACHE IV with the ICH specific score. The results showed that although the general ICU score included more physiological variables, it lacked imaging parameters, while the ICH specific score, although designed concisely, often overlooked common organ dysfunction and complications in the ICU, limiting its predictive ability for severe ICH patients in the ICU\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn addition, there are many difficulties in clinical practice.Firstly, the dynamic changes in early bleeding volume (such as hematoma expansion) are difficult to quantify in real time\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Secondly, severe patients with multiple organ failure, mechanical ventilation, severe infections, and other factors make it difficult to accurately reflect the overall prognosis based solely on admission imaging or neurological status scores\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Finally, most existing models are derived from general neurology or stroke ward patients, and rarely focus on ICU cerebral hemorrhage patients (such as those with multiple critical complications or those undergoing mechanical ventilation)\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTherefore, the applicability and decision support effectiveness of current clinical evaluation methods in the high-risk group of ICU cerebral hemorrhage patients are still significantly insufficient, and research in this field is still insufficient. Therefore, this study aims to construct and validate a clinical prognostic model for patients with cerebral hemorrhage admitted to the ICU.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003e This study retrospectively collected clinical data of patients with cerebral hemorrhage admitted to the Intensive Care Medicine Department of Neijiang Traditional Chinese Medicine Hospital in Sichuan Province from January 2021 to December 2025. All methods comply with relevant guidelines and regulations. Inclusion criteria: (1) age \u0026gt; 18; (2) all patients were diagnosed with cerebral hemorrhage through imaging testing; (3) all individuals received relevant treatment in accordance with current clinical guidelines. Exclusion criteria: (1) incomplete patient information; (2) the patient did not suffer from any other life-threatening diseases except for cerebral hemorrhage. This study has been approved by the Ethics Committee of Neijiang Traditional Chinese Medicine Hospital in Sichuan Province. Due to the retrospective nature of this study, the ethics committee waived informed consent for this study.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eResearch Endpoint\u003c/h3\u003e\n\u003cp\u003eThe endpoint of this study is the survival period of all patients during their ICU stay, defined as the time from the first day of admission to the ICU until the occurrence of a death event or improvement in their condition during hospitalization and their transfer from the ICU. This study predicted the probability of 3-day, 5-day, and 7-day survival for all patients after admission to the ICU.\u003c/p\u003e\n\u003ch3\u003ePredictive Factors\u003c/h3\u003e\n\u003cp\u003eThis study collected all the information that patients can provide to medical staff through preliminary examinations after admission, including personal basic information, medical history information, admission physical examination report, admission imaging report, admission blood routine examination report, admission coagulation four item examination report, and admission liver and kidney function examination report. The variables included in the predictive model include age, cardiac history, admission pulse, admission consciousness state GCS score, pupil light reflex, muscle tone, physiological reflex, and white blood cells. Age \u0026lt; 65 years old is classified as 0, and age \u0026gt; = 65 years old is classified as 1; no history of heart disease is classified as 0, and history of heart disease is classified as 1; if the admission pulse is less than 82, it is classified as 0; if the admission pulse is greater than or equal to 82, it is classified as 1; a GCS score of less than 9 is classified as 0, while a GCS score of greater than or equal to 9 is classified as 1; Pupil's slow or absent light reflex is classified as 0, while pupil's sensitive or slightly slow light reflex is classified as 1; weakened muscle tone is classified as 0, normal muscle tone is classified as 1, and increased muscle tone is classified as 2; physiological reflex weakened or not induced is classified as 0, while physiological reflex present or normal is classified as 1; white blood cell count \u0026lt; 10 * 10 ^ 9/L is classified as 0, while white blood cell count \u0026gt; = 10 * 10 ^ 9/L is classified as 1. Age and consciousness status GCS scores are classified according to clinical guidelines\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Muscle tone retains the original classification, and other variables are classified using the median of the total data as the boundary.\u003c/p\u003e\n\u003ch3\u003eModel construction and validation\u003c/h3\u003e\n\u003cp\u003eBased on previous research\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, this study collected basic clinical information of patients (age, gender, etc.), admission physical examination reports, hematological tests (blood routine, liver and kidney function, etc.), and imaging reports. Divide the included patients into a training set and a validation set in chronological order, with data from 2021 to 2023 as the training set and data from 2024 to 2025 as the validation set. Subsequently, in the training set, the included clinical information was subjected to univariate Cox regression analysis. Variables with differences (P \u0026lt; 0.05) in the univariate analysis were included in the multivariate analysis, and variables with statistical differences (P \u0026lt; 0.05) in the multivariate analysis were visualized to establish a predictive model.Subsequently, the predictive performance of the model was validated using receiver operating characteristic (ROC), calibration curves, and decision curves in both the training and validation sets. Finally, all patients were scored according to the column chart and divided into low-risk, medium risk, and high-risk groups based on their quartiles. The differentiation effect of the model was validated through survival analysis.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eFor continuous variables, we compared them using t-test and sum of squares test; We use chi square test and Fisher's test to compare categorical variables. Single factor and multi factor Cox regression analysis were used to screen independent predictive factors. Survival analysis was validated through Kalplan-Meier survival curves and log analysis. All comparisons were statistically significant with P \u0026lt; 0.05. The data organization and statistical analysis are completed based on the Storm Statistics Platform or Zstats software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.zstats\u003c/span\u003e\u003c/span\u003e. net) and R version 4.3.3.\u003c/p\u003e \u003c/div\u003e "},{"header":"Result","content":"\u003ch2\u003eBaseline characteristics of patients\u003c/h2\u003e\u003cp\u003eThis study included a total of 135 patients, with 97 (71.85%) in the training set, and 38 (28.15%) in the validation set. Among them, there were 42 (43.3%) males in the training set, and 55 (56.3%) females. There were 61 (62.8%) patients aged \u0026gt; 65 in the training set, and 36 (37.2%) patients aged ≤ 65. There were 15 (15.5%) patients who died within 3 days in the training set, 20 (20.6%) patients who died within 5 days, and 26 (26.8%.) patients who died within 7 days In the validation set, there were 23 ( 60.5%) males, and 15 (39.5%) females. There were 61 (62.8%) patients aged \u0026gt; 65 in the validation set, and 36 (37.2%) patients aged ≤ 65. There were 15 (15.5%) patients who died within 3 days in the validation set, 20 (20.6%) patients who died within 5 days, and 26 (26.8%) patients who died within 7 days. The specific baseline characteristics of the two groups of patients are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab1\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline table for comparison between training set and validation set\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" rowspan=\"2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003e(n = 135)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e(n = 97)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e(n = 38)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eGender, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.686\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e57 (42.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e42 (43.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e15 (39.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e78 (57.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e55 (56.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e23 (60.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eAge, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.977\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026lt; 65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e50 (37.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e36 (37.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e14 (36.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≥ 65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e85 (62.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e61 (62.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e24 (63.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ePrognosis, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSurvive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e91 (67.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e67 (69.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e24 (63.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e44 (32.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e30 (30.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e14 (36.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSmoking, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.368\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e93 (68.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e69 (71.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e24 (63.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e42 (31.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e28 (28.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e14 (36.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eAlcohol, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e96 (71.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e72 (74.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e24 (63.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e39 (28.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e25 (25.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e14 (36.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eHistory of Hypertension, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e61 (45.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e44 (45.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e17 (44.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e74 (54.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e53 (54.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e21 (55.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eHistory of Heart Disease, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e118 (87.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e86 (88.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e32 (84.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e17 (12.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e11 (11.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e6 (15.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eHistory of Diabetes, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e123 (91.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e88 (90.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e35 (92.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e12 (8.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e9 (9.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e3 (7.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eHistory of Brain Disease, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e122 (90.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e87 (89.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e35 (92.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e13 (9.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e10 (10.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e3 (7.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ePulse, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026lt; 82/min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e66 (48.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e50 (51.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e16 (42.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≥ 82/min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e69 (51.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e47 (48.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e22 (57.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSystolic Pressure, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.546\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026lt; 170mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e66 (48.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e49 (50.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e17 (44.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≥ 170mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e69 (51.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e48 (49.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e21 (55.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eDiastolic Pressure, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026lt; .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026lt; 96mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e102 (75.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e81 (83.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e21 (55.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≥ 96mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e33 (24.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e16 (16.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e17 (44.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eGCS, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026lt; 9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e50 (37.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e35 (36.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e15 (39.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≥ 9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e85 (62.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e62 (63.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e23 (60.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ePupil Size, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eUnequal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e23 (17.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e12 (12.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e11 (28.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eEqual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e112 (82.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e85 (87.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e27 (71.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ePupil reflex to light, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.344\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSignificantly Weakened\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e95 (70.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e66 (68.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e29 (76.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNot Significantly Weakened\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e40 (29.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e31 (31.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e9 (23.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eMuscle Tension, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.671\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e16 (11.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e11 (11.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e5 (13.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e99 (73.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e70 (72.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e29 (76.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e20 (14.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e16 (16.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e4 (10.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ePhysiological Reflex, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e19 (14.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e18 (18.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1 (2.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e116 (85.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e79 (81.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e37 (97.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCore Functional Area Bleeding, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.552\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e73 (54.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e54 (55.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e19 (50.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e62 (45.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e43 (44.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e19 (50.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eAmount of Bleeding, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026lt; 15.18mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e77 (57.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e55 (56.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e22 (57.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≥ 15.18mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e58 (42.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e42 (43.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e16 (42.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eMidline Shift, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e72 (53.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e55 (56.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e17 (44.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e63 (46.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e42 (43.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e21 (55.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eVentilator Support, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.506\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e72 (53.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e50 (51.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e22 (57.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e63 (46.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e47 (48.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e16 (42.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eWBC, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.546\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026lt; 10*10^9/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e69 (51.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e48 (49.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e21 (55.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≥ 10*10^9/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e66 (48.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e49 (50.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e17 (44.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eAb, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026lt; 132*10^9/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e64 (47.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e46 (47.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e18 (47.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≥ 132*10^9/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e71 (52.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e51 (52.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e20 (52.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ePlt, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.957\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026lt; 179*10^9/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e68 (50.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e49 (50.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e19 (50.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≥ 179*10^9/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e67 (49.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e48 (49.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e19 (50.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCRP, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≤ 10mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e116 (85.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e83 (85.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e33 (86.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026gt; 10mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e19 (14.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e14 (14.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e5 (13.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ehs-CRP, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≤ 1mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e55 (40.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e46 (47.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e9 (23.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026gt; 1mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e80 (59.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e51 (52.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e29 (76.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ePT, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.872\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≤ 11.2s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e66 (48.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e47 (48.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e19 (50.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026gt; 11.2s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e69 (51.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e50 (51.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e19 (50.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eTT, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026lt; .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≤ 17.4s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e65 (48.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e59 (60.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e6 (15.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026gt; 17.4s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e70 (51.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e38 (39.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e32 (84.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eAPTT, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.413\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≤ 24.5s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e68 (50.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e51 (52.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e17 (44.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026gt; 24.5s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e67 (49.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e46 (47.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e21 (55.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eFIB, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.546\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≤ 2.685g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e69 (51.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e48 (49.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e21 (55.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026gt; 2.685g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e66 (48.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e49 (50.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e17 (44.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eAST/ALT, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.706\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≤ 1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e64 (47.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e45 (46.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e19 (50.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026gt; 1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e71 (52.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e52 (53.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e19 (50.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eGGT, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.413\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≤ 26.5U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e68 (50.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e51 (52.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e17 (44.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026gt; 26.5U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e67 (49.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e46 (47.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e21 (55.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eAlb, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.354\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≤ 41.2g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e66 (48.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e45 (46.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e21 (55.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026gt; 41.2g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e69 (51.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e52 (53.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e17 (44.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCrea, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≤ 64umol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e68 (50.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e55 (56.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e13 (34.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026gt; 64umol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e67 (49.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e42 (43.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e25 (65.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eGFR, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.957\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≤ 78.5ml/min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e68 (50.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e49 (50.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e19 (50.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026gt; 78.5ml/min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e67 (49.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e48 (49.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e19 (50.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eGLU, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.476\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≤ 7.85mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e86 (63.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e60 (61.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e26 (68.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026gt; 7.85mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e49 (36.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e37 (38.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e12 (31.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eK, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.546\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≤ 3.61mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e69 (51.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e48 (49.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e21 (55.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026gt; 3.61mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e66 (48.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e49 (50.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e17 (44.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNa, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≤ 139.78mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e70 (51.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e50 (51.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e20 (52.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026gt; 139.78mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e65 (48.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e47 (48.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e18 (47.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/table\u003e\u003c/div\u003e\u003ch3\u003eScreening of independent predictive factors\u003c/h3\u003e\u003cp\u003eThis study included all variables collected from the training set in univariate and multivariate Cox regression analysis, and included variables with p \u0026lt; 0.05 in the univariate Cox regression analysis in the two-way Cox regression analysis. Through a univariate study, it was found that age \u0026gt; 65 years (HR: 2.74, 95% CI: 1.04 ~ 7.02), history of heart disease (HR: 3.31, 95% CI: 1.32 ~ 8.28), history of cardiovascular medication (HR: 3.68, 95% CI: 1.10 ~ 12.28), and admission pulse \u0026gt; 82 times/ min (HR: 2.58, 95%CI༚1.17–5.67),GCS \u0026gt; 9 (HR༚0.16, 95%CI༚0.07 ~ 0.38). Pupil size is equal (HR: 0.28, 95% CI: 0.12 ~ 0.63), pupil reflex is not weakened (HR: 0.32, 95% CI: 0.11 ~ 0.94), muscle tone is normal (HR: 0.32, 95% CI: 0.12 ~ 0.84), muscle strength is reduced (HR: 0.23, 95% CI: 0.06 ~ 0.98), physiological reflex is present (HR: 0.20, 95% CI: 0.10 ~ 0.42), bleeding volume \u0026gt; 15.1835mL (HR: 2.49, 95% CI: 1.15 ~ 5.37), midline shift (HR: 2.27, 95% CI: 1.05 ~ 4.89), use of ventilator (HR: 12.90, 95% CI: 3.0) 5 ~ 54.54), white blood cells \u0026gt; 10 * 10 ^ 9/L (HR: 3.34, 95% CI: 1.35 ~ 8.23), monocytes \u0026gt; 0.57 * 10 ^ 9/L (HR: 3.97, 95% CI: 1.38 ~ 11.46), total bile acids \u0026gt; 4.2umol/L (HR: 0.45, 95% CI: 0.21 ~ 0.96), and monoamine oxidase \u0026gt; 6.6U/L (HR: 3.62, 95% CI: 1.53 ~ 8.55) are correlated with mortality events during hospitalization (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). By incorporating the variables with differences in univariate analysis into a two-way multivariate analysis, the study found that age \u0026gt; 65 years old (HR: 4.28, 95% CI: 1.25 ~ 14.61, P = 0.020), history of heart disease (HR: 6.16, 95% CI: 1.68 ~ 22.55, P = 0.006), and admission pulse \u0026gt; 82 times/ min(HR༚3.39, 95%CI: 1.27 ~ 9.02, P = 0.014), GCS \u0026gt; 9 (HR: 0.22, 95%CI: 0.06 ~ 0.83, P = 0.025). Weak pupil reflex to light (HR: 3.97, 95% CI: 6.84 ~ 44.84, P = 0.045), normal muscle tone (HR: 0.14, 95% CI: 0.04 ~ 0.47, P = 0.001), presence of physiological reflex (HR: 0.29, 95% CI: 0.11 ~ 0.76, P = 0.012), and white blood cell count \u0026gt; 10 * 10 ^ 9/L (HR: 3.22, 95% CI: 1.09 ~ 9.56, P = 0.035) are independent predictive factors for patient survival during hospitalization (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab2\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSingle factor and multi factor analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\"\u003e \u003cp\u003eSingle Factor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\"\u003e \u003cp\u003eMulti-factor\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eS.E\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eS.E\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eMale vs. Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.95 (0.45 ~ 1.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≤ 65 vs. \u0026gt;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u003cb\u003e0.041\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2.74 (1.04 ~ 7.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e4.28 (1.25 ~ 14.61)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eSmoking\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNo vs. Yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.02 (0.45 ~ 2.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eAlcohol\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNo vs. Yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.62 (0.23 ~ 1.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eHistory of Hypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNo vs. Yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.61 (0.29 ~ 1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eHistory of Heart Disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNo vs. Yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e2.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e3.31 (1.32 ~ 8.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e6.16 (1.68 ~ 22.55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eHistory of Diabetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNo vs. Yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.85 (0.20 ~ 3.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eHistory of Brain Disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNo vs. Yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.23 (0.37 ~ 4.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003ePulse\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026lt; 82/min vs. ≥82/min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2.58 (1.17 ~ 5.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e3.39 (1.27 ~ 9.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eSystolic Pressure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026lt; 170mmHg vs. ≥170mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.09 (0.52 ~ 2.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eDiastolic Pressure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026lt; 96mmHg vs. ≥96mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.89 (0.31 ~ 2.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eGCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026lt; 9 vs. ≥9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-4.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; .001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.16 (0.07 ~ 0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e-2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.22 (0.06 ~ 0.83)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003ePupil Size\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eUnequal vs. Equal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-3.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.28 (0.12 ~ 0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003ePupil reflex to light\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSignificantly Weakened vs. Not Significantly Weakened\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u003cb\u003e0.038\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.32 (0.11 ~ 0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u003cb\u003e0.045\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e6.84 (1.04 ~ 44.84)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eMuscle Tension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eLow vs. Normal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-2.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.32 (0.12 ~ 0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e-3.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.14 (0.04 ~ 0.47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eLow vs. High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.01 (0.36 ~ 2.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e-0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.58 (0.19 ~ 1.72)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003ePhysiological Reflex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNo vs. Yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; .001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.20 (0.10 ~ 0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e-2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.29 (0.11 ~ 0.76)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eCore Functional Area Bleeding\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNo vs. Yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.85 (0.41 ~ 1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eAmount of Bleeding\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026lt; 15.18mL vs. ≥15.18mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e2.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2.49 (1.15 ~ 5.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eMidline Shift\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNo vs. Yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u003cb\u003e0.037\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2.27 (1.05 ~ 4.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eVentilator Support\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNo vs. Yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e2.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e3.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; .001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e12.90 (3.05 ~ 54.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e4.53 (0.81 ~ 25.43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eWBC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026lt; 10*10^9/L vs. ≥10*10^9/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e2.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e3.34 (1.35 ~ 8.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u003cb\u003e0.035\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e3.22 (1.09 ~ 9.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eAb\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026lt; 132*10^9/L vs. ≥132*10^9/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.13 (0.54 ~ 2.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003ePlt\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026lt; 179*10^9/L vs. ≥179*10^9/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.77 (0.37 ~ 1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eCRP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≤ 10mg/L vs. \u0026gt;10mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.52 (0.15 ~ 1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003ehs-CRP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≤ 1mg/L vs. \u0026gt;1mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.90 (0.43 ~ 1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003ePT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≤ 11.2s vs. \u0026gt;11.2s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.82 (0.40 ~ 1.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eTT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≤ 17.4s vs. \u0026gt;17.4s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.79 (0.36 ~ 1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eAPTT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≤ 24.5s vs. \u0026gt;24.5s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.33 (0.63 ~ 2.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eFIB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≤ 2.685g/L vs. \u0026gt;2.685g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.92 (0.44 ~ 1.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eAST/ALT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≤ 1.26 vs. \u0026gt;1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.57 (0.27 ~ 1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eGGT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≤ 26.5U/L vs. \u0026gt;26.5U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.61 (0.77 ~ 3.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eAlb\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≤ 41.2g/L vs. \u0026gt;41.2g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.47 (0.22 ~ 0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eCrea\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≤ 64umol/L vs. \u0026gt;64umol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.78 (0.86 ~ 3.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eGFR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≤ 78.5ml/min vs. \u0026gt;78.5ml/min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.93 (0.45 ~ 1.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eGLU\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≤ 7.85mmol/L vs. \u0026gt;7.85mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.67 (0.80 ~ 3.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eK\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≤ 3.61mmol/L vs. \u0026gt;3.61mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e-0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.78 (0.37 ~ 1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eNa\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≤ 139.78mmol/L vs. \u0026gt;139.78mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.18 (0.57 ~ 2.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/table\u003e\u003c/div\u003e\u003cp\u003eAbbreviation: HR,Hazard Ratio; CI, Confidence Interval.\u003c/p\u003e\u003ch2\u003eModel establishment and validation\u003c/h2\u003e\u003cp\u003eIncorporating the independent predictive factors into the prediction model yields the nomogram shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Each variable corresponds to a specific point value, and a higher total score indicates a greater risk of adverse events during hospitalization. For example, consider a patient aged 67 years with a history of heart disease, an admission pulse rate of 83 beats/min, a Glasgow Coma Scale (GCS) score of 10, sluggish pupillary light reflex, reduced muscle tone, preserved physiological reflexes, and a leukocyte count \u0026gt; 10×10^9/L. The corresponding predictive variables for this patient are as follows: Age = 1, HHD = 1, Pulse = 1, GCS = 1, PRL = 0, TM = 0, PhyReflex = 1, and Leu = 1. The total score, calculated as 70 + 17.5 + 29 + 0 + 0 + 38 + 0 + 38, is 192.5. Based on this score, the estimated probabilities of a favorable prognosis at 3, 5, and 7 days after admission are approximately 75%, 70%, and 60%, respectively.\u003c/p\u003e\u003cp\u003eROC for predicting outcomes at 3, 5, and 7 days were constructed in both the training and validation sets using the independent predictive factors identified. In the training cohort, the AUC for the 3-day prediction was 0.8786 (95% CI: 0.7717 ~ 0.9660); for the 5-day prediction was 0.8235 (95% CI: 0.7091 ~ 0.9380); and for the 7-day prediction was 0.8631 (95% CI: 0.7458 ~ 0.9803). In the validation cohort, the AUC for the 3-day prediction was 0.7273 (95% CI: 0.5208 ~ 0.9338); for the 5-day prediction was 0.7724 (95% CI: 0.5932 ~ 0.9516); and for the 7-day prediction was 0.7614 (95% CI: 0.5632 ~ 0.9596) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). These results indicate that the model demonstrates good discriminative ability in both the training and validation cohorts, with relatively stable generalizability.\u003c/p\u003e\u003cp\u003eThe calibration curves for the 3-day, 5-day, and 7-day predictions in both the training and validation cohorts closely adhered to the 45° reference line, indicating good agreement between the predicted and observed risks and demonstrating a high degree of calibration (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003ch2\u003eClinical practicality verification of the model\u003c/h2\u003e\u003cp\u003eThis study evaluated the net benefit of the \"pr_24\" model, \"Treat All\" strategy, and \"Treat None\" strategy at different threshold probabilities through decision curve analysis. The results are shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eFrom the three decision curves, the net benefit of the “Treat None” strategy remains consistently at zero, indicating no additional net gain associated with this approach. The net benefit of the “Treat All” strategy declines rapidly as the threshold probability increases; although it exceeds that of “Treat None” at very low threshold probabilities (approximately \u0026lt; 20%), it quickly drops below zero thereafter. This suggests that treating all patients provides some benefit only when the threshold probability is extremely low, but beyond this range, its net benefit becomes negative.In contrast, the net benefit curve of the “pr_24” model remains above both the “Treat All” and “Treat None” curves across a broad range of threshold probabilities (from nearly 0% to approximately 70%–80%). This indicates that the “pr_24” model yields greater net benefit for patients within this interval. These findings imply that, compared with the extreme strategies of treating all or treating none, clinical decisions guided by the “pr_24” model can provide substantially greater patient benefit across a wider range of threshold probabilities, demonstrating superior clinical utility.\u003c/p\u003e\u003ch2\u003eModel validity verification\u003c/h2\u003e\u003cp\u003eAll patients were scored using the nomogram, and based on the tertiles of the total score, they were categorized into high-, intermediate-, and low-risk groups. Kaplan–Meier survival analyses for these groups are shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. The results indicate that the survival curve of the low-risk group shows almost no decline, with survival probability remaining close to 100%. The intermediate-risk group exhibits a slight decline, yet survival remains at a relatively high level (\u0026gt; 70%). In contrast, the high-risk group demonstrates a marked decline, with survival dropping below 50% at approximately 10 days. The three curves are well separated with virtually no overlap.The log-rank test yielded p \u0026lt; 0.0001, indicating that the model has strong discriminative performance, effectively differentiating survival probabilities among risk groups. These results suggest that the model possesses robust and stable predictive capability.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study identified age, history of heart disease, admission pulse rate, GCS score, pupillary light reflex, muscle tone, physiological reflexes, and leukocyte count as independent predictors of in-ICU mortality among patients with intracerebral hemorrhage. Based on these factors, a nomogram model was successfully constructed to predict 3-, 5-, and 7-day survival probabilities in ICU patients with intracerebral hemorrhage. The model demonstrated good discrimination (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.7) and calibration in both the training and validation cohorts. Furthermore, decision curve analysis in the validation cohort confirmed that the model provides substantial clinical net benefit.\u003c/p\u003e \u003cp\u003eCompared with existing studies, several of the independent predictors identified in this research\u0026mdash;specifically age, GCS score, and leukocyte count\u0026mdash;have also been reported in previous work. Age, in particular, is included as a key predictor in nearly all prognostic models for ICH. For example, in a dynamic nomogram study published by S. Li et al. in 2025, age was incorporated as an essential variable in their model for predicting 90-day mortality16.\u003c/p\u003e \u003cp\u003eAge reflects the progressive decline in the body\u0026rsquo;s tolerance to hemorrhage, brain injury, and systemic stress responses (such as inflammation and immune activation). Older patients are more prone to concomitant organ dysfunction (e.g., cardiac or renal impairment), which limits their capacity for recovery. In addition, advanced age is associated with cerebral atrophy, reduced cerebrovascular elasticity, poorer control of hematoma expansion, and increased risks of rebleeding and complications such as infections or chronic comorbidities.The GCS score is also a highly consistent and reliable predictor in ICH prognostic models. Numerous nomogram-based studies have incorporated the GCS score and identified it as an independent prognostic factor\u003csup\u003e\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe GCS score reflects the severity of neurological impairment, including the level of consciousness and potential involvement of brainstem function. A reduced level of consciousness often indicates larger hematoma volume, mass effect, brain herniation, or more extensive neurological damage, all of which directly contribute to early mortality. Moreover, patients with low GCS scores are more susceptible to multisystem complications such as respiratory instability, circulatory dysfunction, and infections.In addition, although leukocyte count is not included as an independent predictor in all nomogram models, it has been identified as such in several studies. For example, in the 30-day mortality prediction model published by J. Zou et al. in 2022, leukocyte count was incorporated as one of the independent prognostic factors\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.An elevated leukocyte count can be regarded as an indicator of inflammatory activation and systemic stress response. Following intracerebral hemorrhage, tissue destruction, disruption of the blood\u0026ndash;brain barrier, extracellular matrix remodeling, and infiltration of inflammatory cells (including leukocytes) collectively trigger systemic inflammation. This inflammatory cascade further exacerbates brain injury, promotes cerebral edema and neuronal apoptosis, and ultimately contributes to poor clinical outcomes.In addition, previous studies have reported that inflammatory markers\u0026mdash;such as the neutrophil-to-lymphocyte ratio and neutrophil-to-albumin ratio\u0026mdash;are associated with unfavorable prognosis following intraventricular hemorrhage\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCompared with existing studies, the identification of a history of heart disease, admission pulse rate, pupillary light reflex, and physiological reflexes as independent predictors has rarely been reported; however, these variables enrich prognostic dimensions that are often overlooked in traditional ICH mortality prediction models.\u003c/p\u003e \u003cp\u003ePre-existing cardiovascular disease (such as coronary artery disease, arrhythmias, or cardiomyopathy) may reflect impaired cardiovascular function, including reduced cardiac output, increased thrombotic risk, rhythm instability, and inadequate perfusion. These conditions can exacerbate circulatory instability, compromise cerebral perfusion, and increase the likelihood of secondary injuries in ICH patients, such as rebleeding or ischemia. Moreover, many patients with cardiac disease have a history of using medications such as anticoagulants or antiplatelet agents, which may influence hematoma expansion and recovery.\u003c/p\u003e \u003cp\u003ePulse rate (heart rate) is a key indicator of circulatory status. An elevated heart rate (tachycardia) upon admission in patients with acute ICH may reflect sympathetic overactivation, pain, hemodynamic instability caused by hemorrhage, compensatory responses to hypotension, dehydration, or blood loss. Persistent tachycardia may signal sustained circulatory instability, increased cardiac workload, and impaired cerebral and systemic perfusion, all of which are associated with heightened early mortality risk. As a dynamic physiological marker of sympathetic activation and circulatory stress, heart rate has been underestimated in its prognostic value.\u003c/p\u003e \u003cp\u003eThe pupillary light reflex is an essential neurological sign reflecting brainstem function, particularly midbrain and oculomotor nerve integrity. In ICH, sluggish or absent pupillary light reflex often suggests brainstem compression, impending or ongoing herniation (especially transtentorial), or disruption of neural pathways, all of which are directly linked to life-threatening deterioration and mortality. Traditional prognostic models often omit this clinical neurological examination finding, resulting in incomplete integration of early neurological signs.\u003c/p\u003e \u003cp\u003eMuscle tone abnormalities and primitive reflexes (such as grasp reflex, Babinski sign, or other brainstem/cortical reflexes) serve as sensitive indicators of dysfunction in the central nervous system, particularly upper motor neuron pathways and corticobulbar circuits. In ICH patients, alterations in muscle tone\u0026mdash;either hypertonia or hypotonia\u0026mdash;may reflect structural disruption involving the cortex, basal ganglia, or brainstem. The presence or loss of primitive reflexes indicates impaired higher-level cortical regulation. These signs may reflect poor neurological recovery potential and extensive neural pathway involvement, thereby functioning as proxy markers for elevated mortality risk. Their inclusion provides valuable supplementary information in comprehensive neurological assessment.\u003c/p\u003e \u003cp\u003eThe newly identified independent predictors in this study fill an important gap in current nomogram research by incorporating circulatory stress markers and detailed neurological physical examination findings. These results offer meaningful insights for improving risk stratification and guiding critical care management in patients with intracerebral hemorrhage.\u003c/p\u003e \u003cp\u003eCompared with existing prognostic models that focus on long-term or fixed time-point outcomes in intracranial hemorrhage, the prediction model developed in this study for in-hospital survival offers several notable advantages.\u003c/p\u003e \u003cp\u003eFirst, this model is specifically designed to predict short-term (in-hospital/early) outcomes in the ICU setting, whereas many previously published nomograms or scoring systems use 30-day, 90-day, or long-term functional outcomes as their primary endpoints (for example, several recent nomogram studies focus mainly on 90-day or long-term prognosis). In contrast, our model targets the clinically urgent endpoint of \u0026ldquo;in-hospital adverse events,\u0026rdquo; enabling more direct support for early risk identification and timely intervention in the ICU. This focus addresses an important gap in early dynamic risk assessment for critically ill ICH patients\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSecond, the selection of variables in this study balances readily obtainable bedside neurological signs (such as GCS score, pupillary light reflex, and muscle tone/physiological reflexes) with routine laboratory indicators (e.g., leukocyte count) and relevant medical history (such as a history of heart disease). This design ensures that the model can be rapidly implemented in most tertiary hospitals and ICU settings. Existing research has demonstrated that inflammation- and leukocyte-related markers have prognostic relevance in ICH. By incorporating leukocyte count\u0026thinsp;\u0026gt;\u0026thinsp;10\u0026times;10^9/L into the multivariable model\u0026mdash;and identifying it as an independent predictor\u0026mdash;this study further confirms the value of such easily accessible indicators in short-term risk assessment\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThird, the model\u0026rsquo;s time window and dynamic evaluation framework are more closely aligned with real-world clinical decision-making. By providing predicted probabilities for days 3, 5, and 7 after admission, the model enables healthcare teams to reassess patient status at multiple time points and make informed decisions regarding the continuation of invasive interventions, allocation of intensive care resources, or communication of prognosis with families. Compared with models that focus on a single long-term endpoint, this multi\u0026ndash;time point prediction approach better supports a \u0026ldquo;decision\u0026ndash;reassessment\u0026rdquo; feedback loop, thereby enhancing the practicality and responsiveness of ICU management\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFourth, in terms of methodology and interpretability, this study employed Cox regression for variable selection and presented the predictive tool in the form of a nomogram\u0026mdash;achieving a balance among statistical robustness, readability, and interpretability. Although recent studies increasingly adopt machine learning techniques to enhance predictive performance, such models often function as \u0026ldquo;black boxes\u0026rdquo; and require large sample sizes and extensive feature engineering, limiting their clinical applicability.\u003c/p\u003e \u003cp\u003eIn contrast, the present model maintains transparency while achieving strong discrimination (high AUC in the training cohort and consistently good AUC in the validation cohort) and good calibration. This transparency facilitates clinical acceptance, practical implementation, and secondary validation. Comparisons with recent machine learning\u0026ndash;based prognostic studies further indicate that, although machine learning can improve predictive accuracy in certain tasks, it still falls short in interpretability and real-world clinical operability\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFifth, model validation was conducted using a temporally split training/validation design (training cohort: March 2021\u0026ndash;December 2023; validation cohort: January 2024\u0026ndash;July 2025), which partially simulates temporal extrapolation and more closely reflects real-world external generalizability. This approach provides a more realistic assessment of the model\u0026rsquo;s stability in future clinical cohorts compared with internal validation based solely on random splitting within the same time window. Moreover, decision curve analysis demonstrated that the model provides net clinical benefit across a wide range of threshold probabilities, a criterion increasingly recognized in both domestic and international nomogram studies as an important indicator of clinical feasibility\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn summary, this model combines four key advantages: focus on early/in-hospital endpoints, use of readily obtainable bedside variables, strong interpretability, and support from temporal validation and decision curve analysis. These features collectively enhance its practical utility and potential for implementation in ICU clinical decision-making.\u003c/p\u003e \u003cp\u003eThis model can serve multiple roles in clinical practice, specifically including:\u003c/p\u003e \u003cp\u003eEarly risk stratification and prioritized resource allocation\u003c/p\u003e \u003cp\u003eIn the ICU setting, resources such as beds, ventilators, continuous neuro-monitoring, and surgical interventions are limited and costly. This model provides each patient with a quantified risk of experiencing adverse events at 3, 5, and 7 days after admission, enabling healthcare teams to prioritize limited resources for high-risk patients. For example, high-risk individuals may receive more intensive neuroimaging follow-up, early multidisciplinary consultations, or proactive management of potential complications. Existing literature emphasizes the importance of early dynamic risk identification for improving short-term outcomes, and the short-term predictive capability of this model aligns directly with this clinical need\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIndividualized treatment decisions and family communication support\u003c/p\u003e \u003cp\u003eBy providing intuitive risk scores and corresponding prognostic probabilities through the nomogram, clinicians can engage in more quantitatively informed discussions with patients\u0026rsquo; families\u0026mdash;for example, when considering invasive interventions, transfer to higher-level monitoring, or expected outcomes. This approach enhances decision-making transparency and supports shared decision-making. Compared with a simple \u0026ldquo;high/low risk\u0026rdquo; binary classification, probability-based outputs facilitate a more nuanced assessment of the balance between potential treatment benefits and associated burdens\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eEarly warning of complications and targeted interventions\u003c/p\u003e \u003cp\u003eVariables in the model\u0026mdash;such as elevated leukocyte count, GCS score, pupillary reflex, and muscle tone\u0026mdash;not only reflect disease severity but also indicate potentially modifiable factors, including infection, brain herniation/increased intracranial pressure, or neurological dysfunction. Identifying high-risk individuals allows for prioritized interventions, such as early infection screening and antimicrobial therapy, intensified intracranial pressure management, or prompt rehabilitation measures, with the goal of mitigating reversible risk factors and reducing the incidence of adverse in-hospital outcomes. Previous studies have also highlighted the association of inflammation-related markers (e.g., leukocyte count, PIV, NLR) with ICH prognosis. This model confirms the clinical utility of leukocyte count as an independent predictor and demonstrates its potential role in early complication warning\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBaseline tool for clinical pathways and quality improvement\u003c/p\u003e \u003cp\u003eThis model can serve as a baseline risk assessment tool for designing hospital clinical pathways, early warning systems (EWS), or ICU quality improvement initiatives, enabling evaluation of intervention effects. For example, before and after implementing new infection control measures, rapid imaging protocols, or early brain herniation detection workflows, the model can track changes in the proportion of high-risk patients and the actual incidence of adverse events, providing an objective measure of improvement effectiveness.\u003c/p\u003e \u003cp\u003eFramework for future external multicenter validation and dynamic optimization\u003c/p\u003e \u003cp\u003eAlthough this study conducted temporal validation and demonstrated good discrimination and calibration, external validation across multiple centers and diverse populations is still necessary to assess generalizability. The model\u0026rsquo;s clear variable structure and ease of data collection facilitate rapid replication and data acquisition in different institutions. In the future, it could be expanded by incorporating additional biomarkers, quantitative imaging metrics, or dynamic physiological signals to evolve into a hybrid model or an online tool with continuous updating. Recent studies have explored the integration of nomograms with dynamic or machine learning approaches, and this model could serve as a clinically interpretable baseline for comparison and integration within such combined strategies\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNevertheless, this study has several limitations. First, it is a single-center retrospective study, and its generalizability remains uncertain. The relatively small sample size of 136 patients may lead to chance findings. Moreover, validation was performed using an internal dataset rather than external cohorts, raising the possibility of model overfitting. Second, the study variables were limited to patient admission information and related examination results, which may have excluded other potentially relevant predictors. Therefore, future research should focus on multicenter prospective studies to enhance the model\u0026rsquo;s generalizability. Finally, expanding the sample size would improve the representativeness and applicability of the data. Incorporating additional biomarkers from emerging research could further enrich the model, making it more multidimensional and enabling more accurate prognostic assessment of patients.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe clinical nomogram developed in this study for predicting the risk of adverse events in ICU patients with intracerebral hemorrhage demonstrates strong predictive performance and practical clinical utility. It effectively discriminates among high-, intermediate-, and low-risk patients, making it a valuable tool for clinicians to assess the likelihood of adverse in-hospital outcomes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003col\u003e\n \u003cli\u003eGCS:Glasgow Coma Scale\u003c/li\u003e\n \u003cli\u003eHHD:History of Heart Disease\u003c/li\u003e\n \u003cli\u003eLeu:Leukocyte\u003c/li\u003e\n \u003cli\u003eOS:Overall Survival\u003c/li\u003e\n \u003cli\u003ePhyReflex:Physiological Reflex\u003c/li\u003e\n \u003cli\u003ePRL:Pupils Reflect to Light\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConfirmation of Compliance with Instructions to Authors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm that this manuscript has been prepared in full compliance with all the instructions to authors provided by\u0026nbsp;\u003cem\u003eBMC Medical Informatics and Decision Making\u003c/em\u003e, including but not limited to formatting, ethical guidelines, reference style, and submission requirements. All authors have reviewed and approved the final version of the manuscript for submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDetailed description of individual author contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHanbo Liu, Propose project design and research ideas, conduct data collection, cleaning, validation, and management. Using statistical methods for data analysis, designing charts, visualizing and presenting research data and results. The first writing and initial draft of the core content of the paper have been completed.\u003c/p\u003e\n\u003cp\u003eWeigao Liu, Conduct data collection, cleaning, validation, and management. Develop research methods, experimental plans, and analytical models; Provide guidance, supervision, and oversight for the overall research; Methodological validation and result verification of research findings\u003c/p\u003e\n\u003cp\u003ePing Xue,Methodological validation and result verification of research findings; Repeated revisions, polishing, proofreading, and finalization of the paper\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConfirmation that authorship requirements have been met and the final manuscript was approved by all authors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authorship requirements have been met and the final manuscript was approved by all authors\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePublish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis manuscript has not been published elsewhere and is not under consideration\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eby another journal.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the ethical standards of the institutional research committee and with the 1964 Declaration of Helsinki and its later amendments.This study has been approved by the Ethics Committee of Neijiang Traditional Chinese Medicine Hospital in Sichuan Province. Due to the retrospective nature of this study, the ethics committee waived informed consent for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNA\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConfirmation of the use of a reporting checklist\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNA\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNA\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNA\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHanbo Liu,Complete the thesis topic design, collect data and conduct data analysis, draw the thesis charts, write the initial draft of the thesis and participate in its revision.\u003c/p\u003e\n\u003cp\u003eWeigao Liu,Provide data sources, assist in refining research ideas, offer research methods, and participate in paper revisions.\u003c/p\u003e\n\u003cp\u003ePing Xue,Guide paper writing and participate in paper revision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study is temporarily not publicly available due to ethical approval requirements of Neijiang Traditional Chinese Medicine Hospital. The use of data requires special approval from the Ethics Committee of Neijiang Traditional Chinese Medicine Hospital. Interested researchers can contact the corresponding author [email protected] to consult application process and related requirements.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eXu L, Wang Z, Wu W, Li M, Li Q. Global, regional, and national burden of intracerebral hemorrhage and its attributable risk factors from 1990 to 2021: results from the 2021 Global Burden of Disease Study. BMC Public Health. 2024;24(1):2426.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun T, Yuan Y, Wu K, et al. Trends and patterns in the global burden of intracerebral hemorrhage: a comprehensive analysis from 1990 to 2019. Front Neurol. 2023;14:1241158.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAn SJ, Kim TJ, Yoon B, Epidemiology. Risk Factors, and Clinical Features of Intracerebral Hemorrhage: An Update. J Stroke. 2017;19(1):3\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMagid-Bernstein J, Girard R, Polster S, et al. Cerebral Hemorrhage: Pathophysiology, Treatment, and Future Directions. Circ Res. 2022;130(8):1204\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJames FR, Power N, Laha S. Decision-making in intensive care medicine \u0026ndash; A review. J Intensive Care Soc. 2018;19(3):247\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreenberg SM, Ziai WC, Cordonnier C, et al. 2022 Guideline for the Management of Patients With Spontaneous Intracerebral Hemorrhage: A Guideline From the American Heart Association/American Stroke Association. Stroke. 2022;53(7):e282\u0026ndash;361.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThe ICH, Score.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuiz-Sandoval JL, Chiquete E, Romero-Vargas S, Padilla-Mart\u0026iacute;nez JJ, Gonz\u0026aacute;lez-Cornejo S. Grading Scale for Prediction of Outcome in Primary Intracerebral Hemorrhages. Stroke. 2007;38(5):1641\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreg\u0026oacute;rio T, Pipa S, Cavaleiro P, et al. Prognostic models for intracerebral hemorrhage: systematic review and meta-analysis. BMC Med Res Methodol. 2018;18(1):145.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWitsch J, Siegerink B, Nolte CH, et al. Prognostication after intracerebral hemorrhage: a review. Neurol Res Pract. 2021;3(1):22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAytuluk HG, Basaran S, Dogan NO, Demir N. Comparison of Conventional Intensive Care Scoring Systems and Prognostic Scores Specific for Intracerebral Hemorrhage in Predicting One-Year Mortality. Neurocrit Care. 2021;34(1):92\u0026ndash;101.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim E, Wee JH, Choi YH, et al. Clinical and Radiological Predictors for Early Hematoma Expansion After Spontaneous Intracerebral Hemorrhage: A Retrospective Study. Neurol Int. 2025;17(10):170.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLehmann F, Schenk LM, Ilic I, et al. Prolonged Mechanical Ventilation in Patients with Deep-Seated Intracerebral Hemorrhage: Risk Factors and Clinical Implications. J Clin Med. 2021;10(5):1015.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRodr\u0026iacute;guez-Fern\u0026aacute;ndez S, Castillo-Lorente E, Guerrero-Lopez F, et al. Validation of the ICH score in patients with spontaneous intracerebral haemorrhage admitted to the intensive care unit in Southern Spain. BMJ Open. 2018;8(8):e021719.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClinical. Update\u0026thinsp;\u0026ndash;\u0026thinsp;2022 Guideline for the Management of Patients With Spontaneous Intracerebral Hemorrhage.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi S, Li H, Chen J, et al. Development and external validation of a dynamic nomogram for predicting the risk of functional outcome after 90 days in patients with acute intracerebral hemorrhage. Front Neurol. 2025;16:1519091.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu M, Wang Z, Meng X, et al. Predictive Nomogram for Unfavorable Outcome of Spontaneous Intracerebral Hemorrhage. World Neurosurg. 2022;164:e1111\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan Q, Zuo Z, Su D et al. Identification and Validation of a Novel Nomogram Predicting 7-day Death in Patients with Intracerebral Hemorrhage. Balkan Med J. 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Liu X, Wang J, Pan C, Tang Z. A Nomogram Model for Predicting Prognosis in Spontaneous Intracerebral Hemorrhage Patients. J Integr Neurosci. 2023;22(2):42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZou J, Chen H, Liu C, et al. Development and validation of a nomogram to predict the 30-day mortality risk of patients with intracerebral hemorrhage. Front Neurosci. 2022;16:942100.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang H, Li Y, Ye F, et al. Clinical value of inflammatory indices in predicting poor prognosis and post-hemorrhagic hydrocephalus in patients with intraventricular hemorrhage. BMC Neurol. 2025;25(1):115.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu G, Zhang G, Zhang J, Wang L, Du B. Prognostic value of nomogram model based on clinical risk factors and CT radiohistological features in hypertensive intracerebral hemorrhage. Front Neurol. 2024;15:1502133.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu Z, Zheng J, Guo R, et al. Prognostic impact of leukocytosis in intracerebral hemorrhage. Med (Baltim). 2019;98(28):e16281.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatsumoto K, Ishihara K, Matsuda K, et al. Machine Learning\u0026ndash;Based Prediction for In-Hospital Mortality After Acute Intracerebral Hemorrhage Using Real‐World Clinical and Image Data. J Am Heart Assoc. 2024;13(24):e036447.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang S, Zhang W, Li J, Yang X, Wang Y. Nomogram based on pan-immune-inflammation value to predict short-term prognosis in spontaneous intracerebral hemorrhage. Front Neurol. 2025;16:1606436.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang H, Li S, Nie Y, et al. Online Dynamic Nomogram for Predicting 90-Day Prognosis of Patients With Primary Basal Ganglia Cerebral Hemorrhage After Microscopic Keyhole Craniotomy for Hematoma Removal. Brain Behav. 2025;15(2):e70344.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-informatics-and-decision-making","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"midm","sideBox":"Learn more about [BMC Medical Informatics and Decision Making](http://bmcmedinformdecismak.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/midm/default.aspx","title":"BMC Medical Informatics and Decision Making","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Intracerebral hemorrhage, ICU, nomogram, short-term survival, prognostic Model","lastPublishedDoi":"10.21203/rs.3.rs-8866295/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8866295/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study aims to construct and validate a short-term prognostic nomogram model for Intracerebral hemorrhage (ICH) based on information immediately upon admission, providing support for early risk identification and clinical decision-making in the intensive care unit (ICU).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study retrospectively analysised 135 patients with intracerebral hemorrhage (ICH) admitted to the ICU of Neijiang Hospital of Traditional Chinese Medicine in Sichuan Province from January 2021 to December 2025, and divided them sequentially into a training set (n\u0026thinsp;=\u0026thinsp;97) and a validation set (n\u0026thinsp;=\u0026thinsp;38). Univariate and multivariate Cox regression analyses were employed to identify independent predictive factors, and nomogram were constructed to predict survival probabilities. Model discrimination, calibration, clinical utility, and stratification capability were evaluated using receiver operating characteristic curves, calibration curves, decision curves and Kaplan-Meier survival curves.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMultivariate analysis showed that age\u0026thinsp;\u0026ge;\u0026thinsp;65 years, history of heart disease, admission pulse\u0026thinsp;\u0026ge;\u0026thinsp;82 beats/min, admission GCS\u0026thinsp;\u0026ge;\u0026thinsp;9 points, decreased pupil reflex to light, abnormal muscle tone, physiological reflex loss, and white blood cell count\u0026thinsp;\u0026ge;\u0026thinsp;10 \u0026times; 10 ⁹/L were independent predictive factors for ICU admission mortality. The column chart constructed based on the above indicators had AUCs of 0.878, 0.824, and 0.863 on the 3/5/7 days in the training set, and AUCs of 0.727, 0.772, and 0.761 in the validation set. The Kaplan Meier curve for risk stratification clearly distinguishes between high, intermediate, and low-risk groups ( P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe short-term survival prediction nomogram for ICU intracerebral hemorrhage patients constructed in this study has good predictive performance, robust validation results, and high clinical usability. This model is based on readily available clinical information at the bedside and can achieve early risk stratification for severe ICH patients, providing reliable basis for clinical treatment decision-making, resource allocation, and communication with family members. In the future, multi center prospective studies are needed to further validate its generalization ability.\u003c/p\u003e","manuscriptTitle":"Development and validation a machine learning model based on clinical factors to predict short-term prognosis of ICU intracerebral hemorrhage patients: a retrospective study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-25 06:15:07","doi":"10.21203/rs.3.rs-8866295/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-02T02:42:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"192578555752419509238281530388976824489","date":"2026-03-27T15:37:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-20T09:45:14+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-25T12:07:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-16T11:04:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-16T11:03:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Informatics and Decision Making","date":"2026-02-13T00:58:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-informatics-and-decision-making","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"midm","sideBox":"Learn more about [BMC Medical Informatics and Decision Making](http://bmcmedinformdecismak.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/midm/default.aspx","title":"BMC Medical Informatics and Decision Making","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"531a755c-acc7-4988-b333-1958f92c62ba","owner":[],"postedDate":"March 25th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-25T06:15:07+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-25 06:15:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8866295","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8866295","identity":"rs-8866295","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}