Stress Hyperglycemia Ratio and Glycemic Variability Predict Mortality in Critical Stroke: A Machine Learning Study

Stress Hyperglycemia Ratio and Glycemic Variability Predict Mortality in Critical Stroke: A Machine Learning Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Stress Hyperglycemia Ratio and Glycemic Variability Predict Mortality in Critical Stroke: A Machine Learning Study Yixiao Lu, Chengbao Yang, Fuxin Yi, Chunguang Chen, Fei Meng, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8125196/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 The combined prognostic value of the stress hyperglycemia ratio (SHR) and glycemic variability (GV) for mortality risk stratification across different glucose metabolic states in critically ill cerebrovascular patients remains unexplored. This study aims to evaluate its predictive utility by employing machine learning to identify critical risk predictors. Methods This retrospective cohort study analyzed data from the MIMIC-IV databaseand included 2,281 adult ICU patients with cerebrovascular disease stratified by glycemic status (NGR, Pre-DM, DM). The outcomes were 28-day and 90-day all-cause mortality. Associations and predictive performance of SHR and GV were evaluated via Cox regression, Kaplan‒Meier analysis, and receiver operating characteristic (ROC) curves, with machine learning models (SHAP interpretation) applied for predictor identification. Results Among 2,281 patients, high levels of both SHR and GV were independently associated with increased 28-day (HR 1.53, 95% CI 1.11–2.11) and 90-day mortality (HR 1.53, 95% CI 1.15–2.03), particularly in nondiabetic subgroups. GV exhibited a nonlinear association with mortality risk. Compared with the SHR-GV model alone, the combined SHR–GV model did not significantly improve 28-day mortality prediction. For 90-day mortality in diabetic patients, the combination had a marginally greater AUC (0.584 vs. 0.557), although this difference was not statistically significant. Machine learning interpretation confirmed the SHR as the dominant predictor. Conclusion The SHR outperforms GV in predicting short-term mortality in critical cerebrovascular patients. Although combining both metrics does not significantly improve predictive accuracy, it enables practical risk stratification—particularly in people without diabetes—to guide personalized glucose management. Trial registration: Not applicable. This study is a retrospective analysis of a pre-existing database (MIMIC-IV) and does not report the results of a health care intervention. Therefore, trial registration was not required. Stress–hyperglycemia ratio Glycemic variability Mortality Critical care Cerebrovascular disorders Machine learning Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Cerebrovascular disease represents a leading cause of mortality and disability worldwide, ranking as the fourth-largest contributor in the United States[ 1 ]. Survivors frequently experience persistent disability or neurological impairment. Those requiring intensive care typically present with more severe consciousness disturbances, greater clinical complexity, and elevated mortality rates[ 2 ]. Stress hyperglycemia, a transient condition triggered by inflammation and neurohormonal dysregulation during critical illness, elevates mortality risk in intensive care settings[ 3 – 5 ]. Pathophysiologically, critical illness activates the hypothalamic‒pituitary‒adrenal axis, promoting cortisol-mediated gluconeogenesis, insulin resistance, and peripheral glucose utilization defects. Exogenous nutritional support further aggravates hyperglycemia, which subsequently dysregulates inflammatory cytokines, coagulation, and immune function, accelerating disease progression[ 6 ]. However, owing to chronic glycemic status, admission glucose levels do not accurately reflect stress glucose changes. In recent years, the stress hyperglycemia ratio (SHR), combined with random glucose and glycosylated hemoglobin measurements, has been shown to more accurately capture acute stress hyperglycemia levels[ 7 ]. Concurrently, glucose variability (GV), which represents glucose fluctuations, induces endothelial dysfunction and oxidative stress more profoundly than does sustained hyperglycemia, contributing to cerebrovascular injury and cognitive decline[ 8 , 9 ]. Elevated GV also correlates with increased macrovascular and microvascular risks [ 10 , 11 ]. Therefore, the joint evaluation of the SHR and GV may be important for blood glucose management and disease prognosis. Current evidence indicates a mortality disparity between diabetic and nondiabetic patients, with severely affected diabetic individuals showing reduced susceptibility to acute hyperglycemia—potentially due to chronic metabolic adaptations[ 12 ]. Nevertheless, the influence of glucose tolerance status on outcomes in critical illness patients requires further clarification. This study aimed to assess mortality in critically ill cerebrovascular patients via the SHR, GV, and their combination across different glucose tolerance states while machine learning was employed to identify optimal predictors. 2. Methods This study utilized data from the Medical Information Mart for Intensive Care (MIMIC-IV) database, a publicly available critical care dataset containing deidentified clinical information from over 190,000 patients and 450,000 hospital admissions at Beth Israel Deaconess Medical Center (BIDMC) in Boston, MA, USA, between 2008 and 2019[ 13 ]. The use of the MIMIC-IV was approved by the Massachusetts Institute of Technology Ethics Committee (Certification No. 70803575), which waived the requirement for informed consent owing to the retrospective nature of the analysis and the deidentification of all patient data. Patients were included according to the primary diagnosis of cerebrovascular disease. Patients diagnosed with cerebrovascular disease by searching for ICD-9 and ICD-10 codes are shown in Table A.1. The ICD-9 and ICD-10 codes in the MIMIC-IV database are diagnostic codes perfected by clinicians at the end of a hospital stay. These codes are standardized for billing, administrative, and epidemiological purposes and reflect the final diagnosis of a particular hospital course[ 14 ]. The exclusion criteria were as follows: (1) aged less than 18 years; (2) not admitted to the ICU first; (3) had an ICU stay of less than 24 hours; and (4) had less than three blood glucose measurements or lacked HbA1c or laboratory indicators. Multiple hospitalizations were analyzed on the basis of the first ICU admission data. The patient screening process is shown in Fig. 1 . 2.1 Data extraction Data were extracted via Navicat Premium (v17.0.8) via structured query language (SQL). The following variables were collected: (1) demographics: age, sex, ICU length of stay, and time of death; (2) vital signs: heart rate (HR), respiratory rate (RR), systolic and diastolic blood pressure (SBP/DBP), mean blood pressure (MBP), temperature, and oxygen saturation (SpO₂); (3) laboratory parameters: hemoglobin (Hb), platelet count (Plt), white blood cell count (WBC), blood urea nitrogen (BUN), glucose, glycated hemoglobin (HbA1c), prothrombin time (PT), partial thromboplastin time (PTT), and creatinine; (4) severity scores: Acute Physiology Score III (APSIII), Simplified Acute Physiology Score II (SAPSII), Oxford Acute Severity of Illness Score (OASIS), Glasgow Coma Scale (GCS), and Sequential Organ Failure Assessment (SOFA); (5) comorbidities and treatments: hypertension (HTN), diabetes, congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD), cerebrovascular disease (CVD), myocardial infarction (MI), renal disease (RD), atrial fibrillation (afib), mechanical ventilation, hypoglycemic agents, and insulin therapy. SHR was calculated via the following equation: [ABG (mg/dL)/(28.7 × HbA1c (%) − 46.7)][ 15 ] Due to the lack of meal times in the MIMIC-IV database, blood glucose values were measured at admission as random fasting blood glucose. We chose the coefficient of variation (CV) as the primary measure of GV because of its broad clinical applicability, simplicity of interpretation, and extensive validation in intensive care settings. The CV was calculated as the ratio of the standard deviation to the arithmetic mean of all consecutive glucose measurements obtained during intensive care unit monitoring (CV = SD/mean×100%), normalizing the variability between individuals with different baseline glucose levels. Patients were categorized into three groups on the basis of glucose metabolism criteria: normal glucose regulation (NGR), prediabetes (Pre_DM), and diabetes mellitus (DM). The NGR group was defined by an HbA1c level less than 5.7% and the absence of a history of diabetes. The Pre_DM group was characterized by an HbA1c level equal to or greater than 5.7% but less than 6.5%, with no prior history of diabetes. The DM group included individuals with an HbA1c level exceeding 6.5% or a documented history of diabetes. Vital signs, clinical scores, and other laboratory parameters were measured within 24 hours of ICU admission, and variables with missing data rates greater than 20% were excluded to minimize potential bias. 2.2 Outcome measures The primary outcome measure was all-cause mortality at 28 days, and the secondary outcome measure was all-cause mortality at 90 days. 2.3 Statistical methods Continuous variables were assessed for normality via the Kolmogorov‒Smirnov and Shapiro‒Wilk tests. The data are presented as the means ± SDs or medians [IQRs] and were analyzed with Student’s t test/ANOVA or the Mann–Whitney U test, as appropriate. Categorical variables, expressed as counts (percentages), were compared via the χ² test or Fisher’s exact test. Patients were stratified into tertiles on the basis of the SHR ( 1.15) and GV ( 24.46). Kaplan–Meier survival curves with log-rank tests were used to evaluate group differences in all-cause mortality. Three Cox regression models were constructed: unadjusted (Model 1), partially adjusted (Model 2: age, sex, HR, RR, SBP), and fully adjusted (Model 3: demographics, comorbidities, laboratory values, and severity scores). The variance inflation factor (VIF < 5) confirmed the absence of multicollinearity. Restricted cubic splines were used to analyze dose‒response relationships, whereas Schoenfeld residuals were used to test proportional hazards assumptions. ROC curves were used to compare the predictive performance of the indicators. Subgroup analyses were visualized via forest plots. Sensitivity analyses included excluding patients with hypoglycemia and those with extreme SHR/GV values to validate robustness. On the basis of the predictions of the machine learning model, Boruta ranked the importance of features in patients who died within 28 days and randomly divided the dataset into a training subset (80%) and a test subset (20%). Five models [LR (logistic regression), GBM (gradient boosting machine), RF (random forest), XGBoost (extreme gradient boosting), and LightGBM (light gradient boosting machine)] were used to develop importance features, and the model with the best prediction performance was screened out. Finally, Shapley additive explanations (SHAPs) were used for prediction to determine key predictors. All the analytical methods were run with IBM SPSS Statistics 29.0 and R version 4.4.1, and a two-sided p value less than 0.05 indicated statistical significance. 3. Results A total of 2281 patients, including 1100 (48.2%) females, were analyzed, with a median age of 70 years. By the 28-day and 90-day follow-ups, 1880 (82.42%) and 1758 (77.07%) patients survived, respectively. Compared with survivors, nonsurvivors were older and had a greater prevalence of most comorbidities—except for hypertension and COPD, which were less common or comparable. Nonsurvivors also received fewer hypoglycemic agents but required more mechanical ventilation and insulin. The laboratory results revealed elevated WBC, BUN, creatinine, glucose, PT, SHR, and GV in nonsurvivors, alongside lower hemoglobin and higher clinical severity scores. The patients’ baseline characteristics are summarized in Table 1 . The univariate Cox regression results and variance inflation factors are provided in Tables A.2 and A.3, respectively, indicating no multicollinearity. Table 1 Baseline characteristics according to 28-day mortality Variable Total (n = 2281) Survivors (n = 1880) Non-survivors (n = 401) P Demographics Age, years 70(59,80) 68(58,79) 77(66,84) < 0.001 Female, n (%) 1100(48.20) 889(47.30) 211(48.20) 0.052 Comorbidities, n (%) MI 363(15.90) 286(15.20) 77(19.20) 0.047 CHF 543(23.80) 416(22.10) 127(31.70) < 0.001 CVD 2170(95.1) 1779(94.6) 391(97.5) 0.015 COPD 326(14.30) 276(14.70) 50(12.50) 0.251 RD 399(17.50) 303(16.10) 96(23.90) < 0.001 HTN 1279(56.10) 1074(57.10) 205(51.10) 0.028 Afib 613(26.90) 478(25.40) 135(33.70) < 0.001 Diabetes 773(33.90) 619(32.90) 154(38.40) 0.035 Glucose metabolism state, n (%) NGR 876(38.40) 735(39.10) 141(35.20) 0.141 Pre_DM 589(25.80) 496(26.40) 93(23.20) 0.185 DM 816(35.80) 649(34.50) 167(41.60) 0.007 Treatment, n (%) Antihyperglycemic drug 103(4.50) 100(5.30) 3(0.70) < 0.001 Mechanical ventilation 735(32.20) 493(26.20) 242(60.30) < 0.001 RI 1860(81.50) 1513(80.50) 347(86.50) 0.005 Vital signs HR, bpm 78.16(69.19,88.95) 77.43(68.75,87.96) 83.08(72.00,93.65) < 0.001 RR, bpm 18.48(16.82,20.63) 18.26(16.68,20.28) 19.54(17.57,22.04) < 0.001 SpO2, % 96.92(95.67,98.27) 96.79(95.56,98.06) 97.71(96.37,99.09) < 0.001 Temperature, °C 36.90(36.73,37.14) 36.89(36.72,37.10) 36.98(36.76,37.31) < 0.001 SBP, mmHg 132.29(121.62,143.64) 132.75(121.92,144.00) 130.26(119.08,142.25) 0.010 DBP, mmHg 70.46(62.37,79.49) 71.42(63.40,80.52) 65.48(57.83,73.70) < 0.001 MBP, mmHg 87.94(79.97,96.17) 88.56(80.89,96.62) 83.85(76.48,92.50) < 0.001 Laboratory measurements Hb, g/dL 12.90(11.30,14.20) 13.10(11.60,14.30) 12.10(10.40,13.50) < 0.001 Plt, ×10⁹/L 225.00(179.00,281.00) 225.00(181.00,280.00) 224.00(169.00,285.00) 0.190 Table 1 (continued) Variable Total(n = 2281) Survivors(n = 1880) Non-survivors(n = 401) P WBC, K/µL 10.80(8.40,14.00) 10.50(8.20,13.40) 12.90(9.80,16.09) < 0.001 BUN, mg/dL 18.00(14.00,26.00) 18.00(13.00,24.00) 23.00(16.00,33.00) < 0.001 Creatinine, mg/dL 1.00(0.80,1.30) 1.0(0.8,1.2) 1.1(0.9,1.5) < 0.001 Glucose, mg/dL 122.67(105.39,152.51) 117.99(103.50,144.75) 146.00(125.67,176.33) < 0.001 PT, s 12.80(11.90,14.30) 12.65(11.80,14.00) 13.60(12.30,15.80) < 0.001 PTT, s 29.60(26.90,34.50) 29.60(26.90,34.40) 30.00(27.00,34.60) 0.770 HbA1c, % 5.80(5.40,6.40) 5.80(5.40,6.40) 5.80(5.40,6.60) 0.211 SHR 1.02(0.87,1.22) 1.00(0.86,1.20) 1.13(0.94,1.38) < 0.01 GV, % 16.72(11.08,24.50) 16.23(10.79,23.49) 19.01(12.45,28.81) < 0.01 Clinical scores GCS 14(12,15) 14(12,15) 14(10,15) 0.074 APSIII score 35(27,46) 33(26,44) 44(34,58) < 0.001 Oasis score 31(26,36) 30(25,35) 36(31,41) < 0.001 sofa 3(1,4) 2(1,4) 4(2,6) < 0.001 SAPSII score 32(25,39) 30(24,37) 39(33,46) < 0.001 3.1 Relationship between the SHR and survival rate KM analysis revealed a dose-dependent decrease in survival with increasing stress–hyperglycemia ratio (SHR) across glucose metabolism strata (Fig. 2 A–C), although this trend was not significant in patients with diabetes (log-rank P = 0.11). According to the fully adjusted Cox model, the highest SHR tertile (T3) was associated with a 1.53-fold increased risk of 28-day mortality compared with the lowest tertile (T1) (HR = 1.53, 95% CI: 1.16–2.01, P = 0.002), with a significant dose–response trend (P-trend = 0.003). This trend was not observed for 90-day mortality (HR = 1.24, 95% CI: 0.99–1.56, P = 0.066; P-trend = 0.068). When stratified by glucose metabolism status, the SHR was consistently associated with survival in all subgroups except those with diabetes (HR = 1.07, 95% CI: 0.73–1.58, P = 0.721; P-trend = 0.782) (Table A.4). Restricted cubic spline models revealed a linear SHR–mortality relationship in the NGR (P-nonlinear = 0.107) and DM (P-nonlinear = 0.671) groups but a nonlinear association in the pre-DM group (P-nonlinear = 0.027) (Fig. A.2A). Subgroup analysis revealed a strong interaction effect between the SHR and glucose status (P-interaction < 0.001), with the highest risk observed in the NGR subgroup (HR = 5.81, 95% CI: 3.58–9.44, P < 0.001) (Fig. A.3A). 3.2 Relationship between GV and survival rate KM curves for GV and 28-day survival, stratified by glucose status, are shown in Fig. 2 D–F. Like in SHRs, GV was not significantly associated with 28-day survival in patients with diabetes (log-rank P = 0.079). In adjusted Cox models, GV was not significantly predictive of 28-day mortality in the overall population or in any glucose subgroup, either as a continuous variable (all HRs included 1) or as tertiles (T3 vs T1, all confidence intervals crossed 1). However, the intermediate GV tertile was associated with a significantly lower mortality risk than the low GV tertile in most subgroups, although not in the pre-DM cohort (HR = 0.77, 95% CI: 0.47–1.26, P = 0.297; Table A.4). Restricted cubic spline analysis revealed a linear GV–survival relationship in the NGR (P-nonlinear = 0.822) and pre-DM (P-nonlinear = 0.245) groups but a nonlinear association in the DM group (P-nonlinear = 0.009; Fig. A.2B). Subgroup analysis further revealed significant interaction effects for GV in patients with cerebrovascular disease among those with myocardial infarction (P-interaction = 0.010) or renal disease (P-interaction = 0.043; Fig. A.3B). 3.3 Association of combined SHR and GV with mortality The results of the Kaplan‒Meier analysis of the combined SHR and GV data are presented in Fig. 2 G–I. Elevated SHR (> 1.15) was consistently associated with higher 28-day mortality across glucose metabolism strata. In the NGR subgroup, high SHR combined with high GV (> 24.46) significantly increased both 28-day (HR = 2.69, 95% CI: 1.51–4.80, P = 0.001) and 90-day mortality (HR = 2.54, 95% CI: 1.49–4.34, P = 0.001). Conversely, among Pre-DM patients, high SHR with low GV (< 24.46) had the strongest association with 28-day mortality (HR = 2.57, 95% CI: 1.53–4.33, P < 0.001) and 90-day mortality (HR = 1.72, 95% CI: 1.07–2.77, P = 0.026). No significant associations were observed in the DM subgroup (Table 2 ). Table 2 The associations of the combinations of SHR and GV with all-cause mortality Variables Model 1 Model 2 Model 3 HR (95%CI) P HR (95%CI) P HR (95%CI) P 28-day mortality Overall Group 1 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) Group 2 1.81(1.36 ~ 2.39) < 0.001 1.66(1.25 ~ 2.20) 0.001 1.25(0.92 ~ 1.70) 0.146 Group 3 1.74(1.36 ~ 2.24) < 0.001 1.58(1.23 ~ 2.04) < 0.001 1.28(0.99 ~ 1.66) 0.061 Group 4 2.79(2.12 ~ 3.66) < 0.001 2.55(1.93 ~ 3.38) < 0.001 1.53(1.11 ~ 2.11) 0.010 P for trend 1.37(1.26 ~ 1.49) < 0.001 1.32(1.21 ~ 1.44) < 0.001 1.14(1.03 ~ 1.25) 0.008 Patients with NGR Group 1 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) Group 2 1.73(0.91 ~ 3.30) 0.093 1.54(0.80 ~ 2.94) 0.195 1.15(0.58 ~ 2.31) 0.685 Group 3 1.73(1.19 ~ 2.52) 0.004 1.60(1.10 ~ 2.34) 0.015 1.30(0.88 ~ 1.93) 0.189 Group 4 5.56(3.36 ~ 9.22) < 0.001 4.89(2.87 ~ 8.32) < 0.001 2.69(1.51 ~ 4.80) 0.001 P for trend 1.52(1.30 ~ 1.77) < 0.001 1.45(1.24 ~ 1.70) < 0.001 1.25(1.07 ~ 1.47) 0.006 Patients with Pre-DM Group 1 1.00 (Reference) 1.00 (Reference) Group 2 2.61(1.47 ~ 4.65) 0.001 2.29(1.28 ~ 4.11) 0.005 1.79(0.97 ~ 3.29) 0.061 Group 3 2.87(1.76 ~ 4.68) < 0.001 2.64(1.59 ~ 4.37) < 0.001 2.57(1.53 ~ 4.33) < 0.001 Group 4 3.23(1.57 ~ 6.63) 0.001 2.81(1.35 ~ 5.85) 0.006 2.36(1.06 ~ 5.27) 0.036 P for trend 1.57(1.31 ~ 1.88) < 0.001 1.50(1.24 ~ 1.81) < 0.001 1.45(1.18 ~ 1.78) < 0.001 Patients with DM Group 1 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) Group 2 1.28(0.85 ~ 1.92) 0.238 1.25(0.83 ~ 1.89) 0.278 0.93(0.60 ~ 1.44) 0.735 Group 3 1.15(0.71 ~ 1.86) 0.562 1.04(0.64 ~ 1.68) 0.873 0.77(0.47 ~ 1.29) 0.323 Group 4 1.72(1.16 ~ 2.55) 0.007 1.68(1.13 ~ 2.51) 0.011 1.03(0.65 ~ 1.61) 0.913 P for trend 1.18 (1.03 ~ 1.34) 0.002 1.16(1.02 ~ 1.32) 0.028 0.99(0.86 ~ 1.15) 0.938 90-day mortality Overall Group 1 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) Group 2 1.94(1.53 ~ 2.46) < 0.001 1.77(1.39 ~ 2.25) < 0.001 1.36(1.05 ~ 1.77) 0.019 Group 3 1.56(1.25 ~ 1.96) < 0.001 1.41(1.12 ~ 1.76) 0.003 1.16(0.92 ~ 1.47) 0.196 Group 4 2.67(2.09 ~ 3.40) < 0.001 2.39(1.87 ~ 3.06) < 0.001 1.53(1.15 ~ 2.03) 0.004 P for trend 1.33(1.24 ~ 1.44) < 0.001 1.28(1.18 ~ 1.38) < 0.001 1.11(1.02 ~ 1.21) 0.014 Patients with NGR Group 1 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) Group 2 1.97(1.15 ~ 3.37) 0.013 1.77(1.03 ~ 3.04) 0.040 1.32(0.74 ~ 2.37) 0.345 Group 3 1.69(1.21 ~ 2.35) 0.002 1.55(1.11 ~ 2.16) 0.011 1.32(0.94 ~ 1.87) 0.113 Group 4 4.90(3.05 ~ 7.86) < 0.001 4.33(2.64 ~ 7.10) < 0.001 2.54(1.49 ~ 4.34) 0.001 P for trend 1.46(1.28 ~ 1.68) < 0.001 1.39(1.21 ~ 1.60) < 0.001 1.24(1.07 ~ 1.43) 0.004 Patients with Pre-DM Group 1 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) Table 2 (continued) Variables Model 1 Model 2 Model 3 HR (95%CI) P HR (95%CI) P HR (95%CI) P Group 2 2.37(1.44 ~ 3.88) 0.001 1.93(1.17 ~ 3.19) 0.010 1.59(0.94 ~ 2.69) 0.081 Group 3 2.12(1.36 ~ 3.30) 0.001 1.74(1.10 ~ 2.77) 0.019 1.72(1.07 ~ 2.77) 0.026 Group 4 2.99(1.61 ~ 5.54) 0.001 2.28(1.21 ~ 4.29) 0.011 1.80(0.91 ~ 3.54) 0.091 P for trend 1.45 (1.24 ~ 1.71) < 0.001 1.32 (1.11 ~ 1.57) 0.001 1.26 (1.05 ~ 1.51) 0.012 Patients with DM Group 1 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) Group 2 1.47(1.04 ~ 2.09) 0.029 1.46(1.03 ~ 2.07) 0.034 1.07(0.73 ~ 1.55) 0.738 Group 3 1.08(0.70 ~ 1.67) 0.720 0.99(0.64 ~ 1.53) 0.958 0.75(0.47 ~ 1.18) 0.213 Group 4 1.79(1.26 ~ 2.54) 0.001 1.76(1.23 ~ 2.50) 0.002 1.08(0.73 ~ 1.60) 0.706 P for trend 1.17(1.05 ~ 1.31) 0.005 1.16(1.03 ~ 1.29) 0.013 0.99(0.87 ~ 1.13) 0.902 Model 1: unadjusted Model 2: adjusted for age, sex, HR, RR, and SBP Model 3: Adjusted for Model 2 plus MI, CHF, RD, HTN, Afib, DM, Hb, BUN, creatinine, PT, mechanical ventilation, RI, SOFA, and APSIII Group 1: Low SHR and low GV (SHR < 1.15 and GV < 24.46); Group 2: low SHR and high GV (SHR 24.46); Group 3: high SHR and low GV (SHR > 1.15 and GV 1.15 and GV > 24.46 Proportional hazards assumptions were maintained in the Pre-DM (P = 0.525) and DM (P = 0.586) subgroups but violated in the NGR subgroup. The Schoenfeld residuals indicated stabilization of the beta coefficients around day 5 (Table A.5, Fig. A.1). Landmark analysis confirmed superior survival in NGR patients with low SHR and low GV throughout follow-up (Fig. A.4). 3.4 ROC curve analysis ROC analysis was used to assess the predictive performance of the SHR, GV, and their combination for mortality in patients with cerebrovascular diseases (Table A.8, Fig. 3 ). For 28-day mortality in the NGR and Pre-DM groups, the combined model had a significantly greater AUC than GV alone (0.579 vs. 0.575, P < 0.001) but was not superior to the SHR alone (0.579 vs. 0.640, P = 0.094). In the DM group, all models showed reduced discriminative ability, with the combined model achieving an AUC of 0.560, similar to that of SHR (0.574) and GV (0.557) alone. For 90-day mortality, the combined model again outperformed GV in the NGR and Pre-DM subgroups but not in the SHR subgroup. Notably, in the DM group, the combined model exhibited a greater AUC than both the SHR (0.584 vs. 0.557, P = 0.372) and GV (0.584 vs. 0.581, P = 0.002) models did, although the improvement over the SHR model was not statistically significant. Significant differences between the models for 28-day mortality were observed only in the NGR and Pre-DM groups. Figure 3 additionally compares the investigational metrics with the SOFA and APSIII scores for the prediction of 28- and 90-day mortality. 3.5 Sensitivity analysis Sensitivity analyses confirmed the robustness of our primary findings. After excluding 82 patients with ICU-acquired hypoglycemia, the Cox regression results remained consistent with those of the main analysis (Table A.9). Similarly, the exclusion of 195 subjects with extreme SHR or GV values did not alter the associations between these metrics and cerebrovascular outcomes (Table A.10). 3.6 Machine learning Feature selection via the Boruta algorithm identified 18, 14, and 16 mortality predictors in the NGR, Pre-DM, and DM groups, respectively, with predictor importance decreasing from right to left (Fig. A.5B–D; overall population results in Fig. A.5A). The predictive performance of the models varied across subgroups (Table A.11). LightGBM achieved the highest AUC (0.926) in the NGR cohort (Fig. 4 A), whereas logistic regression performed best in both the Pre-DM (AUC = 0.893) and DM (AUC = 0.782) groups (Fig. 4 B, C). The overall population results are provided in Table A.11 and Fig. A.6. SHAP analysis (Fig. 4 D–F) revealed that SHR and GV contributed the least to predictions in the Pre-DM subgroup (Fig. 4 E). Although GV had marginally greater overall importance than SHR across subgroups, SHAP plots revealed that both variables were concentrated in the SHAP > 0 region, with SHR showing denser clustering, indicating a stronger positive association with mortality risk. The full population SHAP results are shown in Fig. A.7. 4. Discussion In this cohort study of cerebrovascular disease patients, the SHR demonstrated a stronger and more consistent association with short-term mortality than did the GV. Cox regression identified the SHR as a significant predictor in the nondiabetic groups (NGR and Pre-DM), whereas GV exhibited a nonlinear relationship with mortality, with moderate levels conferring a protective effect. The highest 28-day mortality risk was observed in NGR patients with high SHR and high GV and in Pre-DM patients with high SHR and low GV. Although the combined SHR–GV model did not outperform the SHR alone in ROC analysis, it provided incremental predictive value for 90-day mortality in diabetic patients. SHAP analysis from machine learning models confirmed that SHR contributed more substantially than GV did to prediction stability and overall performance. The superior predictive utility of the SHR may stem from its reflection of illness severity, as an elevated SHR is correlated with higher clinical scores and more intensive treatments. In contrast, GV—which solely captures glucose fluctuations—showed a complex nonlinear relationship with outcomes and did not enhance the stability of the combined models, likely because of its susceptibility to confounding clinical factors. Current evidence suggests that the SHR and GV are significant prognostic markers in critical care. The SHR, which integrates acute glucose levels with chronic glycemic status (HbA1c)[ 16 ], has been consistently associated with disease severity and mortality in patients with cardiovascular and cerebrovascular conditions[ 17 – 19 ]. Meta-analyses[ 20 ] and cohort studies[ 21 ] have demonstrated that elevated SHR predicts adverse outcomes in acute myocardial infarction, ST-elevation myocardial infarction, and ischemic stroke, particularly among nondiabetic individuals. Similar results have also been reported for myocardial infarction and nonobstructive coronary artery disease (MINOCA)[ 22 ] and three-vessel disease[ 23 ]. Furthermore, for acute ischemic stroke due to large vessel occlusion, the RESCUE BT test revealed a linear relationship between elevated SHR and poor functional outcomes[ 24 ]. Duan et al.[ 25 ] reported that elevated SHR was associated with early neurological deterioration after thrombolysis in acute stroke, while NHANES data revealed a J/u relationship between SHR and mortality from all cardiovascular diseases[ 26 ]. In these studies, we observed a positive association between SHR and cardiovascular adverse events, which is consistent with our findings. Glucose variability was also associated with adverse outcome events. A retrospective analysis of 4809 critically ill patients with cerebrovascular disease by Cai W. et al. [ 27 ] revealed that glucose variability was approximately linearly associated with severe cognitive decline and in-hospital mortality in CVD patients. GV was demonstrated to be an independent risk factor for adverse outcomes in patients with acute stroke in a prospective multicenter study combined with an animal model in a GLIAS-III translational study. He HM. et al.[ 28 ] High SHR/GV combination levels in individuals without diabetes with CAD were found to predict poor prognosis, whereas high SHR and low GV combination levels in individuals with diabetes were associated with increased mortality. Wang Feng et al.[ 29 ] reached the same conclusion and verified the predictive accuracy of the combined SHR and GV index model. These findings underscore the clinical importance of comprehensive SHR-GV assessment in cerebrovascular disease. Further research should explore their combined utility in guiding personalized glucose management strategies for critically ill patients. Stress-induced hyperglycemia is caused mainly by excessive activation of sympathetic nerves and the release of large amounts of glucocorticoids such as cortisol[ 30 ], which also drives inflammatory cytokine regulation and the amplification of oxidative stress[ 31 ]. On the other hand, stress hyperglycemia may exacerbate acute heart disease in a variety of ways, including exacerbating microvascular obstruction[ 32 ], accelerating endothelial cell damage[ 33 ], and impairing platelet nitric oxide responsiveness[ 34 , 35 ] (nitric oxide deficiency causes sustained vasoconstriction, accelerates vascular sclerosis, and increases the risk of thrombosis and vascular inflammation[ 36 ]). In addition, other vascular injury mechanisms mediated by hyperglycemia are promoted. Stress hyperglycemia may also disrupt the blood‒brain barrier through intracellular acidosis, leading to mitochondrial dysfunction, energy depletion, and apoptosis, further driving adverse outcomes after stroke[ 37 ]. Glycemic variability reflects changes in blood glucose fluctuations, which can lead to endothelial dysfunction[ 38 ] and oxidative stress[ 39 ], further exacerbating plaque vulnerability[ 33 ] and promoting cardiovascular and cerebrovascular diseases. Studies indicate that glycated hemoglobin (A1C) can be converted to estimated average glucose (eAG), reflecting mean glycemic levels over 8–12 weeks[ 40 , 41 ]. Unlike absolute hyperglycemia, relative hyperglycemia—assessing acute glucose changes—remains independent of baseline glucose levels in critical illness patients[ 42 ]. The stress hyperglycemia ratio (SHR) integrates acute glucose levels with A1C to differentiate stress-induced hyperglycemia from chronic dysglycemia, thereby improving the assessment of acute glycemic impact on clinical outcomes. Furthermore, glycemic variability (GV) captures short-term glucose fluctuations, complementing the temporal limitations of SHRs. The combined assessment of SHR and GV provides a rational approach for evaluating stress-mediated hyperglycemia and acute glucose fluctuations in critically ill patients. This study confirmed the prognostic value of SHR-GV integration for 28-day mortality in the NGR and Pre-DM subgroups but not in diabetic patients. Previous evidence indicates greater susceptibility to acute glucose variations in nondiabetic individuals than in diabetic individuals[ 43 , 44 ], potentially due to long-term adaptive responses to oxidative stress and increased glycemic tolerance thresholds in diabetic patients[ 29 , 45 ]. Additionally, ongoing hypoglycemic therapies in diabetic patients may attenuate acute glycemic effects[ 46 ]. Thus, diabetes-specific factors likely confound the interpretation of the SHR and GV metrics. Notably, high GV levels were not significantly associated with poor prognosis in cerebrovascular disease patients. This may stem from methodological limitations, as the quantitative CV index might interfere with GV expression[ 29 ], compounded by the MIMIC-IV database's lack of meal timing data. Furthermore, Cox regression revealed a nonlinear relationship, with moderate GV levels serving as a protective factor, which is consistent with reported threshold effects in critical illness patients[ 47 , 48 ]. These findings suggest that maintaining GV within an optimal range may prove more beneficial than indiscriminate minimization. Future studies should incorporate advanced GV metrics (e.g., TIR, VIM, ARV) to validate these observations. The combined assessment of the SHR and GV provides complementary risk stratification for acute cerebrovascular disease. The SHR captures acute-on-chronic glycemic stress, whereas the GV reflects acute glucose instability—which is particularly relevant in nondiabetic patients (NGR/Pre-DM) who lack adaptive hyperglycemic responses. Although the combined model did not outperform the SHR alone in terms of predictive performance, machine learning interpretation (SHAP) confirmed the stronger overall contribution of the SHR to mortality prediction, whereas GV was more important in specific metabolic subgroups. This suggests that GV may introduce interference rather than synergistic improvement in the combined model. Nevertheless, integrating the SHR and GV—especially in nondiabetic patients—offers a clinically valuable framework for glycemic risk stratification in neurocritical care. Further validation across diverse cerebrovascular cohorts is needed. This study has several limitations. First, despite adjusting for available confounders, residual confounding may persist due to unmeasured variables such as lifestyle factors. Second, the exclusion of patients with missing HbA1c or insufficient glucose measurements may have introduced selection bias. Third, the predominantly white nature of the study population limits its generalizability to other ethnic groups. Fourth, the analysis did not account for differences in treatment strategies between the ischemic and hemorrhagic stroke subtypes. Finally, the retrospective design precludes causal inference. Further prospective studies are needed to validate these findings across diverse cerebrovascular disease populations. 5. Conclusion The SHR is a stronger predictor of short-term mortality than GV is in critically ill cerebrovascular patients. While combining SHR and GV does not significantly enhance predictive modeling, it offers practical risk stratification—particularly in the group without diabetes—which may guide personalized glucose management strategies in neurocritical care. Abbreviations APSIII, Acute Physiology Score; ARV, average real variability; BIDMC, Beth Israel Deaconess Medical Center BUN, blood urea nitrogen; CAD, coronary artery disease CHF, Congestive Heart Failure; COPD, chronic pulmonary disease; CVD, cerebrovascular disease; DBP, Diastolic Blood Pressure; DM, diabetes mellitus; GBM, gradient boosting machine; GCS, Glasgow Coma Scale; GV, glycemic variability; Hb, hemoglobin concentration; HbA1c, glycated hemoglobin; HPA, Hypothalamic‒pituitary‒adrenal HR, Heart rate; HTN, hypertension; KM, Kaplan‒Meier; LightGBM, Light gradient boosting machine; LR, logistic regression; MACCEs, major adverse cardiovascular and cerebrovascular events MBP, mean blood pressure; MI, myocardial infarction; MIMIC-IV, Medical Information Mart for Intensive Care MINOCA, Myocardial Infarction and Nonobstructive Coronary Artery Disease NGR, normal glucose regulation; NHANES, National Health and Nutrition Examination Survey; OASIS, Oxford Acute Severity of Illness Score; Plt, Platelet; Pre_DM, Prediabetes mellitus; PT, Prothrombin time; PTT, partial thromboplastin time; RCS, restricted cubic splines RD, Renal Disease; RF, random forest; RI, Insulin; ROC, receiver operating characteristic RR, Respiratory rate; SAPSII, simplified acute physiology score; SBP, Systolic Blood Pressure; SHAP, Machine learning interpretation; SHR, stress–hyperglycemia ratio; SOFA, Sequential Organ Failure Assessment; STEMI, ST-elevated myocardial infarction; TIR, time-in-range; VIM, variation independent of the mean; VIF, variance inflation factor; WBC, White blood cell Declarations Acknowledgments: This study acknowledges the MIT Computational Physiology Laboratory and Beth Israel Deaconess Medical Centre for providing and maintaining the invaluable public resource MIMIC-IV. Moreover, The author expresses sincere gratitude for the technical support of Liaoyang Central Hospital, the First Affiliated Hospital of Jinzhou Medical University, and all individuals involved in this study. Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Ethics Approval and Consent to Participate: This study utilized data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database, a publicly available, de-identified critical care database. The establishment of the MIMIC-IV database was approved by the Institutional Review Board (IRB) of the Massachusetts Institute of Technology (Cambridge, MA, USA) (Certification No. 70803575). All original data were de-identified to protect patient privacy, and the requirement for individual patient consent was waived by the approving IRB. References Ahmad FB, Cisewski JA, Anderson RN. Leading Causes of Death in the US, 2019–2023. 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14:36:02","extension":"html","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":200381,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8125196/v1/6a63d7ed53abf76a8c8c0944.html"},{"id":97367378,"identity":"d10b9985-d547-42bf-bea4-b489cd35b953","added_by":"auto","created_at":"2025-12-03 16:18:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":776118,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of patient selection. A total of 28,823 patients with severe cerebrovascular disease from the MIMIC-IV database were screened. A total of 2281 patients who met the inclusion criteria were analyzed [exclusion conditions: age\u0026lt;18 years (N=0); admission \u0026lt;24 h (n=14537); missing information (n=12709)] and classified into three groups according to their glycemic status: NGR (n=876), Pre_DM (n=589), and DM (n=816).\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8125196/v1/7472cbf9821798db618f37ea.png"},{"id":97266808,"identity":"692dbeb1-7bb6-4696-bf4c-afc89df33b40","added_by":"auto","created_at":"2025-12-02 14:36:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":744173,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan‒Meier curves of 28-day mortality in SHRs, GVs, and their combination. (\u003cstrong\u003eA, D, G\u003c/strong\u003e) patients with NGR; (\u003cstrong\u003eB, E, H\u003c/strong\u003e) patients with Pre-DM; \u003cstrong\u003e(C, F, I\u003c/strong\u003e) patients with DM\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8125196/v1/4587eba1d6320532cf73373a.png"},{"id":97367729,"identity":"f3b9ccd2-0667-448d-84a3-7c34259c6e6c","added_by":"auto","created_at":"2025-12-03 16:20:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":947566,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic curves of the SHR and GV as biomarkers for predicting 28-day mortality (A–C) and 90-day mortality (D–F). (\u003cstrong\u003eA)\u003c/strong\u003e Prediction of 28-day mortality in NGR patients: SHR+GV vs. SHR, GV, APSIII, and SOFA. (\u003cstrong\u003eB)\u003c/strong\u003e Prediction of 28-day mortality in Pre-DM patients: SHR+GV vs. SHR, GV, APSIII, and SOFA. (\u003cstrong\u003eC)\u003c/strong\u003e Prediction of 28-day mortality in DM patients: SHR+GV vs. SHR, GV, APSIII, and SOFA. (\u003cstrong\u003eD)\u003c/strong\u003e Prediction of 90-day mortality in NGR patients: SHR+GV vs. SHR, GV, APSIII, and SOFA. (\u003cstrong\u003eE)\u003c/strong\u003e Prediction of 90-day mortality in Pre-DM patients: SHR+GV vs. SHR, GV, APSIII, and SOFA. (\u003cstrong\u003eF)\u003c/strong\u003e Prediction of 90-day mortality in DM patients: SHR+GV vs. SHR, GV, APSIII, and SOFA.\u003c/p\u003e","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8125196/v1/8e83602c7029772efe15f162.png"},{"id":97266810,"identity":"f59ed252-bba0-41c9-a978-ce90bad4b15b","added_by":"auto","created_at":"2025-12-02 14:36:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":811983,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic curves and SHAP interpretations of theML-based 28-day mortality prediction models. (\u003cstrong\u003eA, D)\u003c/strong\u003e Patients with NGR; (\u003cstrong\u003eB, E)\u003c/strong\u003epatients with Pre-DM; (\u003cstrong\u003eC, F)\u003c/strong\u003e patients with DM. LR, logistic regression; GBM, gradient boosting machine; RF, random forest; XGBoost, extreme gradient boosting; LightGBM, light gradient boosting machine.\u003c/p\u003e","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8125196/v1/752b16f20cb598bb051b5ce8.png"},{"id":98432155,"identity":"dc73c093-b6ee-489b-8ede-16a8f867480b","added_by":"auto","created_at":"2025-12-17 16:49:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6667960,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8125196/v1/44fbbc40-21aa-436f-9945-e670ce662962.pdf"},{"id":97266824,"identity":"305efbcb-ef93-4733-ae32-074da5addbd7","added_by":"auto","created_at":"2025-12-02 14:36:04","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":69024745,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-8125196/v1/4da8ed4592d4a83afac23c04.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eStress Hyperglycemia Ratio and Glycemic Variability Predict Mortality in Critical Stroke: A Machine Learning Study\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCerebrovascular disease represents a leading cause of mortality and disability worldwide, ranking as the fourth-largest contributor in the United States[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Survivors frequently experience persistent disability or neurological impairment. Those requiring intensive care typically present with more severe consciousness disturbances, greater clinical complexity, and elevated mortality rates[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eStress hyperglycemia, a transient condition triggered by inflammation and neurohormonal dysregulation during critical illness, elevates mortality risk in intensive care settings[\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Pathophysiologically, critical illness activates the hypothalamic‒pituitary‒adrenal axis, promoting cortisol-mediated gluconeogenesis, insulin resistance, and peripheral glucose utilization defects. Exogenous nutritional support further aggravates hyperglycemia, which subsequently dysregulates inflammatory cytokines, coagulation, and immune function, accelerating disease progression[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, owing to chronic glycemic status, admission glucose levels do not accurately reflect stress glucose changes. In recent years, the stress hyperglycemia ratio (SHR), combined with random glucose and glycosylated hemoglobin measurements, has been shown to more accurately capture acute stress hyperglycemia levels[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Concurrently, glucose variability (GV), which represents glucose fluctuations, induces endothelial dysfunction and oxidative stress more profoundly than does sustained hyperglycemia, contributing to cerebrovascular injury and cognitive decline[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Elevated GV also correlates with increased macrovascular and microvascular risks [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Therefore, the joint evaluation of the SHR and GV may be important for blood glucose management and disease prognosis.\u003c/p\u003e\u003cp\u003eCurrent evidence indicates a mortality disparity between diabetic and nondiabetic patients, with severely affected diabetic individuals showing reduced susceptibility to acute hyperglycemia\u0026mdash;potentially due to chronic metabolic adaptations[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Nevertheless, the influence of glucose tolerance status on outcomes in critical illness patients requires further clarification.\u003c/p\u003e\u003cp\u003eThis study aimed to assess mortality in critically ill cerebrovascular patients via the SHR, GV, and their combination across different glucose tolerance states while machine learning was employed to identify optimal predictors.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003eThis study utilized data from the Medical Information Mart for Intensive Care (MIMIC-IV) database, a publicly available critical care dataset containing deidentified clinical information from over 190,000 patients and 450,000 hospital admissions at Beth Israel Deaconess Medical Center (BIDMC) in Boston, MA, USA, between 2008 and 2019[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The use of the MIMIC-IV was approved by the Massachusetts Institute of Technology Ethics Committee (Certification No. 70803575), which waived the requirement for informed consent owing to the retrospective nature of the analysis and the deidentification of all patient data.\u003c/p\u003e\u003cp\u003ePatients were included according to the primary diagnosis of cerebrovascular disease. Patients diagnosed with cerebrovascular disease by searching for ICD-9 and ICD-10 codes are shown in Table A.1. The ICD-9 and ICD-10 codes in the MIMIC-IV database are diagnostic codes perfected by clinicians at the end of a hospital stay. These codes are standardized for billing, administrative, and epidemiological purposes and reflect the final diagnosis of a particular hospital course[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The exclusion criteria were as follows: (1) aged less than 18 years; (2) not admitted to the ICU first; (3) had an ICU stay of less than 24 hours; and (4) had less than three blood glucose measurements or lacked HbA1c or laboratory indicators. Multiple hospitalizations were analyzed on the basis of the first ICU admission data. The patient screening process is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Data extraction\u003c/h2\u003e\u003cp\u003eData were extracted via Navicat Premium (v17.0.8) via structured query language (SQL). The following variables were collected:\u003c/p\u003e\u003cp\u003e(1) demographics: age, sex, ICU length of stay, and time of death;\u003c/p\u003e\u003cp\u003e(2) vital signs: heart rate (HR), respiratory rate (RR), systolic and diastolic blood pressure (SBP/DBP), mean blood pressure (MBP), temperature, and oxygen saturation (SpO₂);\u003c/p\u003e\u003cp\u003e(3) laboratory parameters: hemoglobin (Hb), platelet count (Plt), white blood cell count (WBC), blood urea nitrogen (BUN), glucose, glycated hemoglobin (HbA1c), prothrombin time (PT), partial thromboplastin time (PTT), and creatinine;\u003c/p\u003e\u003cp\u003e(4) severity scores: Acute Physiology Score III (APSIII), Simplified Acute Physiology Score II (SAPSII), Oxford Acute Severity of Illness Score (OASIS), Glasgow Coma Scale (GCS), and Sequential Organ Failure Assessment (SOFA);\u003c/p\u003e\u003cp\u003e(5) comorbidities and treatments: hypertension (HTN), diabetes, congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD), cerebrovascular disease (CVD), myocardial infarction (MI), renal disease (RD), atrial fibrillation (afib), mechanical ventilation, hypoglycemic agents, and insulin therapy.\u003c/p\u003e\u003cp\u003eSHR was calculated via the following equation: [ABG (mg/dL)/(28.7 \u0026times; HbA1c (%)\u0026thinsp;\u0026minus;\u0026thinsp;46.7)][\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] Due to the lack of meal times in the MIMIC-IV database, blood glucose values were measured at admission as random fasting blood glucose. We chose the coefficient of variation (CV) as the primary measure of GV because of its broad clinical applicability, simplicity of interpretation, and extensive validation in intensive care settings. The CV was calculated as the ratio of the standard deviation to the arithmetic mean of all consecutive glucose measurements obtained during intensive care unit monitoring (CV\u0026thinsp;=\u0026thinsp;SD/mean\u0026times;100%), normalizing the variability between individuals with different baseline glucose levels.\u003c/p\u003e\u003cp\u003ePatients were categorized into three groups on the basis of glucose metabolism criteria: normal glucose regulation (NGR), prediabetes (Pre_DM), and diabetes mellitus (DM). The NGR group was defined by an HbA1c level less than 5.7% and the absence of a history of diabetes. The Pre_DM group was characterized by an HbA1c level equal to or greater than 5.7% but less than 6.5%, with no prior history of diabetes. The DM group included individuals with an HbA1c level exceeding 6.5% or a documented history of diabetes.\u003c/p\u003e\u003cp\u003eVital signs, clinical scores, and other laboratory parameters were measured within 24 hours of ICU admission, and variables with missing data rates greater than 20% were excluded to minimize potential bias.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Outcome measures\u003c/h2\u003e\u003cp\u003eThe primary outcome measure was all-cause mortality at 28 days, and the secondary outcome measure was all-cause mortality at 90 days.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Statistical methods\u003c/h2\u003e\u003cp\u003eContinuous variables were assessed for normality via the Kolmogorov‒Smirnov and Shapiro‒Wilk tests. The data are presented as the means\u0026thinsp;\u0026plusmn;\u0026thinsp;SDs or medians [IQRs] and were analyzed with Student\u0026rsquo;s t test/ANOVA or the Mann\u0026ndash;Whitney U test, as appropriate. Categorical variables, expressed as counts (percentages), were compared via the χ\u0026sup2; test or Fisher\u0026rsquo;s exact test.\u003c/p\u003e\u003cp\u003ePatients were stratified into tertiles on the basis of the SHR (\u0026lt;\u0026thinsp;0.90, 0.90\u0026ndash;1.15, \u0026gt;\u0026thinsp;1.15) and GV (\u0026lt;\u0026thinsp;14.92, 14.92\u0026ndash;24.46, \u0026gt;\u0026thinsp;24.46). Kaplan\u0026ndash;Meier survival curves with log-rank tests were used to evaluate group differences in all-cause mortality. Three Cox regression models were constructed: unadjusted (Model 1), partially adjusted (Model 2: age, sex, HR, RR, SBP), and fully adjusted (Model 3: demographics, comorbidities, laboratory values, and severity scores). The variance inflation factor (VIF\u0026thinsp;\u0026lt;\u0026thinsp;5) confirmed the absence of multicollinearity.\u003c/p\u003e\u003cp\u003eRestricted cubic splines were used to analyze dose‒response relationships, whereas Schoenfeld residuals were used to test proportional hazards assumptions. ROC curves were used to compare the predictive performance of the indicators. Subgroup analyses were visualized via forest plots. Sensitivity analyses included excluding patients with hypoglycemia and those with extreme SHR/GV values to validate robustness.\u003c/p\u003e\u003cp\u003eOn the basis of the predictions of the machine learning model, Boruta ranked the importance of features in patients who died within 28 days and randomly divided the dataset into a training subset (80%) and a test subset (20%). Five models [LR (logistic regression), GBM (gradient boosting machine), RF (random forest), XGBoost (extreme gradient boosting), and LightGBM (light gradient boosting machine)] were used to develop importance features, and the model with the best prediction performance was screened out. Finally, Shapley additive explanations (SHAPs) were used for prediction to determine key predictors. All the analytical methods were run with IBM SPSS Statistics 29.0 and R version 4.4.1, and a two-sided \u003cem\u003ep\u003c/em\u003e value less than 0.05 indicated statistical significance.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eA total of 2281 patients, including 1100 (48.2%) females, were analyzed, with a median age of 70 years. By the 28-day and 90-day follow-ups, 1880 (82.42%) and 1758 (77.07%) patients survived, respectively. Compared with survivors, nonsurvivors were older and had a greater prevalence of most comorbidities\u0026mdash;except for hypertension and COPD, which were less common or comparable. Nonsurvivors also received fewer hypoglycemic agents but required more mechanical ventilation and insulin. The laboratory results revealed elevated WBC, BUN, creatinine, glucose, PT, SHR, and GV in nonsurvivors, alongside lower hemoglobin and higher clinical severity scores. The patients\u0026rsquo; baseline characteristics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The univariate Cox regression results and variance inflation factors are provided in Tables A.2 and A.3, respectively, indicating no multicollinearity.\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\u003eBaseline characteristics according to 28-day mortality\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2281)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSurvivors\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1880)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNon-survivors\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;401)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eDemographics\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAge, years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e70(59,80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e68(58,79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e77(66,84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eFemale, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e1100(48.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e889(47.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e211(48.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eComorbidities, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e363(15.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e286(15.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e77(19.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0.047\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eCHF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e543(23.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e416(22.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e127(31.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eCVD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e2170(95.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1779(94.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e391(97.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eCOPD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e326(14.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e276(14.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e50(12.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0.251\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eRD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e399(17.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e303(16.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e96(23.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHTN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e1279(56.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1074(57.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e205(51.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAfib\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e613(26.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e478(25.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e135(33.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e773(33.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e619(32.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e154(38.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGlucose metabolism state, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eNGR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e876(38.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e735(39.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e141(35.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0.141\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePre_DM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e589(25.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e496(26.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e93(23.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0.185\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eDM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e816(35.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e649(34.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e167(41.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTreatment, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eAntihyperglycemic\u003c/p\u003e\u003cp\u003edrug\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e103(4.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100(5.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3(0.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMechanical ventilation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e735(32.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e493(26.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e242(60.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e1860(81.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1513(80.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e347(86.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVital signs\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHR, bpm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e78.16(69.19,88.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e77.43(68.75,87.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e83.08(72.00,93.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eRR, bpm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e18.48(16.82,20.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18.26(16.68,20.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19.54(17.57,22.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eSpO2, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e96.92(95.67,98.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e96.79(95.56,98.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e97.71(96.37,99.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eTemperature, \u0026deg;C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e36.90(36.73,37.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e36.89(36.72,37.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e36.98(36.76,37.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eSBP, mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e132.29(121.62,143.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e132.75(121.92,144.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e130.26(119.08,142.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eDBP, mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e70.46(62.37,79.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e71.42(63.40,80.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e65.48(57.83,73.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMBP, mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e87.94(79.97,96.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e88.56(80.89,96.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e83.85(76.48,92.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLaboratory measurements\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHb, g/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e12.90(11.30,14.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.10(11.60,14.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.10(10.40,13.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePlt, \u0026times;10⁹/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e225.00(179.00,281.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e225.00(181.00,280.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e224.00(169.00,285.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0.190\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\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e(continued)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"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\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\u003eTotal(n\u0026thinsp;=\u0026thinsp;2281)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSurvivors(n\u0026thinsp;=\u0026thinsp;1880)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNon-survivors(n\u0026thinsp;=\u0026thinsp;401)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eWBC, K/\u0026micro;L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.80(8.40,14.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.50(8.20,13.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.90(9.80,16.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eBUN, mg/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.00(14.00,26.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.00(13.00,24.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23.00(16.00,33.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\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\u003eCreatinine, mg/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1.00(0.80,1.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.0(0.8,1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.1(0.9,1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\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, mg/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e122.67(105.39,152.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e117.99(103.50,144.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e146.00(125.67,176.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\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\u003ePT, s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e12.80(11.90,14.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.65(11.80,14.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.60(12.30,15.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\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\u003ePTT, s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e29.60(26.90,34.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29.60(26.90,34.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30.00(27.00,34.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.770\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHbA1c, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e5.80(5.40,6.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.80(5.40,6.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.80(5.40,6.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.211\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1.02(0.87,1.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00(0.86,1.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.13(0.94,1.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGV, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e16.72(11.08,24.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.23(10.79,23.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19.01(12.45,28.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eClinical scores\u003c/b\u003e\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\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e14(12,15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14(12,15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14(10,15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.074\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAPSIII score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e35(27,46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33(26,44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e44(34,58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\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 score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e31(26,36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30(25,35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e36(31,41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\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\u003esofa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e3(1,4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2(1,4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4(2,6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\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\u003eSAPSII score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e32(25,39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30(24,37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39(33,46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\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\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Relationship between the SHR and survival rate\u003c/h2\u003e\u003cp\u003eKM analysis revealed a dose-dependent decrease in survival with increasing stress\u0026ndash;hyperglycemia ratio (SHR) across glucose metabolism strata (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u0026ndash;C), although this trend was not significant in patients with diabetes (log-rank P\u0026thinsp;=\u0026thinsp;0.11). According to the fully adjusted Cox model, the highest SHR tertile (T3) was associated with a 1.53-fold increased risk of 28-day mortality compared with the lowest tertile (T1) (HR\u0026thinsp;=\u0026thinsp;1.53, 95% CI: 1.16\u0026ndash;2.01, P\u0026thinsp;=\u0026thinsp;0.002), with a significant dose\u0026ndash;response trend (P-trend\u0026thinsp;=\u0026thinsp;0.003). This trend was not observed for 90-day mortality (HR\u0026thinsp;=\u0026thinsp;1.24, 95% CI: 0.99\u0026ndash;1.56, P\u0026thinsp;=\u0026thinsp;0.066; P-trend\u0026thinsp;=\u0026thinsp;0.068). When stratified by glucose metabolism status, the SHR was consistently associated with survival in all subgroups except those with diabetes (HR\u0026thinsp;=\u0026thinsp;1.07, 95% CI: 0.73\u0026ndash;1.58, P\u0026thinsp;=\u0026thinsp;0.721; P-trend\u0026thinsp;=\u0026thinsp;0.782) (Table A.4). Restricted cubic spline models revealed a linear SHR\u0026ndash;mortality relationship in the NGR (P-nonlinear\u0026thinsp;=\u0026thinsp;0.107) and DM (P-nonlinear\u0026thinsp;=\u0026thinsp;0.671) groups but a nonlinear association in the pre-DM group (P-nonlinear\u0026thinsp;=\u0026thinsp;0.027) (Fig. A.2A). Subgroup analysis revealed a strong interaction effect between the SHR and glucose status (P-interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with the highest risk observed in the NGR subgroup (HR\u0026thinsp;=\u0026thinsp;5.81, 95% CI: 3.58\u0026ndash;9.44, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig. A.3A).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Relationship between GV and survival rate\u003c/h2\u003e\u003cp\u003eKM curves for GV and 28-day survival, stratified by glucose status, are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD\u0026ndash;F. Like in SHRs, GV was not significantly associated with 28-day survival in patients with diabetes (log-rank P\u0026thinsp;=\u0026thinsp;0.079). In adjusted Cox models, GV was not significantly predictive of 28-day mortality in the overall population or in any glucose subgroup, either as a continuous variable (all HRs included 1) or as tertiles (T3 vs T1, all confidence intervals crossed 1). However, the intermediate GV tertile was associated with a significantly lower mortality risk than the low GV tertile in most subgroups, although not in the pre-DM cohort (HR\u0026thinsp;=\u0026thinsp;0.77, 95% CI: 0.47\u0026ndash;1.26, P\u0026thinsp;=\u0026thinsp;0.297; Table A.4). Restricted cubic spline analysis revealed a linear GV\u0026ndash;survival relationship in the NGR (P-nonlinear\u0026thinsp;=\u0026thinsp;0.822) and pre-DM (P-nonlinear\u0026thinsp;=\u0026thinsp;0.245) groups but a nonlinear association in the DM group (P-nonlinear\u0026thinsp;=\u0026thinsp;0.009; Fig. A.2B). Subgroup analysis further revealed significant interaction effects for GV in patients with cerebrovascular disease among those with myocardial infarction (P-interaction\u0026thinsp;=\u0026thinsp;0.010) or renal disease (P-interaction\u0026thinsp;=\u0026thinsp;0.043; Fig. A.3B).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Association of combined SHR and GV with mortality\u003c/h2\u003e\u003cp\u003eThe results of the Kaplan‒Meier analysis of the combined SHR and GV data are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG\u0026ndash;I. Elevated SHR (\u0026gt;\u0026thinsp;1.15) was consistently associated with higher 28-day mortality across glucose metabolism strata. In the NGR subgroup, high SHR combined with high GV (\u0026gt;\u0026thinsp;24.46) significantly increased both 28-day (HR\u0026thinsp;=\u0026thinsp;2.69, 95% CI: 1.51\u0026ndash;4.80, P\u0026thinsp;=\u0026thinsp;0.001) and 90-day mortality (HR\u0026thinsp;=\u0026thinsp;2.54, 95% CI: 1.49\u0026ndash;4.34, P\u0026thinsp;=\u0026thinsp;0.001). Conversely, among Pre-DM patients, high SHR with low GV (\u0026lt;\u0026thinsp;24.46) had the strongest association with 28-day mortality (HR\u0026thinsp;=\u0026thinsp;2.57, 95% CI: 1.53\u0026ndash;4.33, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 90-day mortality (HR\u0026thinsp;=\u0026thinsp;1.72, 95% CI: 1.07\u0026ndash;2.77, P\u0026thinsp;=\u0026thinsp;0.026). No significant associations were observed in the DM subgroup (Table\u0026nbsp;\u003cspan refid=\"Tab4\" 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 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe associations of the combinations of SHR and GV with all-cause mortality\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\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\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e28-day mortality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOverall\u003c/b\u003e\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\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.81(1.36\u0026thinsp;~\u0026thinsp;2.39)\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.66(1.25\u0026thinsp;~\u0026thinsp;2.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.25(0.92\u0026thinsp;~\u0026thinsp;1.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.146\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.74(1.36\u0026thinsp;~\u0026thinsp;2.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.58(1.23\u0026thinsp;~\u0026thinsp;2.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.28(0.99\u0026thinsp;~\u0026thinsp;1.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.79(2.12\u0026thinsp;~\u0026thinsp;3.66)\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.55(1.93\u0026thinsp;~\u0026thinsp;3.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.53(1.11\u0026thinsp;~\u0026thinsp;2.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.37(1.26\u0026thinsp;~\u0026thinsp;1.49)\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.32(1.21\u0026thinsp;~\u0026thinsp;1.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.14(1.03\u0026thinsp;~\u0026thinsp;1.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePatients with NGR\u003c/b\u003e\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\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.73(0.91\u0026thinsp;~\u0026thinsp;3.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.54(0.80\u0026thinsp;~\u0026thinsp;2.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.15(0.58\u0026thinsp;~\u0026thinsp;2.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.685\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.73(1.19\u0026thinsp;~\u0026thinsp;2.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.60(1.10\u0026thinsp;~\u0026thinsp;2.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.30(0.88\u0026thinsp;~\u0026thinsp;1.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.189\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.56(3.36\u0026thinsp;~\u0026thinsp;9.22)\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.89(2.87\u0026thinsp;~\u0026thinsp;8.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.69(1.51\u0026thinsp;~\u0026thinsp;4.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.52(1.30\u0026thinsp;~\u0026thinsp;1.77)\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.45(1.24\u0026thinsp;~\u0026thinsp;1.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.25(1.07\u0026thinsp;~\u0026thinsp;1.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePatients with Pre-DM\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.61(1.47\u0026thinsp;~\u0026thinsp;4.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.29(1.28\u0026thinsp;~\u0026thinsp;4.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.79(0.97\u0026thinsp;~\u0026thinsp;3.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.87(1.76\u0026thinsp;~\u0026thinsp;4.68)\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.64(1.59\u0026thinsp;~\u0026thinsp;4.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.57(1.53\u0026thinsp;~\u0026thinsp;4.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\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\u003eGroup 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.23(1.57\u0026thinsp;~\u0026thinsp;6.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.81(1.35\u0026thinsp;~\u0026thinsp;5.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.36(1.06\u0026thinsp;~\u0026thinsp;5.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.57(1.31\u0026thinsp;~\u0026thinsp;1.88)\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.50(1.24\u0026thinsp;~\u0026thinsp;1.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.45(1.18\u0026thinsp;~\u0026thinsp;1.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\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\u003cb\u003ePatients with DM\u003c/b\u003e\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\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.28(0.85\u0026thinsp;~\u0026thinsp;1.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.25(0.83\u0026thinsp;~\u0026thinsp;1.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.278\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.93(0.60\u0026thinsp;~\u0026thinsp;1.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.735\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.15(0.71\u0026thinsp;~\u0026thinsp;1.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.562\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.04(0.64\u0026thinsp;~\u0026thinsp;1.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.873\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.77(0.47\u0026thinsp;~\u0026thinsp;1.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.323\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.72(1.16\u0026thinsp;~\u0026thinsp;2.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.68(1.13\u0026thinsp;~\u0026thinsp;2.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.03(0.65\u0026thinsp;~\u0026thinsp;1.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.913\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.18 (1.03\u0026thinsp;~\u0026thinsp;1.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.16(1.02\u0026thinsp;~\u0026thinsp;1.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.99(0.86\u0026thinsp;~\u0026thinsp;1.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.938\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e90-day mortality\u003c/b\u003e\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\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverall\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\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.94(1.53\u0026thinsp;~\u0026thinsp;2.46)\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.77(1.39\u0026thinsp;~\u0026thinsp;2.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.36(1.05\u0026thinsp;~\u0026thinsp;1.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.56(1.25\u0026thinsp;~\u0026thinsp;1.96)\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.41(1.12\u0026thinsp;~\u0026thinsp;1.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.16(0.92\u0026thinsp;~\u0026thinsp;1.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.196\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.67(2.09\u0026thinsp;~\u0026thinsp;3.40)\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.39(1.87\u0026thinsp;~\u0026thinsp;3.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.53(1.15\u0026thinsp;~\u0026thinsp;2.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.33(1.24\u0026thinsp;~\u0026thinsp;1.44)\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.28(1.18\u0026thinsp;~\u0026thinsp;1.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.11(1.02\u0026thinsp;~\u0026thinsp;1.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePatients with NGR\u003c/b\u003e\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\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.97(1.15\u0026thinsp;~\u0026thinsp;3.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.77(1.03\u0026thinsp;~\u0026thinsp;3.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.32(0.74\u0026thinsp;~\u0026thinsp;2.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.345\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.69(1.21\u0026thinsp;~\u0026thinsp;2.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.55(1.11\u0026thinsp;~\u0026thinsp;2.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.32(0.94\u0026thinsp;~\u0026thinsp;1.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.113\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.90(3.05\u0026thinsp;~\u0026thinsp;7.86)\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.33(2.64\u0026thinsp;~\u0026thinsp;7.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.54(1.49\u0026thinsp;~\u0026thinsp;4.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.46(1.28\u0026thinsp;~\u0026thinsp;1.68)\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.39(1.21\u0026thinsp;~\u0026thinsp;1.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.24(1.07\u0026thinsp;~\u0026thinsp;1.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePatients with Pre-DM\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e(continued)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eGroup 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.37(1.44\u0026thinsp;~\u0026thinsp;3.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.93(1.17\u0026thinsp;~\u0026thinsp;3.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.59(0.94\u0026thinsp;~\u0026thinsp;2.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.081\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eGroup 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.12(1.36\u0026thinsp;~\u0026thinsp;3.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.74(1.10\u0026thinsp;~\u0026thinsp;2.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.72(1.07\u0026thinsp;~\u0026thinsp;2.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eGroup 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.99(1.61\u0026thinsp;~\u0026thinsp;5.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.28(1.21\u0026thinsp;~\u0026thinsp;4.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.80(0.91\u0026thinsp;~\u0026thinsp;3.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.091\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.45 (1.24\u0026thinsp;~\u0026thinsp;1.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.32 (1.11\u0026thinsp;~\u0026thinsp;1.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.26 (1.05\u0026thinsp;~\u0026thinsp;1.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePatients with DM\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.47(1.04\u0026thinsp;~\u0026thinsp;2.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.46(1.03\u0026thinsp;~\u0026thinsp;2.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.07(0.73\u0026thinsp;~\u0026thinsp;1.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.738\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.08(0.70\u0026thinsp;~\u0026thinsp;1.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.720\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.99(0.64\u0026thinsp;~\u0026thinsp;1.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.958\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.75(0.47\u0026thinsp;~\u0026thinsp;1.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.79(1.26\u0026thinsp;~\u0026thinsp;2.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.76(1.23\u0026thinsp;~\u0026thinsp;2.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.08(0.73\u0026thinsp;~\u0026thinsp;1.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.706\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.17(1.05\u0026thinsp;~\u0026thinsp;1.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.16(1.03\u0026thinsp;~\u0026thinsp;1.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.99(0.87\u0026thinsp;~\u0026thinsp;1.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.902\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"11\"\u003eModel 1: unadjusted\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"11\"\u003eModel 2: adjusted for age, sex, HR, RR, and SBP\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"11\"\u003eModel 3: Adjusted for Model 2 plus MI, CHF, RD, HTN, Afib, DM, Hb, BUN, creatinine, PT, mechanical ventilation, RI, SOFA, and APSIII\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"11\"\u003eGroup 1: Low SHR and low GV (SHR\u0026thinsp;\u0026lt;\u0026thinsp;1.15 and GV\u0026thinsp;\u0026lt;\u0026thinsp;24.46); Group 2: low SHR and high GV (SHR\u0026thinsp;\u0026lt;\u0026thinsp;1.15 and GV\u0026thinsp;\u0026gt;\u0026thinsp;24.46); Group 3: high SHR and low GV (SHR\u0026thinsp;\u0026gt;\u0026thinsp;1.15 and GV\u0026thinsp;\u0026lt;\u0026thinsp;24.46); Group 4: high SHR and high GV (SHR\u0026thinsp;\u0026gt;\u0026thinsp;1.15 and GV\u0026thinsp;\u0026gt;\u0026thinsp;24.46\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eProportional hazards assumptions were maintained in the Pre-DM (P\u0026thinsp;=\u0026thinsp;0.525) and DM (P\u0026thinsp;=\u0026thinsp;0.586) subgroups but violated in the NGR subgroup. The Schoenfeld residuals indicated stabilization of the beta coefficients around day 5 (Table A.5, Fig. A.1). Landmark analysis confirmed superior survival in NGR patients with low SHR and low GV throughout follow-up (Fig. A.4).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.4 ROC curve analysis\u003c/h2\u003e\u003cp\u003eROC analysis was used to assess the predictive performance of the SHR, GV, and their combination for mortality in patients with cerebrovascular diseases (Table A.8, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For 28-day mortality in the NGR and Pre-DM groups, the combined model had a significantly greater AUC than GV alone (0.579 vs. 0.575, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) but was not superior to the SHR alone (0.579 vs. 0.640, P\u0026thinsp;=\u0026thinsp;0.094). In the DM group, all models showed reduced discriminative ability, with the combined model achieving an AUC of 0.560, similar to that of SHR (0.574) and GV (0.557) alone.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFor 90-day mortality, the combined model again outperformed GV in the NGR and Pre-DM subgroups but not in the SHR subgroup. Notably, in the DM group, the combined model exhibited a greater AUC than both the SHR (0.584 vs. 0.557, P\u0026thinsp;=\u0026thinsp;0.372) and GV (0.584 vs. 0.581, P\u0026thinsp;=\u0026thinsp;0.002) models did, although the improvement over the SHR model was not statistically significant. Significant differences between the models for 28-day mortality were observed only in the NGR and Pre-DM groups. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e additionally compares the investigational metrics with the SOFA and APSIII scores for the prediction of 28- and 90-day mortality.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Sensitivity analysis\u003c/h2\u003e\u003cp\u003eSensitivity analyses confirmed the robustness of our primary findings. After excluding 82 patients with ICU-acquired hypoglycemia, the Cox regression results remained consistent with those of the main analysis (Table A.9). Similarly, the exclusion of 195 subjects with extreme SHR or GV values did not alter the associations between these metrics and cerebrovascular outcomes (Table A.10).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Machine learning\u003c/h2\u003e\u003cp\u003eFeature selection via the Boruta algorithm identified 18, 14, and 16 mortality predictors in the NGR, Pre-DM, and DM groups, respectively, with predictor importance decreasing from right to left (Fig. A.5B\u0026ndash;D; overall population results in Fig. A.5A).\u003c/p\u003e\u003cp\u003eThe predictive performance of the models varied across subgroups (Table A.11). LightGBM achieved the highest AUC (0.926) in the NGR cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), whereas logistic regression performed best in both the Pre-DM (AUC\u0026thinsp;=\u0026thinsp;0.893) and DM (AUC\u0026thinsp;=\u0026thinsp;0.782) groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, C). The overall population results are provided in Table A.11 and Fig. A.6.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSHAP analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD\u0026ndash;F) revealed that SHR and GV contributed the least to predictions in the Pre-DM subgroup (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). Although GV had marginally greater overall importance than SHR across subgroups, SHAP plots revealed that both variables were concentrated in the SHAP\u0026thinsp;\u0026gt;\u0026thinsp;0 region, with SHR showing denser clustering, indicating a stronger positive association with mortality risk. The full population SHAP results are shown in Fig. A.7.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this cohort study of cerebrovascular disease patients, the SHR demonstrated a stronger and more consistent association with short-term mortality than did the GV. Cox regression identified the SHR as a significant predictor in the nondiabetic groups (NGR and Pre-DM), whereas GV exhibited a nonlinear relationship with mortality, with moderate levels conferring a protective effect. The highest 28-day mortality risk was observed in NGR patients with high SHR and high GV and in Pre-DM patients with high SHR and low GV. Although the combined SHR\u0026ndash;GV model did not outperform the SHR alone in ROC analysis, it provided incremental predictive value for 90-day mortality in diabetic patients. SHAP analysis from machine learning models confirmed that SHR contributed more substantially than GV did to prediction stability and overall performance. The superior predictive utility of the SHR may stem from its reflection of illness severity, as an elevated SHR is correlated with higher clinical scores and more intensive treatments. In contrast, GV\u0026mdash;which solely captures glucose fluctuations\u0026mdash;showed a complex nonlinear relationship with outcomes and did not enhance the stability of the combined models, likely because of its susceptibility to confounding clinical factors.\u003c/p\u003e\u003cp\u003eCurrent evidence suggests that the SHR and GV are significant prognostic markers in critical care. The SHR, which integrates acute glucose levels with chronic glycemic status (HbA1c)[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], has been consistently associated with disease severity and mortality in patients with cardiovascular and cerebrovascular conditions[\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Meta-analyses[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and cohort studies[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] have demonstrated that elevated SHR predicts adverse outcomes in acute myocardial infarction, ST-elevation myocardial infarction, and ischemic stroke, particularly among nondiabetic individuals. Similar results have also been reported for myocardial infarction and nonobstructive coronary artery disease (MINOCA)[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] and three-vessel disease[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Furthermore, for acute ischemic stroke due to large vessel occlusion, the RESCUE BT test revealed a linear relationship between elevated SHR and poor functional outcomes[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Duan et al.[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] reported that elevated SHR was associated with early neurological deterioration after thrombolysis in acute stroke, while NHANES data revealed a J/u relationship between SHR and mortality from all cardiovascular diseases[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In these studies, we observed a positive association between SHR and cardiovascular adverse events, which is consistent with our findings. Glucose variability was also associated with adverse outcome events.\u003c/p\u003e\u003cp\u003eA retrospective analysis of 4809 critically ill patients with cerebrovascular disease by Cai W. et al. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] revealed that glucose variability was approximately linearly associated with severe cognitive decline and in-hospital mortality in CVD patients. GV was demonstrated to be an independent risk factor for adverse outcomes in patients with acute stroke in a prospective multicenter study combined with an animal model in a GLIAS-III translational study. He HM. et al.[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] High SHR/GV combination levels in individuals without diabetes with CAD were found to predict poor prognosis, whereas high SHR and low GV combination levels in individuals with diabetes were associated with increased mortality. Wang Feng et al.[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] reached the same conclusion and verified the predictive accuracy of the combined SHR and GV index model. These findings underscore the clinical importance of comprehensive SHR-GV assessment in cerebrovascular disease. Further research should explore their combined utility in guiding personalized glucose management strategies for critically ill patients.\u003c/p\u003e\u003cp\u003eStress-induced hyperglycemia is caused mainly by excessive activation of sympathetic nerves and the release of large amounts of glucocorticoids such as cortisol[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], which also drives inflammatory cytokine regulation and the amplification of oxidative stress[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. On the other hand, stress hyperglycemia may exacerbate acute heart disease in a variety of ways, including exacerbating microvascular obstruction[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], accelerating endothelial cell damage[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], and impairing platelet nitric oxide responsiveness[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] (nitric oxide deficiency causes sustained vasoconstriction, accelerates vascular sclerosis, and increases the risk of thrombosis and vascular inflammation[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]). In addition, other vascular injury mechanisms mediated by hyperglycemia are promoted. Stress hyperglycemia may also disrupt the blood‒brain barrier through intracellular acidosis, leading to mitochondrial dysfunction, energy depletion, and apoptosis, further driving adverse outcomes after stroke[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Glycemic variability reflects changes in blood glucose fluctuations, which can lead to endothelial dysfunction[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] and oxidative stress[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], further exacerbating plaque vulnerability[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] and promoting cardiovascular and cerebrovascular diseases.\u003c/p\u003e\u003cp\u003eStudies indicate that glycated hemoglobin (A1C) can be converted to estimated average glucose (eAG), reflecting mean glycemic levels over 8\u0026ndash;12 weeks[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Unlike absolute hyperglycemia, relative hyperglycemia\u0026mdash;assessing acute glucose changes\u0026mdash;remains independent of baseline glucose levels in critical illness patients[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The stress hyperglycemia ratio (SHR) integrates acute glucose levels with A1C to differentiate stress-induced hyperglycemia from chronic dysglycemia, thereby improving the assessment of acute glycemic impact on clinical outcomes. Furthermore, glycemic variability (GV) captures short-term glucose fluctuations, complementing the temporal limitations of SHRs.\u003c/p\u003e\u003cp\u003eThe combined assessment of SHR and GV provides a rational approach for evaluating stress-mediated hyperglycemia and acute glucose fluctuations in critically ill patients. This study confirmed the prognostic value of SHR-GV integration for 28-day mortality in the NGR and Pre-DM subgroups but not in diabetic patients. Previous evidence indicates greater susceptibility to acute glucose variations in nondiabetic individuals than in diabetic individuals[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], potentially due to long-term adaptive responses to oxidative stress and increased glycemic tolerance thresholds in diabetic patients[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Additionally, ongoing hypoglycemic therapies in diabetic patients may attenuate acute glycemic effects[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Thus, diabetes-specific factors likely confound the interpretation of the SHR and GV metrics.\u003c/p\u003e\u003cp\u003eNotably, high GV levels were not significantly associated with poor prognosis in cerebrovascular disease patients. This may stem from methodological limitations, as the quantitative CV index might interfere with GV expression[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], compounded by the MIMIC-IV database's lack of meal timing data. Furthermore, Cox regression revealed a nonlinear relationship, with moderate GV levels serving as a protective factor, which is consistent with reported threshold effects in critical illness patients[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. These findings suggest that maintaining GV within an optimal range may prove more beneficial than indiscriminate minimization. Future studies should incorporate advanced GV metrics (e.g., TIR, VIM, ARV) to validate these observations.\u003c/p\u003e\u003cp\u003eThe combined assessment of the SHR and GV provides complementary risk stratification for acute cerebrovascular disease. The SHR captures acute-on-chronic glycemic stress, whereas the GV reflects acute glucose instability\u0026mdash;which is particularly relevant in nondiabetic patients (NGR/Pre-DM) who lack adaptive hyperglycemic responses. Although the combined model did not outperform the SHR alone in terms of predictive performance, machine learning interpretation (SHAP) confirmed the stronger overall contribution of the SHR to mortality prediction, whereas GV was more important in specific metabolic subgroups. This suggests that GV may introduce interference rather than synergistic improvement in the combined model. Nevertheless, integrating the SHR and GV\u0026mdash;especially in nondiabetic patients\u0026mdash;offers a clinically valuable framework for glycemic risk stratification in neurocritical care. Further validation across diverse cerebrovascular cohorts is needed.\u003c/p\u003e\u003cp\u003eThis study has several limitations. First, despite adjusting for available confounders, residual confounding may persist due to unmeasured variables such as lifestyle factors. Second, the exclusion of patients with missing HbA1c or insufficient glucose measurements may have introduced selection bias. Third, the predominantly white nature of the study population limits its generalizability to other ethnic groups. Fourth, the analysis did not account for differences in treatment strategies between the ischemic and hemorrhagic stroke subtypes. Finally, the retrospective design precludes causal inference. Further prospective studies are needed to validate these findings across diverse cerebrovascular disease populations.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe SHR is a stronger predictor of short-term mortality than GV is in critically ill cerebrovascular patients. While combining SHR and GV does not significantly enhance predictive modeling, it offers practical risk stratification\u0026mdash;particularly in the group without diabetes\u0026mdash;which may guide personalized glucose management strategies in neurocritical care.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAPSIII, Acute Physiology Score;\u003c/p\u003e\n\u003cp\u003eARV, average real variability;\u003c/p\u003e\n\u003cp\u003eBIDMC, Beth Israel Deaconess Medical Center\u003c/p\u003e\n\u003cp\u003eBUN, blood urea nitrogen;\u003c/p\u003e\n\u003cp\u003eCAD, coronary artery disease\u003c/p\u003e\n\u003cp\u003eCHF, Congestive Heart Failure;\u003c/p\u003e\n\u003cp\u003eCOPD, chronic pulmonary disease;\u003c/p\u003e\n\u003cp\u003eCVD, cerebrovascular disease;\u003c/p\u003e\n\u003cp\u003eDBP, Diastolic Blood Pressure;\u003c/p\u003e\n\u003cp\u003eDM, diabetes mellitus;\u003c/p\u003e\n\u003cp\u003eGBM, gradient boosting machine;\u003c/p\u003e\n\u003cp\u003eGCS, Glasgow Coma Scale;\u003c/p\u003e\n\u003cp\u003eGV, glycemic variability;\u003c/p\u003e\n\u003cp\u003eHb, hemoglobin concentration;\u003c/p\u003e\n\u003cp\u003eHbA1c, glycated hemoglobin;\u003c/p\u003e\n\u003cp\u003eHPA, Hypothalamic‒pituitary‒adrenal\u003c/p\u003e\n\u003cp\u003eHR, Heart rate;\u003c/p\u003e\n\u003cp\u003eHTN, hypertension;\u003c/p\u003e\n\u003cp\u003eKM, Kaplan‒Meier;\u003c/p\u003e\n\u003cp\u003eLightGBM, Light gradient boosting machine;\u003c/p\u003e\n\u003cp\u003eLR, logistic regression;\u003c/p\u003e\n\u003cp\u003eMACCEs, major adverse cardiovascular and cerebrovascular events\u003c/p\u003e\n\u003cp\u003eMBP, mean blood pressure;\u003c/p\u003e\n\u003cp\u003eMI, myocardial infarction;\u003c/p\u003e\n\u003cp\u003eMIMIC-IV, Medical Information Mart for Intensive Care\u003c/p\u003e\n\u003cp\u003eMINOCA, Myocardial Infarction and Nonobstructive Coronary Artery Disease\u003c/p\u003e\n\u003cp\u003eNGR, normal glucose regulation;\u003c/p\u003e\n\u003cp\u003eNHANES, National Health and Nutrition Examination Survey;\u003c/p\u003e\n\u003cp\u003eOASIS, Oxford Acute Severity of Illness Score;\u003c/p\u003e\n\u003cp\u003ePlt, Platelet;\u003c/p\u003e\n\u003cp\u003ePre_DM, Prediabetes mellitus;\u003c/p\u003e\n\u003cp\u003ePT, Prothrombin time;\u003c/p\u003e\n\u003cp\u003ePTT, partial thromboplastin time;\u003c/p\u003e\n\u003cp\u003eRCS, restricted cubic splines\u003c/p\u003e\n\u003cp\u003eRD, Renal Disease;\u003c/p\u003e\n\u003cp\u003eRF, random forest;\u003c/p\u003e\n\u003cp\u003eRI, Insulin;\u003c/p\u003e\n\u003cp\u003eROC, receiver operating characteristic\u003c/p\u003e\n\u003cp\u003eRR, Respiratory rate;\u003c/p\u003e\n\u003cp\u003eSAPSII, simplified acute physiology score;\u003c/p\u003e\n\u003cp\u003eSBP, Systolic Blood Pressure;\u003c/p\u003e\n\u003cp\u003eSHAP, Machine learning interpretation;\u003c/p\u003e\n\u003cp\u003eSHR, stress\u0026ndash;hyperglycemia ratio;\u003c/p\u003e\n\u003cp\u003eSOFA, Sequential Organ Failure Assessment;\u003c/p\u003e\n\u003cp\u003eSTEMI, ST-elevated myocardial infarction;\u003c/p\u003e\n\u003cp\u003eTIR, time-in-range;\u003c/p\u003e\n\u003cp\u003eVIM, variation independent of the mean;\u003c/p\u003e\n\u003cp\u003eVIF, variance inflation factor;\u003c/p\u003e\n\u003cp\u003eWBC, White blood cell\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study acknowledges the MIT Computational Physiology Laboratory and Beth Israel Deaconess Medical Centre for providing and maintaining the invaluable public resource MIMIC-IV. Moreover, The author expresses sincere gratitude for the technical support of Liaoyang Central Hospital, the First Affiliated Hospital of Jinzhou Medical University, and all individuals involved in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database, a publicly available, de-identified critical care database. The establishment of the MIMIC-IV database was approved by the Institutional Review Board (IRB) of the Massachusetts Institute of Technology (Cambridge, MA, USA) (Certification No. 70803575). All original data were de-identified to protect patient privacy, and the requirement for individual patient consent was waived by the approving IRB.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhmad FB, Cisewski JA, Anderson RN. Leading Causes of Death in the US, 2019\u0026ndash;2023. JAMA. 2024;332:957\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1001/jama.2024.15563\u003c/span\u003e\u003cspan address=\"10.1001/jama.2024.15563\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChang CWJ, Provencio JJ, Shah S, Neurological Critical Care. The Evolution of Cerebrovascular Critical Care. 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Sci Rep. 2025;15:7820. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-025-92415-9\u003c/span\u003e\u003cspan address=\"10.1038/s41598-025-92415-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\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":"Stress–hyperglycemia ratio, Glycemic variability, Mortality, Critical care, Cerebrovascular disorders, Machine learning","lastPublishedDoi":"10.21203/rs.3.rs-8125196/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8125196/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe combined prognostic value of the stress hyperglycemia ratio (SHR) and glycemic variability (GV) for mortality risk stratification across different glucose metabolic states in critically ill cerebrovascular patients remains unexplored. This study aims to evaluate its predictive utility by employing machine learning to identify critical risk predictors.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis retrospective cohort study analyzed data from the MIMIC-IV databaseand included 2,281 adult ICU patients with cerebrovascular disease stratified by glycemic status (NGR, Pre-DM, DM). The outcomes were 28-day and 90-day all-cause mortality. Associations and predictive performance of SHR and GV were evaluated via Cox regression, Kaplan‒Meier analysis, and receiver operating characteristic (ROC) curves, with machine learning models (SHAP interpretation) applied for predictor identification.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAmong 2,281 patients, high levels of both SHR and GV were independently associated with increased 28-day (HR 1.53, 95% CI 1.11\u0026ndash;2.11) and 90-day mortality (HR 1.53, 95% CI 1.15\u0026ndash;2.03), particularly in nondiabetic subgroups. GV exhibited a nonlinear association with mortality risk. Compared with the SHR-GV model alone, the combined SHR\u0026ndash;GV model did not significantly improve 28-day mortality prediction. For 90-day mortality in diabetic patients, the combination had a marginally greater AUC (0.584 vs. 0.557), although this difference was not statistically significant. Machine learning interpretation confirmed the SHR as the dominant predictor.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe SHR outperforms GV in predicting short-term mortality in critical cerebrovascular patients. Although combining both metrics does not significantly improve predictive accuracy, it enables practical risk stratification\u0026mdash;particularly in people without diabetes\u0026mdash;to guide personalized glucose management.\u003c/p\u003e\u003ch2\u003eTrial registration:\u003c/h2\u003e\u003cp\u003eNot applicable. This study is a retrospective analysis of a pre-existing database (MIMIC-IV) and does not report the results of a health care intervention. Therefore, trial registration was not required.\u003c/p\u003e","manuscriptTitle":"Stress Hyperglycemia Ratio and Glycemic Variability Predict Mortality in Critical Stroke: A Machine Learning Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-02 14:35:57","doi":"10.21203/rs.3.rs-8125196/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":"917fd720-b2bf-4589-af2a-3eeeb26109f4","owner":[],"postedDate":"December 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-15T07:24:59+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-02 14:35:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8125196","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8125196","identity":"rs-8125196","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}