Relationship between Pan-immuno-inflammatory value and hospitalized all-cause mortality in ischemic stroke patients: a retrospective cohort study and predictive modeling

Relationship between Pan-immuno-inflammatory value and hospitalized all-cause mortality in ischemic stroke patients: a retrospective cohort study and predictive modeling | 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 Relationship between Pan-immuno-inflammatory value and hospitalized all-cause mortality in ischemic stroke patients: a retrospective cohort study and predictive modeling Yuze Zhai, Shaolei Huang, Yican Yang, Dezheng Wang, Shuai Guo, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6615694/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 Background Systemic inflammation and immune response are major factors in the development and progression of ischemic stroke (IS). Numerous studies have shown how Pan-immuno-inflammatory value (PIV) affects the chance of dying from a serious disease. However, the value of PIV in IS patients in the ICU remains unclear. The objective of this study was to explore the correlation between PIV and IS, and to construct a machine learning (ML) model for in-hospital mortality risk in IS patients using variables related to PIV. Methods In the present study, patients who had been diagnosed with IS and admitted to the ICU were retrospectively pulled from the publicly accessible MIMIC-IV v3.0 database. The primary result was defined as in-hospital mortality. To investigate the association between Log2-transformed PIV and clinical outcomes in IS patients, a Cox proportional hazards regression using with restricted cubic splines (RCS) was undertaken. The optimum model within the validation cohort was chosen based on accuracy and area under the curve (AUC). Furthermore, the SHAP method was utilized to determine the significance of model features and assess the influence of the top three characteristics on model predictions. Results The research included 2,223 participants with IS. The connection between the probability of in-hospital mortality in IS and Log2PIV was nonlinear. Among the 14 ML algorithms, the GBDT model has higher prediction accuracy, better clinical decision-making performance, and better overall performance. Furthermore, the SHAP algorithm analysis revealed that Log2-PIV, Hemoglobin, and LODS were the three clinical characteristics that most significantly influenced the GBDT model's outputs. Conclusion The study findings revealed a U-shaped association between Log2-PIV and the risk of in-hospital mortality from IS. Furthermore, the GBDT model emerged as the most effective predictor, enabling clinicians to pinpoint high-risk patients and take proactive measures to minimize mortality. Health sciences/Biomarkers Health sciences/Neurology Health sciences/Risk factors Inflammation MIMIC-IV Mortality predictive modeling ischemic stroke Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Ischemic stroke (IS) happens when the blood supply to a specific area of the brain is substantially diminished or stopped, either due to vascular embolism or inadequate systemic blood perfusion. This deprivation of nourishment to the brain tissue results in neuronal damage and neurological dysfunction. Globally, Globally, stroke is the second greatest cause of mortality. Notably, 80% of the strokes are ischemic, with approximately 20% recurring within 5 years and one-third of patients succumbing to the condition [ 1 ] . Reliable clinical predictors and predictive models are essential for determining the likelihood of poor prognosis in critically sick IS patients. They facilitate timely and appropriate management, ultimately aiming to reduce the overall burden of IS [ 2 ] . However, existing clinical outcome predictors and models primarily rely on demographics and clinical parameters, which may introduce biases and limit the reliability of clinical decision-making. This underscores the need for blood-based noninvasive biomarkers. Machine-learning-based clinical prediction models offer the potential to enhance prognostic insights by harnessing data readily available in clinical settings [ 3 ] . Systemic inflammation and immune response play pivotal roles in the development and progression of critical care IS. When central nervous system (CNS) tissue is damaged by ischemia, it produces inflammatory chemicals related with damage and pathogens. This initiates an immunological response, resulting in the activation and infiltration of inflammatory cells. While the initial inflammation and immune response are physiological processes crucial for tissue repair, excessive reactions can cause further tissue damage. In the CNS, microglia phagocytose lysogenic cells and pathogens; however, during IS, they persist in an M1 state.This condition stimulates peripheral neurons and immune cells through pro-inflammatory molecules including tumor necrosis factor(TNF)-α and interleukin(IL)-1β, generating a vicious loop that worsens nerve injury. Consequently, blood biomarkers linked to inflammation and immunity hold the potential for predicting clinical outcomes in critically ill IS patients. Previous research has established the predictive validity of the neutrophil/lymphocyte ratio (NLR), derived NLR, platelet/lymphocyte ratio (PLR), and monocyte/lymphocyte ratio (MLR) in assessing stroke severity and clinical outcome [ 4 ] . Pan-immuno-inflammatory value (PIV) represents a novel composite index that integrates neutrophils, platelets, monocytes, and lymphocytes to comprehensively assess the host's inflammatory and immune response. Prior research has established PIV's considerable superiority over inflammatory markers, including NLR, PLR, and MLR, in prognosticating colorectal cancer [ 5 ] , sepsis [ 6 ] , and anti-neurocyte cytoplasmic antibody-associated vasculitis [ 7 ] . Given the diverse etiologies of severe stroke, traditional diagnostic methods such as neuron-specific enolase (NSE) testing, electroencephalography, and cranial imaging can offer valuable insights but often struggle to provide tailored, long-term prognostic predictions [ 8 ] . However, with significant advancements in computing power, machine learning (ML) algorithms can now integrate a wide range of clinical data, refine prognostic assessments, and deliver more precise predictions. These algorithms have gained widespread application in prognostic evaluations for various critical illnesses. Numerous previous studies have shown that based on ML models excel in assessing neurological function and predicting the risk of disease recurrence among patients with severe IS [ 9 ] . In light of these findings, our study examined the association between PIV and in-hospital mortality in this patient population. Furthermore, we developed an ML model aimed at predicting the risk of death during hospitalization for severe stroke patients. Finally, we useed the SHapley Additive explanations (SHAP) algorithm to interpret our best model, facilitating clinical decision-making by combining a new inflammatory signal with our prediction model. Materials and methods 1 Data source The data employed in this study were sourced from the Medical Information Mart for Intensive Care IV (MIMIC-IV) v3.0 electronic health record dataset, accessible at https://mimic.physionet.org . The Massachusetts Institute of Technology maintains this extensive database, which includes 364,627 patients hospitalized to Beth Israel Deaconess Medical Center's Intensive Care Unit (ICU) between 2008 and 2022. To acquire access to this sensitive data, we completed the National Institutes of Health's human subjects protection training and the Collaborative Institutional Training Program test (certification number 46538344). We also obtained informed consent waivers and permission to use research materials from the Institutional Review Boards at the Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center. 2 Study design and population This retrospective investigation included patients with IS who were later hospitalized and admitted to the ICU for the first time. IS was found in 8,217 individuals using International Classification of Diseases (ICD) codes, especially ICD-9 codes 433, 434, 436, 437.0, 437.1, and ICD-10 codes I63, I65. However, 4,556 individuals were eliminated because they did not match the criteria for first ICU admission. Furthermore, 3,571 patients were eliminated using the following exclusion criteria: To be eligible for ICU admission, (1) patients age < 18 years old; (2) have a severe disease like end-stage renal insufficiency, cirrhosis, or cancer; (3) have missing neutrophil, platelet, monocyte, or lymphocyte counts on the first day; (4) have an ICU hospitalization duration of < 24 hours; (5) have incomplete survival or time of death data. For patients with multiple hospitalizations owing to IS, only the data from the initial admission were retrieved.(Fig. 1 ). 3 Data Extractions We employed PostgreSQL software (version 9.6) to gather data on various factors such as demographic parameters, comorbidities, vital signs, laboratory values, and scoring systems from the patient's first day in the ICU. The study encompassed basic patient details like age, gender, height, weight, BMI, LODS, GCS, OASIS, ICU mortality, and ICU mortality time. Additionally, we collected physical examination findings, specifically heart rate, SBP, DBP, temperature, and respiratory rate; Laboratory Examinations: SO 2 , Hemoglobin, WBC, Aniongap, Blood Urea Nitrogen, Blood calcium, Blood creatinine, Glucose, Blood sodium, Potassium, PH, PO 2 , PCO 2 , TCO 2 , Aado 2 . Comorbidities: Heart failure, Chronic obstructive pulmonary disease, Renal disease, Severe liver disease. All of the data above has been extracted and can be seen in Table 1 . The PIV was calculated using the formula “neutrophil (k/ul) × monocyte count (k/ul) × platelet (k/ul)/lymphocyte (k/ul)” [ 10 ] 。 The primary goal of this study was to investigate in-hospital all-cause mortality among patients with IS. Laboratory results were gathered only within the first 24 hours after the patient's admission to the ICU. To limit the danger of bias, variables having missing values of more than 20% were eliminated from the analysis. For variables with missing values of under 20% we used the Random Forest interpolation approach in R software to do multiple interpolations. 4 statistical analysis PIV's distribution was skewed, thus a Log2 transformation was given to it before analysis. Continuous variables were given as mean ± standard deviation(SD) and compared using one-way ANOVA or the Kruskal-Wallis test based on data appropriateness. Categorical variables were reported as frequencies (%) and evaluated using chi-squared or Fisher's exact test, as applicable. Kaplan-Meier (K-M) survival curves for IS patients in the ICU were created and analyzed using the log-rank test to determine differences in survival outcomes. Univariate analysis, along with a multivariate Cox proportional-hazards model, was used to assess the effort of Log2-PIV on ICU in-hospital all-cause mortality among patients with IS. Specifically, we evaluated three models: Model 1 was unadjusted; Model 2 was adjusted for age and gender; and Model 3 further adjusted for height, weight, and BMI using Model 2. To examine potential nonlinear relationships between Log2-PIV and survival endpoints, we employed RCS analysis and smoothed curve fitting techniques. In cases where nonlinear correlations were identified, a two-stage regression modeling approach was utilized to clarify these nonlinearities. Additionally, Cox proportional risk regression analyses were conducted to assess the regressions based on various factors, including gender, Heart failure, Chronic obstructive pulmonary disease, Renal disease, Severe liver disease, age, BMI, heart rate, and hemoglobin, as significant differences were observed between the survival and non-survival groups. All statistical analyses were performed using Python 3.9.12 and R version 4.1.3. Statistical significance was determined at the P < 0.05. 5 ML model construction and performance evaluation In this study, we employed 14 distinct ML algorithms to construct predictive models, including Latent Dirichlet Allocation (LDA), ExtraTrees (ET), Random Forest (RF), Logistic Regression (LR), Stochastic Gradient Descent (SGD), Naive Bayes (NB), Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Gradient Boosting Decision Tree (GBDT), CatBoost, AdaBoost, LightGBM, and XGBoost. Initially, utilizing multiple interpolated post-data, we randomly partitioned the population, allocating 80% for derivation data and reserving 20% for validation. During model development, we relied on internal validation to ascertain the stability of the predictive model within the training dataset. Specifically, we implemented 10-fold cross-validation as a resampling technique to identify the optimal hyperparameters. In each iteration, the model was trained on 9 folds while the remaining fold was dedicated to fine-tuning the hyperparameters. To evaluate the performance of each model, we computed the Area Under Curve (AUC) in the test dataset and compared the sensitivity, specificity, and accuracy across all models. To assess clinical utility, we generated calibration curves and decision curves, analyzing the clinical benefit derived from model calibration against varying thresholds. Additionally, we enhanced the interpretability of our final models using the SHAP framework. SHAP summary plots illuminated the influence of various model features, while SHAP dependency plots revealed the extent of each feature's impact on model outcomes. Lastly, SHAP force diagrams visually represent how key features shape the final model's predictions for individual patients. Results 1 Correlation analysis Baseline characteristics Based on the defined inclusion and exclusion criteria, this study encompassed 2,233 patients with IS from the database. These patients were stratified into four groups according to the Log2-PIV quartiles. The resulting cohort comprised 1076 (48.19%) males and 1157 (51.81%) females, with a mean age of 69.81 ± 15.56 years. Our findings indicated that blood calcium levels tended to decrease as Log2-PIV increased. Conversely, there was a trend towards an increase in LODS, OASIS, heart rate, respiratory rate, white blood cell (WBC), anion gap, blood urea nitrogen, glucose levels, and ICU mortality (Table 1 )。 Table 1 Basic characteristics of included patients grouped according to Log2-PIV quartiles. Log2-Pan-immune-inflammation value (Log2-PIV) a P b Variables Total Q1 Q2 Q3 Q4 N = 2233 N = 558 N = 557 N = 559 N = 559 Age,y 69.81 ± 15.56 c 71.57 ± 14.48 71.94 ± 14.36 68.71 ± 16.10 69.49 ± 15.65 0.005 Male, n(%) 1076 (48.19%) 264 (47.31%) 261 (46.86%) 268 (47.94%) 283 (50.63%) 0.591 Height, cm 168.70 ± 10.78 168.20 ± 11.30 168.67 ± 10.07 169.84 ± 10.68 168.08 ± 10.97 0.026 Weight, kg 80.49 ± 22.49 80.22 ± 23.87 81.80 ± 22.11 81.26 ± 21.37 78.70 ± 22.46 0.103 BMI, kg/m 2 28.68 ± 7.74 29.19 ± 10.21 28.49 ± 6.45 28.83 ± 6.82 28.22 ± 6.87 0.175 Charlson 6.59 ± 2.88 6.63 ± 2.78 6.64 ± 2.77 6.57 ± 3.05 6.52 ± 2.92 0.892 GCS 12.82 ± 2.95 12.96 ± 2.90 12.87 ± 2.94 12.91 ± 2.74 12.54 ± 3.20 0.072 LODS 3.95 ± 2.92 3.86 ± 3.01 3.77 ± 2.71 3.71 ± 2.87 4.46 ± 3.03 < 0.001 OASIS 31.62 ± 8.48 30.81 ± 8.48 31.24 ± 8.22 31.22 ± 8.22 33.18 ± 8.80 < 0.001 Physical Examinations Heart Rate, beats/min 81.27 ± 15.54 78.72 ± 14.69 80.32 ± 14.84 81.78 ± 15.93 84.24 ± 16.14 < 0.001 SBP, mmHg 131.28 ± 18.74 131.78 ± 18.85 132.79 ± 19.19 131.02 ± 18.55 129.52 ± 18.26 0.029 DBP, mmHg 70.54 ± 13.04 70.68 ± 13.47 70.97 ± 13.02 71.45 ± 12.95 69.07 ± 12.62 0.015 Respiratory Rate, beats/min 19.44 ± 3.49 19.01 ± 3.33 19.15 ± 3.20 19.61 ± 3.50 20.01 ± 3.81 < 0.001 Temperature, ℃ 36.91 ± 0.47 36.88 ± 0.40 36.90 ± 0.40 36.96 ± 0.47 36.91 ± 0.59 0.06 Laboratory Examinations SO₂ 96.88 ± 1.90 96.85 ± 1.75 96.84 ± 1.86 96.86 ± 1.98 96.98 ± 2.01 0.613 Hemoglobin, g/dl 11.85 ± 2.28 11.85 ± 2.19 11.95 ± 2.24 11.96 ± 2.25 11.66 ± 2.42 0.102 WBC, k/ul 12.30 ± 8.15 10.66 ± 11.38 11.11 ± 4.48 11.86 ± 4.42 15.54 ± 9.08 < 0.001 Aniongap, mmol/L 14.46 ± 3.47 13.88 ± 3.02 14.35 ± 3.45 14.52 ± 3.28 15.10 ± 3.94 < 0.001 Blood Urea Nitrogen, mg/dl 23.29 ± 18.13 22.20 ± 16.65 22.08 ± 16.75 23.58 ± 18.85 25.29 ± 19.91 0.01 Blood calcium, mg/dl 8.70 ± 0.69 8.76 ± 0.70 8.73 ± 0.70 8.68 ± 0.66 8.65 ± 0.70 0.035 Blood creatinine, mg/dL 1.28 ± 1.30 1.22 ± 1.11 1.29 ± 1.38 1.34 ± 1.54 1.28 ± 1.13 0.486 Glucose, mg/dL 143.09 ± 58.90 136.70 ± 52.93 140.38 ± 55.05 143.31 ± 60.79 151.95 ± 65.10 < 0.001 Blood sodium, mmol/L 139.25 ± 4.21 139.48 ± 4.10 139.16 ± 4.06 139.25 ± 4.27 139.11 ± 4.39 0.465 Potassium, mEq/L 4.16 ± 0.54 4.16 ± 0.54 4.17 ± 0.55 4.14 ± 0.51 4.16 ± 0.56 0.703 PH 7.38 ± 0.07 7.38 ± 0.06 7.38 ± 0.06 7.38 ± 0.06 7.37 ± 0.07 0.207 PO 2 , % 128.97 ± 74.89 133.89 ± 76.36 128.96 ± 73.48 124.29 ± 72.76 128.73 ± 76.77 0.204 PCO 2 ,% 40.85 ± 7.53 40.47 ± 7.18 41.25 ± 7.47 40.97 ± 7.40 40.70 ± 8.02 0.338 TCO 2 ,% 24.52 ± 4.23 24.37 ± 4.03 24.68 ± 4.04 24.82 ± 4.38 24.19 ± 4.44 0.053 Aado 2 ,% 213.25 ± 108.53 206.59 ± 101.16 211.05 ± 112.29 218.28 ± 104.96 217.06 ± 115.01 0.239 Comorbidities Heart failure, n(%) 0.187 Yes 1689(75.64%) 441 (79.03%) 418 (75.04%) 414 (74.06%) 416 (74.42%) No 544 (24.36%) 117 (20.97%) 139 (24.96%) 145 (25.94%) 143 (25.58%) Chronicobstructive pulmonary disease, n(%) 0.201 Yes 1851(82.89%) 479 (85.84%) 457 (82.05%) 459 (82.11%) 456 (81.57%) No 382 (17.11%) 79 (14.16%) 100 (17.95%) 100 (17.89%) 103 (18.43%) Renal disease, n(%) 0.472 Yes 1797(80.47%) 441 (79.03%) 443 (79.53%) 452 (80.86%) 461 (82.47%) No 436 (19.53%) 117 (20.97%) 114 (20.47%) 107 (19.14%) 98 (17.53%) Severe Liver Disease, n(%) 0.830 Yes 2192(98.16%) 546 (97.85%) 546 (98.03%) 549 (98.21%) 551 (98.57%) No 41 (1.84%) 12 (2.15%) 11 (1.97%) 10 (1.79%) 8 (1.43%) Events ICU mortality, n (%) 390 (17.47%) 74 (13.26%) 80 (14.36%) 91 (16.28%) 145 (25.94%) < 0.001 ICU mortality time, days 11.29 ± 16.22 11.31 ± 15.41 10.61 ± 15.22 11.36 ± 14.62 11.86 ± 19.24 0.64 Values in bold indicate significance at P < 0.05. a: Pan-immune-inflammation value(PIV) was divided into four groups, Q1 and Q4 are the lowest and highest quintile groups, respectively. b: ANOVA and chi-square tests are used to assess the significance of continuous and categorical variables, respectively c: Continuous values are presented as mean and standard error. d: Categorical values are presented as %. BMI, body mass index; GCS, Glasgow Coma Scale scores; LODS, Logistic Organ Dysfunction System; OASIS, Oxford acute severity of illness score; SBP, Systolic Blood Pressure; DBP, Diastolic Blood Pressure; SPO 2 , peripheral capillary oxygen saturation; WBC, White blood cell count; PH, Pondus Hydrogenii; PO 2 , Partial pressure of oxygen; PCO 2 , Partial Pressure of Carbon Dioxide; TCO 2 , Total carbon dioxide; Aado 2 , Difference of alveoli-arterial oxygen pressure, ICU: Intensive care unit. 2 Univariate analysis of in-hospital mortality in patients with IS In the univariate analysis conducted in this study, no significant correlation was observed between the risk of in-hospital death among patients with IS and several factors, including Gender, Height, Weight, BMI, Temperature, and Blood sodium. However, a positive correlation was found between this risk and Age ( HR = 1.92, 95% CI = 1.01, 1.03), Heart Rate ( HR = 1.03, 95% CI = 1.02, 1.03), Respiratory Rate ( HR = 1.17, 95% CI = 1.13, 1.20), SPO2 ( HR = 1.17, 95% CI = 1.10, 1.25), Log2-PIV ( HR = 1.17, 95% CI = 1.10, 1.25), WBC ( HR = 1.12, 95% CI = 1.10, 1.14), Aniongap ( HR = 1.13, 95% CI = 1.09, 1.16), Blood Urea Nitrogen ( HR = 1.02, 95% CI = 1.02, 1.03), Blood creatinine ( HR = 1.16, 95% CI = 1.08, 1.24), Glucose ( HR = 1.01, 95% CI = 1.00, 1.01), and Potassium ( HR = 1.64, 95% CI = 1.35, 1.99) (All P < 0.05). Conversely, a significant negative correlation was observed with SBP ( HR = 0.98, 95% CI = 0.98, 0.99), DBP ( HR = 0.96, 95% CI = 0.95, 0.97), Hemoglobin ( HR = 0.82, 95% CI = 0.78, 0.87), and Blood calcium ( HR = 0.54, 95% CI = 0.46, 0.64, Table 2 ). Table 2 Univariate analysis of in-hospital mortality in patients with IS. Variable HR (95%CI) P Age 1.02 (1.01, 1.03) < 0.001 Gender Male Reference Female 0.93 (0.75, 1.15) 0.50 Height 0.99 (0.98, 1.00) 0.19 Weight 1.00 (0.99, 1.00) 0.06 BMI 1.00 (0.99, 1.02) 0.59 Physical Examinations Heart Rate 1.03 (1.02, 1.03) < 0.001 SBP 0.98 (0.98, 0.99) < 0.001 DBP 0.96 (0.95, 0.97) < 0.001 Respiratory Rate 1.17 (1.13, 1.20) < 0.001 Temperature 1.13 (0.89, 1.44) 0.30 Laboratory Examinations SPO 2 1.17 (1.10, 1.25) < 0.001 Log2 PIV 1.17 (1.10, 1.25) < 0.001 Hemoglobin 0.82 (0.78, 0.87) < 0.001 WBC 1.12 (1.10, 1.14) < 0.001 Aniongap 1.13 (1.09, 1.16) < 0.001 Blood Urea Nitrogen 1.02 (1.02, 1.03) < 0.001 Blood calcium 0.54 (0.46, 0.64) < 0.001 Blood creatinine 1.16 (1.08, 1.24) < 0.001 Glucose 1.01 (1.00, 1.01) < 0.001 Blood sodium 1.02 (0.99, 1.05) 0.17 Potassium 1.64 (1.35, 1.99) < 0.001 Values in bold indicate significance at P < 0.05. CI, confidence interval; BMI, body mass index; SBP, Systolic Blood Pressure; DBP, Diastolic Blood Pressure; SPO 2 , peripheral capillary oxygen saturation; PIV, Pan-immune-inflammation value; WBC, White blood cell count. 3 Cox proportional risk model of in-hospital mortality To confirm the correlation between Log2-PIV and in-hospital prognostic outcomes in IS patients, we selected Cox regression models for analysis. The results indicated that Log2-PIV was significantly associated with an elevated risk of in-hospital all-cause mortality across three models (Model 1: HR = 1.17, 95% CI = 1.10, 1.25, P < 0.001; Model 2: HR = 1.27, 95% CI = 1.17, 1.38, P < 0.001; Model 3: HR = 1.27, 95% CI = 1.17, 1.38, P < 0.001). Furthermore, when analyzing Log2-PIV as an interquartile-based categorical variable, the fully adjusted model (Model 3) revealed a positive association between Log2-PIV and the risk of in-hospital all-cause mortality in IS patients ( HR = 1.29, 95% CI = 1.17, 1.42, P for trend < 0.001). Specifically, for every quartile increase in Log2-PIV, IS patients faced a 29% higher risk of in-hospital all-cause mortality (Table 3 ). Additionally, K-M survival plots supported these findings, showing similar results ( P for log-rank test < 0.001, Fig. 2 ). Table 3 Cox regression modeling of in-hospital mortality Variable Model 1 Model 2 Model 3 HR (95%CI) P HR (95%CI) P HR (95%CI) P Log2-PIV 1.17(1.10,1.25) < 0.001 1.27(1.17,1.38) < 0.001 1.27(1.17,1.38) < 0.001 Quartile Q1 Reference Q2 1.16(0.85,1.60) 0.33 1.23(0.89,1.68) 0.21 1.25(0.91,1.71) 0.17 Q3 1.22(0.90,1.66) 0.20 1.30(0.96,1.77) 0.09 1.33(0.98,1.81) 0.70 Q4 1.95(1.47,2.58) < 0.001 2.07(1.56,2.74) < 0.001 2.10(1.58,2.78) < 0.001 P for trend 1.26(1.15,1.39) < 0.001 1.29(1.17,1.40) < 0.001 1.29(1.17,1.42) < 0.001 Values in bold indicate significance at P < 0.05. Model 1 adjusted for none; Model 2 adjusted for age, gender; Model 3 adjusted for age, gender, hight, and BMI. PIV, Pan-immune-inflammation value; CI, confidence interval; BMI, Body Mass Index. Subsequently, the relationship between Log2-PIV and the risk of in-hospital death in IS patients was further analyzed using smoothed curve fitting and Restricted Cubic Spline (RCS) plots.The results, after adjusting for relevant confounders, revealed a U-shaped nonlinear association between Log2-PIV and the risk of in-hospital all-cause mortality among patients with IS (Figure 3-4). The curves depicted represent the estimated adjusted risk ratios, along with 95% confidence intervals indicated between them. The plotted curves depict the estimated adjusted risk ratios, while the red-shaded areas denote the 95% confidence intervals. The horizontal dotted line marks a risk ratio of 1.0, serving as a reference point. Threshold effect analysis identified a turning point (K) of 8.54. Below this inflection point, Log2-PIV exhibited a negative correlation with the risk of in-hospital all-cause mortality among patients with IS ( HR = 0.77, 95% CI = 0.65, 0.90, P = 0.0017). Conversely, beyond this point, a significant positive association was observed ( HR = 1.26, 95% CI = 1.17, 1.35, P < 0.001). The log-likelihood ratio test yielded a P < 0.001 (Table 4 ). Table 4 Threshold and effect value analysis of the relationship between Log2-PIV and in-hospital mortality in ICU stroke patients. Outcome HR(95%CI) P Model I One line effect 1.18(1.10,1.27) < 0.001 Model II Turning point(K) 8.54 K effect 2 1.26(1.17,1.35) < 0.001 Effect 2 − 1 1.64(1.35,2.00) < 0.001 LRT test < 0.001 Values in bold indicate significance at P < 0.05 Model 1 adjusted for none; Model 2 adjusted for age, gender, hight, and BMI. CI, confidence interval; BMI, Body Mass Index. 4 Subgroup analysis To assess the generalizability of the relationship between Log2-PIV and the risk of in-hospital all-cause mortality among patients with IS, this study stratified the population based on Gender, Heart Failure (Yes, No), Chronic Obstructive Pulmonary Disease (Yes, No), Severe Liver Disease (Yes, No), Age (≤ 65y, >65y), BMI (28.0 kg/m2), Heart Rate (< 100 beats/min, ≥ 100 beats/min), and Hemoglobin (< 12 g/dl, ≥ 12 g/dl). The results revealed a significant interaction between BMI and Hemoglobin, both before and after adjustment ( P < 0.05). Specifically, the association between Log2-PIV and the risk of in-hospital all-cause mortality in IS patients was found to vary among subgroups defined by different BMI and Hemoglobin levels. In other words, the impact of Log2-PIV on the risk of in-hospital all-cause mortality in patients with IS differed depending on their BMI and Hemoglobin status (Fig. 5). 5 Modeling optimal forecasts Based on the aforementioned study, we included a total of 33 clinical features to construct 14 ML models, each incorporating the same 33 features. By assessing model performance using the test set data, we found that the GBDT, CatBoost, and RF models exhibited superior fitting performance, achieving AUC of 0.832, 0.830, and 0.828, respectively, in the validation cohort (Table S1 and Fig. 6A). The calibration represents the disparity between the model's predicted probabilities and the actual probabilities. A smaller value signifies that the model's predictions align more closely with the actual results, thus indicating greater accuracy. The calibration curve results demonstrate that GBDT's calibration is notably superior to that of CatBoost and RF (Fig. 6B). The threshold probability for clinical decision-making (the likelihood of the outcome variable's occurrence) falls between 5% and 90%. Within this range, predicting the risk of in-hospital death among modeled patients offers greater net benefits. Furthermore, the decision curve analysis demonstrates that the GBDT model exhibits superior value for clinical application (Fig. 6C). 6 Analysis of model interpretability In this study, we employed the SHAP algorithm to examine the interpretability of the GBDT model. The resulting SHAP summary plot illustrates how clinical features influence the GBDT model's output results (Fig. 7A). Specifically, a positive value signifies that the corresponding feature positively impacts the output, while a negative value denotes a negative impact. The color coding—with red indicating higher values and blue indicating lower ones—further highlights the strength of each feature's effect on the target variable, with darker colors representing a more pronounced influence. The accompanying bar graph reveals the contribution of each feature to the overall model, where a higher SHAP value signifies greater importance and a more substantial impact on the model's output (Fig. 7B). Our findings identified Log2-PIV, Hemoglobin, LODS, Glucose, Aniongap, Spo2, WBC, Height, Respiratory Rate, and OASIS as the top 10 clinical features influencing the GBDT model's output results. Discussion This study uncovered a notable U-shaped nonlinear relationship between Log2-PIV and the risk of in-hospital death among patients with IS, with an inflection point value of 8.54. Segmental regression analysis further demonstrated that Log2-PIV negatively correlated with the risk of death on the left side of this inflection point ( HR = 0.77, P = 0.0017), while showing a positive correlation on the right side ( HR = 1.26, P < 0.0001). Additionally, our model construction, utilizing 14 learning algorithms, revealed that the GBDT model excelled in predicting the risk of in-hospital death for IS patients, achieving an AUC of 0.8367. The SHAP interpretability analysis further validated Log2-PIV as the foremost characterizing variable of the model, aligning with the findings from the Cox regression ( HR = 1.27, 95% CI = 1.17, 1.38, P < 0.0001) and K-M survival analysis ( P for log-rank test < 0.0001). Previous literature demonstrates that machine learning techniques exhibit a distinct advantage in predicting critical illness prognosis. These techniques are adept at recognizing crucial features of predictive target variables through modeling randomness [ 1 1] . Zhao et al. developed a prediction model for acute kidney injury, employing the random forest algorithm. Their model demonstrated superior predictive power compared to traditional logistic regression analysis, particularly in forecasting early functional recovery or short-term reversibility of kidney injury (AUC: 0.85 vs 0.78) [ 1 2] . The deep neural network model crafted by Heo J et al. notably surpassed the ASTRAL score in terms of predicting the long-term prognosis of IS. Furthermore, the ML approach demonstrated considerable benefits in addressing various stages of post-stroke cognitive dysfunction [ 13 ] , early neurological deterioration, and risk stratification [ 14 ] . Based on an analysis of 14 machine learning algorithms developed by our research team, the GBDT model emerged as a superior predictor of in-hospital mortality risk among patients with IS. The SHAP algorithm's results highlighted the significant role of Log2-PIV in forecasting all-cause mortality for IS patients during their hospital stay. Crucially, Cox regression analysis, coupled with K-M survival plots, further validated the positive association between Log2-PIV and the risk of in-hospital mortality in this patient population, aligning with the findings of the SHAP algorithm. Consequently, we posit that Log2-PIV serves as a reliable predictor of in-hospital all-cause mortality among IS patients. As a composite measure encompassing neutrophils, monocytes, platelets, and lymphocytes, the biological underpinnings of PIV are rooted in the multifaceted immune response triggered by IS. Previous research has indicated that ischemic neuronal injury arises when pattern recognition receptors identify injury-associated molecular patterns. This recognition triggers an excessive release of glutamate, subsequently causing overactivation of N-methyl-D-aspartate receptors and a significant influx of Ca 2+ Consequently, this chain of events leads to cell death by excitotoxicity [ 15 ] . Dead neuronal cells and cellular debris can trigger both innate and adaptive immune responses, which are profoundly implicated in the entire process of atherosclerotic plaque formation, endothelial dysfunction, and vascular rupture. This occurs through their collective promotion of chronic low-grade inflammation, which subsequently orchestrates lipid metabolism, immune cell activation, and vascular wall remodeling [ 16 ] . Consequently, inflammation is tightly linked to the progression and prognosis of IS [ 17 ] . The sympathetic-adrenal axis becomes activated following an IS episode, leading to the swift mobilization of neutrophils. These neutrophils then infiltrate the molluscum contagiosum through a rolling adhesion process that involves the mediation of TNF-α, IL-8, and intercellular cell adhesion molecule (ICAM)-1 [ 1 8] . This infiltration ultimately contributes to a more severe disruption of the blood-brain barrier [ 1 9] . Furthermore, in a mouse model of middle cerebral artery occlusion (MCAO), neutrophils escalate the inflammatory response by discharging extracellular traps composed of desmosomal chromatin and granular contents. This process facilitates the liberation of high mobility group protein B1 (HMGB1), ultimately intensifying delayed immune cell infiltration and cerebrovascular damage [ 2 0] . Monocytes, which circulate in the bloodstream as peripheral immune cells and can differentiate into either macrophages or dendritic cells based on the local tissue milieu, exhibit a dual function in the advancement of IS. C-C motif chemokine receptor (CCR)-2 and chemokine ligand (CCL)-2 not only facilitate the excessive migration of monocytes to the affected area but also augment the expression of IL-1 and TNF genes, thus exacerbating neuroinflammation. Conversely, our prior research has indicated that CCL2 can stimulate the downstream target P2X4R, triggering the production of BDNF and facilitating neurologic recovery [ 2 1] . It is crucial to maintain monocytes within a specific range to optimize neurologic recovery in cases of IS. Following endothelial damage or the rupture of atherosclerotic plaques, platelets undergo functional changes. Surface receptors on these platelets, including GP Ib-IX-V and GP VI, interact with von Willebrand factor to create a transient hemostatic thrombus. Concurrently, they release mediators like ADP and thromboxane A2, which enhance platelet aggregation and thrombus growth, ultimately leading to a reperfusion injury [22] . Furthermore, platelets subjected to stress have the capacity to release excessive amounts of pro-inflammatory factors, including P-selectin, CD40 Ligand, and IL-1β. These factors serve to recruit leukocytes and activate endothelial cells, thereby intensifying the local inflammatory response [23] . Lymphocytes, as key peripheral immune cells, exhibit a dual function in postischemic neuroinflammation. Rodent stroke studies have demonstrated that an elevation in lymphocytes leads to the upregulation of the anti-inflammatory cytokine IL-10, while suppressing the expression of IL-6 and TNF-α, ultimately promoting neuroprotection. However, a decrease in lymphocytes is associated with reduced cerebral infarction and mitigated neurological deficits [24] . A study examining ischemic stroke patients revealed a correlation between lymphocyte counts and the extent of cerebral white matter damage. Furthermore, lymphocyte counts emerged as an independent factor protecting cognitive function in cerebrovascular patients [25] . Consequently, immune activation at moderate levels aids in tissue recovery following the onset of IS. However, an excessive immune response leading to secondary injury could underlie the distinct U-shaped relationship observed between PIV and the in-hospital mortality risk in IS cases. To our knowledge, this study stands as the first to explore the correlation between PIV and IS, and to construct a predictive model based on a comprehensive epidemiological investigation. Recent studies have shown that PIV, as a novel composite immune-inflammatory marker that integrates neutrophils, monocytes, platelets, and lymphocytes, significantly outperforms other traditional markers of inflammation in predicting survival outcomes. A comprehensive systematic review and meta-analysis, encompassing 4,942 patients with tumors, revealed a notable association: patients exhibiting higher PIV levels faced a significantly elevated risk of mortality and were more prone to disease progression [ 26 ] . Furthermore, PIV demonstrated its efficacy in prognosticating the outcomes of patients with both non-metastatic and metastatic cancers [ 27 ] . Indeed, PIV has been identified as an independent factor influencing complete pathological remission in patients with non-small cell lung cancer [ 28 ] . Furthermore, its predictive value extends beyond cancer, being observed in non-cancerous conditions as well. Chen Jin et al. analyzed data from the American Health and Nutrition Examination Survey, revealing a notable positive correlation between PIV and abdominal aortic calcification [ 29 ] . In predicting adverse events following coronary intervention for myocardial infarction, PIV demonstrated superior predictive accuracy compared to the systemic immunoinflammatory index. Furthermore, PIV showed a significant advantage in assessing the risk of death from aortic coarctation and in prognosticating diseases like coronary artery stenosis [ 30 , 31 ] . In this study, Cox regression analysis revealed that Log2-PIV serves as an independent risk factor for in-hospital all-cause mortality among patients with IS. The smoothed curve fitting, when compared to RCS curves, exhibited a U-shaped nonlinear association between Log2-PIV and the mortality risk in this patient population. Notably, the threshold turning point was identified at 8.54. Below this threshold, an increase in Log2 -PIV was associated with a decrease in the risk of in-hospital all-cause mortality among IS patients. Conversely, beyond this threshold, the risk of death escalated with increasing Log2-PIV. These findings strongly suggest that maintaining PIV within a reasonable range, rather than targeting a specific level, is crucial for IS patients. We must acknowledge certain limitations inherent in this study. Firstly, as a retrospective analysis, the data from MIMIC-IV v3.0 is confined to the United States, potentially introducing biases and limiting the global applicability of our prediction model and findings. To verify our results, future prospective multicenter studies with expanded sample sizes are necessary. Secondly, our methodology involved multiple interpolations to address missing data, which could result in deviations from actual values. Conclusions In conclusion, a U-shaped relationship exists between Log2-PIV and the risk of in-hospital mortality among critically ill patients with IS. Elevated Log2-PIV levels are significantly linked to a heightened risk of adverse events, suggesting that Log2-PIV could serve as a predictor of unfavorable outcomes in this patient population. Furthermore, our machine learning modeling indicated that GBDT is a promising tool for predicting the risk of in-hospital death in IS patients. Nevertheless, further large-scale, multicenter, prospective studies are required to corroborate the findings of our current investigation. Declarations Availability of data and material More information is available on the hyperlink as the Data Availability statement page. The data used in this study utilized publicly available datasets, which can be accessed at https://mimic.physionet.org. Ethics Approval Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements. Author contributions YZhai: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology. SH: Project administration, Resources, Software, Supervision, Validation. ZW: Visualization, Writing – original draft, Writing – review & editing. SG: Writing – review & editing. YW: Visualization, Writing – review & editing. DW: Data curation, Writing – review & editing.SG: Data curation, Writing – review & editing. MW: Visualization, Writing – review & editing. YZhang: Supervision, Writing – review & editing. Funding Jinan Science and Technology Plan Project, No 202430052; Key Project of Medical and Health Science and Technology in Shandong Province, No.202416010384；Natural Science Foundation of Shandong Province under Grant No.ZR2021MH023 (Yang Zhang) Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. References Van der Zee C E, Nielander H B, Vos J P, et al. Expression of growth-associated protein B-50 (GAP43) in dorsal root ganglia and sciatic nerve during regenerative sprouting[J]. J Neurosci, 1989,9(10):3505-3512. Correction to: Role of Blood-Based Biomarkers in Ischemic Stroke Prognosis: A Systematic Review[J]. Stroke, 2021,52(3): e106. Montellano F A, Ungethum K, Ramiro L, et al. Role of Blood-Based Biomarkers in Ischemic Stroke Prognosis: A Systematic Review[J]. Stroke, 2021,52(2):543-551. Memis Z, Gurkas E, Ozdemir A O, et al. Impact of Neutrophil-to-Lymphocyte Ratio on Stroke Severity and Clinical Outcome in Anterior Circulation Large Vessel Occlusion Stroke[J]. Diagnostics (Basel), 2024,14(24). Fuca G, Guarini V, Antoniotti C, et al. The Pan-Immune-Inflammation Value is a new prognostic biomarker in metastatic colorectal cancer: results from a pooled-analysis of the Valentino and TRIBE first-line trials[J]. Br J Cancer, 2020,123(3):403-409. Xu H B, Xu Y H, He Y, et al. Association between admission pan-immune-inflammation value and short-term mortality in septic patients: a retrospective cohort study[J]. Sci Rep, 2024,14(1):15205. Lee L E, Ahn S S, Pyo J Y, et al. Pan-immune-inflammation value at diagnosis independently predicts all-cause mortality in patients with antineutrophil cytoplasmic antibody-associated vasculitis[J]. Clin Exp Rheumatol, 2021,39 Suppl 129(2):88-93. Bai B, Huang S, Liu P, et al. Nomogram for Predicting 90-day Outcomes in Patients with Acute Vertebrobasilar Artery Occlusion Undergoing Endovascular Treatment: A Multicenter Cohort Study[J]. AJNR Am J Neuroradiol, 2025. Liu H, Huang X, Yang Y X, et al. Altered Static and Dynamic Functional Network Connectivity and Combined Machine Learning in Stroke[J]. Brain Topogr, 2025,38(2):21. Fuca G, Guarini V, Antoniotti C, et al. The Pan-Immune-Inflammation Value is a new prognostic biomarker in metastatic colorectal cancer: results from a pooled-analysis of the Valentino and TRIBE first-line trials[J]. Br J Cancer, 2020,123(3):403-409. Hou N, Li M, He L, et al. Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost[J]. J Transl Med, 2020,18(1):462. Zhao X, Lu Y, Li S, et al. Predicting renal function recovery and short-term reversibility among acute kidney injury patients in the ICU: comparison of machine learning methods and conventional regression[J]. Ren Fail, 2022,44(1):1326-1337. Lee M, Yeo N Y, Ahn H J, et al. Prediction of post-stroke cognitive impairment after acute ischemic stroke using machine learning[J]. Alzheimers Res Ther, 2023,15(1):147. Yang H, Lv Z, Wang W, et al. Machine Learning Models for Predicting Early Neurological Deterioration and Risk Classification of Acute Ischemic Stroke[J]. Clin Appl Thromb Hemost, 2023,29:1299564054. Maida C D, Norrito R L, Daidone M, et al. Neuroinflammatory Mechanisms in Ischemic Stroke: Focus on Cardioembolic Stroke, Background, and Therapeutic Approaches[J]. Int J Mol Sci, 2020,21(18). Popa-Fotea N M, Ferdoschi C E, Micheu M M. Molecular and cellular mechanisms of inflammation in atherosclerosis[J]. Front Cardiovasc Med, 2023,10:1200341. Kim E, Cho S. Microglia and Monocyte-Derived Macrophages in Stroke[J]. Neurotherapeutics, 2016,13(4):702-718. Kong L L, Wang Z Y, Han N, et al. Neutralization of chemokine-like factor 1, a novel C-C chemokine, protects against focal cerebral ischemia by inhibiting neutrophil infiltration via MAPK pathways in rats[J]. J Neuroinflammation, 2014,11:112. Ren X, Gao X, Li Z, et al. Electroacupuncture ameliorates neuroinflammation by inhibiting TRPV4 channel in ischemic stroke[J]. CNS Neurosci Ther, 2024,30(2):e14618. Oh S A, Seol S I, Davaanyam D, et al. Platelet-derived HMGB1 induces NETosis, exacerbating brain damage in the photothrombotic stroke model[J]. Mol Med, 2025,31(1):46. Teng Y, Zhang Y, Yue S, et al. Intrathecal injection of bone marrow stromal cells attenuates neuropathic pain via inhibition of P2X4R in spinal cord microglia[J]. J Neuroinflammation, 2019,16(1):271. Huang L, Shao B. New insights of glycoprotein Ib-IX-V complex organization and glycoprotein Ibalpha in platelet biogenesis[J]. Curr Opin Hematol, 2024,31(6):294-301. Maciejewska-Renkowska J, Wachowiak J, Telec M, et al. Prospective Quantitative and Phenotypic Analysis of Platelet-Derived Extracellular Vesicles and Its Clinical Relevance in Ischemic Stroke Patients[J]. Int J Mol Sci, 2024,25(20). Ren H, Liu X, Wang L, et al. Lymphocyte-to-Monocyte Ratio: A Novel Predictor of the Prognosis of Acute Ischemic Stroke[J]. J Stroke Cerebrovasc Dis, 2017,26(11):2595-2602. Memis Z, Gurkas E, Ozdemir A O, et al. Impact of Neutrophil-to-Lymphocyte Ratio on Stroke Severity and Clinical Outcome in Anterior Circulation Large Vessel Occlusion Stroke[J]. Diagnostics (Basel), 2024,14(24). Yang X C, Liu H, Liu D C, et al. Prognostic value of pan-immune-inflammation value in colorectal cancer patients: A systematic review and meta-analysis[J]. Front Oncol, 2022,12:1036890. Yang X C, Liu H, Liu D C, et al. Prognostic value of pan-immune-inflammation value in colorectal cancer patients: A systematic review and meta-analysis[J]. Front Oncol, 2022,12:1036890. Zhai W Y, Duan F F, Lin Y B, et al. Pan-Immune-Inflammatory Value in Patients with Non-Small-Cell Lung Cancer Undergoing Neoadjuvant Immunochemotherapy[J]. J Inflamm Res, 2023,16:3329-3339. Jin C, Li X, Luo Y, et al. Associations between pan-immune-inflammation value and abdominal aortic calcification: a cross-sectional study[J]. Front Immunol, 2024,15:1370516. Yu X, Chen Y, Peng Y, et al. The Pan-Immune Inflammation Value at Admission Predicts Postoperative in-hospital Mortality in Patients with Acute Type A Aortic Dissection[J]. J Inflamm Res, 2024,17:5223-5234. Yang L, Guo J, Chen M, et al. Pan-Immune-Inflammatory Value is Superior to Other Inflammatory Indicators in Predicting Inpatient Major Adverse Cardiovascular Events and Severe Coronary Artery Stenosis after Percutaneous Coronary Intervention in STEMI Patients[J]. Rev Cardiovasc Med, 2024,25(8):294. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6615694","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":509851049,"identity":"745b3600-7fb5-4fdc-b28d-39c9de6eb02d","order_by":0,"name":"Yuze Zhai","email":"","orcid":"","institution":"Shandong University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yuze","middleName":"","lastName":"Zhai","suffix":""},{"id":509851050,"identity":"1748de96-409e-4a7a-a1bc-025a82553aef","order_by":1,"name":"Shaolei Huang","email":"","orcid":"","institution":"Affiliated Hospital of Shandong University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Shaolei","middleName":"","lastName":"Huang","suffix":""},{"id":509851051,"identity":"808a7274-c62a-4c55-8204-374c7a51cb58","order_by":2,"name":"Yican Yang","email":"","orcid":"","institution":"Shandong University Cheeloo college of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yican","middleName":"","lastName":"Yang","suffix":""},{"id":509851052,"identity":"fafcc32c-b72d-457d-96e1-010db74bbb9b","order_by":3,"name":"Dezheng Wang","email":"","orcid":"","institution":"Shandong University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Dezheng","middleName":"","lastName":"Wang","suffix":""},{"id":509851053,"identity":"cfff75eb-465e-4c6c-a009-31ea4127cbf3","order_by":4,"name":"Shuai Guo","email":"","orcid":"","institution":"Shandong University Cheeloo college of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Shuai","middleName":"","lastName":"Guo","suffix":""},{"id":509851054,"identity":"c0094f2f-b30a-4735-ad60-645d28d582e2","order_by":5,"name":"Mutong Wang","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Mutong","middleName":"","lastName":"Wang","suffix":""},{"id":509851055,"identity":"0d02f634-6176-4f3f-882a-a771ad15adbe","order_by":6,"name":"Zishan Wang","email":"","orcid":"","institution":"Shandong University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zishan","middleName":"","lastName":"Wang","suffix":""},{"id":509851056,"identity":"f39c3feb-141f-4343-a545-51f8a85a5464","order_by":7,"name":"Yang Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEUlEQVRIiWNgGAWjYDACCRiDvfnggwSDGjk29vYDRGrhOZZs8KHimDEfz5kEIrVI5JhJzjjDnDhPwsEArw752c3PHn5ts8uTj0hLkOZtY0tvk2BIYPhRsQ2nFsY5x8yNZduSiw3PPD5gzNsmk9sm3XiAsefMbZxamCUSzKQl25gTN7anJSQDbcltkzmQwMzYhlsLm0T6N6CW+sSNDTkGh3nbmNPZJBIM8GrhAfn6Y9vhxPkcOYaNQO8nENQiIZFTJs1w7njiBmAgMwAD2bANGMgH8flFfkb6NskfZdWJ89ubj/8ARqW8fHv7wQc/KnBrAQcBLxsDg8EBJJEDOFTCAeOPP0DrGggpGwWjYBSMghELAIIvXDLvdDAcAAAAAElFTkSuQmCC","orcid":"","institution":"University of Traditional Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Yang","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-05-08 01:53:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6615694/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6615694/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90883876,"identity":"4824e965-97f2-4292-b52d-d91ec2afcefa","added_by":"auto","created_at":"2025-09-09 09:56:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":430602,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart for patient selection and development of predictive models.\u003c/p\u003e\n\u003cp\u003eMIMIC: Medical Information Mort for Intensive Care; ICU: Intensive care unit\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6615694/v1/491b655af1b8cbc4c07c97ba.png"},{"id":90881231,"identity":"f243faf5-785a-4d48-8409-7f9f726466c6","added_by":"auto","created_at":"2025-09-09 09:40:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":414165,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe K-M analysis examining the prognostic impact of Log2-PIV on in-hospital mortality\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6615694/v1/b872aea58ce7d9cf17c282e2.png"},{"id":90883877,"identity":"0cacf01c-7d1b-4f68-b110-ee1344d496de","added_by":"auto","created_at":"2025-09-09 09:56:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":114276,"visible":true,"origin":"","legend":"\u003cp\u003eSmoothed curve fit of Log2-PIV to the risk of in-hospital all-cause mortality among patients with IS.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6615694/v1/152607a2ea217238dd473a21.png"},{"id":90881728,"identity":"381f56ad-d9e2-4893-a5ae-1e341f275767","added_by":"auto","created_at":"2025-09-09 09:48:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":83183,"visible":true,"origin":"","legend":"\u003cp\u003eThe restricted cubic regression analysis between Log2-PIV and in-hospital mortality in a fully adjusted model.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6615694/v1/1dd9f21e6c9e54efe24227a1.png"},{"id":90881729,"identity":"fc45dfdb-f2f2-49f7-8126-676e629d26d0","added_by":"auto","created_at":"2025-09-09 09:48:05","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":965120,"visible":true,"origin":"","legend":"\u003cp\u003eLog2-PIV and Cox proportional hazards regression analysis, along with a subgroup analysis, examining the risk of in-hospital all-cause mortality among patients with IS.\u003c/p\u003e\n\u003cp\u003eA: Unadjusted predictive model; B: Model corrected for Height, Weight, SBP, DBP, Resprate, and SPO2. BMI, body mass index; SBP, Systolic Blood Pressure; DBP, Diastolic Blood Pressure; OR: Hazard ratio.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6615694/v1/9a9624cb272977d974bf83bb.png"},{"id":90881245,"identity":"be9e4c35-02b6-43e1-8045-bdb4672b2a0d","added_by":"auto","created_at":"2025-09-09 09:40:05","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":491969,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u0026nbsp;\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6615694/v1/d8b2436f70e0dc7ba89202bd.png"},{"id":90881737,"identity":"8ef7fd5f-1603-4818-808f-5a6edfaa63ef","added_by":"auto","created_at":"2025-09-09 09:48:05","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":685297,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSHAP Summary Chart. \u003c/strong\u003eA. SHAP values of different clinical features on the affectivity of model output results; Figure7 B: Mean absolute SHAP values for each clinical feature.SHAP: Shapley's Additive Interpretation；PIV, Pan-immune-inflammation value; LODS, Logistic Organ Dysfunction System; WBC, White blood cell count; OASIS, Oxford acute severity of illness score; SBP, Systolic Blood Pressure; Aado2, Difference of alveoli-arterial oxygen pressure; DBP, Diastolic Blood Pressure; TCO\u003csub\u003e2\u003c/sub\u003e, Total carbon dioxide; GCS, Glasgow Coma Scale scores.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6615694/v1/c0dbec100eefbbd57ac716b9.png"},{"id":93361630,"identity":"c716dbf2-6307-45a8-b373-b03d9e0a0c7c","added_by":"auto","created_at":"2025-10-13 03:32:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4322230,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6615694/v1/05f37be6-8111-41ac-9721-4f0406043136.pdf"},{"id":90881724,"identity":"e7e7b4c4-e575-4e8c-8be7-caf084c4bcff","added_by":"auto","created_at":"2025-09-09 09:48:05","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":57114,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfiles.docx","url":"https://assets-eu.researchsquare.com/files/rs-6615694/v1/4c680e5147816276a041af44.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Relationship between Pan-immuno-inflammatory value and hospitalized all-cause mortality in ischemic stroke patients: a retrospective cohort study and predictive modeling","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIschemic stroke (IS) happens when the blood supply to a specific area of the brain is substantially diminished or stopped, either due to vascular embolism or inadequate systemic blood perfusion. This deprivation of nourishment to the brain tissue results in neuronal damage and neurological dysfunction. Globally, Globally, stroke is the second greatest cause of mortality. Notably, 80% of the strokes are ischemic, with approximately 20% recurring within 5 years and one-third of patients succumbing to the condition\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Reliable clinical predictors and predictive models are essential for determining the likelihood of poor prognosis in critically sick IS patients. They facilitate timely and appropriate management, ultimately aiming to reduce the overall burden of IS\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. However, existing clinical outcome predictors and models primarily rely on demographics and clinical parameters, which may introduce biases and limit the reliability of clinical decision-making. This underscores the need for blood-based noninvasive biomarkers. Machine-learning-based clinical prediction models offer the potential to enhance prognostic insights by harnessing data readily available in clinical settings\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eSystemic inflammation and immune response play pivotal roles in the development and progression of critical care IS. When central nervous system (CNS) tissue is damaged by ischemia, it produces inflammatory chemicals related with damage and pathogens. This initiates an immunological response, resulting in the activation and infiltration of inflammatory cells. While the initial inflammation and immune response are physiological processes crucial for tissue repair, excessive reactions can cause further tissue damage. In the CNS, microglia phagocytose lysogenic cells and pathogens; however, during IS, they persist in an M1 state.This condition stimulates peripheral neurons and immune cells through pro-inflammatory molecules including tumor necrosis factor(TNF)-α and interleukin(IL)-1β, generating a vicious loop that worsens nerve injury. Consequently, blood biomarkers linked to inflammation and immunity hold the potential for predicting clinical outcomes in critically ill IS patients. Previous research has established the predictive validity of the neutrophil/lymphocyte ratio (NLR), derived NLR, platelet/lymphocyte ratio (PLR), and monocyte/lymphocyte ratio (MLR) in assessing stroke severity and clinical outcome\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003ePan-immuno-inflammatory value (PIV) represents a novel composite index that integrates neutrophils, platelets, monocytes, and lymphocytes to comprehensively assess the host's inflammatory and immune response. Prior research has established PIV's considerable superiority over inflammatory markers, including NLR, PLR, and MLR, in prognosticating colorectal cancer\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e, sepsis\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e, and anti-neurocyte cytoplasmic antibody-associated vasculitis\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eGiven the diverse etiologies of severe stroke, traditional diagnostic methods such as neuron-specific enolase (NSE) testing, electroencephalography, and cranial imaging can offer valuable insights but often struggle to provide tailored, long-term prognostic predictions\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. However, with significant advancements in computing power, machine learning (ML) algorithms can now integrate a wide range of clinical data, refine prognostic assessments, and deliver more precise predictions. These algorithms have gained widespread application in prognostic evaluations for various critical illnesses. Numerous previous studies have shown that based on ML models excel in assessing neurological function and predicting the risk of disease recurrence among patients with severe IS\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. In light of these findings, our study examined the association between PIV and in-hospital mortality in this patient population. Furthermore, we developed an ML model aimed at predicting the risk of death during hospitalization for severe stroke patients. Finally, we useed the SHapley Additive explanations (SHAP) algorithm to interpret our best model, facilitating clinical decision-making by combining a new inflammatory signal with our prediction model.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e1 Data source\u003c/p\u003e\u003cp\u003eThe data employed in this study were sourced from the Medical Information Mart for Intensive Care IV (MIMIC-IV) v3.0 electronic health record dataset, accessible at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mimic.physionet.org\u003c/span\u003e\u003cspan address=\"https://mimic.physionet.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eThe Massachusetts Institute of Technology maintains this extensive database, which includes 364,627 patients hospitalized to Beth Israel Deaconess Medical Center's Intensive Care Unit (ICU) between 2008 and 2022. To acquire access to this sensitive data, we completed the National Institutes of Health's human subjects protection training and the Collaborative Institutional Training Program test (certification number 46538344). We also obtained informed consent waivers and permission to use research materials from the Institutional Review Boards at the Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center.\u003c/p\u003e\u003cp\u003e2 Study design and population\u003c/p\u003e\u003cp\u003eThis retrospective investigation included patients with IS who were later hospitalized and admitted to the ICU for the first time. IS was found in 8,217 individuals using International Classification of Diseases (ICD) codes, especially ICD-9 codes 433, 434, 436, 437.0, 437.1, and ICD-10 codes I63, I65. However, 4,556 individuals were eliminated because they did not match the criteria for first ICU admission. Furthermore, 3,571 patients were eliminated using the following exclusion criteria: To be eligible for ICU admission, (1) patients age\u0026thinsp;\u0026lt;\u0026thinsp;18 years old; (2) have a severe disease like end-stage renal insufficiency, cirrhosis, or cancer; (3) have missing neutrophil, platelet, monocyte, or lymphocyte counts on the first day; (4) have an ICU hospitalization duration of \u0026lt;\u0026thinsp;24 hours; (5) have incomplete survival or time of death data. For patients with multiple hospitalizations owing to IS, only the data from the initial admission were retrieved.(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e3 Data Extractions\u003c/p\u003e\u003cp\u003eWe employed PostgreSQL software (version 9.6) to gather data on various factors such as demographic parameters, comorbidities, vital signs, laboratory values, and scoring systems from the patient's first day in the ICU. The study encompassed basic patient details like age, gender, height, weight, BMI, LODS, GCS, OASIS, ICU mortality, and ICU mortality time. Additionally, we collected physical examination findings, specifically heart rate, SBP, DBP, temperature, and respiratory rate; Laboratory Examinations: SO\u003csub\u003e2\u003c/sub\u003e, Hemoglobin, WBC, Aniongap, Blood Urea Nitrogen, Blood calcium, Blood creatinine, Glucose, Blood sodium, Potassium, PH, PO\u003csub\u003e2\u003c/sub\u003e, PCO\u003csub\u003e2\u003c/sub\u003e, TCO\u003csub\u003e2\u003c/sub\u003e, Aado\u003csub\u003e2\u003c/sub\u003e. Comorbidities: Heart failure, Chronic obstructive pulmonary disease, Renal disease, Severe liver disease. All of the data above has been extracted and can be seen in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The PIV was calculated using the formula \u0026ldquo;neutrophil (k/ul) \u0026times; monocyte count (k/ul) \u0026times; platelet (k/ul)/lymphocyte (k/ul)\u0026rdquo;\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e。\u003c/p\u003e\u003cp\u003eThe primary goal of this study was to investigate in-hospital all-cause mortality among patients with IS. Laboratory results were gathered only within the first 24 hours after the patient's admission to the ICU. To limit the danger of bias, variables having missing values of more than 20% were eliminated from the analysis. For variables with missing values of under 20% we used the Random Forest interpolation approach in R software to do multiple interpolations.\u003c/p\u003e\u003cp\u003e4 statistical analysis\u003c/p\u003e\u003cp\u003ePIV's distribution was skewed, thus a Log2 transformation was given to it before analysis. Continuous variables were given as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation(SD) and compared using one-way ANOVA or the Kruskal-Wallis test based on data appropriateness. Categorical variables were reported as frequencies (%) and evaluated using chi-squared or Fisher's exact test, as applicable. Kaplan-Meier (K-M) survival curves for IS patients in the ICU were created and analyzed using the log-rank test to determine differences in survival outcomes.\u003c/p\u003e\u003cp\u003eUnivariate analysis, along with a multivariate Cox proportional-hazards model, was used to assess the effort of Log2-PIV on ICU in-hospital all-cause mortality among patients with IS. Specifically, we evaluated three models: Model 1 was unadjusted; Model 2 was adjusted for age and gender; and Model 3 further adjusted for height, weight, and BMI using Model 2. To examine potential nonlinear relationships between Log2-PIV and survival endpoints, we employed RCS analysis and smoothed curve fitting techniques. In cases where nonlinear correlations were identified, a two-stage regression modeling approach was utilized to clarify these nonlinearities. Additionally, Cox proportional risk regression analyses were conducted to assess the regressions based on various factors, including gender, Heart failure, Chronic obstructive pulmonary disease, Renal disease, Severe liver disease, age, BMI, heart rate, and hemoglobin, as significant differences were observed between the survival and non-survival groups.\u003c/p\u003e\u003cp\u003eAll statistical analyses were performed using Python 3.9.12 and R version 4.1.3. Statistical significance was determined at the \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003cp\u003e5 ML model construction and performance evaluation\u003c/p\u003e\u003cp\u003eIn this study, we employed 14 distinct ML algorithms to construct predictive models, including Latent Dirichlet Allocation (LDA), ExtraTrees (ET), Random Forest (RF), Logistic Regression (LR), Stochastic Gradient Descent (SGD), Naive Bayes (NB), Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Gradient Boosting Decision Tree (GBDT), CatBoost, AdaBoost, LightGBM, and XGBoost. Initially, utilizing multiple interpolated post-data, we randomly partitioned the population, allocating 80% for derivation data and reserving 20% for validation. During model development, we relied on internal validation to ascertain the stability of the predictive model within the training dataset. Specifically, we implemented 10-fold cross-validation as a resampling technique to identify the optimal hyperparameters. In each iteration, the model was trained on 9 folds while the remaining fold was dedicated to fine-tuning the hyperparameters. To evaluate the performance of each model, we computed the Area Under Curve (AUC) in the test dataset and compared the sensitivity, specificity, and accuracy across all models. To assess clinical utility, we generated calibration curves and decision curves, analyzing the clinical benefit derived from model calibration against varying thresholds. Additionally, we enhanced the interpretability of our final models using the SHAP framework. SHAP summary plots illuminated the influence of various model features, while SHAP dependency plots revealed the extent of each feature's impact on model outcomes. Lastly, SHAP force diagrams visually represent how key features shape the final model's predictions for individual patients.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e1 Correlation analysis\u003c/p\u003e\u003cp\u003eBaseline characteristics\u003c/p\u003e\u003cp\u003eBased on the defined inclusion and exclusion criteria, this study encompassed 2,233 patients with IS from the database. These patients were stratified into four groups according to the Log2-PIV quartiles. The resulting cohort comprised 1076 (48.19%) males and 1157 (51.81%) females, with a mean age of 69.81\u0026thinsp;\u0026plusmn;\u0026thinsp;15.56 years. Our findings indicated that blood calcium levels tended to decrease as Log2-PIV increased. Conversely, there was a trend towards an increase in LODS, OASIS, heart rate, respiratory rate, white blood cell (WBC), anion gap, blood urea nitrogen, glucose levels, and ICU mortality (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)。\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBasic characteristics of included patients grouped according to Log2-PIV quartiles.\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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eLog2-Pan-immune-inflammation value (Log2-PIV)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eN\u0026thinsp;=\u0026thinsp;2233\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eN\u0026thinsp;=\u0026thinsp;558\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eN\u0026thinsp;=\u0026thinsp;557\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eN\u0026thinsp;=\u0026thinsp;559\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eN\u0026thinsp;=\u0026thinsp;559\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,y\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e69.81\u0026thinsp;\u0026plusmn;\u0026thinsp;15.56\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71.57\u0026thinsp;\u0026plusmn;\u0026thinsp;14.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e71.94\u0026thinsp;\u0026plusmn;\u0026thinsp;14.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e68.71\u0026thinsp;\u0026plusmn;\u0026thinsp;16.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e69.49\u0026thinsp;\u0026plusmn;\u0026thinsp;15.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1076 (48.19%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e264 (47.31%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e261 (46.86%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e268 (47.94%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e283 (50.63%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.591\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeight, cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e168.70\u0026thinsp;\u0026plusmn;\u0026thinsp;10.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e168.20\u0026thinsp;\u0026plusmn;\u0026thinsp;11.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e168.67\u0026thinsp;\u0026plusmn;\u0026thinsp;10.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e169.84\u0026thinsp;\u0026plusmn;\u0026thinsp;10.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e168.08\u0026thinsp;\u0026plusmn;\u0026thinsp;10.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight, kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80.49\u0026thinsp;\u0026plusmn;\u0026thinsp;22.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80.22\u0026thinsp;\u0026plusmn;\u0026thinsp;23.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e81.80\u0026thinsp;\u0026plusmn;\u0026thinsp;22.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e81.26\u0026thinsp;\u0026plusmn;\u0026thinsp;21.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e78.70\u0026thinsp;\u0026plusmn;\u0026thinsp;22.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.103\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28.68\u0026thinsp;\u0026plusmn;\u0026thinsp;7.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.19\u0026thinsp;\u0026plusmn;\u0026thinsp;10.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28.49\u0026thinsp;\u0026plusmn;\u0026thinsp;6.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e28.83\u0026thinsp;\u0026plusmn;\u0026thinsp;6.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e28.22\u0026thinsp;\u0026plusmn;\u0026thinsp;6.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.175\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharlson\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.59\u0026thinsp;\u0026plusmn;\u0026thinsp;2.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.63\u0026thinsp;\u0026plusmn;\u0026thinsp;2.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.64\u0026thinsp;\u0026plusmn;\u0026thinsp;2.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.57\u0026thinsp;\u0026plusmn;\u0026thinsp;3.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.52\u0026thinsp;\u0026plusmn;\u0026thinsp;2.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.892\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.82\u0026thinsp;\u0026plusmn;\u0026thinsp;2.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.96\u0026thinsp;\u0026plusmn;\u0026thinsp;2.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.87\u0026thinsp;\u0026plusmn;\u0026thinsp;2.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.91\u0026thinsp;\u0026plusmn;\u0026thinsp;2.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.54\u0026thinsp;\u0026plusmn;\u0026thinsp;3.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.072\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLODS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.95\u0026thinsp;\u0026plusmn;\u0026thinsp;2.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.86\u0026thinsp;\u0026plusmn;\u0026thinsp;3.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.77\u0026thinsp;\u0026plusmn;\u0026thinsp;2.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.71\u0026thinsp;\u0026plusmn;\u0026thinsp;2.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.46\u0026thinsp;\u0026plusmn;\u0026thinsp;3.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOASIS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31.62\u0026thinsp;\u0026plusmn;\u0026thinsp;8.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.81\u0026thinsp;\u0026plusmn;\u0026thinsp;8.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.24\u0026thinsp;\u0026plusmn;\u0026thinsp;8.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31.22\u0026thinsp;\u0026plusmn;\u0026thinsp;8.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e33.18\u0026thinsp;\u0026plusmn;\u0026thinsp;8.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003ePhysical Examinations\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart Rate, beats/min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81.27\u0026thinsp;\u0026plusmn;\u0026thinsp;15.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e78.72\u0026thinsp;\u0026plusmn;\u0026thinsp;14.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e80.32\u0026thinsp;\u0026plusmn;\u0026thinsp;14.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e81.78\u0026thinsp;\u0026plusmn;\u0026thinsp;15.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e84.24\u0026thinsp;\u0026plusmn;\u0026thinsp;16.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP, mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e131.28\u0026thinsp;\u0026plusmn;\u0026thinsp;18.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e131.78\u0026thinsp;\u0026plusmn;\u0026thinsp;18.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e132.79\u0026thinsp;\u0026plusmn;\u0026thinsp;19.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e131.02\u0026thinsp;\u0026plusmn;\u0026thinsp;18.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e129.52\u0026thinsp;\u0026plusmn;\u0026thinsp;18.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBP, mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70.54\u0026thinsp;\u0026plusmn;\u0026thinsp;13.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70.68\u0026thinsp;\u0026plusmn;\u0026thinsp;13.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e70.97\u0026thinsp;\u0026plusmn;\u0026thinsp;13.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e71.45\u0026thinsp;\u0026plusmn;\u0026thinsp;12.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e69.07\u0026thinsp;\u0026plusmn;\u0026thinsp;12.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRespiratory Rate, beats/min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19.44\u0026thinsp;\u0026plusmn;\u0026thinsp;3.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.01\u0026thinsp;\u0026plusmn;\u0026thinsp;3.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.15\u0026thinsp;\u0026plusmn;\u0026thinsp;3.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19.61\u0026thinsp;\u0026plusmn;\u0026thinsp;3.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20.01\u0026thinsp;\u0026plusmn;\u0026thinsp;3.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTemperature, ℃\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e36.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e36.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eLaboratory Examinations\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSO₂\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e96.88\u0026thinsp;\u0026plusmn;\u0026thinsp;1.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e96.85\u0026thinsp;\u0026plusmn;\u0026thinsp;1.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e96.84\u0026thinsp;\u0026plusmn;\u0026thinsp;1.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e96.86\u0026thinsp;\u0026plusmn;\u0026thinsp;1.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e96.98\u0026thinsp;\u0026plusmn;\u0026thinsp;2.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.613\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin, g/dl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.85\u0026thinsp;\u0026plusmn;\u0026thinsp;2.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.85\u0026thinsp;\u0026plusmn;\u0026thinsp;2.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.95\u0026thinsp;\u0026plusmn;\u0026thinsp;2.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.96\u0026thinsp;\u0026plusmn;\u0026thinsp;2.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.66\u0026thinsp;\u0026plusmn;\u0026thinsp;2.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.102\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC, k/ul\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.30\u0026thinsp;\u0026plusmn;\u0026thinsp;8.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.66\u0026thinsp;\u0026plusmn;\u0026thinsp;11.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.11\u0026thinsp;\u0026plusmn;\u0026thinsp;4.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.86\u0026thinsp;\u0026plusmn;\u0026thinsp;4.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15.54\u0026thinsp;\u0026plusmn;\u0026thinsp;9.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAniongap, mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.46\u0026thinsp;\u0026plusmn;\u0026thinsp;3.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.88\u0026thinsp;\u0026plusmn;\u0026thinsp;3.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.35\u0026thinsp;\u0026plusmn;\u0026thinsp;3.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14.52\u0026thinsp;\u0026plusmn;\u0026thinsp;3.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15.10\u0026thinsp;\u0026plusmn;\u0026thinsp;3.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood Urea Nitrogen, mg/dl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.29\u0026thinsp;\u0026plusmn;\u0026thinsp;18.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.20\u0026thinsp;\u0026plusmn;\u0026thinsp;16.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.08\u0026thinsp;\u0026plusmn;\u0026thinsp;16.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23.58\u0026thinsp;\u0026plusmn;\u0026thinsp;18.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e25.29\u0026thinsp;\u0026plusmn;\u0026thinsp;19.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood calcium, mg/dl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood creatinine, mg/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.28\u0026thinsp;\u0026plusmn;\u0026thinsp;1.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.22\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.29\u0026thinsp;\u0026plusmn;\u0026thinsp;1.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.34\u0026thinsp;\u0026plusmn;\u0026thinsp;1.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.28\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.486\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlucose, mg/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e143.09\u0026thinsp;\u0026plusmn;\u0026thinsp;58.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e136.70\u0026thinsp;\u0026plusmn;\u0026thinsp;52.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e140.38\u0026thinsp;\u0026plusmn;\u0026thinsp;55.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e143.31\u0026thinsp;\u0026plusmn;\u0026thinsp;60.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e151.95\u0026thinsp;\u0026plusmn;\u0026thinsp;65.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood sodium, mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e139.25\u0026thinsp;\u0026plusmn;\u0026thinsp;4.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e139.48\u0026thinsp;\u0026plusmn;\u0026thinsp;4.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e139.16\u0026thinsp;\u0026plusmn;\u0026thinsp;4.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e139.25\u0026thinsp;\u0026plusmn;\u0026thinsp;4.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e139.11\u0026thinsp;\u0026plusmn;\u0026thinsp;4.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.465\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePotassium, mEq/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.703\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.207\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePO\u003csub\u003e2\u003c/sub\u003e, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e128.97\u0026thinsp;\u0026plusmn;\u0026thinsp;74.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e133.89\u0026thinsp;\u0026plusmn;\u0026thinsp;76.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e128.96\u0026thinsp;\u0026plusmn;\u0026thinsp;73.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e124.29\u0026thinsp;\u0026plusmn;\u0026thinsp;72.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e128.73\u0026thinsp;\u0026plusmn;\u0026thinsp;76.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.204\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePCO\u003csub\u003e2\u003c/sub\u003e,%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40.85\u0026thinsp;\u0026plusmn;\u0026thinsp;7.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40.47\u0026thinsp;\u0026plusmn;\u0026thinsp;7.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41.25\u0026thinsp;\u0026plusmn;\u0026thinsp;7.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e40.97\u0026thinsp;\u0026plusmn;\u0026thinsp;7.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e40.70\u0026thinsp;\u0026plusmn;\u0026thinsp;8.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.338\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTCO\u003csub\u003e2\u003c/sub\u003e,%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.52\u0026thinsp;\u0026plusmn;\u0026thinsp;4.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.37\u0026thinsp;\u0026plusmn;\u0026thinsp;4.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.68\u0026thinsp;\u0026plusmn;\u0026thinsp;4.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24.82\u0026thinsp;\u0026plusmn;\u0026thinsp;4.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e24.19\u0026thinsp;\u0026plusmn;\u0026thinsp;4.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.053\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAado\u003csub\u003e2\u003c/sub\u003e,%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e213.25\u0026thinsp;\u0026plusmn;\u0026thinsp;108.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e206.59\u0026thinsp;\u0026plusmn;\u0026thinsp;101.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e211.05\u0026thinsp;\u0026plusmn;\u0026thinsp;112.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e218.28\u0026thinsp;\u0026plusmn;\u0026thinsp;104.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e217.06\u0026thinsp;\u0026plusmn;\u0026thinsp;115.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.239\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eComorbidities\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart failure, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.187\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1689(75.64%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e441 (79.03%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e418 (75.04%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e414 (74.06%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e416 (74.42%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e544 (24.36%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e117 (20.97%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e139 (24.96%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e145 (25.94%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e143 (25.58%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronicobstructive pulmonary disease, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.201\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1851(82.89%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e479 (85.84%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e457 (82.05%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e459 (82.11%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e456 (81.57%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e382 (17.11%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e79 (14.16%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100 (17.95%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100 (17.89%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e103 (18.43%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRenal disease, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.472\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1797(80.47%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e441 (79.03%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e443 (79.53%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e452 (80.86%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e461 (82.47%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e436 (19.53%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e117 (20.97%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e114 (20.47%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e107 (19.14%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e98 (17.53%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSevere Liver Disease, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.830\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2192(98.16%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e546 (97.85%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e546 (98.03%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e549 (98.21%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e551 (98.57%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41 (1.84%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (2.15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11 (1.97%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10 (1.79%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8 (1.43%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eEvents\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eICU mortality, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e390 (17.47%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74 (13.26%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e80 (14.36%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e91 (16.28%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e145 (25.94%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eICU mortality time, days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.29\u0026thinsp;\u0026plusmn;\u0026thinsp;16.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.31\u0026thinsp;\u0026plusmn;\u0026thinsp;15.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.61\u0026thinsp;\u0026plusmn;\u0026thinsp;15.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.36\u0026thinsp;\u0026plusmn;\u0026thinsp;14.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.86\u0026thinsp;\u0026plusmn;\u0026thinsp;19.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eValues in bold indicate significance at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003cp\u003ea: Pan-immune-inflammation value(PIV) was divided into four groups, Q1 and Q4 are the lowest and highest quintile groups, respectively.\u003c/p\u003e\u003cp\u003eb: ANOVA and chi-square tests are used to assess the significance of continuous and categorical variables, respectively\u003c/p\u003e\u003cp\u003ec: Continuous values are presented as mean and standard error.\u003c/p\u003e\u003cp\u003ed: Categorical values are presented as %.\u003c/p\u003e\u003cp\u003eBMI, body mass index; GCS, Glasgow Coma Scale scores; LODS, Logistic Organ Dysfunction System; OASIS, Oxford acute severity of illness score; SBP, Systolic Blood Pressure; DBP, Diastolic Blood Pressure; SPO\u003csub\u003e2\u003c/sub\u003e, peripheral capillary oxygen saturation; WBC, White blood cell count; PH, Pondus Hydrogenii; PO\u003csub\u003e2\u003c/sub\u003e, Partial pressure of oxygen; PCO\u003csub\u003e2\u003c/sub\u003e, Partial Pressure of Carbon Dioxide; TCO\u003csub\u003e2\u003c/sub\u003e, Total carbon dioxide; Aado\u003csub\u003e2\u003c/sub\u003e, Difference of alveoli-arterial oxygen pressure, ICU: Intensive care unit.\u003c/p\u003e\u003cp\u003e2 Univariate analysis of in-hospital mortality in patients with IS\u003c/p\u003e\u003cp\u003eIn the univariate analysis conducted in this study, no significant correlation was observed between the risk of in-hospital death among patients with IS and several factors, including Gender, Height, Weight, BMI, Temperature, and Blood sodium. However, a positive correlation was found between this risk and Age (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.92, 95% CI\u0026thinsp;=\u0026thinsp;1.01, 1.03), Heart Rate (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.03, 95% CI\u0026thinsp;=\u0026thinsp;1.02, 1.03), Respiratory Rate (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.17, 95% CI\u0026thinsp;=\u0026thinsp;1.13, 1.20), SPO2 (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.17, 95% CI\u0026thinsp;=\u0026thinsp;1.10, 1.25), Log2-PIV (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.17, 95% CI\u0026thinsp;=\u0026thinsp;1.10, 1.25), WBC (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.12, 95% CI\u0026thinsp;=\u0026thinsp;1.10, 1.14), Aniongap (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.13, 95% CI\u0026thinsp;=\u0026thinsp;1.09, 1.16), Blood Urea Nitrogen (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.02, 95% CI\u0026thinsp;=\u0026thinsp;1.02, 1.03), Blood creatinine (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.16, 95% CI\u0026thinsp;=\u0026thinsp;1.08, 1.24), Glucose (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.01, 95% CI\u0026thinsp;=\u0026thinsp;1.00, 1.01), and Potassium (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.64, 95% CI\u0026thinsp;=\u0026thinsp;1.35, 1.99) (All \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Conversely, a significant negative correlation was observed with SBP (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.98, 95% CI\u0026thinsp;=\u0026thinsp;0.98, 0.99), DBP (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.96, 95% CI\u0026thinsp;=\u0026thinsp;0.95, 0.97), Hemoglobin (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.82, 95% CI\u0026thinsp;=\u0026thinsp;0.78, 0.87), and Blood calcium (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.54, 95% CI\u0026thinsp;=\u0026thinsp;0.46, 0.64, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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\u003eUnivariate analysis of in-hospital mortality in patients with IS.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eHR\u003c/em\u003e(95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.02 (1.01, 1.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.93 (0.75, 1.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.99 (0.98, 1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (0.99, 1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.06\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\u003e1.00 (0.99, 1.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003ePhysical Examinations\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\u003e1.03 (1.02, 1.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.98 (0.98, 0.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.96 (0.95, 0.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRespiratory Rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.17 (1.13, 1.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTemperature\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.13 (0.89, 1.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eLaboratory Examinations\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSPO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.17 (1.10, 1.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLog2 PIV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.17 (1.10, 1.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.82 (0.78, 0.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.12 (1.10, 1.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAniongap\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.13 (1.09, 1.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003e1.02 (1.02, 1.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood calcium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.54 (0.46, 0.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood creatinine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.16 (1.08, 1.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlucose\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.01 (1.00, 1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood sodium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.02 (0.99, 1.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePotassium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.64 (1.35, 1.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eValues in bold indicate significance at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003cp\u003eCI, confidence interval; BMI, body mass index; SBP, Systolic Blood Pressure; DBP, Diastolic Blood Pressure; SPO\u003csub\u003e2\u003c/sub\u003e, peripheral capillary oxygen saturation; PIV, Pan-immune-inflammation value; WBC, White blood cell count.\u003c/p\u003e\u003cp\u003e3 Cox proportional risk model of in-hospital mortality\u003c/p\u003e\u003cp\u003eTo confirm the correlation between Log2-PIV and in-hospital prognostic outcomes in IS patients, we selected Cox regression models for analysis. The results indicated that Log2-PIV was significantly associated with an elevated risk of in-hospital all-cause mortality across three models (Model 1: \u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.17, 95% CI\u0026thinsp;=\u0026thinsp;1.10, 1.25, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Model 2: \u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.27, 95% CI\u0026thinsp;=\u0026thinsp;1.17, 1.38, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Model 3: \u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.27, 95% CI\u0026thinsp;=\u0026thinsp;1.17, 1.38, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Furthermore, when analyzing Log2-PIV as an interquartile-based categorical variable, the fully adjusted model (Model 3) revealed a positive association between Log2-PIV and the risk of in-hospital all-cause mortality in IS patients (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.29, 95% CI\u0026thinsp;=\u0026thinsp;1.17, 1.42, \u003cem\u003eP\u003c/em\u003e for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Specifically, for every quartile increase in Log2-PIV, IS patients faced a 29% higher risk of in-hospital all-cause mortality (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Additionally, K-M survival plots supported these findings, showing similar results (\u003cem\u003eP\u003c/em\u003e for log-rank test\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\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\u003eCox regression modeling of in-hospital mortality\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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLog2-PIV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.17(1.10,1.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.27(1.17,1.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.27(1.17,1.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eQuartile\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.16(0.85,1.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.23(0.89,1.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.25(0.91,1.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.22(0.90,1.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.30(0.96,1.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.33(0.98,1.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.95(1.47,2.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.07(1.56,2.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.10(1.58,2.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.26(1.15,1.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.29(1.17,1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.29(1.17,1.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eValues in bold indicate significance at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003cp\u003eModel 1 adjusted for none; Model 2 adjusted for age, gender; Model 3 adjusted for age, gender, hight, and BMI.\u003c/p\u003e\u003cp\u003ePIV, Pan-immune-inflammation value;\u003c/p\u003e\u003cp\u003eCI, confidence interval;\u003c/p\u003e\u003cp\u003eBMI, Body Mass Index.\u003c/p\u003e\u003cp\u003eSubsequently, the relationship between Log2-PIV and the risk of in-hospital death in IS patients was further analyzed using smoothed curve fitting and Restricted Cubic Spline (RCS) plots.The results, after adjusting for relevant confounders, revealed a U-shaped nonlinear association between Log2-PIV and the risk of in-hospital all-cause mortality among patients with IS (Figure 3-4).\u003c/p\u003e\u003cp\u003eThe curves depicted represent the estimated adjusted risk ratios, along with 95% confidence intervals indicated between them.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe plotted curves depict the estimated adjusted risk ratios, while the red-shaded areas denote the 95% confidence intervals. The horizontal dotted line marks a risk ratio of 1.0, serving as a reference point. Threshold effect analysis identified a turning point (K) of 8.54. Below this inflection point, Log2-PIV exhibited a negative correlation with the risk of in-hospital all-cause mortality among patients with IS (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.77, 95% CI\u0026thinsp;=\u0026thinsp;0.65, 0.90, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0017). Conversely, beyond this point, a significant positive association was observed (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.26, 95% CI\u0026thinsp;=\u0026thinsp;1.17, 1.35, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The log-likelihood ratio test yielded a \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\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\u003eThreshold and effect value analysis of the relationship between Log2-PIV and in-hospital mortality in ICU stroke patients.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOutcome\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR(95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\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\u003eModel I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOne line effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.18(1.10,1.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTurning point(K)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt; K effect 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.77(0.65,0.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;K effect 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.26(1.17,1.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEffect 2\u0026thinsp;\u0026minus;\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.64(1.35,2.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLRT test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eValues in bold indicate significance at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eModel 1 adjusted for none; Model 2 adjusted for age, gender, hight, and BMI.\u003c/p\u003e\u003cp\u003eCI, confidence interval;\u003c/p\u003e\u003cp\u003eBMI, Body Mass Index.\u003c/p\u003e\u003cp\u003e4 Subgroup analysis\u003c/p\u003e\u003cp\u003eTo assess the generalizability of the relationship between Log2-PIV and the risk of in-hospital all-cause mortality among patients with IS, this study stratified the population based on Gender, Heart Failure (Yes, No), Chronic Obstructive Pulmonary Disease (Yes, No), Severe Liver Disease (Yes, No), Age (\u0026le;\u0026thinsp;65y, \u0026gt;65y), BMI (\u0026lt;\u0026thinsp;18.5 kg/m2, 18.5\u0026ndash;24.0 kg/m2, 24.0\u0026ndash;28.0 kg/m2, \u0026gt;28.0 kg/m2), Heart Rate (\u0026lt;\u0026thinsp;100 beats/min, \u0026ge; 100 beats/min), and Hemoglobin (\u0026lt;\u0026thinsp;12 g/dl, \u0026ge; 12 g/dl). The results revealed a significant interaction between BMI and Hemoglobin, both before and after adjustment (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Specifically, the association between Log2-PIV and the risk of in-hospital all-cause mortality in IS patients was found to vary among subgroups defined by different BMI and Hemoglobin levels. In other words, the impact of Log2-PIV on the risk of in-hospital all-cause mortality in patients with IS differed depending on their BMI and Hemoglobin status (Fig. 5).\u003c/p\u003e\n\u003cp\u003e5 Modeling optimal forecasts\u003c/p\u003e\n\u003cp\u003eBased on the aforementioned study, we included a total of 33 clinical features to construct 14 ML models, each incorporating the same 33 features. By assessing model performance using the test set data, we found that the GBDT, CatBoost, and RF models exhibited superior fitting performance, achieving AUC of 0.832, 0.830, and 0.828, respectively, in the validation cohort (Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e and Fig. 6A).\u003c/p\u003e\n\u003cp\u003eThe calibration represents the disparity between the model\u0026apos;s predicted probabilities and the actual probabilities. A smaller value signifies that the model\u0026apos;s predictions align more closely with the actual results, thus indicating greater accuracy. The calibration curve results demonstrate that GBDT\u0026apos;s calibration is notably superior to that of CatBoost and RF (Fig.\u0026nbsp;6B).\u003c/p\u003e\n\u003cp\u003eThe threshold probability for clinical decision-making (the likelihood of the outcome variable\u0026apos;s occurrence) falls between 5% and 90%. Within this range, predicting the risk of in-hospital death among modeled patients offers greater net benefits. Furthermore, the decision curve analysis demonstrates that the GBDT model exhibits superior value for clinical application (Fig. 6C).\u003c/p\u003e\n\u003cp\u003e6 Analysis of model interpretability\u003c/p\u003e\n\u003cp\u003eIn this study, we employed the SHAP algorithm to examine the interpretability of the GBDT model. The resulting SHAP summary plot illustrates how clinical features influence the GBDT model\u0026apos;s output results (Fig. 7A). Specifically, a positive value signifies that the corresponding feature positively impacts the output, while a negative value denotes a negative impact. The color coding\u0026mdash;with red indicating higher values and blue indicating lower ones\u0026mdash;further highlights the strength of each feature\u0026apos;s effect on the target variable, with darker colors representing a more pronounced influence. The accompanying bar graph reveals the contribution of each feature to the overall model, where a higher SHAP value signifies greater importance and a more substantial impact on the model\u0026apos;s output (Fig. 7B). Our findings identified Log2-PIV, Hemoglobin, LODS, Glucose, Aniongap, Spo2, WBC, Height, Respiratory Rate, and OASIS as the top 10 clinical features influencing the GBDT model\u0026apos;s output results.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study uncovered a notable U-shaped nonlinear relationship between Log2-PIV and the risk of in-hospital death among patients with IS, with an inflection point value of 8.54. Segmental regression analysis further demonstrated that Log2-PIV negatively correlated with the risk of death on the left side of this inflection point (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.77, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0017), while showing a positive correlation on the right side (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.26, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Additionally, our model construction, utilizing 14 learning algorithms, revealed that the GBDT model excelled in predicting the risk of in-hospital death for IS patients, achieving an AUC of 0.8367. The SHAP interpretability analysis further validated Log2-PIV as the foremost characterizing variable of the model, aligning with the findings from the Cox regression (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.27, 95% CI\u0026thinsp;=\u0026thinsp;1.17, 1.38, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and K-M survival analysis (\u003cem\u003eP\u003c/em\u003e for log-rank test\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Previous literature demonstrates that machine learning techniques exhibit a distinct advantage in predicting critical illness prognosis. These techniques are adept at recognizing crucial features of predictive target variables through modeling randomness\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e1]\u003c/sup\u003e. Zhao et al. developed a prediction model for acute kidney injury, employing the random forest algorithm. Their model demonstrated superior predictive power compared to traditional logistic regression analysis, particularly in forecasting early functional recovery or short-term reversibility of kidney injury (AUC: 0.85 vs 0.78) \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e2]\u003c/sup\u003e. The deep neural network model crafted by Heo J et al. notably surpassed the ASTRAL score in terms of predicting the long-term prognosis of IS. Furthermore, the ML approach demonstrated considerable benefits in addressing various stages of post-stroke cognitive dysfunction\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e, early neurological deterioration, and risk stratification \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Based on an analysis of 14 machine learning algorithms developed by our research team, the GBDT model emerged as a superior predictor of in-hospital mortality risk among patients with IS. The SHAP algorithm's results highlighted the significant role of Log2-PIV in forecasting all-cause mortality for IS patients during their hospital stay. Crucially, Cox regression analysis, coupled with K-M survival plots, further validated the positive association between Log2-PIV and the risk of in-hospital mortality in this patient population, aligning with the findings of the SHAP algorithm. Consequently, we posit that Log2-PIV serves as a reliable predictor of in-hospital all-cause mortality among IS patients. As a composite measure encompassing neutrophils, monocytes, platelets, and lymphocytes, the biological underpinnings of PIV are rooted in the multifaceted immune response triggered by IS. Previous research has indicated that ischemic neuronal injury arises when pattern recognition receptors identify injury-associated molecular patterns. This recognition triggers an excessive release of glutamate, subsequently causing overactivation of N-methyl-D-aspartate receptors and a significant influx of Ca\u003csup\u003e2+\u003c/sup\u003e Consequently, this chain of events leads to cell death by excitotoxicity\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Dead neuronal cells and cellular debris can trigger both innate and adaptive immune responses, which are profoundly implicated in the entire process of atherosclerotic plaque formation, endothelial dysfunction, and vascular rupture. This occurs through their collective promotion of chronic low-grade inflammation, which subsequently orchestrates lipid metabolism, immune cell activation, and vascular wall remodeling\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Consequently, inflammation is tightly linked to the progression and prognosis of IS \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. The sympathetic-adrenal axis becomes activated following an IS episode, leading to the swift mobilization of neutrophils. These neutrophils then infiltrate the molluscum contagiosum through a rolling adhesion process that involves the mediation of TNF-α, IL-8, and intercellular cell adhesion molecule (ICAM)-1\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e8]\u003c/sup\u003e. This infiltration ultimately contributes to a more severe disruption of the blood-brain barrier\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e9]\u003c/sup\u003e. Furthermore, in a mouse model of middle cerebral artery occlusion (MCAO), neutrophils escalate the inflammatory response by discharging extracellular traps composed of desmosomal chromatin and granular contents. This process facilitates the liberation of high mobility group protein B1 (HMGB1), ultimately intensifying delayed immune cell infiltration and cerebrovascular damage\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e0]\u003c/sup\u003e. Monocytes, which circulate in the bloodstream as peripheral immune cells and can differentiate into either macrophages or dendritic cells based on the local tissue milieu, exhibit a dual function in the advancement of IS. C-C motif chemokine receptor (CCR)-2 and chemokine ligand (CCL)-2 not only facilitate the excessive migration of monocytes to the affected area but also augment the expression of IL-1 and TNF genes, thus exacerbating neuroinflammation. Conversely, our prior research has indicated that CCL2 can stimulate the downstream target P2X4R, triggering the production of BDNF and facilitating neurologic recovery\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e1]\u003c/sup\u003e. It is crucial to maintain monocytes within a specific range to optimize neurologic recovery in cases of IS. Following endothelial damage or the rupture of atherosclerotic plaques, platelets undergo functional changes. Surface receptors on these platelets, including GP Ib-IX-V and GP VI, interact with von Willebrand factor to create a transient hemostatic thrombus. Concurrently, they release mediators like ADP and thromboxane A2, which enhance platelet aggregation and thrombus growth, ultimately leading to a reperfusion injury\u003csup\u003e[22]\u003c/sup\u003e. Furthermore, platelets subjected to stress have the capacity to release excessive amounts of pro-inflammatory factors, including P-selectin, CD40 Ligand, and IL-1β. These factors serve to recruit leukocytes and activate endothelial cells, thereby intensifying the local inflammatory response\u003csup\u003e[23]\u003c/sup\u003e. Lymphocytes, as key peripheral immune cells, exhibit a dual function in postischemic neuroinflammation. Rodent stroke studies have demonstrated that an elevation in lymphocytes leads to the upregulation of the anti-inflammatory cytokine IL-10, while suppressing the expression of IL-6 and TNF-α, ultimately promoting neuroprotection. However, a decrease in lymphocytes is associated with reduced cerebral infarction and mitigated neurological deficits\u003csup\u003e[24]\u003c/sup\u003e. A study examining ischemic stroke patients revealed a correlation between lymphocyte counts and the extent of cerebral white matter damage. Furthermore, lymphocyte counts emerged as an independent factor protecting cognitive function in cerebrovascular patients\u003csup\u003e[25]\u003c/sup\u003e. Consequently, immune activation at moderate levels aids in tissue recovery following the onset of IS. However, an excessive immune response leading to secondary injury could underlie the distinct U-shaped relationship observed between PIV and the in-hospital mortality risk in IS cases.\u003c/p\u003e\u003cp\u003eTo our knowledge, this study stands as the first to explore the correlation between PIV and IS, and to construct a predictive model based on a comprehensive epidemiological investigation. Recent studies have shown that PIV, as a novel composite immune-inflammatory marker that integrates neutrophils, monocytes, platelets, and lymphocytes, significantly outperforms other traditional markers of inflammation in predicting survival outcomes. A comprehensive systematic review and meta-analysis, encompassing 4,942 patients with tumors, revealed a notable association: patients exhibiting higher PIV levels faced a significantly elevated risk of mortality and were more prone to disease progression\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Furthermore, PIV demonstrated its efficacy in prognosticating the outcomes of patients with both non-metastatic and metastatic cancers\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Indeed, PIV has been identified as an independent factor influencing complete pathological remission in patients with non-small cell lung cancer \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Furthermore, its predictive value extends beyond cancer, being observed in non-cancerous conditions as well. Chen Jin et al. analyzed data from the American Health and Nutrition Examination Survey, revealing a notable positive correlation between PIV and abdominal aortic calcification\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. In predicting adverse events following coronary intervention for myocardial infarction, PIV demonstrated superior predictive accuracy compared to the systemic immunoinflammatory index. Furthermore, PIV showed a significant advantage in assessing the risk of death from aortic coarctation and in prognosticating diseases like coronary artery stenosis\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn this study, Cox regression analysis revealed that Log2-PIV serves as an independent risk factor for in-hospital all-cause mortality among patients with IS. The smoothed curve fitting, when compared to RCS curves, exhibited a U-shaped nonlinear association between Log2-PIV and the mortality risk in this patient population. Notably, the threshold turning point was identified at 8.54. Below this threshold, an increase in Log2 -PIV was associated with a decrease in the risk of in-hospital all-cause mortality among IS patients. Conversely, beyond this threshold, the risk of death escalated with increasing Log2-PIV. These findings strongly suggest that maintaining PIV within a reasonable range, rather than targeting a specific level, is crucial for IS patients.\u003c/p\u003e\u003cp\u003eWe must acknowledge certain limitations inherent in this study. Firstly, as a retrospective analysis, the data from MIMIC-IV v3.0 is confined to the United States, potentially introducing biases and limiting the global applicability of our prediction model and findings. To verify our results, future prospective multicenter studies with expanded sample sizes are necessary. Secondly, our methodology involved multiple interpolations to address missing data, which could result in deviations from actual values.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, a U-shaped relationship exists between Log2-PIV and the risk of in-hospital mortality among critically ill patients with IS. Elevated Log2-PIV levels are significantly linked to a heightened risk of adverse events, suggesting that Log2-PIV could serve as a predictor of unfavorable outcomes in this patient population. Furthermore, our machine learning modeling indicated that GBDT is a promising tool for predicting the risk of in-hospital death in IS patients. Nevertheless, further large-scale, multicenter, prospective studies are required to corroborate the findings of our current investigation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAvailability of data and material\u003c/p\u003e\n\u003cp\u003eMore information is available on the hyperlink as the Data Availability statement page.\u003c/p\u003e\n\u003cp\u003eThe data used in this study utilized publicly available datasets, which can be accessed at https://mimic.physionet.org.\u003c/p\u003e\n\u003cp\u003eEthics Approval\u003c/p\u003e\n\u003cp\u003eWritten informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.\u003c/p\u003e\n\u003cp\u003eAuthor contributions\u003c/p\u003e\n\u003cp\u003eYZhai: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology. SH: Project administration, Resources, Software, Supervision, Validation. ZW: Visualization, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing. SG: Writing \u0026ndash; review \u0026amp; editing. YW: Visualization, Writing \u0026ndash; review \u0026amp; editing. DW: Data curation, Writing \u0026ndash; review\u0026nbsp;\u0026amp; editing.SG: Data curation, Writing \u0026ndash; review \u0026amp; editing. MW: Visualization, Writing \u0026ndash; review \u0026amp; editing. YZhang: Supervision, Writing\u0026nbsp;\u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eJinan Science and Technology Plan Project, No 202430052; Key Project of Medical and Health Science and Technology in Shandong Province, No.202416010384；Natural Science Foundation of Shandong Province under Grant No.ZR2021MH023 (Yang Zhang)\u003c/p\u003e\n\u003cp\u003eConflict of interest\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003ePublisher\u0026rsquo;s note\u003c/p\u003e\n\u003cp\u003eAll claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eVan der Zee C E, Nielander H B, Vos J P, et al. Expression of growth-associated protein B-50 (GAP43) in dorsal root ganglia and sciatic nerve during regenerative sprouting[J]. J Neurosci, 1989,9(10):3505-3512.\u003c/li\u003e\n\u003cli\u003eCorrection to: Role of Blood-Based Biomarkers in Ischemic Stroke Prognosis: A Systematic Review[J]. Stroke, 2021,52(3): e106.\u003c/li\u003e\n\u003cli\u003eMontellano F A, Ungethum K, Ramiro L, et al. Role of Blood-Based Biomarkers in Ischemic Stroke Prognosis: A Systematic Review[J]. Stroke, 2021,52(2):543-551.\u003c/li\u003e\n\u003cli\u003eMemis Z, Gurkas E, Ozdemir A O, et al. Impact of Neutrophil-to-Lymphocyte Ratio on Stroke Severity and Clinical Outcome in Anterior Circulation Large Vessel Occlusion Stroke[J]. 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Front Immunol, 2024,15:1370516.\u003c/li\u003e\n\u003cli\u003eYu X, Chen Y, Peng Y, et al. The Pan-Immune Inflammation Value at Admission Predicts Postoperative in-hospital Mortality in Patients with Acute Type A Aortic Dissection[J]. J Inflamm Res, 2024,17:5223-5234.\u003c/li\u003e\n\u003cli\u003eYang L, Guo J, Chen M, et al. Pan-Immune-Inflammatory Value is Superior to Other Inflammatory Indicators in Predicting Inpatient Major Adverse Cardiovascular Events and Severe Coronary Artery Stenosis after Percutaneous Coronary Intervention in STEMI Patients[J]. Rev Cardiovasc Med, 2024,25(8):294.\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":"Inflammation, MIMIC-IV, Mortality, predictive modeling, ischemic stroke","lastPublishedDoi":"10.21203/rs.3.rs-6615694/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6615694/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground Systemic inflammation and immune response are major factors in the development and progression of ischemic stroke (IS). Numerous studies have shown how Pan-immuno-inflammatory value (PIV) affects the chance of dying from a serious disease. However, the value of PIV in IS patients in the ICU remains unclear. The objective of this study was to explore the correlation between PIV and IS, and to construct a machine learning (ML) model for in-hospital mortality risk in IS patients using variables related to PIV.\u003c/p\u003e\u003cp\u003eMethods In the present study, patients who had been diagnosed with IS and admitted to the ICU were retrospectively pulled from the publicly accessible MIMIC-IV v3.0 database. The primary result was defined as in-hospital mortality. To investigate the association between Log2-transformed PIV and clinical outcomes in IS patients, a Cox proportional hazards regression using with restricted cubic splines (RCS) was undertaken. The optimum model within the validation cohort was chosen based on accuracy and area under the curve (AUC). Furthermore, the SHAP method was utilized to determine the significance of model features and assess the influence of the top three characteristics on model predictions.\u003c/p\u003e\u003cp\u003eResults The research included 2,223 participants with IS. The connection between the probability of in-hospital mortality in IS and Log2PIV was nonlinear. Among the 14 ML algorithms, the GBDT model has higher prediction accuracy, better clinical decision-making performance, and better overall performance. Furthermore, the SHAP algorithm analysis revealed that Log2-PIV, Hemoglobin, and LODS were the three clinical characteristics that most significantly influenced the GBDT model's outputs.\u003c/p\u003e\u003cp\u003eConclusion The study findings revealed a U-shaped association between Log2-PIV and the risk of in-hospital mortality from IS. Furthermore, the GBDT model emerged as the most effective predictor, enabling clinicians to pinpoint high-risk patients and take proactive measures to minimize mortality.\u003c/p\u003e","manuscriptTitle":"Relationship between Pan-immuno-inflammatory value and hospitalized all-cause mortality in ischemic stroke patients: a retrospective cohort study and predictive modeling","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-09 09:40:00","doi":"10.21203/rs.3.rs-6615694/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":"a6d2bb4e-19ae-438d-88a8-b1735a014826","owner":[],"postedDate":"September 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":54154438,"name":"Health sciences/Biomarkers"},{"id":54154439,"name":"Health sciences/Neurology"},{"id":54154440,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-10-13T03:23:53+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-09 09:40:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6615694","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6615694","identity":"rs-6615694","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}