Linear Chart Model for Adverse Prognosis within one Year in Acute Ischemic Stroke Patients

Linear Chart Model for Adverse Prognosis within one Year in Acute Ischemic Stroke Patients | 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 Article Linear Chart Model for Adverse Prognosis within one Year in Acute Ischemic Stroke Patients Wei-xin ZHANG, Ting HUANG, Wen-ting ZOU, Ting HUANG, Yin GAO This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5264566/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective The aim of this study was to explore the risk factors influencing adverse outcomes in patients with acute ischemic stroke (AIS)within one year and establish a linear prediction model based on them. Methods We conducted a retrospective analysis of 600 AIS patients treated at our hospital from January 2019 to June 2023. They were divided into an observation group (n=100, adverse prognosis) and a control group (n=500, good prognosis) based on the occurrence of adverse events within one year. Statistical analysis of intergroup differences was performed using the chi-square test, independent sample t-test, and Mann-Whitney U test. Single-factor, multiple-factor logistic regression, and Lasso regression analyses were conducted using the glmnet package to identify independent risk factors affecting AIS. Risk factors influencing adverse outcomes in AIS were depicted using column charts with the "rms" package.Bootstrap method was used for internal validation of the model. Results Single-factor logistic regression showed that age, admission NIHSS score, blood sugar, creatinine, blood urea nitrogen, white blood cell count, smoking history, stroke history, concurrent pneumonia, inability to walk within 48 hours of admission, and atrial fibrillation were the main risk factors ( P <0.05). Multiple-factor logistic regression revealed that age, admission NIHSS score, concurrent pneumonia, inability to walk within 48 hours of admission, and atrial fibrillation were independent risk factors influencing adverse outcomes in AIS patients within one year ( P <0.05). The ROC curve for the AIS adverse prognosis column chart model within one year showed high credibility, with a training set AUC of 0.993 (0.988-0.998) and a validation set AUC of 0.987 (0.969-1.000). Conclusion We has successfully constructed a risk prediction model based on a linear chart, which can be used to predict adverse outcomes in AIS patients within one year with high reliability. Health sciences/Neurology Health sciences/Risk factors Acute Ischemic Stroke Risk Factors Prognosis Prediction Model Figures Figure 1 Figure 2 Background Acute ischemic stroke (AIS) is a prevalent central nervous system disorder that significantly impacts patients' quality of life and functional recovery [ 1 ]. AIS arises from the interruption or severe reduction of blood supply to the brain, typically caused by arterial occlusion or stenosis [ 2 ]. This can result from thrombosis, atherosclerosis, or arterial spasms in the brain [ 3 ]. The incidence varies across different age groups, but the elderly and those with cardiovascular risk factors are more susceptible [ 4 ]. When blood flow to brain cells is interrupted, causing cell damage or death, patients may experience various neurological deficits. The clinical manifestations of AIS depend on the location and extent of brain damage, with common symptoms including sudden weakness or numbness in the face, arm, or leg, sudden speech difficulties, sudden vision problems, headache, dizziness, and balance and coordination disorders [ 5 – 6 ]. Prognosis varies depending on the severity of the stroke, early treatment, rehabilitation, and the patient's underlying health status; severe AIS can lead to coma and life-threatening conditions [ 7 ]. The first year following AIS onset is considered a critical period for treatment and rehabilitation. Clinical progress and prognosis during this period are crucial for the long-term impact on patients' lives [ 8 ]. Investigating adverse outcomes within one year of AIS is vital for improving patient treatment and rehabilitation strategies. By deep understanding these factors, we can better identify high-risk patients and take measures to reduce the risk of adverse outcomes. Materials and Methods General Data This retrospective analysis included 600 stroke patients treated at the Second Affiliated Hospital of Qiqihar Medical University from January 2019 to June 2023. Based on the occurrence of adverse events within one year, patients were divided into an observation group (n=100, adverse prognosis) and a control group (n=500, good prognosis). The observation group consisted of 39 females (39.0%) and 61 males (61.0%), with ages ranging from 61 to 86 years and an average age of （72.87±11.52 ）years. The control group included 185 females (37.0%) and 315 males (63.0%), with ages ranging from 51 to 75 years and an average age of （62.76±11.70） years.This study was approved by the Medical Ethics Committee of Qiqihar Medical University (Qi) Lun Shen [2021] No. 162. Inclusion Criteria (1) Meeting the diagnostic criteria for ischemic stroke according to the AHA/ASA guidelines [9]; (2) Stroke diagnosis confirmed by CT and/or MRI; (3) Disease onset within 3 days; (4) Signed informed consent. Exclusion Criteria (1) Hemorrhagic or other types of stroke; (2) Incomplete clinical case data; (3) Loss to follow-up; (4) Presence of severe cardiopulmonary, hepatic, renal, or other organ dysfunction, and malignancies. Patient Follow-up Clinical data, past medical history, and blood test results of enrolled patients were collected. Follow-ups were conducted via telephone or outpatient (inpatient) visits at 30, 60, 180, and 360 days after diagnosis (with a one-week time fluctuation). The primary endpoint was the occurrence of adverse events, with the modified Rankin scale used to evaluate adverse events. A score of ≤2 on the modified Rankin scale indicates a good prognosis, while a score of ≥3 indicates a poor prognosis. For detailed assessment criteria, see Table 1 [10-11]. Statistical Methods This retrospective study utilized SPSS 23.0 for general data analysis. Data model establishment and validation were performed using R language (R-4.1.0). Categorical data were represented by counts, normally distributed data by mean±standard deviation (SD), and non-normally distributed data by P[M(Q1,Q3)]. Data were statistically analyzed using chi-square tests, independent samples t-tests, and Mann-Whitney U tests. Risk factors for adverse outcomes within one year of ischemic stroke were analyzed using the glmnet package for single-factor and multi-factor logistic regression and Lasso regression. Column charts (nomograms) depicting risk factors influencing adverse outcomes in stroke were created using the "rms" package. Internal validation of the column chart was conducted using 1000 bootstrap resamples, and the pROC package was used for processing.The "rms" package was used for column chart drawing, the pROC package for validation ROC curve drawing, and the calibration package for calibration curve drawing and C-index and HL goodness-of-fit evaluation. The Bootstrap method was used for internal model validation. P <0.05 indicated statistical significance. Table 1 Modified Rankin Scale Score Interpretation 0 No symptoms 1 No significant disability. Able to carry out all usual activities, despite some symptoms. 2 Slight disability. Able to look after own affairs without assistance, but unable to carry out all previous activities. 3 Moderate disability; requires some assistance but can walk independently 4 Moderately severe disability; cannot walk independently; requires help with daily activities 5 Severe disability; bedridden; incontinent; completely dependent on others for daily activities 6 Death Results Intergroup Differences As shown in Table 2 , significant differences were observed between the observation and control groups in terms of age, smoking, stroke history, concurrent pneumonia, inability to walk within 48 hours of admission, atrial fibrillation, admission NIHSS, blood sugar, creatinine, blood urea nitrogen, and white blood cell count ( P < 0.05). Table 2 Comparison of Parameters between Two Groups Item Total(n = 600) Observation Group(n = 100) Control Group(n = 500) Diff(95%CI) χ 2 /t/z P Gender, n(%) / Female/Male 224(37.3%)/376(62.7%) 39(39.0%)/61(61.0%) 185(37.0%)/315(63.0%) - 0.142 0.706 Age 64.44 ± 12.25 72.87 ± 11.52 62.76 ± 11.70 10.11(7.60,12.62) 7.912 0.001 Education, n(%) / Primary School/Junior High School/Senior High School and above 280/114/206 55/15/30 225/99/176 - 3.453 0.178 Smoking, n(%) / None/Quit/Still smoking 310/118/172 40/19/41 270/99/131 - 9.556 0.008 Alcohol consumption, n(%) / No/Yes 445(74.2%)/155(25.8%) 81(81.0%)/19(19.0%) 364(72.8%)/136(27.2%) - 2.925 0.087 Stroke history, n(%) / No/Yes 434(72.3%)/166(27.7%) 62(62.0%)/38(38.0%) 372(74.4%)/128(25.6%) - 6.403 0.011 Concurrent pneumonia, n(%) / No/Yes 569(94.8%)/31(5.2%) 81(81.0%)/19(19.0%) 488(97.6%)/12(2.4%) - 46.867 0.001 Inability to walk within 48 hours of admission, n(%) / No/Yes 429(71.5%)/171(28.5%) 44(44.0%)/56(56.0%) 385(77.0%)/115(23.0%) - 44.534 0.001 Hypertension, n(%) / No/Yes 165(27.5%)/435(72.5%) 29(29.0%)/71(71.0%) 136(27.2%)/364(72.8%) - 0.135 0.713 Diabetes, n(%) / No/Yes 463(77.2%)/137(22.8%) 80(80.0%)/20(20.0%) 383(76.6%)/117(23.4%) - 0.547 0.460 Atrial fibrillation, n(%) / No/Yes 563(93.8%)/37(6.2%) 85(85.0%)/15(15.0%) 478(95.6%)/22(4.4%) - 16.182 0.001 Admission NIHSS 3.79 ± 2.21 7.66 ± 2.09 3.01 ± 1.17 4.65(4.36,4.94) 31.082 0.001 Total cholesterol 4.40 ± 1.09 4.47 ± 1.04 4.39 ± 1.11 0.08(−0.15,0.32) 0.701 0.483 Triglycerides 1.74 ± 1.41 1.56 ± 0.93 1.78 ± 1.48 −0.22(−0.52,0.08) 1.425 0.155 HDL cholesterol 1.15 ± 0.31 1.20 ± 0.31 1.14 ± 0.31 0.05(−0.01,0.12) 1.585 0.114 LDL cholesterol 2.56 ± 0.78 2.46 ± 0.88 2.57 ± 0.76 −0.11(−0.28,0.05) 1.329 0.184 Blood sugar 5.91 ± 2.26 6.56 ± 2.76 5.78 ± 2.12 0.77(0.29,1.26) 3.153 0.002 Alkaline phosphatase 79.43 ± 26.22 80.81 ± 28.02 79.16 ± 25.86 1.65(−4.00,7.29) 0.573 0.567 Creatinine 74.73 ± 35.36 81.61 ± 57.75 73.36 ± 28.77 8.25(0.66,15.83) 2.135 0.033 Blood urea nitrogen 4.96 ± 1.90 5.39 ± 2.27 4.87 ± 1.81 0.51(0.11,0.92) 2.471 0.014 International normalized ratio 1.03 ± 0.20 1.04 ± 0.16 1.02 ± 0.21 0.02(−0.02,0.07) 0.946 0.344 Uric acid 271.78 ± 110.87 277.36 ± 83.65 270.66 ± 115.59 6.70(−17.17,30.57) 0.551 0.582 White blood cell count 6.86 ± 2.50 7.83 ± 3.16 6.66 ± 2.31 1.17(0.64,1.70) 4.315 0.001 BMI 23.81 ± 3.66 23.53 ± 4.20 23.87 ± 3.54 −0.34(−1.12,0.45) 0.837 0.403 Heart rate 74.54 ± 10.14 74.76 ± 13.19 74.50 ± 9.43 0.26(−1.92,2.44) 0.233 0.816 Alanine aminotransferase 21.0(17.5,24.3) 21.1(17.0,23.9) 21.0(17.6,24.4) −0.0(−0.9,0.8) 0.088 0.930 Aspartate aminotransferase 22.7(20.3,25.7) 22.3(19.3,25.4) 22.8(20.4,25.7) −0.5(−1.2,0.2) 1.307 0.191 Homocysteine 19.2(15.6,22.7) 18.9(16.0,22.1) 19.3(15.5,22.8) −0.2(−1.1,0.7) 0.392 0.695 Construction and Validation of Stroke Risk Factors As shown in Fig. 1 , 28 significant clinical characteristics were selected from patient data. The LASSO Logistic regression model was used to select the most significant features for prediction model construction; when the minimum lambda was 0.005, 11 potential predictive factors related to adverse outcomes within one year of stroke were identified with non-zero coefficients. A model was constructed using the selected features and their respective weights. Single-Factor Logistic Regression As shown in Table 4 , single-factor Logistic regression analysis was performed on the aforementioned risk factors influencing adverse outcomes within one year of stroke. Age, admission NIHSS, blood sugar, creatinine, blood urea nitrogen, white blood cell count, smoking history, stroke history, concurrent pneumonia, inability to walk within 48 hours of admission, and atrial fibrillation were identified as the main risk factors ( P < 0.05),with the assignment situation seen in Table 3 . Table 3 The assignment of the independent variable Item Assignment Item Assignment Age < 60 years old = 0,≥60 years old = 1 Smoking None = 0, Quit = 1, Still smoking = 2 Admission NIHSS Original value entered Stroke history No = 0, Yes = 1 Blood sugar Original value entered Concurrent pneumonia No = 0, Yes = 1 Creatinine Original value entered Inability to walk within 48h of admission No = 0, Yes = 1 Blood urea nitrogen Original value entered Atrial fibrillation No = 0, Yes = 1 White blood cell count Original value entered Table 4 Risk factors influencing adverse outcomes within one year of stroke Item Partial regression coefficient Standard error Z value OR(95%CI) P Age 0.073 0.010 7.033 1.076(1.054,1.098) 0.001 Admission NIHSS 3.528 0.611 5.771 34.065(10.278,112.907) 0.001 Blood sugar 0.153 0.049 3.105 1.166(1.058,1.284) 0.002 Creatinine 0.007 0.003 2.124 1.007(1.001,1.013) 0.034 Blood urea nitrogen 0.144 0.059 2.449 1.155(1.029,1.296) 0.014 White blood cell count 0.188 0.045 4.177 1.207(1.105,1.318) 0.001 Smoking, n(%) / None/Quit/Still smoking 0.259 0.302 0.856 Reference/1.295(0.716,2.343) 0.392 0.748 0.246 3.035 2.113(1.303,3.424) 0.002 Stroke history, n(%) / No/Yes 0.577 0.230 2.509 Reference/1.781(1.135,2.796) 0.012 Concurrent pneumonia, n(%) / No/Yes 2.255 0.388 5.816 Reference/9.539(4.461,20.398) 0.001 Inability to walk within 48 hours of admission, n(%) / No/Yes 1.449 0.228 6.364 Reference/4.261(2.727,6.658) 0.001 Atrial fibrillation, n(%) / No/Yes 1.344 0.355 3.787 Reference/3.834(1.912,7.688) 0.001 Multi-Factor Logistic Regression As shown in Table 5 , multi-factor logistic regression was performed on the risk factors, revealing that age, admission NIHSS, concurrent pneumonia, inability to walk within 48 hours of admission, and atrial fibrillation are independent risk factors for adverse outcomes within one year of stroke ( P < 0.05). Table 5 Independent risk factors influencing adverse outcomes within one year of stroke Item Partial regression coefficient Standard error Z value OR(95%CI) P Age 0.076 0.031 2.423 1.079(1.015,1.147) 0.015 Admission NIHSS 3.836 0.948 4.048 46.339(7.232,296.904) 0.001 Blood sugar 0.224 0.144 1.552 1.251(0.943,1.660) 0.121 Creatinine 0.014 0.009 1.538 1.014(0.996,1.032) 0.124 Blood urea nitrogen 0.033 0.159 0.208 1.034(0.756,1.412) 0.835 White blood cell count 0.220 0.129 1.699 1.245(0.967,1.604) 0.089 Smoking, n(%) / None/Quit/Still smoking 0.879 0.865 1.015 Reference/2.408(0.442,13.133) 0.310 1.018 0.835 1.219 2.767(0.539,14.211) 0.223 Stroke history, n(%) / No/Yes 0.471 0.723 0.652 Reference/1.602(0.388,6.605) 0.515 Concurrent pneumonia, n(%) / No/Yes 3.249 1.182 2.749 Reference/25.769(2.541,261.370) 0.006 Inability to walk within 48 hours of admission, n(%) / No/Yes 1.691 0.683 2.477 Reference/5.426(1.423,20.687) 0.013 Atrial fibrillation, n(%) / No/Yes 2.885 0.920 3.134 Reference17.897(2.947,108.686) 0.002 Prediction Model Construction and Validation As shown in Fig. 2 a, a nomogram model for adverse outcomes within one year of stroke was constructed based on five risk factors: age, admission NIHSS, concurrent pneumonia, inability to walk within 48 hours of admission, and atrial fibrillation. Interpretation method: A vertical line is drawn for a patient's variable, and the corresponding Points are the scores for that factor. The sum of the points for the aforementioned variables of the patient (Total Points) corresponds to the Disease Risk, which is the probability of adverse outcomes. Figure 2 b is the validation ROC curve of the nomogram, showing a training set AUC: 0.917 (0.885–0.949), and a validation set AUC: 0.900 (0.845–0.955), indicating that the prediction model is highly credible. Figure 2 c is the calibration curve of the training set, with a C-index of 0.917 and an HL goodness-of-fit value of 22.64, p = 0.053. Figure 2 d is the calibration curve of the validation set, with a C-index of 0.902 and an HL goodness-of-fit value of 12.177, p = 0.435, suggesting that the model has good real-world value. Discussion Adverse outcomes within one year after AIS diagnosis are associated with various factors, and early prevention and proper management of risks are prerequisites for preventing AIS recurrence and achieving good outcomes. AIS, as a severe neurological disorder, often results in long-term adverse effects [ 12 – 13 ]. Within one year after diagnosis, patients may face multiple adverse outcomes, including neurological deficits, reduced quality of life, and increased risk of recurrence [ 14 – 15 ]. Therefore, understanding and accurately predicting the risk factors for adverse outcomes is crucial for optimizing patient treatment and rehabilitation strategies. Identification of risk factors can provide clinicians with powerful tools to identify high-risk patients and develop individualized treatment plans. This study showed that age, admission NIHSS score, inability to walk within 48 hours of admission, concurrent pneumonia, and atrial fibrillation are independent risk factors for poor outcomes in AIS, and further established a highly credible linear prediction model. This model not only promises accurate predictions but also helps better understand the pathophysiological mechanisms of adverse outcomes in AIS patients. Age as a key factor in AIS adverse outcomes has been widely confirmed. As age increases, patients' nervous system regeneration and repair capabilities gradually diminish, leading to a significant increase in the risk of adverse outcomes [ 16 ]. BEUKER et al. [ 17 ] conducted a retrospective cohort study to assess the relationship between age and long-term outcomes in AIS patients after mechanical thrombectomy. The study included 18,506 patients and found that patients aged ≥ 80 years had higher mortality and more common disabilities after surgery, and fewer could recover to no or mild disability one year later. This study highlights the impact of age on the long-term outcomes of AIS patients after thrombectomy, which may be related to changes in vascular health, metabolic stability, inflammatory responses, and other physiological and biochemical processes [ 18 ]. Therefore, in the management of AIS patients, treatment strategies need to be adjusted according to age factors, including more aggressive rehabilitation and monitoring. Admission NIHSS score is a commonly used clinical assessment tool to measure the severity of patients' neurological deficits. This scoring system indicates that an increased score means more severe neurological damage, which may lead to reduced recovery ability and an increased risk of adverse outcomes. Studies have pointed out [ 19 – 20 ] that an increased NIHSS score at admission is a key risk factor for early neurological deterioration in stroke patients and can predict AIS recurrence and early adverse outcomes. The prediction model in this study shows that although the NIHSS score at admission is an important factor in the risk of adverse outcomes in AIS patients, it is not the only factor. Therefore, relying solely on the NIHSS score to assess the prognosis of stroke is insufficient. Studies have shown that the inability to walk within 48 hours of admission is a risk factor for stroke-associated pneumonia [ 21 ], which is consistent with the results of this study. At the same time, studies have shown that the ability to walk within 48 hours of admission is a protective factor [ 22 ], and exercise helps prevent pulmonary infections in patients [ 23 ]. Clinical guidelines recommend that acute ischemic stroke patients with mobility should develop individualized rehabilitation plans for early rehabilitation activities [ 24 – 25 ]. The immunosuppressed state after stroke, dysphagia, and limited positional changes increase the risk of concurrent pneumonia. Concurrent pneumonia can lead to inflammatory responses, hypoxia, and systemic stress reactions, further aggravating brain damage [ 26 – 27 ]. Therefore, in the management of AIS patients, early pneumonia screening, prevention, and treatment are particularly important. Atrial fibrillation is significantly associated with the risk of thromboembolism, which can lead to stroke recurrence. ZHU et al. [ 28 ] conducted a meta-analysis to assess the impact of early rhythm control versus rate control on clinical outcomes in patients with newly diagnosed atrial fibrillation. The study pooled data from 8 studies involving 447,000 patients with atrial fibrillation and found that early rhythm control treatment was associated with better clinical outcomes. Compared with rate control, an early rhythm control strategy was significantly related to a reduced risk of major composite outcomes, including AIS. The occurrence of AIS is closely related to thrombus formation, which can block cerebral arteries, leading to brain tissue ischemia and infarction. In addition, microcirculatory disturbances are also a key mechanism of adverse outcomes, as they can cause local tissue ischemia and microvascular damage [ 29 ]. These pathophysiological changes may continue to affect brain tissue regeneration and repair, thereby adversely affecting the recovery of AIS patients. Therefore, in managing AIS patients, special attention should be paid to the screening and treatment of arrhythmias. This study provides insights into the factors associated with adverse outcomes within one year after AIS diagnosis, but it also has some limitations. First, the study used retrospective data, which may lead to selection bias in the information. Second, although this study identified several risk factors associated with adverse outcomes in AIS, a more in-depth study of the relationship between these factors and pathophysiological mechanisms is still needed. Finally, the results of this study are based on data from a single medical center and may be influenced by specific geographical and population characteristics. To increase the external validity of the study, future research could consider multicenter cooperation to validate the results in different regions and populations. Overall, AIS prognosis research is a complex and challenging field, but it has important clinical significance for improving patient rehabilitation and quality of life. Through continued in-depth research, a better understanding of the mechanisms of adverse outcomes in AIS can be achieved, and more effective intervention strategies can be developed. Declarations The datasets used and analysed during the current study available from the corresponding author on reasonable request. Acknowledgements Thanks to Qiqihar Medical University and the second affiliated Hospitals of Qiqihar Medical University for their scientific research help Author contributions Wei-xin ZHANG and Ting HUANG 1 completed the main writing of the article, and and Yin GAO completed the review of the article.Wen-ting ZOU and Ting HUANG 2 collected the dates. Funding Basic scientific research expenses of Heilongjiang Provincial Colleges and Universities .Project Project number: 2021-KYYWF-0389. Ethics approval and consent to participate All patients provided signed informed consent and volunteered to participate in the study. This study was approved by the Ethics Committee of Qiqihar Medical University Ethics Committee.All methods were performed in accordance with the relevant guidelines and regulations. Consent for publication Not Applicable. Competing interests The authors declare no conflicts of interest. Corresponding author Weixin ZHANG References Gao P, Wang T, Wang D, et al. Effect of Stenting Plus Medical Therapy vs Medical Therapy Alone on Risk of Stroke and Death in Patients With Symptomatic Intracranial Stenosis: The CASSISS Randomized Clinical Trial[J]. JAMA, 2022, 328(6): 534-542. Buckley B, Harrison S L, Hill A, et al. Stroke-Heart Syndrome: Incidence and Clinical Outcomes of Cardiac Complications Following Stroke[J]. Stroke, 2022, 53(5): 1759-1763. Wang N, Yang Y, Qiu B, et al. Correlation of the systemic immune-inflammation index with short- and long-term prognosis after acute ischemic stroke[J]. Aging (Albany NY), 2022, 14(16): 6567-6578. Lee G, Choi S, Chang J, et al. Association of L-alpha Glycerylphosphorylcholine With Subsequent Stroke Risk After 10 Years[J]. JAMA Netw Open, 2021, 4(11): e2136008. Lee K J, Kim B J, Han M K, et al. One-Year Blood Pressure Trajectory After Acute Ischemic Stroke[J]. J Am Heart Assoc, 2022, 11(5): e23747. Liu D, Yang K, Gu H, et al. Predictive effect of triglyceride-glucose index on clinical events in patients with acute ischemic stroke and type 2 diabetes mellitus[J]. Cardiovasc Diabetol, 2022, 21(1): 280. Hoshino T, Mizuno T, Ishizuka K, et al. Triglyceride-glucose index as a prognostic marker after ischemic stroke or transient ischemic attack: a prospective observational study[J]. Cardiovasc Diabetol, 2022, 21(1): 264. Takahashi S, Ishizuka K, Hoshino T, et al. Long-Term Outcome in Patients With Acute Ischemic Stroke and Heart Failure[J]. Circ J, 2023, 87(3): 401-408. Ford B, Peela S, Roberts C. Secondary Prevention of Ischemic Stroke: Updated Guidelines From AHA/ASA[J]. Am Fam Physician, 2022, 105(1): 99-102. Yi K, Nakajima M, Ikeda T, Yoshigai M, Ueda M. Modified Rankin scale assessment by telephone using a simple questionnaire. J Stroke Cerebrovasc Dis. 2022 Oct;31(10):106695. doi: 10.1016/j.jstrokecerebrovasdis.2022.106695. Epub 2022 Aug 30. PMID: 36054972. Banks JL, Marotta CA. Outcomes validity and reliability of the modified Rankin scale: implications for stroke clinical trials: a literature review and synthesis[J]. Stroke. 2007 38(3): 1091-6. LU Z J,YIN Z G,XIE X,et al.Validation of TM score and sFABS score in recognizing stroke mimics in patients with acute ischemic stroke[J].Chin J Geriatr Heart Brain Vesel Dis,2022,24(10):1073-1075. Li J, Qiu Y, Zhang C, et al. The role of protein glycosylation in the occurrence and outcome of acute ischemic stroke[J]. Pharmacol Res, 2023, 191: 106726. WANG W, GAO H W, HUO Y M,et al. Effect of early precision exercise rehabilitation on the levels of serum cytokine and walking ability in patients with hemiplegia after acute ischemic stroke[J].Chinese Journal of PracticalNervous Diseases, 2023,26(10):1275-1280. Khurshid S, Li X, Ashburner J M, et al. Usefulness of Rhythm Monitoring Following Acute Ischemic Stroke[J]. Am J Cardiol, 2021, 147: 44-51. Li L H, Chen C T, Chang Y C, et al. Prognostic role of neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and systemic immune inflammation index in acute ischemic stroke: a STROBE-compliant retrospective study[J]. Medicine (Baltimore), 2021, 100(25): e26354. Beuker C, Koppe J, Feld J, et al. Association of age with 1-year outcome in patients with acute ischaemic stroke treated with thrombectomy: real-world analysis in 18 506 patients[J]. J Neurol Neurosurg Psychiatry, 2023, 94(8): 631-637. Paul S, Candelario-Jalil E. Emerging neuroprotective strategies for the treatment of ischemic stroke: an overview of clinical and preclinical studies[J]. Exp Neurol, 2021, 335: 113518. YAN C,GONG Y,HUANG P, et al. Early poor prognosis and predictive model construction in patients with acute ischemic stroke[J]South China J Prev Med,2023,49(10):1213-1217. QIN Y F, GAO S Y,ZHANG H L, et al.Analysis of influencing factors of one-year poor prognosis of acute ischemic stroke[J]. Beijing Medical Journal,2023,45(05): 393-397. Chumbler NR, Williams LS, Wells CK, et al. Derivation and validation of a clinical system for predicting pneumonia in acute stroke[J]. Neuroepidemiology. 2010; 34(4): 193-199. Li YM, Zhao L, Liu YG, et al. Novel Predictors of Stroke-Associated Pneumonia: A Single Center Analysis[J]. Front Neurol. 2022, 13: 857420. Song Y, Ren F, Sun D, et al. Benefits of Exercise on Influenza or Pneumonia in Older Adults: A Systematic Review[J]. Int J Environ Res Public Health. 2020, 17(8): 2655. ZHOU S Y,YANG Z,ZHENG T H.Early rehabilitation activity for stroke patients: a review [J].China Prev Med J,2024,36(02):127-130. Marzolini S, Robertson AD, Oh P, et al. Aerobic Training and Mobilization Early Post-stroke: Cautions and Considerations[J]. Front Neurol. 2019, 10: 1187. Fan J L, Nogueira R C, Brassard P, et al. Integrative physiological assessment of cerebral hemodynamics and metabolism in acute ischemic stroke[J]. J Cereb Blood Flow Metab, 2022, 42(3): 454-470. DENG T,CHEN J M, LIU X M, et al.Risk factors of stroke-associated pneumonia for patients with mild to moderate acute ischemic stroke[J].Chin J Rehabil Theory Pract, 2023, 29 (06): 708-713. Zhu W, Wu Z, Dong Y, et al. Effectiveness of early rhythm control in improving clinical outcomes in patients with atrial fibrillation: a systematic review and meta-analysis[J]. BMC Med, 2022, 20(1): 340. Saeed O, Zhang S, Patel S R, et al. Oral Anticoagulation and Adverse Outcomes after Ischemic Stroke in Heart Failure Patients without Atrial Fibrillation[J]. J Card Fail, 2021, 27(8): 857-864. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5264566","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":380935798,"identity":"5e813198-61c4-48ec-8771-87f5c4bb712b","order_by":0,"name":"Wei-xin ZHANG","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArUlEQVRIiWNgGAWjYDACCSD+AGEaEK+FcQbJWph5SNIiP7vHTNqmbFtiA3vzNgmGmjuEtTDOOWNsnHPudmIDz7EyCYZjzwhrYZbI3fg4tw2oRSLHTIKx4TBhLWwSuRsOW4K0yL8hUgsPyBZGsC08RGqRkMj/bNhz7rZxG09asUXCMSK0yM9IS5P4UXZbtp/98MYbH2qI0AIBbGDEwJBArAao+lEwCkbBKBgFOAAAJbE2Ir30UZMAAAAASUVORK5CYII=","orcid":"","institution":"Qiqihar Medical University","correspondingAuthor":true,"prefix":"","firstName":"Wei-xin","middleName":"","lastName":"ZHANG","suffix":""},{"id":380935800,"identity":"96122be8-c045-4ebc-8599-14e9346aa1d5","order_by":1,"name":"Ting HUANG","email":"","orcid":"","institution":"Qiqihar Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ting","middleName":"","lastName":"HUANG","suffix":""},{"id":380935801,"identity":"0338ff18-391f-4b83-bcf7-cac32debc627","order_by":2,"name":"Wen-ting ZOU","email":"","orcid":"","institution":"Department of neurosurgery，The second affiliated hospital of Qiqihar Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wen-ting","middleName":"","lastName":"ZOU","suffix":""},{"id":380935802,"identity":"1701b645-5498-4180-93a1-36b7adec4472","order_by":3,"name":"Ting HUANG","email":"","orcid":"","institution":"Department of neurosurgery，The second affiliated hospital of Qiqihar Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ting","middleName":"","lastName":"HUANG","suffix":""},{"id":380935803,"identity":"33bffd6c-1f9d-4e4f-b9b3-32228d6e5b8b","order_by":4,"name":"Yin GAO","email":"","orcid":"","institution":"Qiqihar Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yin","middleName":"","lastName":"GAO","suffix":""}],"badges":[],"createdAt":"2024-10-15 03:08:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5264566/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5264566/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71002915,"identity":"62f12078-6db3-4d29-bc75-55d4506d49bd","added_by":"auto","created_at":"2024-12-10 06:01:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":49522,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction and validation of adverse outcomes within one year of stroke.\u003c/p\u003e\n\u003cp\u003e*Note: The LASSO Logistic regression model was used for clinical case feature selection. Figure 1a: A plot of partial likelihood deviation versus log (lambda) is presented. The y-axis represents partial likelihood deviation, with the lower x-axis representing log(lambda) and the upper x-axis representing the average number of predictive variables. A vertical dashed line is drawn at the optimal value based on the minimum criterion and one standard error of the minimum criterion. The adjustment parameter (λ) in the LASSO model is selected based on 10-fold cross-validation based on the lowest criterion. Figure 1b: Distribution of LASSO coefficients for 28 clinical parameter features. Coefficients (y-axis) are plotted according to log(lambda), and 6 features with non-zero coefficients are selected to construct a model for adverse outcomes within one year of stroke.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5264566/v1/58056e41e639967a774be7f4.png"},{"id":71003265,"identity":"3e284764-fa25-47c8-ab2e-4ede786feadf","added_by":"auto","created_at":"2024-12-10 06:02:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":86422,"visible":true,"origin":"","legend":"\u003cp\u003eColumn chart and validation curve for adverse outcomes within one year of stroke.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5264566/v1/8ef65a3f67424030e6bfc494.png"},{"id":92568554,"identity":"369425da-8694-4dcd-9061-493921bbc2ca","added_by":"auto","created_at":"2025-10-01 07:09:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":964004,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5264566/v1/2ae59d0e-0a57-4b78-aafc-b046165bb786.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Linear Chart Model for Adverse Prognosis within one Year in Acute Ischemic Stroke Patients","fulltext":[{"header":"Background","content":"\u003cp\u003eAcute ischemic stroke (AIS) is a prevalent central nervous system disorder that significantly impacts patients' quality of life and functional recovery [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. AIS arises from the interruption or severe reduction of blood supply to the brain, typically caused by arterial occlusion or stenosis [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This can result from thrombosis, atherosclerosis, or arterial spasms in the brain [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The incidence varies across different age groups, but the elderly and those with cardiovascular risk factors are more susceptible [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. When blood flow to brain cells is interrupted, causing cell damage or death, patients may experience various neurological deficits. The clinical manifestations of AIS depend on the location and extent of brain damage, with common symptoms including sudden weakness or numbness in the face, arm, or leg, sudden speech difficulties, sudden vision problems, headache, dizziness, and balance and coordination disorders [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Prognosis varies depending on the severity of the stroke, early treatment, rehabilitation, and the patient's underlying health status; severe AIS can lead to coma and life-threatening conditions [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The first year following AIS onset is considered a critical period for treatment and rehabilitation. Clinical progress and prognosis during this period are crucial for the long-term impact on patients' lives [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Investigating adverse outcomes within one year of AIS is vital for improving patient treatment and rehabilitation strategies. By deep understanding these factors, we can better identify high-risk patients and take measures to reduce the risk of adverse outcomes.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eGeneral Data\u003c/p\u003e\n\u003cp\u003eThis retrospective analysis included 600 stroke patients treated at the Second Affiliated Hospital of Qiqihar Medical University from January 2019 to June 2023. Based on the occurrence of adverse events within one year, patients were divided into an observation group (n=100, adverse prognosis) and a control group (n=500, good prognosis). The observation group consisted of 39 females (39.0%) and 61 males (61.0%), with ages ranging from 61 to 86 years and an average age of （72.87\u0026plusmn;11.52 ）years. The control group included 185 females (37.0%) and 315 males (63.0%), with ages ranging from 51 to 75 years and an average age of （62.76\u0026plusmn;11.70） years.This study was approved by the Medical Ethics Committee of Qiqihar Medical University (Qi) Lun Shen [2021] No. 162.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInclusion Criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(1) Meeting the diagnostic criteria for ischemic stroke according to the AHA/ASA guidelines [9]; (2) Stroke diagnosis confirmed by CT and/or MRI; (3) Disease onset within 3 days; (4) Signed informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExclusion Criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(1) Hemorrhagic or other types of stroke; (2) Incomplete clinical case data; (3) Loss to follow-up; (4) Presence of severe cardiopulmonary, hepatic, renal, or other organ dysfunction, and malignancies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient Follow-up\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical data, past medical history, and blood test results of enrolled patients were collected. Follow-ups were conducted via telephone or outpatient (inpatient) visits at 30, 60, 180, and 360 days after diagnosis (with a one-week time fluctuation). The primary endpoint was the occurrence of adverse events, with the modified Rankin scale used to evaluate adverse events. A score of \u0026le;2 on the modified Rankin scale indicates a good prognosis, while a score of \u0026ge;3 indicates a poor prognosis. For detailed assessment criteria, see Table 1 [10-11].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective study utilized SPSS 23.0 for general data analysis. Data model establishment and validation were performed using R language (R-4.1.0). Categorical data were represented by counts, normally distributed data by mean\u0026plusmn;standard deviation (SD), and non-normally distributed data by P[M(Q1,Q3)]. Data were statistically analyzed using chi-square tests, independent samples t-tests, and Mann-Whitney U tests. Risk factors for adverse outcomes within one year of ischemic stroke were analyzed using the glmnet package for single-factor and multi-factor logistic regression and Lasso regression. Column charts (nomograms) depicting risk factors influencing adverse outcomes in stroke were created using the \u0026quot;rms\u0026quot; package. Internal validation of the column chart was conducted using 1000 bootstrap resamples, and the pROC package was used for processing.The \u0026quot;rms\u0026quot; package was used for column chart drawing, the pROC package for validation ROC curve drawing, and the calibration package for calibration curve drawing and C-index and HL goodness-of-fit evaluation. The Bootstrap method was used for internal model validation. \u0026nbsp;\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 indicated statistical significance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003e Modified Rankin Scale\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"665\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.5789%;\"\u003e\n \u003cp\u003eScore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68.4211%;\"\u003e\n \u003cp\u003eInterpretation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.5789%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68.4211%;\"\u003e\n \u003cp\u003eNo symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.5789%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68.4211%;\"\u003e\n \u003cp\u003eNo significant disability. Able to carry out all usual activities, despite some symptoms.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.5789%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68.4211%;\"\u003e\n \u003cp\u003eSlight disability. Able to look after own affairs without assistance, but unable to carry out all previous activities.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.5789%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68.4211%;\"\u003e\n \u003cp\u003eModerate disability; requires some assistance but can walk independently\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.5789%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68.4211%;\"\u003e\n \u003cp\u003eModerately severe disability; cannot walk independently; requires help with daily activities\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.5789%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68.4211%;\"\u003e\n \u003cp\u003eSevere disability; bedridden; incontinent; completely dependent on others for daily activities\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.5789%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68.4211%;\"\u003e\n \u003cp\u003eDeath\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eIntergroup Differences\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, significant differences were observed between the observation and control groups in terms of age, smoking, stroke history, concurrent pneumonia, inability to walk within 48 hours of admission, atrial fibrillation, admission NIHSS, blood sugar, creatinine, blood urea nitrogen, and white blood cell count (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of Parameters between Two Groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal(n\u0026thinsp;=\u0026thinsp;600)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eObservation Group(n\u0026thinsp;=\u0026thinsp;100)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControl Group(n\u0026thinsp;=\u0026thinsp;500)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDiff(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e/t/z\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, n(%) / Female/Male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e224(37.3%)/376(62.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39(39.0%)/61(61.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e185(37.0%)/315(63.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.706\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.44\u0026thinsp;\u0026plusmn;\u0026thinsp;12.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.87\u0026thinsp;\u0026plusmn;\u0026thinsp;11.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.76\u0026thinsp;\u0026plusmn;\u0026thinsp;11.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.11(7.60,12.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation, n(%) / Primary School/Junior High School/Senior High School and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e280/114/206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55/15/30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e225/99/176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking, n(%) / None/Quit/Still smoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e310/118/172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40/19/41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e270/99/131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol consumption, n(%) / No/Yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e445(74.2%)/155(25.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81(81.0%)/19(19.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e364(72.8%)/136(27.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke history, n(%) / No/Yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e434(72.3%)/166(27.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62(62.0%)/38(38.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e372(74.4%)/128(25.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConcurrent pneumonia, n(%) / No/Yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e569(94.8%)/31(5.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81(81.0%)/19(19.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e488(97.6%)/12(2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e46.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInability to walk within 48 hours of admission, n(%) / No/Yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e429(71.5%)/171(28.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44(44.0%)/56(56.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e385(77.0%)/115(23.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e44.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, n(%) / No/Yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e165(27.5%)/435(72.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29(29.0%)/71(71.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e136(27.2%)/364(72.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.713\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes, n(%) / No/Yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e463(77.2%)/137(22.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80(80.0%)/20(20.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e383(76.6%)/117(23.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.460\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtrial fibrillation, n(%) / No/Yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e563(93.8%)/37(6.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85(85.0%)/15(15.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e478(95.6%)/22(4.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdmission NIHSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.79\u0026thinsp;\u0026plusmn;\u0026thinsp;2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.66\u0026thinsp;\u0026plusmn;\u0026thinsp;2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.01\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.65(4.36,4.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e31.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.40\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.47\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.39\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.08(\u0026minus;0.15,0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.483\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.74\u0026thinsp;\u0026plusmn;\u0026thinsp;1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.78\u0026thinsp;\u0026plusmn;\u0026thinsp;1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.22(\u0026minus;0.52,0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05(\u0026minus;0.01,0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.11(\u0026minus;0.28,0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood sugar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.91\u0026thinsp;\u0026plusmn;\u0026thinsp;2.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.56\u0026thinsp;\u0026plusmn;\u0026thinsp;2.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.78\u0026thinsp;\u0026plusmn;\u0026thinsp;2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.77(0.29,1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlkaline phosphatase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.43\u0026thinsp;\u0026plusmn;\u0026thinsp;26.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.81\u0026thinsp;\u0026plusmn;\u0026thinsp;28.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.16\u0026thinsp;\u0026plusmn;\u0026thinsp;25.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.65(\u0026minus;4.00,7.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.567\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74.73\u0026thinsp;\u0026plusmn;\u0026thinsp;35.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81.61\u0026thinsp;\u0026plusmn;\u0026thinsp;57.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73.36\u0026thinsp;\u0026plusmn;\u0026thinsp;28.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.25(0.66,15.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood urea nitrogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.96\u0026thinsp;\u0026plusmn;\u0026thinsp;1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.39\u0026thinsp;\u0026plusmn;\u0026thinsp;2.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.87\u0026thinsp;\u0026plusmn;\u0026thinsp;1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.51(0.11,0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternational normalized ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02(\u0026minus;0.02,0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.344\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e271.78\u0026thinsp;\u0026plusmn;\u0026thinsp;110.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e277.36\u0026thinsp;\u0026plusmn;\u0026thinsp;83.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e270.66\u0026thinsp;\u0026plusmn;\u0026thinsp;115.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.70(\u0026minus;17.17,30.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.582\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite blood cell count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.86\u0026thinsp;\u0026plusmn;\u0026thinsp;2.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.83\u0026thinsp;\u0026plusmn;\u0026thinsp;3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.66\u0026thinsp;\u0026plusmn;\u0026thinsp;2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.17(0.64,1.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.81\u0026thinsp;\u0026plusmn;\u0026thinsp;3.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.53\u0026thinsp;\u0026plusmn;\u0026thinsp;4.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.87\u0026thinsp;\u0026plusmn;\u0026thinsp;3.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.34(\u0026minus;1.12,0.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74.54\u0026thinsp;\u0026plusmn;\u0026thinsp;10.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.76\u0026thinsp;\u0026plusmn;\u0026thinsp;13.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.50\u0026thinsp;\u0026plusmn;\u0026thinsp;9.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.26(\u0026minus;1.92,2.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlanine aminotransferase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.0(17.5,24.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.1(17.0,23.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.0(17.6,24.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.0(\u0026minus;0.9,0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.930\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAspartate aminotransferase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.7(20.3,25.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.3(19.3,25.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.8(20.4,25.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.5(\u0026minus;1.2,0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHomocysteine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.2(15.6,22.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.9(16.0,22.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.3(15.5,22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.2(\u0026minus;1.1,0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.695\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eConstruction and Validation of Stroke Risk Factors\u003c/h3\u003e\n\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, 28 significant clinical characteristics were selected from patient data. The LASSO Logistic regression model was used to select the most significant features for prediction model construction; when the minimum lambda was 0.005, 11 potential predictive factors related to adverse outcomes within one year of stroke were identified with non-zero coefficients. A model was constructed using the selected features and their respective weights.\u003c/p\u003e\n\u003ch3\u003eSingle-Factor Logistic Regression\u003c/h3\u003e\n\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, single-factor Logistic regression analysis was performed on the aforementioned risk factors influencing adverse outcomes within one year of stroke. Age, admission NIHSS, blood sugar, creatinine, blood urea nitrogen, white blood cell count, smoking history, stroke history, concurrent pneumonia, inability to walk within 48 hours of admission, and atrial fibrillation were identified as the main risk factors (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05),with the assignment situation seen in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe assignment of the independent variable\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssignment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAssignment\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;60 years old\u0026thinsp;=\u0026thinsp;0,\u0026ge;60 years old\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNone\u0026thinsp;=\u0026thinsp;0, Quit\u0026thinsp;=\u0026thinsp;1, Still smoking\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdmission NIHSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOriginal value entered\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStroke history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u0026thinsp;=\u0026thinsp;0, Yes\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood sugar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOriginal value entered\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConcurrent pneumonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u0026thinsp;=\u0026thinsp;0, Yes\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOriginal value entered\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInability to walk within 48h of admission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u0026thinsp;=\u0026thinsp;0, Yes\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood urea nitrogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOriginal value entered\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAtrial fibrillation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u0026thinsp;=\u0026thinsp;0, Yes\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite blood cell count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOriginal value entered\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRisk factors influencing adverse outcomes within one year of stroke\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePartial regression coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard\u003c/p\u003e \u003cp\u003eerror\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.076(1.054,1.098)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdmission NIHSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.065(10.278,112.907)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood sugar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.166(1.058,1.284)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.007(1.001,1.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood urea nitrogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.155(1.029,1.296)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite blood cell count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.207(1.105,1.318)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSmoking, n(%) / None/Quit/Still smoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference/1.295(0.716,2.343)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.392\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.113(1.303,3.424)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke history, n(%) / No/Yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference/1.781(1.135,2.796)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConcurrent pneumonia, n(%) / No/Yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference/9.539(4.461,20.398)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInability to walk within 48 hours of admission, n(%) / No/Yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference/4.261(2.727,6.658)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtrial fibrillation, n(%) / No/Yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference/3.834(1.912,7.688)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMulti-Factor Logistic Regression\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, multi-factor logistic regression was performed on the risk factors, revealing that age, admission NIHSS, concurrent pneumonia, inability to walk within 48 hours of admission, and atrial fibrillation are independent risk factors for adverse outcomes within one year of stroke (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIndependent risk factors influencing adverse outcomes within one year of stroke\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePartial regression coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard\u003c/p\u003e \u003cp\u003eerror\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.079(1.015,1.147)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdmission NIHSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46.339(7.232,296.904)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood sugar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.251(0.943,1.660)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.014(0.996,1.032)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood urea nitrogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.034(0.756,1.412)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.835\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite blood cell count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.245(0.967,1.604)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSmoking, n(%) / None/Quit/Still smoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference/2.408(0.442,13.133)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.310\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.767(0.539,14.211)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke history, n(%) / No/Yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference/1.602(0.388,6.605)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.515\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConcurrent pneumonia, n(%) / No/Yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference/25.769(2.541,261.370)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInability to walk within 48 hours of admission, n(%) / No/Yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference/5.426(1.423,20.687)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtrial fibrillation, n(%) / No/Yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference17.897(2.947,108.686)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePrediction Model Construction and Validation\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, a nomogram model for adverse outcomes within one year of stroke was constructed based on five risk factors: age, admission NIHSS, concurrent pneumonia, inability to walk within 48 hours of admission, and atrial fibrillation. Interpretation method: A vertical line is drawn for a patient's variable, and the corresponding Points are the scores for that factor. The sum of the points for the aforementioned variables of the patient (Total Points) corresponds to the Disease Risk, which is the probability of adverse outcomes. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb is the validation ROC curve of the nomogram, showing a training set AUC: 0.917 (0.885\u0026ndash;0.949), and a validation set AUC: 0.900 (0.845\u0026ndash;0.955), indicating that the prediction model is highly credible. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec is the calibration curve of the training set, with a C-index of 0.917 and an HL goodness-of-fit value of 22.64, p\u0026thinsp;=\u0026thinsp;0.053. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed is the calibration curve of the validation set, with a C-index of 0.902 and an HL goodness-of-fit value of 12.177, p\u0026thinsp;=\u0026thinsp;0.435, suggesting that the model has good real-world value.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAdverse outcomes within one year after AIS diagnosis are associated with various factors, and early prevention and proper management of risks are prerequisites for preventing AIS recurrence and achieving good outcomes. AIS, as a severe neurological disorder, often results in long-term adverse effects [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Within one year after diagnosis, patients may face multiple adverse outcomes, including neurological deficits, reduced quality of life, and increased risk of recurrence [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Therefore, understanding and accurately predicting the risk factors for adverse outcomes is crucial for optimizing patient treatment and rehabilitation strategies. Identification of risk factors can provide clinicians with powerful tools to identify high-risk patients and develop individualized treatment plans. This study showed that age, admission NIHSS score, inability to walk within 48 hours of admission, concurrent pneumonia, and atrial fibrillation are independent risk factors for poor outcomes in AIS, and further established a highly credible linear prediction model. This model not only promises accurate predictions but also helps better understand the pathophysiological mechanisms of adverse outcomes in AIS patients.\u003c/p\u003e \u003cp\u003eAge as a key factor in AIS adverse outcomes has been widely confirmed. As age increases, patients' nervous system regeneration and repair capabilities gradually diminish, leading to a significant increase in the risk of adverse outcomes [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. BEUKER et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] conducted a retrospective cohort study to assess the relationship between age and long-term outcomes in AIS patients after mechanical thrombectomy. The study included 18,506 patients and found that patients aged\u0026thinsp;\u0026ge;\u0026thinsp;80 years had higher mortality and more common disabilities after surgery, and fewer could recover to no or mild disability one year later. This study highlights the impact of age on the long-term outcomes of AIS patients after thrombectomy, which may be related to changes in vascular health, metabolic stability, inflammatory responses, and other physiological and biochemical processes [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Therefore, in the management of AIS patients, treatment strategies need to be adjusted according to age factors, including more aggressive rehabilitation and monitoring.\u003c/p\u003e \u003cp\u003eAdmission NIHSS score is a commonly used clinical assessment tool to measure the severity of patients' neurological deficits. This scoring system indicates that an increased score means more severe neurological damage, which may lead to reduced recovery ability and an increased risk of adverse outcomes. Studies have pointed out [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] that an increased NIHSS score at admission is a key risk factor for early neurological deterioration in stroke patients and can predict AIS recurrence and early adverse outcomes. The prediction model in this study shows that although the NIHSS score at admission is an important factor in the risk of adverse outcomes in AIS patients, it is not the only factor. Therefore, relying solely on the NIHSS score to assess the prognosis of stroke is insufficient.\u003c/p\u003e \u003cp\u003eStudies have shown that the inability to walk within 48 hours of admission is a risk factor for stroke-associated pneumonia [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], which is consistent with the results of this study. At the same time, studies have shown that the ability to walk within 48 hours of admission is a protective factor [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], and exercise helps prevent pulmonary infections in patients [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Clinical guidelines recommend that acute ischemic stroke patients with mobility should develop individualized rehabilitation plans for early rehabilitation activities [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe immunosuppressed state after stroke, dysphagia, and limited positional changes increase the risk of concurrent pneumonia. Concurrent pneumonia can lead to inflammatory responses, hypoxia, and systemic stress reactions, further aggravating brain damage [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Therefore, in the management of AIS patients, early pneumonia screening, prevention, and treatment are particularly important.\u003c/p\u003e \u003cp\u003eAtrial fibrillation is significantly associated with the risk of thromboembolism, which can lead to stroke recurrence. ZHU et al. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] conducted a meta-analysis to assess the impact of early rhythm control versus rate control on clinical outcomes in patients with newly diagnosed atrial fibrillation. The study pooled data from 8 studies involving 447,000 patients with atrial fibrillation and found that early rhythm control treatment was associated with better clinical outcomes. Compared with rate control, an early rhythm control strategy was significantly related to a reduced risk of major composite outcomes, including AIS. The occurrence of AIS is closely related to thrombus\u003c/p\u003e \u003cp\u003eformation, which can block cerebral arteries, leading to brain tissue ischemia and infarction. In addition, microcirculatory disturbances are also a key mechanism of adverse outcomes, as they can cause local tissue ischemia and microvascular damage [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. These pathophysiological changes may continue to affect brain tissue regeneration and repair, thereby adversely affecting the recovery of AIS patients. Therefore, in managing AIS patients, special attention should be paid to the screening and treatment of arrhythmias.\u003c/p\u003e \u003cp\u003eThis study provides insights into the factors associated with adverse outcomes within one year after AIS diagnosis, but it also has some limitations. First, the study used retrospective data, which may lead to selection bias in the information. Second, although this study identified several risk factors associated with adverse outcomes in AIS, a more in-depth study of the relationship between these factors and pathophysiological mechanisms is still needed. Finally, the results of this study are based on data from a single medical center and may be influenced by specific geographical and population characteristics. To increase the external validity of the study, future research could consider multicenter cooperation to validate the results in different regions and populations. Overall, AIS prognosis research is a complex and challenging field, but it has important clinical significance for improving patient rehabilitation and quality of life. Through continued in-depth research, a better understanding of the mechanisms of adverse outcomes in AIS can be achieved, and more effective intervention strategies can be developed.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe datasets used and analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThanks to Qiqihar Medical University \u0026nbsp;and the second affiliated Hospitals of Qiqihar\u003c/p\u003e\n\u003cp\u003eMedical University for their scientific research help\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWei-xin ZHANG and Ting HUANG\u003csup\u003e1\u003c/sup\u003e completed the main writing of the article, and and Yin GAO completed the review of the article.Wen-ting ZOU and Ting HUANG\u003csup\u003e2\u003c/sup\u003e collected the dates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBasic scientific research expenses of Heilongjiang Provincial Colleges and Universities .Project Project number: 2021-KYYWF-0389.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll patients provided signed informed consent and volunteered to participate in the study. This study was approved by the Ethics Committee of Qiqihar Medical University Ethics Committee.All methods were performed in accordance with the relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Weixin ZHANG\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGao P, Wang T, Wang D, et al. Effect of Stenting Plus Medical Therapy vs Medical Therapy Alone on Risk of Stroke and Death in Patients With Symptomatic Intracranial Stenosis: The CASSISS Randomized Clinical Trial[J]. JAMA, 2022, 328(6): 534-542.\u003c/li\u003e\n\u003cli\u003eBuckley B, Harrison S L, Hill A, et al. Stroke-Heart Syndrome: Incidence and Clinical Outcomes of Cardiac Complications Following Stroke[J]. Stroke, 2022, 53(5): 1759-1763.\u003c/li\u003e\n\u003cli\u003eWang N, Yang Y, Qiu B, et al. Correlation of the systemic immune-inflammation index with short- and long-term prognosis after acute ischemic stroke[J]. Aging (Albany NY), 2022, 14(16): 6567-6578.\u003c/li\u003e\n\u003cli\u003eLee G, Choi S, Chang J, et al. Association of L-alpha Glycerylphosphorylcholine With Subsequent Stroke Risk After 10 Years[J]. JAMA Netw Open, 2021, 4(11): e2136008.\u003c/li\u003e\n\u003cli\u003eLee K J, Kim B J, Han M K, et al. One-Year Blood Pressure Trajectory After Acute Ischemic Stroke[J]. J Am Heart Assoc, 2022, 11(5): e23747.\u003c/li\u003e\n\u003cli\u003eLiu D, Yang K, Gu H, et al. Predictive effect of triglyceride-glucose index on clinical events in patients with acute ischemic stroke and type 2 diabetes mellitus[J]. Cardiovasc Diabetol, 2022, 21(1): 280.\u003c/li\u003e\n\u003cli\u003eHoshino T, Mizuno T, Ishizuka K, et al. Triglyceride-glucose index as a prognostic marker after ischemic stroke or transient ischemic attack: a prospective observational study[J]. Cardiovasc Diabetol, 2022, 21(1): 264.\u003c/li\u003e\n\u003cli\u003eTakahashi S, Ishizuka K, Hoshino T, et al. Long-Term Outcome in Patients With Acute Ischemic Stroke and Heart Failure[J]. Circ J, 2023, 87(3): 401-408.\u003c/li\u003e\n\u003cli\u003eFord B, Peela S, Roberts C. Secondary Prevention of Ischemic Stroke: Updated Guidelines From AHA/ASA[J]. Am Fam Physician, 2022, 105(1): 99-102.\u003c/li\u003e\n\u003cli\u003eYi K, Nakajima M, Ikeda T, Yoshigai M, Ueda M. Modified Rankin scale assessment by telephone using a simple questionnaire. J Stroke Cerebrovasc Dis. 2022 Oct;31(10):106695. doi: 10.1016/j.jstrokecerebrovasdis.2022.106695. Epub 2022 Aug 30. PMID: 36054972.\u003c/li\u003e\n\u003cli\u003eBanks JL, Marotta CA. Outcomes validity and reliability of the modified Rankin scale: implications for stroke clinical trials: a literature review and synthesis[J]. Stroke. 2007 38(3): 1091-6.\u003c/li\u003e\n\u003cli\u003eLU Z J,YIN Z G,XIE X,et al.Validation of TM score and sFABS score in recognizing stroke mimics in patients with acute ischemic stroke[J].Chin J Geriatr Heart Brain Vesel Dis,2022,24(10):1073-1075.\u003c/li\u003e\n\u003cli\u003eLi J, Qiu Y, Zhang C, et al. The role of protein glycosylation in the occurrence and outcome of acute ischemic stroke[J]. Pharmacol Res, 2023, 191: 106726.\u003c/li\u003e\n\u003cli\u003eWANG W, GAO H W, HUO Y M,et al. Effect of early precision exercise rehabilitation on the levels of serum cytokine and walking ability in patients with hemiplegia after acute ischemic stroke[J].Chinese Journal of PracticalNervous Diseases, 2023,26(10):1275-1280.\u003c/li\u003e\n\u003cli\u003eKhurshid S, Li X, Ashburner J M, et al. Usefulness of Rhythm Monitoring Following Acute Ischemic Stroke[J]. Am J Cardiol, 2021, 147: 44-51.\u003c/li\u003e\n\u003cli\u003eLi L H, Chen C T, Chang Y C, et al. Prognostic role of neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and systemic immune inflammation index in acute ischemic stroke: a STROBE-compliant retrospective study[J]. Medicine (Baltimore), 2021, 100(25): e26354.\u003c/li\u003e\n\u003cli\u003eBeuker C, Koppe J, Feld J, et al. Association of age with 1-year outcome in patients with acute ischaemic stroke treated with thrombectomy: real-world analysis in 18 506 patients[J]. J Neurol Neurosurg Psychiatry, 2023, 94(8): 631-637.\u003c/li\u003e\n\u003cli\u003ePaul S, Candelario-Jalil E. Emerging neuroprotective strategies for the treatment of ischemic stroke: an overview of clinical and preclinical studies[J]. Exp Neurol, 2021, 335: 113518.\u003c/li\u003e\n\u003cli\u003eYAN C,GONG Y,HUANG P, et al. Early poor prognosis and predictive model construction in patients with acute ischemic stroke[J]South China J Prev Med,2023,49(10):1213-1217. \u003c/li\u003e\n\u003cli\u003eQIN Y F, GAO S Y,ZHANG H L, et al.Analysis of influencing factors of one-year poor prognosis of acute ischemic stroke[J]. Beijing Medical Journal,2023,45(05): 393-397.\u003c/li\u003e\n\u003cli\u003eChumbler NR, Williams LS, Wells CK, et al. Derivation and validation of a clinical system for predicting pneumonia in acute stroke[J]. Neuroepidemiology. 2010; 34(4): 193-199.\u003c/li\u003e\n\u003cli\u003eLi YM, Zhao L, Liu YG, et al. Novel Predictors of Stroke-Associated Pneumonia: A Single Center Analysis[J]. Front Neurol. 2022, 13: 857420.\u003c/li\u003e\n\u003cli\u003eSong Y, Ren F, Sun D, et al. Benefits of Exercise on Influenza or Pneumonia in Older Adults: A Systematic Review[J]. Int J Environ Res Public Health. 2020, 17(8): 2655.\u003c/li\u003e\n\u003cli\u003eZHOU S Y,YANG Z,ZHENG T H.Early rehabilitation activity for stroke patients: a review [J].China Prev Med J,2024,36(02):127-130.\u003c/li\u003e\n\u003cli\u003eMarzolini S, Robertson AD, Oh P, et al. Aerobic Training and Mobilization Early Post-stroke: Cautions and Considerations[J]. Front Neurol. 2019, 10: 1187.\u003c/li\u003e\n\u003cli\u003eFan J L, Nogueira R C, Brassard P, et al. Integrative physiological assessment of cerebral hemodynamics and metabolism in acute ischemic stroke[J]. J Cereb Blood Flow Metab, 2022, 42(3): 454-470.\u003c/li\u003e\n\u003cli\u003eDENG T,CHEN J M, LIU X M, et al.Risk factors of stroke-associated pneumonia for patients with mild to moderate acute ischemic stroke[J].Chin J Rehabil Theory Pract, 2023, 29 (06): 708-713. \u003c/li\u003e\n\u003cli\u003eZhu W, Wu Z, Dong Y, et al. Effectiveness of early rhythm control in improving clinical outcomes in patients with atrial fibrillation: a systematic review and meta-analysis[J]. BMC Med, 2022, 20(1): 340.\u003c/li\u003e\n\u003cli\u003eSaeed O, Zhang S, Patel S R, et al. Oral Anticoagulation and Adverse Outcomes after Ischemic Stroke in Heart Failure Patients without Atrial Fibrillation[J]. J Card Fail, 2021, 27(8): 857-864.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Acute Ischemic Stroke, Risk Factors, Prognosis, Prediction Model","lastPublishedDoi":"10.21203/rs.3.rs-5264566/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5264566/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e The aim of this study was to explore the risk factors influencing adverse outcomes in patients with acute ischemic stroke (AIS)within one year and establish a linear prediction model based on them.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e We conducted a retrospective analysis of 600 AIS patients treated at our hospital from January 2019 to June 2023. They were divided into an observation group (n=100, adverse prognosis) and a control group (n=500, good prognosis) based on the occurrence of adverse events within one year. Statistical analysis of intergroup differences was performed using the chi-square test, independent sample t-test, and Mann-Whitney U test. Single-factor, multiple-factor logistic regression, and Lasso regression analyses were conducted using the glmnet package to identify independent risk factors affecting AIS. Risk factors influencing adverse outcomes in AIS were depicted using column charts with the \"rms\" package.Bootstrap method was used for internal validation of the model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eSingle-factor logistic regression showed that age, admission NIHSS score, blood sugar, creatinine, blood urea nitrogen, white blood cell count, smoking history, stroke history, concurrent pneumonia, inability to walk within 48 hours of admission, and atrial fibrillation were the main risk factors (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). Multiple-factor logistic regression revealed that age, admission NIHSS score, concurrent pneumonia, inability to walk within 48 hours of admission, and atrial fibrillation were independent risk factors influencing adverse outcomes in AIS patients within one year (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). The ROC curve for the AIS adverse prognosis column chart model within one year showed high credibility, with a training set AUC of 0.993 (0.988-0.998) and a validation set AUC of 0.987 (0.969-1.000).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e We has successfully constructed a risk prediction model based on a linear chart, which can be used to predict adverse outcomes in AIS patients within one year with high reliability.\u003c/p\u003e","manuscriptTitle":"Linear Chart Model for Adverse Prognosis within one Year in Acute Ischemic Stroke Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-10 05:45:10","doi":"10.21203/rs.3.rs-5264566/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"01ad8ace-1981-4538-bd0a-faed471df4db","owner":[],"postedDate":"December 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":40570957,"name":"Health sciences/Neurology"},{"id":40570958,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-10-01T07:08:51+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-10 05:45:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5264566","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5264566","identity":"rs-5264566","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}