Prognostic Value of Time-Weighted Average Glucose on All- Cause Mortality in Critically Ill Patients with Ischemic Stroke: A Retrospective Cohort Analysis

Prognostic Value of Time-Weighted Average Glucose on All- Cause Mortality in Critically Ill Patients with Ischemic Stroke: A Retrospective Cohort Analysis | 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 Prognostic Value of Time-Weighted Average Glucose on All- Cause Mortality in Critically Ill Patients with Ischemic Stroke: A Retrospective Cohort Analysis Jie Peng, Xingzhan Zhang, Huanhuan Wu, Hongzhi Chen, Ling Zhao, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7573088/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 Ischemic stroke poses an increased risk of disability and mortality. Time-weighted average glucose (TWAG) represents an integrated index of glycemic exposure and serves as an independent prognostic marker in populations with critical illness. Nonetheless, its clinical significance for patients with ischemic stroke in intensive care units remains unclear. Investigating TWAG in critically ill patients with stroke may provide a promising approach to improving risk stratification in this vulnerable population. Methods This retrospective cohort study employed the MIMIC-IV database, encompassing 1,408 critically ill patients with ischemic stroke. Tertiles of TWAG values were defined on the basis of cutoff points at the 33rd and 66th percentiles. The main endpoint was 30-day all-cause mortality, with 90-day mortality assessed as a secondary outcome. Cox proportional hazards models, adjusted for demographic factors, illness severity scores, comorbidities, laboratory results, and treatment variables, were employed for assessing associations. Kaplan–Meier curves and restricted cubic spline plots were used for visualization, and subgroup analyses evaluated effect modifications related to diabetes status and other clinical characteristics. Results This study, comprising 1,408 patients with ischemic stroke, revealed that increased TWAG was significantly associated with higher short-term all-cause mortality, as estimated by Cox proportional hazards models. Subgroup evaluations further affirmed these associations. Conclusions In the intensive care setting, TWAG was independently correlated with increased short-term mortality in patients with ischemic stroke, with a stronger impact on individuals without diabetes. These findings suggest that TWAG can serve as a useful marker for early risk stratification and guide more targeted glycemic management protocols to improve clinical outcomes. time-weighted average glucose ischemic stroke MIMIC-IV database critically ill patient all-cause mortality Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Ischemic stroke, which arises from a cerebral artery occlusion, accounts for approximately 70% of all strokes and remains a leading contributor to disability and mortality worldwide( 1 ). Despite the use of endovascular therapy and intravenous recombinant tissue plasminogen activator (rt-PA), patients with ischemic stroke, particularly those in the intensive care unit (ICU), continue to experience a high risk of poor clinical outcomes( 2 , 3 ). Ischemic stroke incidence has been closely associated with pathological and behavioral factors, including dietary patterns, metabolic abnormalities, and tobacco use( 4 ). Several studies have similarly demonstrated associations between ischemic stroke and metabolic disturbances, including elevated blood glucose and lipid levels( 5 , 6 ). Published evidence has reported that diabetes represents a major risk factor for cardiovascular and cerebrovascular diseases( 7 , 8 ). Optimal glycemic management is widely considered a critical factor influencing clinical outcomes in patients following a stroke event( 9 ), particularly among those receiving intensive care( 10 ). Emerging evidence highlights the significance of acute stress hyperglycemia as a key prognostic factor in stroke. Research shows that the stress hyperglycemia ratio, which compares admission glucose levels with chronic glycemic markers, is a significant predictor of increased short- and long-term mortality risks in patients with acute ischemic stroke, irrespective of diabetes status( 11 ). Hyperglycemia and hypoglycemia are common in hospital admissions and are associated with higher risks of adverse outcomes and mortality, irrespective of diabetes status( 10 , 12 , 13 ). Existing evidence links dysglycemia, including hyperglycemia, hypoglycemia and glycemic variability, to adverse outcomes following stroke( 14 ). Historically, metabolic status has been evaluated using single-point or average glucose levels; however, these metrics do not account for the dynamic glucose fluctuations in critically ill patients( 15 ). Time-weighted average glucose (TWAG) has become a comprehensive measure of glycemic exposure, incorporating both the magnitude and duration of glucose levels. By incorporating time into its calculation, this metric offers a precise evaluation of in-hospital glycemic control( 15 ). Prior evidence has demonstrated that TWAG is significantly associated with ICU mortality( 16 ). By integrating glycemic exposure over time, this method reflects prolonged hyperglycemia, which may exert greater harm than short-lived fluctuations, thereby providing a profile of clearer clinical relevance. Evidence on the association between TWAG and mortality in critically ill patients with ischemic stroke remains scarce. This study aimed to evaluate the association between TWAG and 30- and 90-day all-cause mortality in ICU patients with ischemic stroke. Methods Study population This retrospective cohort study utilized the MIMIC-IV (version 3.1) repository, a significant open-access critical care database developed by MIT and affiliated organizations. This study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology guidelines for documenting observational epidemiological research( 17 ). The dataset comprised comprehensive clinical records of emergency department and ICU admissions at Beth Israel Deaconess Medical Center from 2008 to 2022. The extensive database facilitated the evaluation of longitudinal glucose measurements and clinical outcomes spanning > 10 years. Author Jie Peng complied with required protocols for database access and performed data extraction. To maintain consistency and accuracy, data extraction adhered to strict quality control protocols. As the MIMIC-IV database contains de-identified data, informed consent was not required. This study adhered to ethical standards and regulatory requirements. The Institutional Review Boards overseeing the MIMIC-IV database approved the study protocol. This study comprised critically ill individuals admitted to the ICU with a diagnosis of ischemic stroke based on ICD-9 and ICD-10 criteria. The following were the exclusion criteria: patients who had multiple ICU admissions for ischemic stroke, retaining only the initial admission data; aged < 18 years at the first admission; ICU stay of < 24h; or fewer than three glucose measurements during their ICU stay. This study encompassed 1,408 patients, categorized into three groups according to TWAG tertiles (Fig. 1 ). Exclusion criteria were applied to secure adequate data for glycemic trajectory evaluation and reduce potential bias arising from brief or incomplete ICU stays. Data collection Data were collected using Structured Query Language (SQL) in PostgreSQL (version 13.7.2) and Navicat Premium (version 16) environments. Variables potentially associated with outcomes were categorized into the following six groups: ( 1 ) Demographics , including age, sex, and race. ( 2 ) Vital signs , including heart rate, blood pressure (systolic and diastolic), respiratory rate, and body temperature. ( 3 ) Severity of illness scores , this study incorporated the Sequential Organ Failure Assessment, Glasgow Coma Scale, Logistic Organ Dysfunction System (LODS), and Charlson Comorbidity Index (CCI). ( 4 ) Comorbidities , including myocardial infarction, congestive heart failure, liver disease, diabetes, and hypertension. Hypertension was identified using ICD codes, whereas other conditions were determined using the CCI. ( 5 ) On the basis of clinical relevance, the following variables were selected: urine output volume, red and white blood cell counts, platelet count, serum sodium and potassium, blood urea nitrogen (BUN), serum creatinine, prothrombin time, partial thromboplastin time, international normalized ratio (INR), and TWAG( 18 ). ( 6 ) Treatments , including insulin administration, rt-PA, continuous renal replacement therapy, and mechanical ventilation (MV). The dataset’s extensive coverage enabled the adjustment of multiple potential confounding factors in subsequent analyses. Follow-up commenced at hospital admission and was censored at the date of death. Death dates were mainly obtained from hospital records, supplemented by administrative status data when necessary. Deaths occurring following discharge were verified using Social Security death records( 19 ). This approach facilitated precise ascertainment of mortality events, including those occurring following hospital discharge. Laboratory measurements and severity scoring systems were derived from data collected during the initial 24 h of ICU admission. Accordingly, data on baseline physiological and laboratory indices were collected to reflect the initial critical phase during ICU admission. In this study, blood glucose levels were recorded solely throughout the ICU stay to ensure that the TWAG reliably reflected glycemic variations specific to this acute clinical phase. Restriction of TWAG calculation to in-ICU glucose measurements excluded pre- and post-ICU values, thereby focusing analysis on the critical illness period and ensuring relevance to ICU-related stress hyperglycemia. TWAG was computed for each individual in the cohort to reduce potential bias arising from irregular sampling frequencies( 15 ). TWAG was calculated to reflect the dynamic exposure to glucose over the ICU stay. For each participant, i was defined as the order of glucose measurements (i = 1, 2, …, n). The glucose value obtained at the i-th measurement was denoted as G i . The time interval between the i-th and the (i + 1)-th measurements was denoted as ΔT i , while the terminal interval ΔTₙ was defined as the duration from the final glucose measurement to either hospital discharge or death. TWAG was calculated using the following equation: TWAG = Σ (Gi × ΔTi)/Σ (ΔTi) (in mg/dL, with ΔT i in %). Consequently, prolonged hyperglycemia periods exert a greater impact on the TWAG metric than brief glycemic fluctuations. TWAG was stratified into three categories on the basis of thresholds at the 33rd and 66th percentiles (T1, 67.02–113.52; T2, 113.56–142.55; and T3, 142.56–363.97). Variables with > 20% missing data were excluded to prevent bias (Supplementary Table S1 ) . Missing values for all other variables were imputed by means of a random forest algorithm using the “mice” package in R software( 20 , 21 ). This approach maintained the sample size and mitigated potential bias by assuming data were missing at random and utilizing observed patterns in the available information. Clinical outcomes The study’s primary outcome was 30-day all-cause mortality following hospital admission, and the secondary outcome was 90-day all-cause mortality. Thirty- and ninety-day timeframes were selected to denote short-term and intermediate outcomes, consistent with conventions in prognostic research within critical care. Statistical analysis The Kolmogorov–Smirnov test was employed for evaluating the normality of continuous variables( 22 ), which were presented as medians with interquartile ranges (IQRs) owing to their non-normal distributions, and group comparisons utilized the Kruskal–Wallis test. Categorical variables were presented as percentages and compared via chi-square tests. The Kaplan–Meier analysis estimated survival probabilities per TWAG group, with log-rank tests evaluating significance. Cox regression models yielded hazard ratios (HRs) and 95% confidence intervals (CIs) for outcome associations with TWAG. Model 1 received no adjustment; model 2 incorporated adjustments for age, sex, and race, acknowledging their impact on stroke and ICU prognosis. Confounding was controlled by incorporating variables that were either significant at P < 0.05 in univariate analysis or considered clinically relevant( 23 , 24 ). The minimal adjustment model evaluated the independent relationship between TWAG and outcomes, accounting for basic confounding factors. The full adjustment model included adjustments for sex, race, diabetes, hypertension, rt-PA, insulin, MV, age, heart rate, respiratory rate, white blood cell count, potassium, BUN, and INR. To assess the nonlinear impact of TWAG on all-cause mortality, a restricted cubic spline model with three knots was applied. Knot selection prioritized minimal CI width to enhance smoothness and avoid overfitting. The approach robustly captures complex risk patterns. In all analyses, TWAG was treated as continuous and tertiles, with the lowest of which being considered the reference. Subgroup analyses evaluated TWAG’s prognostic robustness across age groups (< 65 vs. ≥65 years), and histories of congestive heart failure, diabetes, hypertension, insulin use, and rt-PA treatment. Likelihood ratio tests for interaction were applied. An E-value analysis was performed to evaluate unmeasured confounding, estimating the minimum association strength required between an unmeasured confounder and both the exposure and outcome to explain the observed association. A two-sided P-value of < 0.05 was considered statistically significant. All statistical analyses were performed using R (version 4.2.2) and Statistical Package for the Social Sciences (version 22.0; IBM Corp, Armonk, NY, USA). Results This study enrolled 1,408 critically ill patients with ischemic stroke. The median age of the participants was 71.94 (IQR, 61.44–81.69) years, with an even sex distribution. The median TWAG level was 126.55 (IQR, 107.74–153.34). Mortality due to any cause was recorded as 23% at 30 days and 30% at 90 days (Table 1 ). All tables are included at the end of the manuscript for reference. Table 1 baseline characteristics. Characteristic Overall N = 1,408 T1 N = 465 T2 N = 478 T3 N = 465 P-value TWAG Tertile 67.02-363.97 67.02-113.52 113.56-142.55 142.56-363.97 Age (year) 71.94 (61.44, 81.69) 73.06 (60.90, 82.04) 72.66 (62.72, 82.79) 69.87 (61.40, 79.85) 0.043 Sex, n (%) 0.040 Female 697 (50%) 251 (54%) 219 (46%) 227 (49%) Male 711 (50%) 214 (46%) 259 (54%) 238 (51%) Race, n (%) 0.011 Other 525 (37%) 149 (32%) 184 (38%) 192 (41%) White 883 (63%) 316 (68%) 294 (62%) 273 (59%) Heart rate (bmp) 82.00 (72.00, 97.00) 78.00 (68.00, 90.00) 81.00 (70.00, 95.00) 88.00 (76.00, 103.00) < 0.001 SBP (mmHg) 133.00 (114.00, 152.00) 134.00 (117.00, 153.00) 132.00 (112.00, 152.00) 133.00 (114.00, 152.00) 0.392 DBP (mmHg) 72.00 (60.00, 86.00) 73.00 (61.00, 85.00) 71.00 (58.00, 87.00) 72.00 (60.00, 86.00) 0.674 Respiratory rate (bmp) 18.00 (15.00, 22.00) 18.00 (15.00, 21.00) 18.00 (15.00, 22.00) 19.00 (16.00, 24.00) < 0.001 Temperature (℃) 36.72 (36.44, 37.06) 36.67 (36.44, 36.94) 36.67 (36.40, 37.00) 36.78 (36.44, 37.11) 0.006 SOFA 1.00 (0.00, 2.00) 1.00 (0.00, 2.00) 1.00 (0.00, 3.00) 1.00 (0.00, 3.00) 0.312 GCS 15.00 (14.00, 15.00) 15.00 (14.00, 15.00) 15.00 (13.00, 15.00) 15.00 (14.00, 15.00) 0.824 LODS 4.00 (2.00, 6.00) 3.00 (2.00, 5.00) 4.00 (2.00, 6.00) 5.00 (3.00, 7.00) < 0.001 CCI 7.00 (5.00, 9.00) 7.00 (4.00, 9.00) 7.00 (5.00, 9.00) 7.00 (5.00, 9.00) 0.002 Myocardial infarct, n (%) 300 (21%) 88 (19%) 104 (22%) 108 (23%) 0.265 Congestive heart failure, n (%) 465 (33%) 127 (27%) 162 (34%) 176 (38%) 0.003 Liver disease, n (%) 93 (7%) 30 (6%) 32 (7%) 31 (7%) 0.987 Diabetes, n (%) 460 (33%) 61 (13%) 94 (20%) 305 (66%) < 0.001 Hypertension, n (%) 1,082 (77%) 330 (71%) 369 (77%) 383 (82%) < 0.001 RBC (10 9 /L) 3.83 (3.27, 4.39) 3.84 (3.29, 4.38) 3.80 (3.26, 4.35) 3.83 (3.25, 4.42) 0.688 WBC (10 9 /L) 10.40 (8.00, 13.65) 9.30 (7.30, 11.90) 10.70 (8.10, 13.80) 11.50 (8.80, 15.10) < 0.001 Platelet (10 9 /L) 207.00 (156.50, 270.50) 208.00 (161.00, 270.00) 203.50 (150.00, 258.00) 209.00 (156.00, 281.00) 0.262 Potassium (mmol/L) 4.10 (3.80, 4.50) 4.10 (3.70, 4.50) 4.10 (3.80, 4.50) 4.20 (3.80, 4.60) 0.018 Sodium (mmol/L) 139.00 (136.00, 141.00) 139.00 (136.00, 142.00) 139.00 (136.00, 141.00) 138.00 (135.00, 141.00) 0.027 BUN (mg/dL) 19.00 (13.00, 28.00) 17.00 (12.00, 25.00) 18.00 (13.00, 27.00) 22.00 (15.00, 32.00) < 0.001 Creatinine (mg/dL) 1.00 (0.70, 1.30) 0.90 (0.70, 1.30) 0.90 (0.70, 1.20) 1.10 (0.80, 1.50) < 0.001 PT (S) 13.20 (12.00, 15.45) 12.80 (11.80, 14.80) 13.60 (12.20, 15.60) 13.40 (12.10, 15.80) < 0.001 PTT (S) 29.70 (26.35, 35.50) 30.00 (26.60, 36.20) 29.90 (26.60, 35.50) 29.30 (26.00, 35.10) 0.306 INR 1.20 (1.10, 1.40) 1.20 (1.10, 1.30) 1.20 (1.10, 1.40) 1.20 (1.10, 1.40) < 0.001 Urine output (mL) 1,500.00 (946.50, 2,250.00) 1,485.00 (950.00, 2,170.00) 1,512.50 (965.00, 2,325.00) 1,470.00 (915.00, 2,240.00) 0.503 rt-PA, n (%) 113 (8%) 26 (6%) 43 (9%) 44 (9%) 0.060 Insulin, n (%) 746 (53%) 204 (44%) 228 (48%) 314 (68%) < 0.001 MV, n (%) 1,074 (76%) 301 (65%) 392 (82%) 381 (82%) < 0.001 CRRT, n (%) 61 (4%) 18 (4%) 16 (3%) 27 (6%) 0.150 TWAG (mg/dL) 126.55 (107.74, 153.34) 101.68 (94.25, 107.42) 126.55 (119.37, 134.19) 171.90 (153.86, 205.38) < 0.001 Los hospital (day) 10.20 (6.12, 17.86) 8.44 (5.22, 14.89) 10.99 (6.77, 19.00) 11.74 (6.43, 21.54) < 0.001 Hospital Mortality, n (%) 262 (19%) 44 (9%) 79 (17%) 139 (30%) < 0.001 Los ICU (day) 4.06 (2.17, 7.89) 2.90 (1.76, 5.50) 4.29 (2.31, 8.09) 5.31 (2.68, 9.95) < 0.001 ICU Mortality, n (%) 173 (12%) 26 (6%) 54 (11%) 93 (20%) < 0.001 30-day hospital Mortality (%) 326 (23%) 65 (14%) 110 (23%) 151 (32%) < 0.001 90-day hospital Mortality (%) 417 (30%) 97 (21%) 139 (29%) 181 (39%) < 0.001 SOFA: Sequential organ failure assessment, LODS: Logistic Organ Dysfunction Score, CCI: Charlson Comorbidity Index, SpO2: Oxygen saturation, SBP: Systolic blood pressure, DBP: Diastolic blood pressure, AKI: Acute kidney injury, WBC: White blood cell count, RBC: Red blood cell count, Platelet: Platelet count, INR: International normalized ratio, MV: Mechanical Ventilation, CRRT: Continuous renal replacement therapy. Baseline characteristics The baseline characteristics of critically ill patients with ischemic stroke are presented in Table 1 . Continuous variables were presented as medians with IQRs (25th–75th percentiles), whereas categorical variables were expressed as numbers (percentages). Participants were divided into three groups according to TWAG tertiles, with the 33rd and 66th percentiles as cutoffs: T1 (67.02–113.52 mg/dL), T2 (113.56–142.55 mg/dL), and T3 (142.56–363.97 mg/dL). Median TWAG values for each tertile were 101.68 (IQR, 94.25–107.42), 126.55 (IQR, 119.37–134.19), and 171.90 (IQR, 153.86–205.38) mg/dL, respectively. Patients in the highest TWAG category demonstrated a higher prevalence of congestive heart failure, diabetes, and hypertension, as well as elevated LODS and CCI scores. Laboratory indices revealed higher BUN and creatinine levels in this group. Lengths of stay were longer with increasing TWAG levels: hospital stay averaged 8.44, 10.99, and 11.74 days across tertiles (P < 0.001), whereas ICU duration was 2.90, 4.29, and 5.31 days, respectively (P < 0.001). Furthermore, mortality differed across tertiles, with 30-day rates of 14%, 23%, and 32%, and 90-day rates of 21%, 29%, and 39% (P < 0.001). Primary outcomes The Kaplan–Meier survival analysis indicated that higher TWAG tertiles were correlated with elevated short-term mortality (log-rank P < 0.001; Fig. 2 ). The univariable Cox analysis results are presented in Supplementary Table S2. Variables with P < 0.05 and those clinically relevant were retained for multivariable adjustment. A 10-unit increase in the TWAG level was consistently associated with higher 30-day mortality across all models: unadjusted (HR, 1.08; 95% CI, 1.06–1.10; P < 0.001), partially adjusted (HR, 1.08; 95% CI, 1.06–1.10; P < 0.001), and fully adjusted (HR, 1.08; 95% CI, 1.05–1.11; P < 0.001). The HRs for TWAG and mortality remained consistent across adjusted models, indicating stability of the association after accounting for demographic characteristics and other measured covariates. When analyzed as a categorical variable, membership in the highest TWAG tertile was independently associated with an increased risk of 30-day mortality across all Cox models: unadjusted (HR, 2.66; 95% CI, 1.99–3.55; P < 0.001), partially adjusted (HR, 2.62; 95% CI, 1.96–3.51; P < .001), and fully adjusted (HR, 2.42; 95% CI, 1.75–3.37; P < .001) compared with the lowest tertile. Following full adjustment, patients in the highest TWAG category had more than twice the hazard of mortality compared with those in the lowest category. Ninety-day mortality exhibited a comparable pattern, with the highest TWAG group demonstrating a higher risk than the lowest group (Table 2 ). Table 2 relationship between TWAG and mortality Variables Model 1 Model 2 Model 3 HR (95%CI) P HR (95%CI) P HR (95%CI) P D30 TWAG 10 1.08 (1.06 ~ 1.10) < .001 1.08 (1.06 ~ 1.10) < .001 1.08 (1.05 ~ 1.11) < .001 TWAG Tertile T1 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) T2 1.75 (1.29 ~ 2.37) < .001 1.64 (1.20 ~ 2.23) 0.002 1.48 (1.08 ~ 2.02) 0.014 T3 2.66 (1.99 ~ 3.55) < .001 2.62 (1.96 ~ 3.51) < .001 2.42 (1.75 ~ 3.37) < .001 D90 TWAG 10 1.07 (1.05 ~ 1.09) < .001 1.07 (1.05 ~ 1.09) < .001 1.06 (1.04 ~ 1.09) < .001 TWAG Tertile T1 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) T2 1.49 (1.15 ~ 1.93) 0.003 1.41 (1.09 ~ 1.83) 0.010 1.26 (0.96 ~ 1.64) 0.091 T3 2.19 (1.71 ~ 2.80) < .001 2.18 (1.71 ~ 2.80) < .001 1.97 (1.48 ~ 2.61) < .001 HR: Hazard Ratio, CI: Confidence Interval D30: 30-day all-cause mortality; D90: 90-day all-cause mortality TWAG 10: TWAG (per 10 mg/dL increase) Model 1: Crude Model 2: Adjust: Sex, Race, Age Model 3: Adjust: Sex, Race, Diabetes, Hypertension, rt-PA, Insulin, MV, Age, Heart rate, RR, WBC, Potassium, BUN, INR rt-PA: recombinant tissue plasminogen activator; MV: Mechanical Ventilation; RR: Respiratory rate; WBC: White blood cell count; BUN: blood urea nitrogen; INR: International normalized ratio In RCS analyses considering potential confounders, elevated TWAG levels were significantly associated with an increased risk of mortality. For 30-day mortality, the association was statistically significant, with glycemic exposure showing a nonlinear association with 30-day mortality (P for overall < 0.001; P for nonlinearity = 0.044), indicating a complex association (Fig. 3 A). The significant association between TWAG and 90-day mortality (P < 0.001) demonstrated a linear trend (P for nonlinearity = 0.219), indicating a consistent increase in risk across the glycemic spectrum over an extended period (Fig. 3 B). Sensitivity analysis involved calculated E-values to evaluate the robustness of the association between TWAG and mortality. In the fully adjusted model, the E-values were 3.07 and 2.57 for 30- (Fig. 4 A) and 90-day mortality (Fig. 4 B), respectively, indicating that a significant unmeasured confounder would be necessary to invalidate the observed associations. Subgroup analysis A prespecified subgroup analysis was performed to determine whether the association between TWAG and the primary outcome differed across the subgroups. Subgroup HRs and 95% CIs were calculated, and interaction P values were employed to evaluate potential effect modification. Subgroup analyses investigated the association between TWAG and 30-day mortality across clinically relevant strata. The positive association remained consistent across the subgroups defined by age, heart failure, hypertension, rt-PA use, and insulin therapy (all interaction P > 0.05), supporting the robustness of the association. A significant interaction was identified regarding diabetes status (interaction P = 0.006), with patients without diabetes demonstrating a stronger association (HR, 1.13; 95% CI, 1.09–1.18; P < 0.001) than those with diabetes (HR, 1.04; 95% CI, 1.00–1.08; P = 0.065), suggesting potential effect modification by baseline glycemic condition (Fig. 5 A). Analyses of secondary outcomes revealed comparable patterns (Fig. 5 B). Other variables showed no significant interactions, and the association between TWAG and mortality was consistent across the patient subgroups. Among critically ill patients with stroke, those without pre-existing diabetes showed a steeper risk gradient, whereas those with diabetes demonstrated a comparatively attenuated pattern. Discussion This retrospective cohort study indicated that critically ill patients with ischemic stroke with elevated TWAG levels during ICU admission exhibited significantly higher risks of 30- and 90-day all-cause mortality, even after comprehensive adjustment for potential confounders. The association between TWAG and short-term mortality was nonlinear. A significant interaction effect was identified with diabetes status, indicating that patients with diabetes experienced a higher mortality risk associated with elevated TWAG levels. These findings suggest that TWAG can serve as a clinically useful marker of glycemic exposure and prognostic indicator in this patient population. These results align with those of previous studies in general critical care populations identifying TWAG as a mortality risk factor( 25 ) and further demonstrate its relevance in patients with ischemic stroke. Several biological mechanisms support the association between elevated TWAG levels and increased mortality in patients with severe ischemic stroke. Acute hyperglycemia is common following stroke events, even in patients without a history of diabetes, and has an established association with poorer neurological prognosis and higher death rates( 14 , 26 , 27 ). Hyperglycemia aggravates ischemic brain injury through several mechanisms, including enhanced oxidative stress, impaired mitochondrial function, and increased excitotoxic neuronal damage( 28 , 29 ). Elevated blood glucose levels accelerate anaerobic metabolic activity, causing lactic acidosis that contributes to further neuronal damage in the ischemic boundary zone( 30 , 31 ). Moreover, hyperglycemia impairs blood–brain barrier function, aggravates brain edema, and increases the likelihood of hemorrhagic transformation in the context of reperfusion strategies( 32 , 33 ). Hyperglycemia has been associated with systemic inflammatory response amplification and immune function impairment, likely contributing to higher rates of stroke-associated infections, including pneumonia, conditions that are associated with worse outcomes( 7 ). Besides acute elevations in glucose levels, variability in glycemic levels and sustained hyperglycemia, as quantified by TWAG, may contribute to poor prognosis. Such fluctuations enhance oxidative stress, intensify inflammatory pathways, disrupt endothelial function, and promote thrombogenic states( 34 – 36 ). These pathophysiological processes may increase the risk of secondary complications, including infections, acute kidney injury, and cardiovascular events, collectively contributing to heightened mortality among ICU patients( 37 , 38 ). Conventional indices, including isolated or average glucose readings, fail to reflect glycemic excursion duration and severity. TWAG, however, incorporates both aspects, facilitating a more comprehensive evaluation of glucose burden over time( 39 , 40 ). Thus, TWAG reflects both sustained and variable glycemic exposures, and may offer greater prognostic value for outcomes in critical illness than conventional glycemic indices. The analysis revealed a nonlinear association between TWAG and short-term mortality in patients with ischemic stroke. This finding aligns with those of earlier studies indicating J- or U-shaped associations between glucose levels and clinical outcomes in critically ill patients, suggesting that hyperglycemia and hypoglycemia can elevate risk( 41 – 43 ). In the present study, elevated TWAG levels demonstrated the strongest effect, underscoring the vulnerability of ischemic cerebral tissues to extended hyperglycemic stress. Although previous studies have suggested a U-shaped association, no excess mortality was observed at lower TWAG values in this cohort, where profound hypoglycemia events were relatively uncommon. Subgroup analyses based on age, comorbidity burden, and treatment type consistently indicated that TWAG independently predicted mortality. In patients with diabetes, the association was less pronounced (HR, 1.04; 95% CI, 1.00–1.08; P = 0.065), suggesting adaptive metabolic responses to chronic glycemic variability( 44 , 45 ). A recent study on critically ill patients aged > 75 years reported that stress-induced hyperglycemia significantly increased short-term morality in individuals without diabetes, whereas those with diabetes seemed partially protected, suggesting an adaptive effect of chronic hyperglycemia on acute glucose spikes( 38 ). Brownlee et al. described a similar phenomenon, suggesting that chronic hyperglycemia can blunt stress responses to acute glucose changes( 44 ). These findings align with those of previous studies and may be attributed to several mechanisms. Therefore, pre-existing diabetes may influence the physiological response to acute glucose elevations, possibly through chronic glycol–metabolic stress-related adaptations. In this context, chronic hyperglycemia in diabetes may confer partial tolerance to acute glucose surges, consistent with the attenuated risk observed in patients with diabetes. First, patients with chronic hyperglycemia due to diabetes may develop adaptive mechanisms that mitigate the deleterious effects of acute glucose elevations( 46 , 47 ). Extended exposure to elevated glucose levels has been demonstrated to improve antioxidant defense mechanisms and alter cerebral glucose transporter expression, potentially providing limited neuroprotection during acute ischemic episodes( 48 , 49 ). Even in the presence of adaptive mechanisms, patients with diabetes who developed stroke and exhibiting marked acute hyperglycemia demonstrate a persistently elevated mortality risk compared with those with well-controlled diabetes, indicating that tolerance to glucose surges is only partial( 11 ). In contrast, individuals without diabetes who develop stress-related hyperglycemia may not possess similar adaptive mechanisms, thereby increasing their susceptibility to the detrimental effects of acute glucose surges( 14 ). Second, in individuals without diabetes, stress-induced hyperglycemia frequently serves as an indicator of significant physiological disturbance, reflecting intensified neuroendocrine activity and systemic inflammatory responses( 50 , 51 ). In this context, elevated TWAG levels may act as a surrogate marker of underlying illness severity rather than exerting a direct pathogenic effect. Third, glycemic management strategies in the ICU frequently vary by diabetes status, with patients with diabetes more frequently receiving insulin therapy and undergoing more intensive glucose monitoring( 52 ). Although intensive insulin therapy has demonstrated improved clinical outcomes in certain ICU groups, the ideal glucose control threshold remains unclear( 52 , 53 ). The clinical impact of strict glucose regulation may depend on pre-existing glycemic status, and individuals without diabetes may derive more marked benefits from limiting hyperglycemia( 54 ). Contemporary critical care guidelines advocate for tailoring glycemic targets to patient-specific factors, including diabetes status, rather than adopting a uniform strategy, reinforcing the rationale for individualized glycemic management( 54 ). From a clinical perspective, TWAG offers a practical and physiologically informative measure incorporating the magnitude and persistence of hyperglycemic exposure. First, unlike conventional glycemic indicators, TWAG more accurately represents cumulative dysglycemia burden. Integrating this metric into standard ICU monitoring may improve early risk stratification and support targeted glucose management approaches. Second, these results highlight the significance of establishing diabetes status-specific glucose targets in critically ill patients with ischemic stroke. Aggressively preventing prolonged hyperglycemia in individuals without pre-existing diabetes is particularly crucial, whereas those with known diabetes may require moderately relaxed thresholds. Prospective research is warranted to identify the optimal TWAG range that balances hypoglycemia and hyperglycemia risks and maximizes survival outcomes( 55 , 56 ). Third, the nonlinear association between TWAG and short-term mortality suggests that even modest elevations in glycemic exposure pose significant prognostic implications. Maintaining early and consistent glycemic control during the acute phase of ischemic stroke could serve as a modifiable therapeutic target to enhance ICU outcomes( 57 ). Integrating TWAG into decision support systems may facilitate real-time mortality risk stratification and proactive clinical management. To strengthen the early identification of high-risk patients and refine resource utilization strategies in intensive care settings, future investigations should explore the utility of dynamic glucose-based metrics in predictive algorithms. Advances in continuous glucose monitoring and integration with electronic health records may enable real-time assessment of TWAG and related glycemic indices( 58 ), supporting timely clinical responses. Furthermore, to enhance outcome prediction and inform intervention strategies, machine learning approaches can incorporate TWAG alongside other clinical variables. This study provides valuable insights into the association between TWAG and clinical outcomes in ischemic stroke; however, several limitations merit consideration. The retrospective nature of the analysis precluded causal inference and may have introduced unmeasured confounding. Several potentially significant variables, including dietary habits, lifestyle factors, socioeconomic status, and detailed stroke severity metrics, were either unavailable or inadequately documented in the MIMIC-IV database, limiting the ability to adjust for these factors. Despite each patient having at least three glucose measurements, differences in testing frequency may have influenced the accuracy of TWAG calculations and excluding those with fewer measurements may have resulted in selection bias. The lack of data on glucose-lowering medications and nutritional support further limits outcome interpretation. Generalizability may be constrained using data from a single U.S. ICU center over an extended period. Moreover, TWAG was exclusively assessed during the ICU stay, with no capture of post-discharge glycemic patterns that may influence long-term survival. To confirm these findings and refine glycemic targets for patients with stroke, further prospective studies are warranted. Conclusion Elevated TWAG level during ICU admission was independently correlated with increased mortality in patients with ischemic stroke, with individuals without diabetes showing a stronger association. These results highlight TWAG’s potential as a prognostic tool and the significance of continuous glycemic monitoring in critically ill patients. To investigate whether tailored interventions targeting TWAG can enhance survival and improve overall clinical outcomes, prospective studies are required. Abbreviations TWAG, time-weighted average glucose; ICU, intensive care unit; SQL, Structured Query Language; LODS, Logistic Organ Dysfunction System; CCI, Charlson Comorbidity Index; BUN, blood urea nitrogen; INR, international normalized ratio; MV, mechanical ventilation; IQR, interquartile range; CI, confidence interval; HR, hazard ratio; rt-PA, recombinant tissue plasminogen activator Declarations Data Availability This study utilizes data from the publicly accessible MIMIC-IV database, available at https://mimic.physionet.org. Ethics approval and consent to participate The study used the publicly available Medical Information Mart for Intensive Care (MIMIC) database, which contains de-identified health-related data. Because all data are anonymized, individual patient consent and additional institutional review board (IRB) approval were not required. Access to the database was granted to the authors after completion of the mandatory Collaborative Institutional Training Initiative (CITI) program. Consent for publication Not applicable. The study used only de-identified data from the publicly available MIMIC database, and therefore individual consent for publication was not required. Funding None Competing Interests The authors declare that they have no competing interests. Acknowledgements The authors would like to thank the Massachusetts Institute of Technology Laboratory for Computational Physiology and collaborating research groups for developing and maintaining the MIMIC database, which provided the data foundation for this study. Author Contributions Jie Peng and Zhanguo Liu designed the study. Huanhuan Wu and Hongzhi Chen collected and analyzed data. Jie Peng and Xingzhan Zhang prepared tables and figures. Jie Peng, Xingzhan Zhang, Huanhuan Wu, Hongzhi Chen, Ling Zhao, Jianbin Guan, Zhanguo Liu reviewed the results, interpreted data, and wrote the manuscript. All authors have made an intellectual contribution to the manuscript and approved the submission. Language editing assistance Portions of this manuscript were refined using a Large Language Model (ChatGPT, OpenAI, San Francisco, CA, USA) to improve clarity and readability. The model was not involved in study design, data analysis, interpretation, or authorship responsibilities. References Li L, Pan Y, Wang M, Jing J, Meng X, Jiang Y, et al. Trends and predictors of myocardial infarction or vascular death after ischaemic stroke or TIA in China, 2007-2018: insights from China National Stroke Registries. Stroke Vasc Neurol. 2021 June;6(2):214–21. Zhao Z, Zhang J, Jiang X, Wang L, Yin Z, Hall M, et al. Is Endovascular Treatment Still Good for Ischemic Stroke in Real World? Stroke. 2020 Nov;51(11):3250–63. Sweid A, Hammoud B, Ramesh S, Wong D, Alexander TD, Weinberg JH, et al. Acute ischaemic stroke interventions: large vessel occlusion and beyond. Stroke Vasc Neurol. 2020 Mar;5(1):80–5. Barthels D, Das H. Current advances in ischemic stroke research and therapies. Biochim Biophys Acta BBA - Mol Basis Dis. 2020 Apr;1866(4):165260. Muhammad IF, Borné Y, Zaigham S, Söderholm M, Johnson L, Persson M, et al. Comparison of risk factors for ischemic stroke and coronary events in a population-based cohort. BMC Cardiovasc Disord. 2021 Dec;21(1):536. Ergul A, Kelly-Cobbs A, Abdalla M, C. Fagan S. Cerebrovascular Complications of Diabetes: Focus on Stroke. Endocr Metab Immune Disord - Drug Targets. 2012 May 1;12(2):148–58. Yuan M, Li Q, Zhang R, Zhang W, Zou N, Qin X, et al. Risk factors for and impact of poststroke pneumonia in patients with acute ischemic stroke. Medicine (Baltimore). 2021 Mar 26;100(12):e25213. Sun CJ, McCudden C, Brisson D, Shaw J, Gaudet D, Ooi TC. Comparison of Three Methods for LDLC Calculation for Cardiovascular Disease Risk Categorisation in Three Distinct Patient Populations. Can J Cardiol. 2023 May;39(5):668–77. Fuentes B, Ntaios G, Putaala J, Thomas B, Turc G, Díez-Tejedor E. European Stroke Organisation (ESO) guidelines on glycaemia management in acute stroke. Eur Stroke J. 2018 Mar;3(1):5–21. Hermanides J, Bosman RJ, Vriesendorp TM, Dotsch R, Rosendaal FR, Zandstra DF, et al. Hypoglycemia is associated with intensive care unit mortality. Crit Care Med. 2010 June;38(6):1430–4. Pan H, Xiong Y, Huang Y, Zhao J, Wan H. Association between stress hyperglycemia ratio with short-term and long-term mortality in critically ill patients with ischemic stroke. Acta Diabetol. 2024 July;61(7):859–68. Danaei G, Finucane MM, Lu Y, Singh GM, Cowan MJ, Paciorek CJ, et al. National, regional, and global trends in fasting plasma glucose and diabetes prevalence since 1980: systematic analysis of health examination surveys and epidemiological studies with 370 country-years and 2·7 million participants. The Lancet. 2011 July;378(9785):31–40. Duning T, van den Heuvel I, Dickmann A, Volkert T, Wempe C, Reinholz J, et al. Hypoglycemia Aggravates Critical Illness–Induced Neurocognitive Dysfunction. Diabetes Care. 2010 Mar 1;33(3):639–44. Capes SE, Hunt D, Malmberg K, Pathak P, Gerstein HC. Stress Hyperglycemia and Prognosis of Stroke in Nondiabetic and Diabetic Patients: A Systematic Overview. Stroke. 2001 Oct;32(10):2426–32. Badawi O, Yeung SY, Rosenfeld BA. Evaluation of Glycemic Control Metrics for Intensive Care Unit Populations. Am J Med Qual. 2009 July;24(4):310–20. Feng M, Zhou J. Relationship between time-weighted average glucose and mortality in critically ill patients: a retrospective analysis of the MIMIC-IV database. Sci Rep. 2024 Feb 27;14(1):4721. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. The Lancet. 2007 Oct;370(9596):1453–7. Schrock JW, Glasenapp M, Drogell K. Elevated blood urea nitrogen/creatinine ratio is associated with poor outcome in patients with ischemic stroke. Clin Neurol Neurosurg. 2012 Sept;114(7):881–4. Johnson AEW, Bulgarelli L, Shen L, Gayles A, Shammout A, Horng S, et al. MIMIC-IV, a freely accessible electronic health record dataset. Sci Data. 2023 Jan 3;10(1):1. Austin PC, White IR, Lee DS, van Buuren S. Missing Data in Clinical Research: A Tutorial on Multiple Imputation. Can J Cardiol. 2021 Sept;37(9):1322–31. Blazek K, van Zwieten A, Saglimbene V, Teixeira-Pinto A. A practical guide to multiple imputation of missing data in nephrology. Kidney Int. 2021 Jan;99(1):68–74. Gupta A, Mishra P, Pandey C, Singh U, Sahu C, Keshri A. Descriptive statistics and normality tests for statistical data. Ann Card Anaesth. 2019;22(1):67. Montellano FA, Ungethüm K, Ramiro L, Nacu A, Hellwig S, Fluri F, et al. Role of Blood-Based Biomarkers in Ischemic Stroke Prognosis. Stroke. 2021 Feb;52(2):543–51. Gibson CL. Cerebral Ischemic Stroke: is Gender Important? J Cereb Blood Flow Metab. 2013 Sept;33(9):1355–61. Wang S, Li R, Zhang L, Xie T, Wang X. Association between triglyceride glucose–body mass index and acute kidney injury and renal replacement therapy in critically ill patients with sepsis: analysis of the MIMIC-IV database. Front Endocrinol [Internet]. 2025 July 21 [cited 2025 July 21];16. Available from: https://www.frontiersin.org/articles/10.3389/fendo.2025.1561228/full Kruyt ND, Biessels GJ, DeVries JH, Roos YB. Hyperglycemia in acute ischemic stroke: pathophysiology and clinical management. Nat Rev Neurol. 2010 Mar;6(3):145–55. Yong M, Kaste M. Dynamic of Hyperglycemia as a Predictor of Stroke Outcome in the ECASS-II Trial. Stroke. 2008 Oct;39(10):2749–55. Jin M, Wang Y, Zhang S, Zhang Y, Liu T, Luo Y, et al. Endothelial cells secrete small extracellular vesicles to promote neuron endoplasmic reticulum stress injury via miR-146a-5p/Eif4g2 axis in ischemic stroke. J Adv Res. 2025 July;S2090-1232(25)00507-7. Suh SW, Shin BS, Ma H, Van Hoecke M, Brennan AM, Yenari MA, et al. Glucose and NADPH oxidase drive neuronal superoxide formation in stroke. Ann Neurol. 2008 Dec;64(6):654–63. Bruno A, Levine SR, Frankel MR, Brott TG, Lin Y, Tilley BC, et al. Admission glucose level and clinical outcomes in the NINDS rt-PA Stroke Trial. Neurology. 2002 Sept 10;59(5):669–74. Nedergaard M, Goldman S, Desai S, Pulsinelli W. Acid-induced death in neurons and glia. J Neurosci. 1991 Aug 1;11(8):2489–97. Irvine HJ, Ostwaldt AC, Bevers MB, Dixon S, Battey TW, Campbell BC, et al. Reperfusion after ischemic stroke is associated with reduced brain edema. J Cereb Blood Flow Metab. 2018 Oct;38(10):1807–17. Scheen AJ. Central nervous system: a conductor orchestrating metabolic regulations harmed by both hyperglycaemia and hypoglycaemia. Diabetes Metab. 2010 Oct;36:S31–8. Egi M, Bellomo R, Stachowski E, French CJ, Hart G. Variability of Blood Glucose Concentration and Short-term Mortality in Critically Ill Patients. Anesthesiology. 2006 Aug 1;105(2):244–52. Krinsley JS. Glycemic variability: A strong independent predictor of mortality in critically ill patients. Crit Care Med. 2008 Nov;36(11):3008–13. Krinsley JS. Glycemic Variability and Mortality in Critically ill Patients: The Impact of Diabetes. J Diabetes Sci Technol. 2009 Nov;3(6):1292–301. Marik PE, Bellomo R. Stress hyperglycemia: an essential survival response! Crit Care. 2013;17(2):305. Tian Y, Li T, Zhang M, Wang R, Ma C, Guo Y, et al. Impact of the Time-Weighted Average Glucose Concentration and Diabetes on In-Hospital Mortality in Critically Ill Patients Older Than 75 Years: A Retrospective Cohort Study. Endocr Pract. 2022 Nov;28(11):1152–8. Hirshberg E, Lacroix J, Sward K, Willson D, Morris AH. Blood Glucose Control in Critically Ill Adults and Children. Chest. 2008 June;133(6):1328–35. Krinsley JS, Preiser JC, Krinsley JS, Preiser JC. Time in blood glucose range 70 to 140 mg/dl > 80% is strongly associated with increased survival in non-diabetic critically ill adults. Crit Care. 2015 Dec;19(1):179. Li Y, Li W, Xu B. Between blood glucose and mortality in critically ill patients: Retrospective analysis of the MIMIC‐IV database. J Diabetes Investig. 2024 July;15(7):931–8. Siegelaar SE, Hermanides J, Oudemans-van Straaten HM, van der Voort PH, Bosman RJ, Zandstra DF, et al. Mean glucose during ICU admission is related to mortality by a U-shaped curve in surgical and medical patients: a retrospective cohort study. Crit Care. 2010;14(6):R224. Preechasuk L, Rilstone S, Tang WX, Man J, Yang M, Zhao E, et al. Glycaemic level and glycaemic variability in acute ischaemic stroke and functional outcome: An observational continuous glucose monitoring study. PLOS One. 2025;20(5):e0318456. Brownlee M, Hirsch IB. Glycemic Variability: A Hemoglobin A1c–Independent Risk Factor for Diabetic Complications. JAMA. 2006 Apr 12;295(14):1707. Monnier L, Mas E, Ginet C, Michel F, Villon L, Cristol JP, et al. Activation of Oxidative Stress by Acute Glucose Fluctuations Compared With Sustained Chronic Hyperglycemia in Patients With Type 2 Diabetes. JAMA. 2006 Apr 12;295(14):1681. Zhang W wen, Zhang L, Hou W kai, Xu Y xin, Xu H, Lou F chen, et al. Dynamic expression of glucose transporters 1 and 3 in the brain of diabetic rats with cerebral ischemia reperfusion. Chin Med J (Engl). 2009 Sept 5;122(17):1996–2001. McCowen KC, Malhotra A, Bistrian BR. Stress-Induced Hyperglycemia. Crit Care Clin. 2001 Jan;17(1):107–24. Harik SI, Gravina SA, Kalaria RN. Glucose Transporter of the Blood‐Brain Barrier and Brain in Chronic Hyperglycemia. J Neurochem. 1988 Dec;51(6):1930–4. Poppe AY, Majumdar SR, Jeerakathil T, Ghali W, Buchan AM, Hill MD. Admission hyperglycemia predicts a worse outcome in stroke patients treated with intravenous thrombolysis. Diabetes Care. 2009 Apr 1;32(4):617–22. Saisho Y. Glycemic Variability and Oxidative Stress: A Link between Diabetes and Cardiovascular Disease? Int J Mol Sci. 2014 Oct 13;15(10):18381–406. Lee DY, Han K, Park S, Yu JH, Seo JA, Kim NH, et al. Glucose variability and the risks of stroke, myocardial infarction, and all-cause mortality in individuals with diabetes: retrospective cohort study. Cardiovasc Diabetol. 2020 Dec;19(1):144. NICE-SUGAR Study Investigators, Finfer S, Chittock DR, Su SYS, Blair D, Foster D, et al. Intensive versus Conventional Glucose Control in Critically Ill Patients. N Engl J Med. 2009 Mar 25;360(13):1283–97. Marik PE, Preiser JC. Toward Understanding Tight Glycemic Control in the ICU. Chest. 2010 Mar;137(3):544–51. Honarmand K, Sirimaturos M, Hirshberg EL, Bircher NG, Agus MSD, Carpenter DL, et al. Society of Critical Care Medicine Guidelines on Glycemic Control for Critically Ill Children and Adults 2024. Crit Care Med. 2024 Apr;52(4):e161–81. Di Muzio F, Presello B, Glassford NJ, Tsuji IY, Eastwood GM, Deane AM, et al. Liberal Versus Conventional Glucose Targets in Critically Ill Diabetic Patients: An Exploratory Safety Cohort Assessment. Crit Care Med. 2016 Sept;44(9):1683–91. Gutiérrez-Zúñiga R, Alonso de Leciñana M, Delgado-Mederos R, Gállego-Cullere J, Rodríguez-Yáñez M, Martínez-Zabaleta M, et al. Beyond hyperglycemia: glycaemic variability as a prognostic factor after acute ischemic stroke. Neurol Engl Ed. 2023 Apr;38(3):150–8. Forti P, Maioli F. The Prognostic Significance of Early Glycemic Profile in Acute Ischemic Stroke Depends on Stroke Subtype. J Clin Med. 2023 Jan;12(5):1794. Marrero DG, Parkin CG, Aleppo G, Hirsch IB, McGill J, Galindo RJ, et al. The Role of Advanced Technologies in Improving Diabetes Outcomes. Am J Manag Care. 2025 Apr 1;31(4):e102–12. Additional Declarations No competing interests reported. Supplementary Files 250831supplementary.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7573088","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":534154783,"identity":"79345b79-137f-4cb7-8abc-ac13ccff42d9","order_by":0,"name":"Jie Peng","email":"","orcid":"","institution":"Zhujiang Hospital, Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Peng","suffix":""},{"id":534154788,"identity":"941b4891-bb00-46eb-8370-5a98b9c12bf6","order_by":1,"name":"Xingzhan Zhang","email":"","orcid":"","institution":"The People's Hospital Medical Group of 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16:16:11","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":393303,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure01flowchart250828.png","url":"https://assets-eu.researchsquare.com/files/rs-7573088/v1/b703984e7ac067bc66f74ee7.png"},{"id":94481976,"identity":"9b5518ff-0ae4-48d7-9461-bff097596ddd","added_by":"auto","created_at":"2025-10-27 16:16:10","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":395277,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure02KMcurve3090250828.png","url":"https://assets-eu.researchsquare.com/files/rs-7573088/v1/7921c4751194bd81271716ce.png"},{"id":94481768,"identity":"a43c5628-4b26-4b3e-bbdc-fde491e2e5ff","added_by":"auto","created_at":"2025-10-27 16:15:17","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":338519,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure03RCScurve3090250828.png","url":"https://assets-eu.researchsquare.com/files/rs-7573088/v1/f9a26c1e0b6b6474556074ee.png"},{"id":94481797,"identity":"9f28d7a3-aacc-4bb4-8140-d6b6bb1d393d","added_by":"auto","created_at":"2025-10-27 16:15:35","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":674898,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure05forestplot3090250828.png","url":"https://assets-eu.researchsquare.com/files/rs-7573088/v1/78be5aded4368ee992bd1a90.png"},{"id":94481972,"identity":"34545c29-c874-40cb-b3e7-7c6af0c5f137","added_by":"auto","created_at":"2025-10-27 16:16:09","extension":"xml","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":161489,"visible":true,"origin":"","legend":"","description":"","filename":"6d303c64c2654a3c9e5c446147f83ad01structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7573088/v1/39ece42ee04575d7058dc10f.xml"},{"id":94481980,"identity":"cd97522c-f09b-47e6-afc8-6b0038946f7d","added_by":"auto","created_at":"2025-10-27 16:16:12","extension":"html","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":171587,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7573088/v1/5c29373197801aa5643bee20.html"},{"id":94481806,"identity":"7a12c970-94f1-4a3c-8b5c-734861977f5f","added_by":"auto","created_at":"2025-10-27 16:15:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":533685,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the study.\u003c/p\u003e","description":"","filename":"Figure01flowchart250828.png","url":"https://assets-eu.researchsquare.com/files/rs-7573088/v1/1ab648cb4005e6cc3a3ea007.png"},{"id":94481796,"identity":"5914b57b-b770-4911-bcfd-16882f9540e0","added_by":"auto","created_at":"2025-10-27 16:15:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":752598,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival curves were used to analyze the mortality rates of critically ill patients with ischemic stroke over two time points: 30 days (A) and 90 days (B).\u003c/p\u003e","description":"","filename":"Figure02KMcurve3090250828.png","url":"https://assets-eu.researchsquare.com/files/rs-7573088/v1/f65edf0e07a3e2b8296eee38.png"},{"id":94481975,"identity":"be621908-d500-4546-a8bb-72f9ada19dab","added_by":"auto","created_at":"2025-10-27 16:16:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":755258,"visible":true,"origin":"","legend":"\u003cp\u003eRestrictive cubic spline (RCS) curves were used to analyze the nonlinear relationship between Time-Weighted Average Glucose (TWAG) and mortality in critically ill patients with ischemic stroke at two time points: 30 days (A) and 90 days (B).\u003c/p\u003e","description":"","filename":"Figure03RCScurve3090250828.png","url":"https://assets-eu.researchsquare.com/files/rs-7573088/v1/cbee2540db4d3dcfe6f7acc6.png"},{"id":94482044,"identity":"937a36ef-9e2f-4917-8335-a549ce820abd","added_by":"auto","created_at":"2025-10-27 16:16:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":638046,"visible":true,"origin":"","legend":"\u003cp\u003eE-values for the association between TWAG and all-cause mortality at 30 (A) and 90 (B) days.\u003c/p\u003e","description":"","filename":"Figure04Evalue3090250828.png","url":"https://assets-eu.researchsquare.com/files/rs-7573088/v1/0bfc066cb9b332f5d62de36f.png"},{"id":94481699,"identity":"7e9061cb-1067-4cac-97bf-30f34e1a2a9c","added_by":"auto","created_at":"2025-10-27 16:14:30","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1649458,"visible":true,"origin":"","legend":"\u003cp\u003eThe subgroup analysis of critically ill patients with ischemic stroke at two time points: 30 days (A) and 90 days (B). HRs are calculated per 10 mg/dL increase in TWAG.\u003c/p\u003e","description":"","filename":"Figure05forestplot3090250828.png","url":"https://assets-eu.researchsquare.com/files/rs-7573088/v1/d4c073352866cb68b19356b8.png"},{"id":105751751,"identity":"1b547aa8-7996-46ae-9f2a-7843aa61f9c7","added_by":"auto","created_at":"2026-03-30 15:40:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5036151,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7573088/v1/5784961d-a62a-471e-8bc6-061132ad0f37.pdf"},{"id":94481767,"identity":"c88619ba-735c-46ea-a52b-8d27c27a7aad","added_by":"auto","created_at":"2025-10-27 16:15:13","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":37251,"visible":true,"origin":"","legend":"","description":"","filename":"250831supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-7573088/v1/1a93b9f0d487e1b2235aafb7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic Value of Time-Weighted Average Glucose on All- Cause Mortality in Critically Ill Patients with Ischemic Stroke: A Retrospective Cohort Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIschemic stroke, which arises from a cerebral artery occlusion, accounts for approximately 70% of all strokes and remains a leading contributor to disability and mortality worldwide(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Despite the use of endovascular therapy and intravenous recombinant tissue plasminogen activator (rt-PA), patients with ischemic stroke, particularly those in the intensive care unit (ICU), continue to experience a high risk of poor clinical outcomes(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIschemic stroke incidence has been closely associated with pathological and behavioral factors, including dietary patterns, metabolic abnormalities, and tobacco use(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Several studies have similarly demonstrated associations between ischemic stroke and metabolic disturbances, including elevated blood glucose and lipid levels(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Published evidence has reported that diabetes represents a major risk factor for cardiovascular and cerebrovascular diseases(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Optimal glycemic management is widely considered a critical factor influencing clinical outcomes in patients following a stroke event(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), particularly among those receiving intensive care(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Emerging evidence highlights the significance of acute stress hyperglycemia as a key prognostic factor in stroke. Research shows that the stress hyperglycemia ratio, which compares admission glucose levels with chronic glycemic markers, is a significant predictor of increased short- and long-term mortality risks in patients with acute ischemic stroke, irrespective of diabetes status(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHyperglycemia and hypoglycemia are common in hospital admissions and are associated with higher risks of adverse outcomes and mortality, irrespective of diabetes status(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Existing evidence links dysglycemia, including hyperglycemia, hypoglycemia and glycemic variability, to adverse outcomes following stroke(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Historically, metabolic status has been evaluated using single-point or average glucose levels; however, these metrics do not account for the dynamic glucose fluctuations in critically ill patients(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Time-weighted average glucose (TWAG) has become a comprehensive measure of glycemic exposure, incorporating both the magnitude and duration of glucose levels. By incorporating time into its calculation, this metric offers a precise evaluation of in-hospital glycemic control(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Prior evidence has demonstrated that TWAG is significantly associated with ICU mortality(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). By integrating glycemic exposure over time, this method reflects prolonged hyperglycemia, which may exert greater harm than short-lived fluctuations, thereby providing a profile of clearer clinical relevance.\u003c/p\u003e\u003cp\u003eEvidence on the association between TWAG and mortality in critically ill patients with ischemic stroke remains scarce. This study aimed to evaluate the association between TWAG and 30- and 90-day all-cause mortality in ICU patients with ischemic stroke.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy population\u003c/h2\u003e\u003cp\u003eThis retrospective cohort study utilized the MIMIC-IV (version 3.1) repository, a significant open-access critical care database developed by MIT and affiliated organizations. This study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology guidelines for documenting observational epidemiological research(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The dataset comprised comprehensive clinical records of emergency department and ICU admissions at Beth Israel Deaconess Medical Center from 2008 to 2022. The extensive database facilitated the evaluation of longitudinal glucose measurements and clinical outcomes spanning\u0026thinsp;\u0026gt;\u0026thinsp;10 years. Author Jie Peng complied with required protocols for database access and performed data extraction. To maintain consistency and accuracy, data extraction adhered to strict quality control protocols. As the MIMIC-IV database contains de-identified data, informed consent was not required. This study adhered to ethical standards and regulatory requirements. The Institutional Review Boards overseeing the MIMIC-IV database approved the study protocol.\u003c/p\u003e\u003cp\u003eThis study comprised critically ill individuals admitted to the ICU with a diagnosis of ischemic stroke based on ICD-9 and ICD-10 criteria.\u003c/p\u003e\u003cp\u003eThe following were the exclusion criteria: patients who had multiple ICU admissions for ischemic stroke, retaining only the initial admission data; aged\u0026thinsp;\u0026lt;\u0026thinsp;18 years at the first admission; ICU stay of \u0026lt;\u0026thinsp;24h; or fewer than three glucose measurements during their ICU stay. This study encompassed 1,408 patients, categorized into three groups according to TWAG tertiles (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Exclusion criteria were applied to secure adequate data for glycemic trajectory evaluation and reduce potential bias arising from brief or incomplete ICU stays.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eData were collected using Structured Query Language (SQL) in PostgreSQL (version 13.7.2) and Navicat Premium (version 16) environments.\u003c/p\u003e\u003cp\u003eVariables potentially associated with outcomes were categorized into the following six groups:\u003c/p\u003e\u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) \u003cb\u003eDemographics\u003c/b\u003e, including age, sex, and race.\u003c/p\u003e\u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) \u003cb\u003eVital signs\u003c/b\u003e, including heart rate, blood pressure (systolic and diastolic), respiratory rate, and body temperature.\u003c/p\u003e\u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) \u003cb\u003eSeverity of illness scores\u003c/b\u003e, this study incorporated the Sequential Organ Failure Assessment, Glasgow Coma Scale, Logistic Organ Dysfunction System (LODS), and Charlson Comorbidity Index (CCI).\u003c/p\u003e\u003cp\u003e(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) \u003cb\u003eComorbidities\u003c/b\u003e, including myocardial infarction, congestive heart failure, liver disease, diabetes, and hypertension. Hypertension was identified using ICD codes, whereas other conditions were determined using the CCI.\u003c/p\u003e\u003cp\u003e(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) On the basis of clinical relevance, the following variables were selected: urine output volume, red and white blood cell counts, platelet count, serum sodium and potassium, blood urea nitrogen (BUN), serum creatinine, prothrombin time, partial thromboplastin time, international normalized ratio (INR), and TWAG(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) \u003cb\u003eTreatments\u003c/b\u003e, including insulin administration, rt-PA, continuous renal replacement therapy, and mechanical ventilation (MV).\u003c/p\u003e\u003cp\u003eThe dataset\u0026rsquo;s extensive coverage enabled the adjustment of multiple potential confounding factors in subsequent analyses.\u003c/p\u003e\u003cp\u003eFollow-up commenced at hospital admission and was censored at the date of death. Death dates were mainly obtained from hospital records, supplemented by administrative status data when necessary. Deaths occurring following discharge were verified using Social Security death records(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). This approach facilitated precise ascertainment of mortality events, including those occurring following hospital discharge. Laboratory measurements and severity scoring systems were derived from data collected during the initial 24 h of ICU admission. Accordingly, data on baseline physiological and laboratory indices were collected to reflect the initial critical phase during ICU admission.\u003c/p\u003e\u003cp\u003eIn this study, blood glucose levels were recorded solely throughout the ICU stay to ensure that the TWAG reliably reflected glycemic variations specific to this acute clinical phase. Restriction of TWAG calculation to in-ICU glucose measurements excluded pre- and post-ICU values, thereby focusing analysis on the critical illness period and ensuring relevance to ICU-related stress hyperglycemia.\u003c/p\u003e\u003cp\u003eTWAG was computed for each individual in the cohort to reduce potential bias arising from irregular sampling frequencies(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). TWAG was calculated to reflect the dynamic exposure to glucose over the ICU stay. For each participant, i was defined as the order of glucose measurements (i\u0026thinsp;=\u0026thinsp;1, 2, \u0026hellip;, n). The glucose value obtained at the i-th measurement was denoted as G\u003csub\u003ei\u003c/sub\u003e. The time interval between the i-th and the (i\u0026thinsp;+\u0026thinsp;1)-th measurements was denoted as ΔT\u003csub\u003ei\u003c/sub\u003e, while the terminal interval ΔTₙ was defined as the duration from the final glucose measurement to either hospital discharge or death. TWAG was calculated using the following equation:\u003c/p\u003e\u003cp\u003eTWAG\u0026thinsp;=\u0026thinsp;Σ (Gi\u0026thinsp;\u0026times;\u0026thinsp;ΔTi)/Σ (ΔTi)\u003c/p\u003e\u003cp\u003e(in mg/dL, with ΔT\u003csub\u003ei\u003c/sub\u003e in %).\u003c/p\u003e\u003cp\u003eConsequently, prolonged hyperglycemia periods exert a greater impact on the TWAG metric than brief glycemic fluctuations.\u003c/p\u003e\u003cp\u003eTWAG was stratified into three categories on the basis of thresholds at the 33rd and 66th percentiles (T1, 67.02\u0026ndash;113.52; T2, 113.56\u0026ndash;142.55; and T3, 142.56\u0026ndash;363.97).\u003c/p\u003e\u003cp\u003eVariables with \u0026gt;\u0026thinsp;20% missing data were excluded to prevent bias \u003cb\u003e(Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e)\u003c/b\u003e. Missing values for all other variables were imputed by means of a random forest algorithm using the \u0026ldquo;mice\u0026rdquo; package in R software(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). This approach maintained the sample size and mitigated potential bias by assuming data were missing at random and utilizing observed patterns in the available information.\u003c/p\u003e\n\u003ch3\u003eClinical outcomes\u003c/h3\u003e\n\u003cp\u003eThe study\u0026rsquo;s primary outcome was 30-day all-cause mortality following hospital admission, and the secondary outcome was 90-day all-cause mortality. Thirty- and ninety-day timeframes were selected to denote short-term and intermediate outcomes, consistent with conventions in prognostic research within critical care.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eThe Kolmogorov\u0026ndash;Smirnov test was employed for evaluating the normality of continuous variables(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), which were presented as medians with interquartile ranges (IQRs) owing to their non-normal distributions, and group comparisons utilized the Kruskal\u0026ndash;Wallis test. Categorical variables were presented as percentages and compared via chi-square tests. The Kaplan\u0026ndash;Meier analysis estimated survival probabilities per TWAG group, with log-rank tests evaluating significance. Cox regression models yielded hazard ratios (HRs) and 95% confidence intervals (CIs) for outcome associations with TWAG. Model 1 received no adjustment; model 2 incorporated adjustments for age, sex, and race, acknowledging their impact on stroke and ICU prognosis. Confounding was controlled by incorporating variables that were either significant at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in univariate analysis or considered clinically relevant(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). The minimal adjustment model evaluated the independent relationship between TWAG and outcomes, accounting for basic confounding factors. The full adjustment model included adjustments for sex, race, diabetes, hypertension, rt-PA, insulin, MV, age, heart rate, respiratory rate, white blood cell count, potassium, BUN, and INR.\u003c/p\u003e\u003cp\u003eTo assess the nonlinear impact of TWAG on all-cause mortality, a restricted cubic spline model with three knots was applied. Knot selection prioritized minimal CI width to enhance smoothness and avoid overfitting. The approach robustly captures complex risk patterns. In all analyses, TWAG was treated as continuous and tertiles, with the lowest of which being considered the reference.\u003c/p\u003e\u003cp\u003eSubgroup analyses evaluated TWAG\u0026rsquo;s prognostic robustness across age groups (\u0026lt;\u0026thinsp;65 vs. \u0026ge;65 years), and histories of congestive heart failure, diabetes, hypertension, insulin use, and rt-PA treatment. Likelihood ratio tests for interaction were applied. An E-value analysis was performed to evaluate unmeasured confounding, estimating the minimum association strength required between an unmeasured confounder and both the exposure and outcome to explain the observed association. A two-sided P-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant. All statistical analyses were performed using R (version 4.2.2) and Statistical Package for the Social Sciences (version 22.0; IBM Corp, Armonk, NY, USA).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThis study enrolled 1,408 critically ill patients with ischemic stroke. The median age of the participants was 71.94 (IQR, 61.44\u0026ndash;81.69) years, with an even sex distribution. The median TWAG level was 126.55 (IQR, 107.74\u0026ndash;153.34). Mortality due to any cause was recorded as 23% at 30 days and 30% at 90 days (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). \u003cem\u003eAll tables are included at the end of the manuscript for reference.\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ebaseline characteristics.\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\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall \u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;1,408\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eT1 \u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;465\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eT2 \u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;478\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eT3 \u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;465\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTWAG Tertile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67.02-363.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e67.02-113.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e113.56-142.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e142.56-363.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (year)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e71.94 (61.44, 81.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73.06 (60.90, 82.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e72.66 (62.72, 82.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e69.87 (61.40, 79.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.043\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.040\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e697 (50%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e251 (54%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e219 (46%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e227 (49%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e711 (50%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e214 (46%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e259 (54%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e238 (51%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRace, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e525 (37%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e149 (32%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e184 (38%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e192 (41%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e883 (63%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e316 (68%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e294 (62%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e273 (59%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart rate (bmp)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e82.00 (72.00, 97.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e78.00 (68.00, 90.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e81.00 (70.00, 95.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e88.00 (76.00, 103.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e133.00 (114.00, 152.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e134.00 (117.00, 153.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e132.00 (112.00, 152.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e133.00 (114.00, 152.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.392\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBP (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e72.00 (60.00, 86.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73.00 (61.00, 85.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e71.00 (58.00, 87.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e72.00 (60.00, 86.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.674\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRespiratory rate (bmp)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18.00 (15.00, 22.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.00 (15.00, 21.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.00 (15.00, 22.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19.00 (16.00, 24.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTemperature (℃)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36.72 (36.44, 37.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36.67 (36.44, 36.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36.67 (36.40, 37.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e36.78 (36.44, 37.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\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\" colname=\"c2\"\u003e\u003cp\u003e1.00 (0.00, 2.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00 (0.00, 2.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00 (0.00, 3.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00 (0.00, 3.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.312\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.00 (14.00, 15.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.00 (14.00, 15.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.00 (13.00, 15.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15.00 (14.00, 15.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.824\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLODS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.00 (2.00, 6.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.00 (2.00, 5.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.00 (2.00, 6.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.00 (3.00, 7.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCCI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.00 (5.00, 9.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.00 (4.00, 9.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.00 (5.00, 9.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.00 (5.00, 9.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMyocardial infarct, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e300 (21%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e88 (19%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e104 (22%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e108 (23%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.265\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCongestive heart failure, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e465 (33%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e127 (27%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e162 (34%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e176 (38%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiver disease, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e93 (7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30 (6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32 (7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31 (7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.987\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e460 (33%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61 (13%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e94 (20%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e305 (66%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,082 (77%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e330 (71%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e369 (77%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e383 (82%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRBC (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.83 (3.27, 4.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.84 (3.29, 4.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.80 (3.26, 4.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.83 (3.25, 4.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.688\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.40 (8.00, 13.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.30 (7.30, 11.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.70 (8.10, 13.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.50 (8.80, 15.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelet (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e207.00 (156.50, 270.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e208.00 (161.00, 270.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e203.50 (150.00, 258.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e209.00 (156.00, 281.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.262\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePotassium (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.10 (3.80, 4.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.10 (3.70, 4.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.10 (3.80, 4.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.20 (3.80, 4.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSodium (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e139.00 (136.00, 141.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e139.00 (136.00, 142.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e139.00 (136.00, 141.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e138.00 (135.00, 141.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.027\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBUN (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19.00 (13.00, 28.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.00 (12.00, 25.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.00 (13.00, 27.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22.00 (15.00, 32.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\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\" colname=\"c2\"\u003e\u003cp\u003e1.00 (0.70, 1.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.90 (0.70, 1.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.90 (0.70, 1.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.10 (0.80, 1.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\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\" colname=\"c2\"\u003e\u003cp\u003e13.20 (12.00, 15.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.80 (11.80, 14.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.60 (12.20, 15.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.40 (12.10, 15.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\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\" colname=\"c2\"\u003e\u003cp\u003e29.70 (26.35, 35.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.00 (26.60, 36.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29.90 (26.60, 35.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e29.30 (26.00, 35.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.306\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.20 (1.10, 1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.20 (1.10, 1.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.20 (1.10, 1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.20 (1.10, 1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrine output (mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,500.00 (946.50, 2,250.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,485.00 (950.00, 2,170.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,512.50 (965.00, 2,325.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1,470.00 (915.00, 2,240.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.503\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ert-PA, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e113 (8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26 (6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43 (9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e44 (9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.060\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInsulin, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e746 (53%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e204 (44%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e228 (48%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e314 (68%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMV, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,074 (76%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e301 (65%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e392 (82%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e381 (82%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCRRT, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61 (4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18 (4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16 (3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27 (6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.150\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTWAG (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e126.55 (107.74, 153.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e101.68 (94.25, 107.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e126.55 (119.37, 134.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e171.90 (153.86, 205.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLos hospital (day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.20 (6.12, 17.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.44 (5.22, 14.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.99 (6.77, 19.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.74 (6.43, 21.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHospital Mortality, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e262 (19%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44 (9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e79 (17%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e139 (30%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLos ICU (day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.06 (2.17, 7.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.90 (1.76, 5.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.29 (2.31, 8.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.31 (2.68, 9.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eICU Mortality, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e173 (12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26 (6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e54 (11%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e93 (20%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30-day hospital Mortality (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e326 (23%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65 (14%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e110 (23%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e151 (32%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e90-day hospital Mortality (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e417 (30%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e97 (21%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e139 (29%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e181 (39%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eSOFA: Sequential organ failure assessment, LODS: Logistic Organ Dysfunction Score, CCI: Charlson Comorbidity Index, SpO2: Oxygen saturation, SBP: Systolic blood pressure, DBP: Diastolic blood pressure, AKI: Acute kidney injury, WBC: White blood cell count, RBC: Red blood cell count, Platelet: Platelet count, INR: International normalized ratio, MV: Mechanical Ventilation, CRRT: Continuous renal replacement therapy.\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=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eBaseline characteristics\u003c/h2\u003e\u003cp\u003eThe baseline characteristics of critically ill patients with ischemic stroke are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Continuous variables were presented as medians with IQRs (25th\u0026ndash;75th percentiles), whereas categorical variables were expressed as numbers (percentages). Participants were divided into three groups according to TWAG tertiles, with the 33rd and 66th percentiles as cutoffs: T1 (67.02\u0026ndash;113.52 mg/dL), T2 (113.56\u0026ndash;142.55 mg/dL), and T3 (142.56\u0026ndash;363.97 mg/dL). Median TWAG values for each tertile were 101.68 (IQR, 94.25\u0026ndash;107.42), 126.55 (IQR, 119.37\u0026ndash;134.19), and 171.90 (IQR, 153.86\u0026ndash;205.38) mg/dL, respectively.\u003c/p\u003e\u003cp\u003ePatients in the highest TWAG category demonstrated a higher prevalence of congestive heart failure, diabetes, and hypertension, as well as elevated LODS and CCI scores. Laboratory indices revealed higher BUN and creatinine levels in this group. Lengths of stay were longer with increasing TWAG levels: hospital stay averaged 8.44, 10.99, and 11.74 days across tertiles (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas ICU duration was 2.90, 4.29, and 5.31 days, respectively (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Furthermore, mortality differed across tertiles, with 30-day rates of 14%, 23%, and 32%, and 90-day rates of 21%, 29%, and 39% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePrimary outcomes\u003c/h3\u003e\n\u003cp\u003eThe Kaplan\u0026ndash;Meier survival analysis indicated that higher TWAG tertiles were correlated with elevated short-term mortality (log-rank P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The univariable Cox analysis results are presented in \u003cb\u003eSupplementary Table S2.\u003c/b\u003e Variables with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and those clinically relevant were retained for multivariable adjustment. A 10-unit increase in the TWAG level was consistently associated with higher 30-day mortality across all models: unadjusted (HR, 1.08; 95% CI, 1.06\u0026ndash;1.10; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), partially adjusted (HR, 1.08; 95% CI, 1.06\u0026ndash;1.10; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and fully adjusted (HR, 1.08; 95% CI, 1.05\u0026ndash;1.11; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The HRs for TWAG and mortality remained consistent across adjusted models, indicating stability of the association after accounting for demographic characteristics and other measured covariates.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWhen analyzed as a categorical variable, membership in the highest TWAG tertile was independently associated with an increased risk of 30-day mortality across all Cox models: unadjusted (HR, 2.66; 95% CI, 1.99\u0026ndash;3.55; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), partially adjusted (HR, 2.62; 95% CI, 1.96\u0026ndash;3.51; P\u0026thinsp;\u0026lt;\u0026thinsp;.001), and fully adjusted (HR, 2.42; 95% CI, 1.75\u0026ndash;3.37; P\u0026thinsp;\u0026lt;\u0026thinsp;.001) compared with the lowest tertile. Following full adjustment, patients in the highest TWAG category had more than twice the hazard of mortality compared with those in the lowest category. Ninety-day mortality exhibited a comparable pattern, with the highest TWAG group demonstrating a higher risk than the lowest group (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003erelationship between TWAG and mortality\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\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\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\" morerows=\"1\" rowspan=\"2\"\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\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\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=\"c5\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTWAG 10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.08 (1.06\u0026thinsp;~\u0026thinsp;1.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.08 (1.06\u0026thinsp;~\u0026thinsp;1.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.08 (1.05\u0026thinsp;~\u0026thinsp;1.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTWAG\u003c/p\u003e\u003cp\u003eTertile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT1\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\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.75 (1.29\u0026thinsp;~\u0026thinsp;2.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.64 (1.20\u0026thinsp;~\u0026thinsp;2.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.48 (1.08\u0026thinsp;~\u0026thinsp;2.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.66 (1.99\u0026thinsp;~\u0026thinsp;3.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.62 (1.96\u0026thinsp;~\u0026thinsp;3.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.42 (1.75\u0026thinsp;~\u0026thinsp;3.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTWAG 10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.07 (1.05\u0026thinsp;~\u0026thinsp;1.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.07 (1.05\u0026thinsp;~\u0026thinsp;1.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.06 (1.04\u0026thinsp;~\u0026thinsp;1.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTWAG\u003c/p\u003e\u003cp\u003eTertile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT1\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\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.49 (1.15\u0026thinsp;~\u0026thinsp;1.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\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 (1.09\u0026thinsp;~\u0026thinsp;1.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.26 (0.96\u0026thinsp;~\u0026thinsp;1.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.091\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.19 (1.71\u0026thinsp;~\u0026thinsp;2.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.18 (1.71\u0026thinsp;~\u0026thinsp;2.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.97 (1.48\u0026thinsp;~\u0026thinsp;2.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e\u003cp\u003eHR: Hazard Ratio, CI: Confidence Interval\u003c/p\u003e\u003cp\u003eD30: 30-day all-cause mortality; D90: 90-day all-cause mortality\u003c/p\u003e\u003cp\u003eTWAG 10: TWAG (per 10 mg/dL increase)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e\u003cp\u003eModel 1: Crude\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e\u003cp\u003eModel 2: Adjust: Sex, Race, Age\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e\u003cp\u003eModel 3: Adjust: Sex, Race, Diabetes, Hypertension, rt-PA, Insulin, MV, Age, Heart rate, RR, WBC, Potassium, BUN, INR\u003c/p\u003e\u003cp\u003ert-PA: recombinant tissue plasminogen activator; MV: Mechanical Ventilation; RR: Respiratory rate; WBC: White blood cell count; BUN: blood urea nitrogen; INR: International normalized ratio\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\u003eIn RCS analyses considering potential confounders, elevated TWAG levels were significantly associated with an increased risk of mortality. For 30-day mortality, the association was statistically significant, with glycemic exposure showing a nonlinear association with 30-day mortality (P for overall\u0026thinsp;\u0026lt;\u0026thinsp;0.001; P for nonlinearity\u0026thinsp;=\u0026thinsp;0.044), indicating a complex association (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The significant association between TWAG and 90-day mortality (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) demonstrated a linear trend (P for nonlinearity\u0026thinsp;=\u0026thinsp;0.219), indicating a consistent increase in risk across the glycemic spectrum over an extended period (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Sensitivity analysis involved calculated E-values to evaluate the robustness of the association between TWAG and mortality. In the fully adjusted model, the E-values were 3.07 and 2.57 for 30- (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA) and 90-day mortality (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), respectively, indicating that a significant unmeasured confounder would be necessary to invalidate the observed associations.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eSubgroup analysis\u003c/h3\u003e\n\u003cp\u003eA prespecified subgroup analysis was performed to determine whether the association between TWAG and the primary outcome differed across the subgroups. Subgroup HRs and 95% CIs were calculated, and interaction P values were employed to evaluate potential effect modification.\u003c/p\u003e\u003cp\u003eSubgroup analyses investigated the association between TWAG and 30-day mortality across clinically relevant strata. The positive association remained consistent across the subgroups defined by age, heart failure, hypertension, rt-PA use, and insulin therapy (all interaction P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), supporting the robustness of the association. A significant interaction was identified regarding diabetes status (interaction P\u0026thinsp;=\u0026thinsp;0.006), with patients without diabetes demonstrating a stronger association (HR, 1.13; 95% CI, 1.09\u0026ndash;1.18; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) than those with diabetes (HR, 1.04; 95% CI, 1.00\u0026ndash;1.08; P\u0026thinsp;=\u0026thinsp;0.065), suggesting potential effect modification by baseline glycemic condition (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Analyses of secondary outcomes revealed comparable patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Other variables showed no significant interactions, and the association between TWAG and mortality was consistent across the patient subgroups. Among critically ill patients with stroke, those without pre-existing diabetes showed a steeper risk gradient, whereas those with diabetes demonstrated a comparatively attenuated pattern.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis retrospective cohort study indicated that critically ill patients with ischemic stroke with elevated TWAG levels during ICU admission exhibited significantly higher risks of 30- and 90-day all-cause mortality, even after comprehensive adjustment for potential confounders. The association between TWAG and short-term mortality was nonlinear. A significant interaction effect was identified with diabetes status, indicating that patients with diabetes experienced a higher mortality risk associated with elevated TWAG levels. These findings suggest that TWAG can serve as a clinically useful marker of glycemic exposure and prognostic indicator in this patient population. These results align with those of previous studies in general critical care populations identifying TWAG as a mortality risk factor(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) and further demonstrate its relevance in patients with ischemic stroke.\u003c/p\u003e\u003cp\u003eSeveral biological mechanisms support the association between elevated TWAG levels and increased mortality in patients with severe ischemic stroke. Acute hyperglycemia is common following stroke events, even in patients without a history of diabetes, and has an established association with poorer neurological prognosis and higher death rates(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Hyperglycemia aggravates ischemic brain injury through several mechanisms, including enhanced oxidative stress, impaired mitochondrial function, and increased excitotoxic neuronal damage(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Elevated blood glucose levels accelerate anaerobic metabolic activity, causing lactic acidosis that contributes to further neuronal damage in the ischemic boundary zone(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Moreover, hyperglycemia impairs blood\u0026ndash;brain barrier function, aggravates brain edema, and increases the likelihood of hemorrhagic transformation in the context of reperfusion strategies(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Hyperglycemia has been associated with systemic inflammatory response amplification and immune function impairment, likely contributing to higher rates of stroke-associated infections, including pneumonia, conditions that are associated with worse outcomes(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBesides acute elevations in glucose levels, variability in glycemic levels and sustained hyperglycemia, as quantified by TWAG, may contribute to poor prognosis. Such fluctuations enhance oxidative stress, intensify inflammatory pathways, disrupt endothelial function, and promote thrombogenic states(\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). These pathophysiological processes may increase the risk of secondary complications, including infections, acute kidney injury, and cardiovascular events, collectively contributing to heightened mortality among ICU patients(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Conventional indices, including isolated or average glucose readings, fail to reflect glycemic excursion duration and severity. TWAG, however, incorporates both aspects, facilitating a more comprehensive evaluation of glucose burden over time(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Thus, TWAG reflects both sustained and variable glycemic exposures, and may offer greater prognostic value for outcomes in critical illness than conventional glycemic indices.\u003c/p\u003e\u003cp\u003eThe analysis revealed a nonlinear association between TWAG and short-term mortality in patients with ischemic stroke. This finding aligns with those of earlier studies indicating J- or U-shaped associations between glucose levels and clinical outcomes in critically ill patients, suggesting that hyperglycemia and hypoglycemia can elevate risk(\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). In the present study, elevated TWAG levels demonstrated the strongest effect, underscoring the vulnerability of ischemic cerebral tissues to extended hyperglycemic stress. Although previous studies have suggested a U-shaped association, no excess mortality was observed at lower TWAG values in this cohort, where profound hypoglycemia events were relatively uncommon.\u003c/p\u003e\u003cp\u003eSubgroup analyses based on age, comorbidity burden, and treatment type consistently indicated that TWAG independently predicted mortality. In patients with diabetes, the association was less pronounced (HR, 1.04; 95% CI, 1.00\u0026ndash;1.08; P\u0026thinsp;=\u0026thinsp;0.065), suggesting adaptive metabolic responses to chronic glycemic variability(\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). A recent study on critically ill patients aged\u0026thinsp;\u0026gt;\u0026thinsp;75 years reported that stress-induced hyperglycemia significantly increased short-term morality in individuals without diabetes, whereas those with diabetes seemed partially protected, suggesting an adaptive effect of chronic hyperglycemia on acute glucose spikes(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Brownlee et al. described a similar phenomenon, suggesting that chronic hyperglycemia can blunt stress responses to acute glucose changes(\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). These findings align with those of previous studies and may be attributed to several mechanisms. Therefore, pre-existing diabetes may influence the physiological response to acute glucose elevations, possibly through chronic glycol\u0026ndash;metabolic stress-related adaptations. In this context, chronic hyperglycemia in diabetes may confer partial tolerance to acute glucose surges, consistent with the attenuated risk observed in patients with diabetes.\u003c/p\u003e\u003cp\u003eFirst, patients with chronic hyperglycemia due to diabetes may develop adaptive mechanisms that mitigate the deleterious effects of acute glucose elevations(\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). Extended exposure to elevated glucose levels has been demonstrated to improve antioxidant defense mechanisms and alter cerebral glucose transporter expression, potentially providing limited neuroprotection during acute ischemic episodes(\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). Even in the presence of adaptive mechanisms, patients with diabetes who developed stroke and exhibiting marked acute hyperglycemia demonstrate a persistently elevated mortality risk compared with those with well-controlled diabetes, indicating that tolerance to glucose surges is only partial(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). In contrast, individuals without diabetes who develop stress-related hyperglycemia may not possess similar adaptive mechanisms, thereby increasing their susceptibility to the detrimental effects of acute glucose surges(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Second, in individuals without diabetes, stress-induced hyperglycemia frequently serves as an indicator of significant physiological disturbance, reflecting intensified neuroendocrine activity and systemic inflammatory responses(\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). In this context, elevated TWAG levels may act as a surrogate marker of underlying illness severity rather than exerting a direct pathogenic effect. Third, glycemic management strategies in the ICU frequently vary by diabetes status, with patients with diabetes more frequently receiving insulin therapy and undergoing more intensive glucose monitoring(\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). Although intensive insulin therapy has demonstrated improved clinical outcomes in certain ICU groups, the ideal glucose control threshold remains unclear(\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). The clinical impact of strict glucose regulation may depend on pre-existing glycemic status, and individuals without diabetes may derive more marked benefits from limiting hyperglycemia(\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). Contemporary critical care guidelines advocate for tailoring glycemic targets to patient-specific factors, including diabetes status, rather than adopting a uniform strategy, reinforcing the rationale for individualized glycemic management(\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFrom a clinical perspective, TWAG offers a practical and physiologically informative measure incorporating the magnitude and persistence of hyperglycemic exposure. First, unlike conventional glycemic indicators, TWAG more accurately represents cumulative dysglycemia burden. Integrating this metric into standard ICU monitoring may improve early risk stratification and support targeted glucose management approaches. Second, these results highlight the significance of establishing diabetes status-specific glucose targets in critically ill patients with ischemic stroke. Aggressively preventing prolonged hyperglycemia in individuals without pre-existing diabetes is particularly crucial, whereas those with known diabetes may require moderately relaxed thresholds. Prospective research is warranted to identify the optimal TWAG range that balances hypoglycemia and hyperglycemia risks and maximizes survival outcomes(\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e). Third, the nonlinear association between TWAG and short-term mortality suggests that even modest elevations in glycemic exposure pose significant prognostic implications. Maintaining early and consistent glycemic control during the acute phase of ischemic stroke could serve as a modifiable therapeutic target to enhance ICU outcomes(\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). Integrating TWAG into decision support systems may facilitate real-time mortality risk stratification and proactive clinical management. To strengthen the early identification of high-risk patients and refine resource utilization strategies in intensive care settings, future investigations should explore the utility of dynamic glucose-based metrics in predictive algorithms. Advances in continuous glucose monitoring and integration with electronic health records may enable real-time assessment of TWAG and related glycemic indices(\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e), supporting timely clinical responses. Furthermore, to enhance outcome prediction and inform intervention strategies, machine learning approaches can incorporate TWAG alongside other clinical variables.\u003c/p\u003e\u003cp\u003eThis study provides valuable insights into the association between TWAG and clinical outcomes in ischemic stroke; however, several limitations merit consideration. The retrospective nature of the analysis precluded causal inference and may have introduced unmeasured confounding. Several potentially significant variables, including dietary habits, lifestyle factors, socioeconomic status, and detailed stroke severity metrics, were either unavailable or inadequately documented in the MIMIC-IV database, limiting the ability to adjust for these factors. Despite each patient having at least three glucose measurements, differences in testing frequency may have influenced the accuracy of TWAG calculations and excluding those with fewer measurements may have resulted in selection bias. The lack of data on glucose-lowering medications and nutritional support further limits outcome interpretation. Generalizability may be constrained using data from a single U.S. ICU center over an extended period. Moreover, TWAG was exclusively assessed during the ICU stay, with no capture of post-discharge glycemic patterns that may influence long-term survival. To confirm these findings and refine glycemic targets for patients with stroke, further prospective studies are warranted.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eElevated TWAG level during ICU admission was independently correlated with increased mortality in patients with ischemic stroke, with individuals without diabetes showing a stronger association. These results highlight TWAG\u0026rsquo;s potential as a prognostic tool and the significance of continuous glycemic monitoring in critically ill patients. To investigate whether tailored interventions targeting TWAG can enhance survival and improve overall clinical outcomes, prospective studies are required.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eTWAG,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003etime-weighted average glucose; ICU, intensive care unit; SQL, Structured Query Language; LODS, Logistic Organ Dysfunction System; CCI, Charlson Comorbidity Index; BUN, blood urea nitrogen; INR, international normalized ratio; MV, mechanical ventilation; IQR, interquartile range; CI, confidence interval; HR, hazard ratio; rt-PA, recombinant tissue plasminogen activator\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilizes data from the publicly accessible MIMIC-IV database, available at https://mimic.physionet.org.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study used the publicly available Medical Information Mart for Intensive Care (MIMIC) database, which contains de-identified health-related data. Because all data are anonymized, individual patient consent and additional institutional review board (IRB) approval were not required. Access to the database was granted to the authors after completion of the mandatory Collaborative Institutional Training Initiative (CITI) program.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent\u003c/strong\u003e \u003cstrong\u003efor publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. The study used only de-identified data from the publicly available MIMIC database, and therefore individual consent for publication was not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the Massachusetts Institute of Technology Laboratory for Computational Physiology and collaborating research groups for developing and maintaining the MIMIC database, which provided the data foundation for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJie Peng and Zhanguo Liu designed the study. Huanhuan Wu and Hongzhi Chen collected and analyzed data. Jie Peng and Xingzhan Zhang prepared tables and figures. Jie Peng, Xingzhan Zhang, Huanhuan Wu, Hongzhi Chen, Ling Zhao, Jianbin Guan, Zhanguo Liu\u003csup\u003e\u0026nbsp;\u003c/sup\u003ereviewed the results, interpreted data, and wrote the manuscript. All authors have made an intellectual contribution to the manuscript and approved the submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLanguage editing assistance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePortions of this manuscript were refined using a Large Language Model (ChatGPT, OpenAI, San Francisco, CA, USA) to improve clarity and readability. The model was not involved in study design, data analysis, interpretation, or authorship responsibilities.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLi L, Pan Y, Wang M, Jing J, Meng X, Jiang Y, et al. Trends and predictors of myocardial infarction or vascular death after ischaemic stroke or TIA in China, 2007-2018: insights from China National Stroke Registries. Stroke Vasc Neurol. 2021 June;6(2):214\u0026ndash;21.\u003c/li\u003e\n\u003cli\u003eZhao Z, Zhang J, Jiang X, Wang L, Yin Z, Hall M, et al. Is Endovascular Treatment Still Good for Ischemic Stroke in Real World? Stroke. 2020 Nov;51(11):3250\u0026ndash;63.\u003c/li\u003e\n\u003cli\u003eSweid A, Hammoud B, Ramesh S, Wong D, Alexander TD, Weinberg JH, et al. Acute ischaemic stroke interventions: large vessel occlusion and beyond. Stroke Vasc Neurol. 2020 Mar;5(1):80\u0026ndash;5.\u003c/li\u003e\n\u003cli\u003eBarthels D, Das H. Current advances in ischemic stroke research and therapies. Biochim Biophys Acta BBA - Mol Basis Dis. 2020 Apr;1866(4):165260.\u003c/li\u003e\n\u003cli\u003eMuhammad IF, Born\u0026eacute; Y, Zaigham S, S\u0026ouml;derholm M, Johnson L, Persson M, et al. Comparison of risk factors for ischemic stroke and coronary events in a population-based cohort. BMC Cardiovasc Disord. 2021 Dec;21(1):536.\u003c/li\u003e\n\u003cli\u003eErgul A, Kelly-Cobbs A, Abdalla M, C. Fagan S. Cerebrovascular Complications of Diabetes: Focus on Stroke. Endocr Metab Immune Disord - Drug Targets. 2012 May 1;12(2):148\u0026ndash;58.\u003c/li\u003e\n\u003cli\u003eYuan M, Li Q, Zhang R, Zhang W, Zou N, Qin X, et al. Risk factors for and impact of poststroke pneumonia in patients with acute ischemic stroke. Medicine (Baltimore). 2021 Mar 26;100(12):e25213.\u003c/li\u003e\n\u003cli\u003eSun CJ, McCudden C, Brisson D, Shaw J, Gaudet D, Ooi TC. Comparison of Three Methods for LDLC Calculation for Cardiovascular Disease Risk Categorisation in Three Distinct Patient Populations. Can J Cardiol. 2023 May;39(5):668\u0026ndash;77.\u003c/li\u003e\n\u003cli\u003eFuentes B, Ntaios G, Putaala J, Thomas B, Turc G, D\u0026iacute;ez-Tejedor E. European Stroke Organisation (ESO) guidelines on glycaemia management in acute stroke. Eur Stroke J. 2018 Mar;3(1):5\u0026ndash;21.\u003c/li\u003e\n\u003cli\u003eHermanides J, Bosman RJ, Vriesendorp TM, Dotsch R, Rosendaal FR, Zandstra DF, et al. Hypoglycemia is associated with intensive care unit mortality. Crit Care Med. 2010 June;38(6):1430\u0026ndash;4.\u003c/li\u003e\n\u003cli\u003ePan H, Xiong Y, Huang Y, Zhao J, Wan H. Association between stress hyperglycemia ratio with short-term and long-term mortality in critically ill patients with ischemic stroke. Acta Diabetol. 2024 July;61(7):859\u0026ndash;68.\u003c/li\u003e\n\u003cli\u003eDanaei G, Finucane MM, Lu Y, Singh GM, Cowan MJ, Paciorek CJ, et al. National, regional, and global trends in fasting plasma glucose and diabetes prevalence since 1980: systematic analysis of health examination surveys and epidemiological studies with 370 country-years and 2\u0026middot;7 million participants. The Lancet. 2011 July;378(9785):31\u0026ndash;40.\u003c/li\u003e\n\u003cli\u003eDuning T, van den Heuvel I, Dickmann A, Volkert T, Wempe C, Reinholz J, et al. Hypoglycemia Aggravates Critical Illness\u0026ndash;Induced Neurocognitive Dysfunction. Diabetes Care. 2010 Mar 1;33(3):639\u0026ndash;44.\u003c/li\u003e\n\u003cli\u003eCapes SE, Hunt D, Malmberg K, Pathak P, Gerstein HC. Stress Hyperglycemia and Prognosis of Stroke in Nondiabetic and Diabetic Patients: A Systematic Overview. Stroke. 2001 Oct;32(10):2426\u0026ndash;32.\u003c/li\u003e\n\u003cli\u003eBadawi O, Yeung SY, Rosenfeld BA. Evaluation of Glycemic Control Metrics for Intensive Care Unit Populations. Am J Med Qual. 2009 July;24(4):310\u0026ndash;20.\u003c/li\u003e\n\u003cli\u003eFeng M, Zhou J. Relationship between time-weighted average glucose and mortality in critically ill patients: a retrospective analysis of the MIMIC-IV database. Sci Rep. 2024 Feb 27;14(1):4721.\u003c/li\u003e\n\u003cli\u003evon Elm E, Altman DG, Egger M, Pocock SJ, G\u0026oslash;tzsche PC, Vandenbroucke JP. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. The Lancet. 2007 Oct;370(9596):1453\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eSchrock JW, Glasenapp M, Drogell K. Elevated blood urea nitrogen/creatinine ratio is associated with poor outcome in patients with ischemic stroke. Clin Neurol Neurosurg. 2012 Sept;114(7):881\u0026ndash;4.\u003c/li\u003e\n\u003cli\u003eJohnson AEW, Bulgarelli L, Shen L, Gayles A, Shammout A, Horng S, et al. MIMIC-IV, a freely accessible electronic health record dataset. Sci Data. 2023 Jan 3;10(1):1.\u003c/li\u003e\n\u003cli\u003eAustin PC, White IR, Lee DS, van Buuren S. Missing Data in Clinical Research: A Tutorial on Multiple Imputation. Can J Cardiol. 2021 Sept;37(9):1322\u0026ndash;31.\u003c/li\u003e\n\u003cli\u003eBlazek K, van Zwieten A, Saglimbene V, Teixeira-Pinto A. A practical guide to multiple imputation of missing data in nephrology. Kidney Int. 2021 Jan;99(1):68\u0026ndash;74.\u003c/li\u003e\n\u003cli\u003eGupta A, Mishra P, Pandey C, Singh U, Sahu C, Keshri A. Descriptive statistics and normality tests for statistical data. Ann Card Anaesth. 2019;22(1):67.\u003c/li\u003e\n\u003cli\u003eMontellano FA, Ungeth\u0026uuml;m K, Ramiro L, Nacu A, Hellwig S, Fluri F, et al. Role of Blood-Based Biomarkers in Ischemic Stroke Prognosis. Stroke. 2021 Feb;52(2):543\u0026ndash;51.\u003c/li\u003e\n\u003cli\u003eGibson CL. Cerebral Ischemic Stroke: is Gender Important? J Cereb Blood Flow Metab. 2013 Sept;33(9):1355\u0026ndash;61.\u003c/li\u003e\n\u003cli\u003eWang S, Li R, Zhang L, Xie T, Wang X. Association between triglyceride glucose\u0026ndash;body mass index and acute kidney injury and renal replacement therapy in critically ill patients with sepsis: analysis of the MIMIC-IV database. Front Endocrinol [Internet]. 2025 July 21 [cited 2025 July 21];16. Available from: https://www.frontiersin.org/articles/10.3389/fendo.2025.1561228/full\u003c/li\u003e\n\u003cli\u003eKruyt ND, Biessels GJ, DeVries JH, Roos YB. Hyperglycemia in acute ischemic stroke: pathophysiology and clinical management. Nat Rev Neurol. 2010 Mar;6(3):145\u0026ndash;55.\u003c/li\u003e\n\u003cli\u003eYong M, Kaste M. Dynamic of Hyperglycemia as a Predictor of Stroke Outcome in the ECASS-II Trial. Stroke. 2008 Oct;39(10):2749\u0026ndash;55.\u003c/li\u003e\n\u003cli\u003eJin M, Wang Y, Zhang S, Zhang Y, Liu T, Luo Y, et al. Endothelial cells secrete small extracellular vesicles to promote neuron endoplasmic reticulum stress injury via miR-146a-5p/Eif4g2 axis in ischemic stroke. J Adv Res. 2025 July;S2090-1232(25)00507-7.\u003c/li\u003e\n\u003cli\u003eSuh SW, Shin BS, Ma H, Van Hoecke M, Brennan AM, Yenari MA, et al. Glucose and NADPH oxidase drive neuronal superoxide formation in stroke. Ann Neurol. 2008 Dec;64(6):654\u0026ndash;63.\u003c/li\u003e\n\u003cli\u003eBruno A, Levine SR, Frankel MR, Brott TG, Lin Y, Tilley BC, et al. Admission glucose level and clinical outcomes in the NINDS rt-PA Stroke Trial. Neurology. 2002 Sept 10;59(5):669\u0026ndash;74.\u003c/li\u003e\n\u003cli\u003eNedergaard M, Goldman S, Desai S, Pulsinelli W. Acid-induced death in neurons and glia. J Neurosci. 1991 Aug 1;11(8):2489\u0026ndash;97.\u003c/li\u003e\n\u003cli\u003eIrvine HJ, Ostwaldt AC, Bevers MB, Dixon S, Battey TW, Campbell BC, et al. Reperfusion after ischemic stroke is associated with reduced brain edema. J Cereb Blood Flow Metab. 2018 Oct;38(10):1807\u0026ndash;17.\u003c/li\u003e\n\u003cli\u003eScheen AJ. Central nervous system: a conductor orchestrating metabolic regulations harmed by both hyperglycaemia and hypoglycaemia. Diabetes Metab. 2010 Oct;36:S31\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eEgi M, Bellomo R, Stachowski E, French CJ, Hart G. Variability of Blood Glucose Concentration and Short-term Mortality in Critically Ill Patients. Anesthesiology. 2006 Aug 1;105(2):244\u0026ndash;52.\u003c/li\u003e\n\u003cli\u003eKrinsley JS. Glycemic variability: A strong independent predictor of mortality in critically ill patients. Crit Care Med. 2008 Nov;36(11):3008\u0026ndash;13.\u003c/li\u003e\n\u003cli\u003eKrinsley JS. Glycemic Variability and Mortality in Critically ill Patients: The Impact of Diabetes. J Diabetes Sci Technol. 2009 Nov;3(6):1292\u0026ndash;301.\u003c/li\u003e\n\u003cli\u003eMarik PE, Bellomo R. Stress hyperglycemia: an essential survival response! Crit Care. 2013;17(2):305.\u003c/li\u003e\n\u003cli\u003eTian Y, Li T, Zhang M, Wang R, Ma C, Guo Y, et al. Impact of the Time-Weighted Average Glucose Concentration and Diabetes on In-Hospital Mortality in Critically Ill Patients Older Than 75 Years: A Retrospective Cohort Study. Endocr Pract. 2022 Nov;28(11):1152\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eHirshberg E, Lacroix J, Sward K, Willson D, Morris AH. Blood Glucose Control in Critically Ill Adults and Children. Chest. 2008 June;133(6):1328\u0026ndash;35.\u003c/li\u003e\n\u003cli\u003eKrinsley JS, Preiser JC, Krinsley JS, Preiser JC. Time in blood glucose range 70 to 140 mg/dl \u0026gt; 80% is strongly associated with increased survival in non-diabetic critically ill adults. Crit Care. 2015 Dec;19(1):179.\u003c/li\u003e\n\u003cli\u003eLi Y, Li W, Xu B. Between blood glucose and mortality in critically ill patients: Retrospective analysis of the MIMIC‐IV database. J Diabetes Investig. 2024 July;15(7):931\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eSiegelaar SE, Hermanides J, Oudemans-van Straaten HM, van der Voort PH, Bosman RJ, Zandstra DF, et al. Mean glucose during ICU admission is related to mortality by a U-shaped curve in surgical and medical patients: a retrospective cohort study. Crit Care. 2010;14(6):R224.\u003c/li\u003e\n\u003cli\u003ePreechasuk L, Rilstone S, Tang WX, Man J, Yang M, Zhao E, et al. Glycaemic level and glycaemic variability in acute ischaemic stroke and functional outcome: An observational continuous glucose monitoring study. PLOS One. 2025;20(5):e0318456.\u003c/li\u003e\n\u003cli\u003eBrownlee M, Hirsch IB. Glycemic Variability: A Hemoglobin A1c\u0026ndash;Independent Risk Factor for Diabetic Complications. JAMA. 2006 Apr 12;295(14):1707.\u003c/li\u003e\n\u003cli\u003eMonnier L, Mas E, Ginet C, Michel F, Villon L, Cristol JP, et al. Activation of Oxidative Stress by Acute Glucose Fluctuations Compared With Sustained Chronic Hyperglycemia in Patients With Type 2 Diabetes. JAMA. 2006 Apr 12;295(14):1681.\u003c/li\u003e\n\u003cli\u003eZhang W wen, Zhang L, Hou W kai, Xu Y xin, Xu H, Lou F chen, et al. Dynamic expression of glucose transporters 1 and 3 in the brain of diabetic rats with cerebral ischemia reperfusion. Chin Med J (Engl). 2009 Sept 5;122(17):1996\u0026ndash;2001.\u003c/li\u003e\n\u003cli\u003eMcCowen KC, Malhotra A, Bistrian BR. Stress-Induced Hyperglycemia. Crit Care Clin. 2001 Jan;17(1):107\u0026ndash;24.\u003c/li\u003e\n\u003cli\u003eHarik SI, Gravina SA, Kalaria RN. Glucose Transporter of the Blood‐Brain Barrier and Brain in Chronic Hyperglycemia. J Neurochem. 1988 Dec;51(6):1930\u0026ndash;4.\u003c/li\u003e\n\u003cli\u003ePoppe AY, Majumdar SR, Jeerakathil T, Ghali W, Buchan AM, Hill MD. Admission hyperglycemia predicts a worse outcome in stroke patients treated with intravenous thrombolysis. Diabetes Care. 2009 Apr 1;32(4):617\u0026ndash;22.\u003c/li\u003e\n\u003cli\u003eSaisho Y. Glycemic Variability and Oxidative Stress: A Link between Diabetes and Cardiovascular Disease? Int J Mol Sci. 2014 Oct 13;15(10):18381\u0026ndash;406.\u003c/li\u003e\n\u003cli\u003eLee DY, Han K, Park S, Yu JH, Seo JA, Kim NH, et al. Glucose variability and the risks of stroke, myocardial infarction, and all-cause mortality in individuals with diabetes: retrospective cohort study. Cardiovasc Diabetol. 2020 Dec;19(1):144.\u003c/li\u003e\n\u003cli\u003eNICE-SUGAR Study Investigators, Finfer S, Chittock DR, Su SYS, Blair D, Foster D, et al. Intensive versus Conventional Glucose Control in Critically Ill Patients. N Engl J Med. 2009 Mar 25;360(13):1283\u0026ndash;97.\u003c/li\u003e\n\u003cli\u003eMarik PE, Preiser JC. Toward Understanding Tight Glycemic Control in the ICU. Chest. 2010 Mar;137(3):544\u0026ndash;51.\u003c/li\u003e\n\u003cli\u003eHonarmand K, Sirimaturos M, Hirshberg EL, Bircher NG, Agus MSD, Carpenter DL, et al. Society of Critical Care Medicine Guidelines on Glycemic Control for Critically Ill Children and Adults 2024. Crit Care Med. 2024 Apr;52(4):e161\u0026ndash;81.\u003c/li\u003e\n\u003cli\u003eDi Muzio F, Presello B, Glassford NJ, Tsuji IY, Eastwood GM, Deane AM, et al. Liberal Versus Conventional Glucose Targets in Critically Ill Diabetic Patients: An Exploratory Safety Cohort Assessment. Crit Care Med. 2016 Sept;44(9):1683\u0026ndash;91.\u003c/li\u003e\n\u003cli\u003eGuti\u0026eacute;rrez-Z\u0026uacute;\u0026ntilde;iga R, Alonso de Leci\u0026ntilde;ana M, Delgado-Mederos R, G\u0026aacute;llego-Cullere J, Rodr\u0026iacute;guez-Y\u0026aacute;\u0026ntilde;ez M, Mart\u0026iacute;nez-Zabaleta M, et al. Beyond hyperglycemia: glycaemic variability as a prognostic factor after acute ischemic stroke. Neurol Engl Ed. 2023 Apr;38(3):150\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eForti P, Maioli F. The Prognostic Significance of Early Glycemic Profile in Acute Ischemic Stroke Depends on Stroke Subtype. J Clin Med. 2023 Jan;12(5):1794.\u003c/li\u003e\n\u003cli\u003eMarrero DG, Parkin CG, Aleppo G, Hirsch IB, McGill J, Galindo RJ, et al. The Role of Advanced Technologies in Improving Diabetes Outcomes. Am J Manag Care. 2025 Apr 1;31(4):e102\u0026ndash;12.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"time-weighted average glucose, ischemic stroke, MIMIC-IV database, critically ill patient, all-cause mortality","lastPublishedDoi":"10.21203/rs.3.rs-7573088/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7573088/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eIschemic stroke poses an increased risk of disability and mortality. Time-weighted average glucose (TWAG) represents an integrated index of glycemic exposure and serves as an independent prognostic marker in populations with critical illness. Nonetheless, its clinical significance for patients with ischemic stroke in intensive care units remains unclear. Investigating TWAG in critically ill patients with stroke may provide a promising approach to improving risk stratification in this vulnerable population.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis retrospective cohort study employed the MIMIC-IV database, encompassing 1,408 critically ill patients with ischemic stroke. Tertiles of TWAG values were defined on the basis of cutoff points at the 33rd and 66th percentiles. The main endpoint was 30-day all-cause mortality, with 90-day mortality assessed as a secondary outcome. Cox proportional hazards models, adjusted for demographic factors, illness severity scores, comorbidities, laboratory results, and treatment variables, were employed for assessing associations. Kaplan\u0026ndash;Meier curves and restricted cubic spline plots were used for visualization, and subgroup analyses evaluated effect modifications related to diabetes status and other clinical characteristics.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThis study, comprising 1,408 patients with ischemic stroke, revealed that increased TWAG was significantly associated with higher short-term all-cause mortality, as estimated by Cox proportional hazards models. Subgroup evaluations further affirmed these associations.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eIn the intensive care setting, TWAG was independently correlated with increased short-term mortality in patients with ischemic stroke, with a stronger impact on individuals without diabetes. These findings suggest that TWAG can serve as a useful marker for early risk stratification and guide more targeted glycemic management protocols to improve clinical outcomes.\u003c/p\u003e","manuscriptTitle":"Prognostic Value of Time-Weighted Average Glucose on All- Cause Mortality in Critically Ill Patients with Ischemic Stroke: A Retrospective Cohort Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-27 15:30:18","doi":"10.21203/rs.3.rs-7573088/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":"a77a3d6b-c78c-4698-ba75-8b0088db568d","owner":[],"postedDate":"October 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-16T13:11:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-27 15:30:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7573088","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7573088","identity":"rs-7573088","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}