Association of glycemic variability with short and long-term mortality among critically ill trauma patients: A retrospective study from the MIMIC-IV database

Association of glycemic variability with short and long-term mortality among critically ill trauma patients: A retrospective study from the MIMIC-IV database | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Association of glycemic variability with short and long-term mortality among critically ill trauma patients: A retrospective study from the MIMIC-IV database Bing Liu, Jianghua Zhang, Chuangye Song, Jianjun Miao, Xiaowu Li, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7246103/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract The relationship between glycemic variability (GV) and mortality in critically ill trauma patients is not well-established. We aimed to evaluate the association between GV, quantified by the coefficient of variation (CV), and both short- and long-term mortality in this population. A cohort of patients was established from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Cox proportional-hazards models, Kaplan-Meier analysis, restricted cubic splines (RCS), and subgroup analyses were used to investigate the association between GV and mortality. A mediation model was constructed to determine the mediating role of sepsis. This study included 4,009 critically ill trauma patients. Higher GV was independently associated with increased 30-day (Adjusted HR 1.49, P < 0.001) and 1-year mortality (Adjusted HR 1.28, P < 0.001). Nonlinear analyses revealed a J-shaped relationship, with mortality risk increasing sharply above a CV of 12.2%. The association was more pronounced in younger patients and those without diabetes. Mediation analysis revealed that sepsis significantly mediated this association, with proportions of 50.7% for 30-day and 70.5% for 1-year mortality. Higher glycemic variability is an independent predictor of both short- and long-term mortality in critically ill trauma patients. The risk appears to have a threshold effect, and sepsis is a major mediating pathway. Health sciences/Biomarkers Health sciences/Diseases Health sciences/Endocrinology Health sciences/Medical research Health sciences/Risk factors trauma glycemic variability critical care mortality MIMIC-IV database Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Trauma remains a dominant contributor to global morbidity and mortality, causing more than five million deaths each year and standing as the leading cause of death among people younger than 45 years 1 . Critically injured patients who require admission to an intensive care unit (ICU) experience particularly high case-fatality rates, owing to the interplay of hemodynamic instability, systemic inflammation and multi-organ dysfunction 2 . Although modern pre-hospital systems, damage-control resuscitation and advanced critical-care techniques have improved short-term survival, overall mortality in severe trauma has plateaued 3 . Furthermore, trauma patients demonstrate persistently elevated long-term mortality rates that exceed those of age-matched populations, indicating that acute care improvements have not addressed the broader impact of traumatic injury on survival 4 . This underscores the need to identify modifiable physiological targets that could further enhance outcomes 5 . Glycemic variability (GV)—the degree of fluctuation in blood-glucose concentrations over time—has emerged as one such target. In the critically ill, sympathetic over-activation, counter-regulatory hormone surges (glucagon, catecholamines, growth hormone, cortisol) and stress-induced insulin resistance combine to destabilize glucose homeostasis 6 , 7 . Among the several indices proposed to quantify GV, the coefficient of variation (CV) is favored for its simplicity, unit-free nature and proven prognostic value 8 , 9 . Accumulating evidence suggests that GV is more deleterious than sustained hyperglycemia alone. Across diverse ICU cohorts—sepsis, heart failure, stroke and acute kidney injury—high GV independently predicts mortality, nosocomial infection and organ failure 10 – 13 . Mechanistic studies indicate that oscillating glucose levels amplify endothelial dysfunction, oxidative stress and inflammatory cytokine release beyond that seen with stable hyperglycemia 14 . Clinically, greater GV has also been linked to malignant arrhythmias, septic complications and progression to multi-organ failure 15 . Whether these observations hold true in trauma, however, remains unclear. The metabolic milieu of trauma is unique, characterized by massive catecholamine surges, acute blood-loss anemia, hyper-coagulability and a pronounced systemic inflammatory response that may alter glucose handling relative to other critical-illness states 16 , 17 . Hyperglycemia in trauma patients appears to be 'an entity of its own' with distinct pathophysiology compared to other ICU populations 18 . Moreover, current glycemic-control recommendations for trauma patients are extrapolated largely from mixed ICU populations, despite wide heterogeneity of injury patterns, resuscitation strategies and operative interventions in trauma care 19 . Against this backdrop we undertook a retrospective cohort study using the Medical Information Mart for Intensive Care IV (MIMIC-IV) database to evaluate the relationship between GV and both short-term (30-day) and long-term (1-year) all-cause mortality in adult ICU trauma patients. We hypothesized that higher CV would be independently associated with increased mortality and that a portion of this risk would be mediated through downstream complications such as sepsis. Clarifying these associations could inform evidence-based glucose-management protocols and help clinicians identify high-risk trauma patients who may benefit from tighter glycemic monitoring and targeted intervention. Results Baseline characteristics The final cohort consisted of 4,009 critically ill trauma patients (mean age ± SD, 61.84 ± 21.75 years), of whom 2,529 (63.08%) were male. Overall, 616 (15.37%) patients died within 30 days and 937 (23.37%) died within 1 year of ICU admission. Patients were stratified into quartiles based on their glucose coefficient of variation (CV) (Q1: n = 1,005; Q2: n = 1,001; Q3: n = 1,001; Q4: n = 1,002). As detailed in Table 1 , baseline characteristics differed markedly across these CV quartiles. Table 1 Baseline characteristics of critically ill trauma patients according to quartile of glycemic variability. Variables Overall (n = 4009) Q1 (n = 1005) Q2 (n = 1001) Q3 (n = 1001) Q4 (n = 1002) P value Demographics Age, years 61.84 ± 21.75 58.16 ± 22.76 60.52 ± 22.24 63.11 ± 21.18 65.59 ± 20.02 < 0.001 Female, n (%) 2529 (63.08%) 661 (65.77%) 671 (67.03%) 611 (61.04%) 586 (58.48%) < 0.001 Race, n (%) 0.759 WHITE, n (%) 2444 (60.96%) 612 (60.90%) 616 (61.54%) 608 (60.74%) 608 (60.68%) BLACK, n (%) 199 (4.96%) 47 (4.68%) 46 (4.60%) 47 (4.70%) 59 (5.89%) HISPANIC/LATINO, n (%) 136 (3.39%) 34 (3.38%) 35 (3.50%) 29 (2.90%) 38 (3.79%) ASIAN, n (%) 78 (1.95%) 19 (1.89%) 16 (1.60%) 17 (1.70%) 26 (2.59%) OTHERS/UNKOWN, n (%) 1152 (28.74%) 293 (29.15%) 288 (28.77%) 300 (29.97%) 271 (27.05%) Vital signs Heart rate, bpm 84.31 ± 15.87 83.31 ± 15.04 85.13 ± 15.58 83.85 ± 16.19 84.95 ± 16.59 0.027 SBP, mmHg 122.94 ± 14.64 124.30 ± 14.80 123.24 ± 14.46 122.95 ± 14.19 121.26 ± 14.97 < 0.001 DBP, mmHg 64.93 ± 10.70 67.01 ± 10.82 64.98 ± 10.35 64.80 ± 10.59 62.91 ± 10.65 < 0.001 MBP, mmHg 81.08 ± 10.53 82.55 ± 10.86 81.32 ± 10.38 81.21 ± 10.32 79.25 ± 10.31 < 0.001 Respiratory rate, /min 18.0 (16.2–20.2) 17.8 (16.0–19.7) 18.0 (16.3–20.0) 18.0 (16.3–20.2) 18.2 (16.2–20.9) 0.002 Temperature, °C 37.0 (36.7–37.3) 37.0 (36.8–37.3) 37.0 (36.8–37.4) 37.0 (36.7–37.3) 36.9 (36.6–37.2) < 0.001 SpO₂, % 97.6 (96.2–98.9) 97.3 (96.0–98.7) 97.8 (96.3–99.1) 97.8 (96.3–99.1) 97.6 (96.3–98.9) < 0.001 Laboratory measurements Hemoglobin, g/dL 10.52 ± 2.22 10.92 ± 2.17 10.58 ± 2.19 10.43 ± 2.18 10.16 ± 2.29 < 0.001 WBC, 10⁹/L 13.2 (9.7–17.4) 12.9 (9.5–17.1) 13.5 (10.2–17.3) 13.3 (9.8–17.6) 13.0 (9.7–17.5) 0.040 Platelets, 10⁹/L 172 (129–220) 178(142–224) 172 (131–217) 170 (124–218) 165 (120–220) < 0.001 PT, sec 13.1 (12.0–14.9) 12.8 (11.8–14.2) 13.1 (12.0–14.7) 13.3 (12.2–15.0) 13.4 (12.1–15.8) < 0.001 PTT, sec 28.6 (26.0–32.5) 28.3 (25.8–31.7) 28.4 (25.8–32.2) 28.8 (26.1–32.4) 29.2 (26.3–34.0) < 0.001 INR 1.20 (1.10–1.40) 1.20 (1.10–1.30) 1.20 (1.10–1.30) 1.20 (1.10–1.40) 1.20 (1.10–1.40) < 0.001 Serum creatinine, mg/dL 1.00 (0.80–1.20) 0.90 (0.80–1.10) 1.00 (0.80–1.20) 0.90 (0.80–1.20) 1.10 (0.80–1.50) < 0.001 BUN, mg/dL 18 (13–25) 16 (12–22) 17 (13–24) 18 (13–24) 21 (15–30) < 0.001 Sodium, mmol/L 138 (135–140) 138 (136–140) 138 (136–140) 138 (135–140) 137 (135–140) 0.011 Potassium, mmol/L 3.90 (3.60–4.20) 3.90 (3.60–4.20) 3.90 (3.60–4.20) 3.80 (3.60–4.20) 3.90 (3.50–4.20) 0.267 Calcium, mg/dL 8.20 (7.70–8.60) 8.30 (7.80–8.70) 8.10 (7.70–8.60) 8.20 (7.70–8.60) 8.10 (7.50–8.60) < 0.001 Admission glucose, mg/dL 132 (110–164) 121 (106–138) 131 (112–156) 137 (111–169) 155 (113–219) < 0.001 Mean glucose, mg/dL 128.1 (113.8–148.1) 118.3 (105.1–131.5) 127.3 (114.8–143.5) 130.8 (116.3–148.1) 143.0 (122.0–174.5) < 0.001 Trauma characteristics ISS 16 (9–25) 16 (9–25) 16 (10–25) 16 (9–25) 16 (9–25) 0.020 TBI, n (%) 2409 (60.09%) 612 (60.90%) 624 (62.34%) 612 (61.14%) 561 (55.99%) 0.020 Chest trauma, n (%) 1613 (40.23%) 393 (39.10%) 432 (43.16%) 384 (38.36%) 404 (40.32%) 0.136 Abdominal trauma, n (%) 844 (21.05%) 232 (23.08%) 208 (20.78%) 190 (18.98%) 214 (21.36%) 0.159 Spinal trauma, n (%) 1304 (32.53%) 322 (32.04%) 315 (31.47%) 330 (32.97%) 337 (33.63%) 0.737 Extremity trauma, n (%) 1423 (35.50%) 347 (34.53%) 383 (38.26%) 330 (32.97%) 363 (36.23%) 0.079 Multiple trauma, n (%) 1388 (34.62%) 355 (35.32%) 370 (36.96%) 324 (32.37%) 339 (33.83%) 0.160 Trauma mechanism, n (%) Blunt, n (%) 1178 (29.38%) 321 (31.94%) 341 (34.07%) 275 (27.47%) 241 (24.05%) < 0.001 Fall, n (%) 1653 (41.23%) 415 (41.29%) 399 (39.86%) 423 (42.26%) 416 (41.52%) Penetrating, n (%) 112 (2.79%) 33 (3.28%) 26 (2.60%) 28 (2.80%) 25 (2.50%) Other, n (%) 1066 (26.59%) 236 (23.48%) 235 (23.48%) 275 (27.47%) 320 (31.94%) Continued Variables Overall (n = 4009) Q1 (n = 1005) Q2 (n = 1001) Q3 (n = 1001) Q4 (n = 1002) P value Comorbidities Charlson Comorbidity Index 3.39 ± 2.82 2.71 ± 2.63 3.16 ± 2.74 3.48 ± 2.70 4.22 ± 3.00 < 0.001 Diabetes, n (%) 786 (19.61%) 81 (8.06%) 145 (14.49%) 183 (18.28%) 377 (37.62%) < 0.001 Myocardial infarction, n (%) 280 (6.98%) 49 (4.88%) 62 (6.19%) 67 (6.69%) 102 (10.18%) < 0.001 Congestive heart failure, n (%) 535 (13.34%) 95 (9.45%) 114 (11.39%) 142 (14.19%) 184 (18.36%) < 0.001 Cerebrovascular disease, n (%) 364 (9.08%) 56 (5.57%) 93 (9.29%) 111 (11.09%) 104 (10.38%) < 0.001 Chronic pulmonary disease, n (%) 611 (15.24%) 136 (13.53%) 161 (16.08%) 136 (13.59%) 178 (17.76%) 0.020 Renal disease, n (%) 408 (10.18%) 55 (5.47%) 81 (8.09%) 94 (9.39%) 178 (17.76%) < 0.001 Malignant cancer, n (%) 142 (3.54%) 31 (3.08%) 39 (3.90%) 32 (3.20%) 40 (3.99%) 0.588 Treatments Mechanical ventilation, n (%) 3365 (83.94%) 783 (77.91%) 874 (87.31%) 862 (86.11%) 846 (84.43%) < 0.001 Vasoactive drugs, n (%) 956 (23.85%) 115 (11.44%) 240 (23.98%) 284 (28.37%) 317 (31.64%) < 0.001 CRRT, n (%) 52 (1.30%) 2 (0.20%) 6 (0.60%) 8 (0.80%) 36 (3.59%) < 0.001 Insulin therapy, n (%) 1226 (30.58%) 197 (19.60%) 306 (30.57%) 349 (34.87%) 374 (37.33%) < 0.001 Emergency surgery, n (%) 2602 (64.90%) 597 (59.40%) 670 (66.93%) 659 (65.83%) 676 (67.47%) < 0.001 Blood transfusion, n (%) 1334 (33.28%) 230 (22.89%) 336 (33.57%) 372 (37.16%) 396 (39.52%) < 0.001 Complications Sepsis, n (%) 1834 (45.75%) 295 (29.35%) 511 (51.05%) 501 (50.05%) 527 (52.59%) < 0.001 AKI, n (%) 2928 (73.04%) 613 (61.00%) 753 (75.22%) 771 (77.02%) 791 (78.94%) < 0.001 Pneumonia, n (%) 377 (9.40%) 57 (5.67%) 111 (11.09%) 113 (11.29%) 96 (9.58%) < 0.001 Severity scores SAPS II 32.58 ± 12.58 28.48 ± 11.55 31.94 ± 11.62 33.89 ± 12.44 36.01 ± 13.39 < 0.001 APS III 36 (28–47) 32 (24–41) 36 (28–46) 38 (29–49) 42 (32–55) < 0.001 OASIS 31.59 ± 7.56 29.43 ± 7.07 31.85 ± 6.98 32.37 ± 7.79 32.73 ± 7.91 < 0.001 GCS 14 (13–15) 14 (13–15) 14 (13–15) 14 (13–15) 15 (13–15) 0.110 SOFA 4 (2–6) 3 (2–5) 4 (3–6) 5 (3–7) 5 (3–7) < 0.001 Outcomes ICU LOS, days 2.93 (1.82–5.75) 2.13 (1.53–3.31) 3.36 (1.91–7.01) 3.77 (2.08–7.29) 3.10 (1.89–6.10) < 0.001 Hospital LOS, days 8.15 (4.89–14.68) 6.60 (4.18–10.80) 9.17 (5.46–16.84) 8.82 (5.45–16.38) 8.29 (4.88–14.74) < 0.001 ICU mortality, n (%) 319 (7.96%) 38 (3.78%) 56 (5.59%) 82 (8.19%) 143 (14.27%) < 0.001 In-hospital mortality, n (%) 420 (10.48%) 58 (5.77%) 80 (7.99%) 110 (10.99%) 172 (17.17%) < 0.001 30-day mortality, n (%) 616 (15.37%) 97 (9.65%) 128 (12.79%) 163 (16.28%) 228 (22.75%) < 0.001 1-year mortality, n (%) 937 (23.37%) 161 (16.02%) 202 (20.18%) 259 (25.87%) 315 (31.44%) < 0.001 Continuous numerical variables are expressed as means ± standard deviations or medians (interquartile range), and categorical variables are presented as numbers (percentages). ICU (intensive care unit), SBP (systolic blood pressure), DBP (diastolic blood pressure), MBP (mean blood pressure), SpO₂ (oxygen saturation), WBC (white blood cell), PT (prothrombin time), PTT (partial thromboplastin time), INR (international normalized ratio), BUN (blood urea nitrogen), ISS (Injury Severity Score), TBI (traumatic brain injury), CRRT (continuous renal replacement therapy), AKI (acute kidney injury), SAPS II (Simplified Acute Physiology Score II), APS III (Acute Physiology Score III), OASIS (Oxford Acute Severity of Illness Score), GCS (Glasgow Coma Scale), SOFA (Sequential Organ Failure Assessment), LOS (length of stay). Compared with Q1, individuals in Q4 were older (65.59 vs 58.16 years) and displayed greater physiological derangement, reflected by higher mean scores for OASIS (32.73 vs. 29.43), median SOFA (5.00 [IQR 3.00–7.00] vs 3.00 [IQR 2.00–5.00]) and Charlson Comorbidity Index (4.22 vs 2.71) (all P < 0.001). The highest-CV quartile also had lower arterial pressures, lower hemoglobin concentrations (10.16 vs 10.92 g/dL), and worse renal biochemistry—creatinine 1.10 mg/dL (IQR 0.80–1.50) versus 0.90 mg/dL (IQR 0.80–1.10) and BUN 21.00 mg/dL (IQR 15.00–30.00) versus 16.00 mg/dL (IQR 12.00–22.00) (all P < 0.001). Comorbid illness was more prevalent in Q4, particularly diabetes mellitus (37.62% vs 8.06%), myocardial infarction (10.18% vs 4.88%) and congestive heart failure (18.36% vs 9.45%). Correspondingly, high-CV patients required more intensive therapeutic support: vasoactive drugs (31.64% vs 11.44%), insulin therapy (37.33% vs 19.60%) and blood transfusion (39.52% vs 22.89%) (all P < 0.001). Complication rates followed a similar pattern, with sepsis present in 52.59% of Q4 versus 29.35% of Q1 and acute kidney injury in 78.94% versus 61.00% (both P < 0.001). Mortality increased stepwise across quartiles: 30-day death rose from 9.65% in Q1 to 22.75% in Q4, and 1-year death from 16.02–31.44% ( P < 0.001 for trend), underscoring a strong unadjusted association between higher glycemic variability and adverse outcomes. The relationship between glycemic variability and mortality Cox proportional-hazards models confirmed that higher glycemic variability was independently associated with both early and late death (Table 2 ). In the crude model, each one-unit increase in log-transformed CV doubled the hazard of 30-day mortality (HR 2.07, 95% CI 1.77–2.43; P < 0.001) and raised the hazard of 1-year mortality by 79% (HR 1.79, 95% CI 1.57–2.03; P < 0.001). After comprehensive adjustment for demographic factors, physiology, comorbidities, laboratory indices, severity scores, trauma burden, and therapeutic interventions, the associations remained robust: HR 1.49 (95% CI 1.23–1.81; P < 0.001) for 30-day death and HR 1.28 (95% CI 1.11–1.49; P < 0.001) for 1-year death. Table 2 The associations of glycemic variability with all-cause mortality in critically ill trauma patients Outcome Variable Model 1 Model 2 Model 3 Model 4 HR (95% CI) P value HR (95% CI) P value HR (95% CI) P value HR (95% CI) P value 30-day mortality Per unit increase in log(CV) 2.07 (1.77–2.43) < 0.001 1.93 (1.64–2.27) < 0.001 1.67 (1.39–1.99) < 0.001 1.49 (1.23–1.81) < 0.001 Q1 (Reference) 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) Q2 1.34 (1.03–1.74) 0.032 1.27 (0.97–1.65) 0.077 1.06 (0.82–1.39) 0.646 0.86 (0.66–1.13) 0.286 Q3 1.74 (1.35–2.24) < 0.001 1.50 (1.17–1.93) 0.002 1.19 (0.92–1.54) 0.182 0.92 (0.70–1.20) 0.522 Q4 2.55 (2.01–3.23) < 0.001 2.17 (1.71–2.75) < 0.001 1.67 (1.29–2.15) < 0.001 1.35 (1.04–1.75) 0.024 P for trend < 0.001 < 0.001 < 0.001 0.003 1-year mortality Per unit increase in log(CV) 1.79 (1.57–2.03) < 0.001 1.59 (1.40–1.81) < 0.001 1.41 (1.22–1.62) < 0.001 1.28 (1.11–1.49) < 0.001 Q1 (Reference) 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) Q2 1.28 (1.04–1.58) 0.018 1.21 (0.99–1.49) 0.067 1.07 (0.86–1.31) 0.553 0.92 (0.74–1.14) 0.445 Q3 1.71 (1.40–2.08) < 0.001 1.47 (1.20–1.79) < 0.001 1.26 (1.03–1.54) 0.024 1.03 (0.84–1.27) 0.764 Q4 2.19 (1.81–2.65) < 0.001 1.79 (1.48–2.17) < 0.001 1.47 (1.20–1.80) < 0.001 1.26 (1.02–1.55) 0.030 P for trend < 0.001 < 0.001 < 0.001 0.005 CV: coefficient of variation (glycemic variability). HR: hazard ratio. CI: confidence interval. Model1: Crude. Model2: Adjusted for gender, age, race. Model 3: Model 2 + vital signs (heart rate, SBP, respiratory rate, body temperature, SpO 2 ), laboratory tests (hemoglobin, WBC, platelets, creatinine, BUN, sodium, potassium, calcium), comorbidities (diabetes, myocardial infarction, congestive heart failure, cerebrovascular disease, chronic pulmonary disease, renal disease, malignant cancer). Model 4: Model 3 + GCS score, trauma variables (ISS score, TBI, chest/abdominal/spinal/extremity/multiple trauma, trauma mechanism), treatments (mechanical ventilation, vasoactive drugs, insulin, emergency surgery, blood transfusion), complications (sepsis, AKI, pneumonia). Treating CV as a categorical variable revealed a clear dose–response pattern. Relative to the lowest quartile (Q1), fully adjusted hazards for 30-day death were 0.80 (95% CI 0.61–1.05) in Q2, 0.90 (95% CI 0.69–1.17) in Q3 and 1.33 (95% CI 1.02–1.73) in Q4 ( P for trend = 0.002). For 1-year death, the corresponding HRs were 0.88 (95% CI 0.71–1.09) in Q2, 1.01 (95% CI 0.82–1.24) in Q3 and 1.25 (95% CI 1.02–1.54) in Q4, respectively ( P for trend = 0.004). Kaplan–Meier curves stratified by quartile corroborated these findings (log-rank P < 0.001 for both time horizons). Survival probabilities diverged early and remained separated, with the greatest gap between Q1 and Q4 (Fig. 2 ). Model discrimination improved markedly with covariate adjustment. For 30-day death, the area under the ROC curve (AUC) increased from 0.614 (95% CI 0.516–0.712) in the unadjusted model to 0.845 (95% CI 0.747–0.943) in the fully adjusted model - a 37.6% relative gain ( Supplementary Fig. S2A ). For 1-year death, the AUC rose from 0.597 (95% CI 0.499–0.695) to 0.831 (95% CI 0.733–0.929), representing a 39.4% improvement ( Supplementary Fig. S2B ). Restricted cubic-spline and threshold analyses To investigate the shape of the association between glycemic variability and mortality, we fitted a series of multivariable Restricted Cubic Splines (RCS) with progressively greater adjustment ( Supplementary Fig. S3 ). Interestingly, the J-shaped relationship only became evident after comprehensive multivariable adjustment. In the crude and partially adjusted models (Models 1–3), the association appeared consistent with linearity (all P for non-linearity > 0.05). However, in the fully adjusted model (Model 4), a significant non-linear, J-shaped relationship emerged for both 30-day ( P for non-linearity = 0.004) and 1-year mortality ( P for non-linearity = 0.010) (Fig. 3 ). The spline curves revealed that mortality risk was lowest at a log(CV) of 2.498, corresponding to a CV of approximately 12.2%, and increased sharply at values above this nadir. Moreover, based on the nadir identified by the RCS analysis, we formally tested this threshold effect using a pre-specified two-piecewise Cox regression model with a knot at a CV of 12.2% (Table 3 ). For 30-day mortality, no significant association was found below this threshold (HR 0.82, 95% CI 0.50–1.33; P = 0.420). However, above a CV of 12.2%, higher glycemic variability was strongly associated with increased mortality (HR 1.75, 95% CI 1.39–2.21; P < 0.001). A nearly identical pattern was observed for 1-year mortality, with a significant increase in risk only above the 12.2% threshold (HR 1.49, 95% CI 1.24–1.80; P < 0.001), while the association below it was non-significant (HR 0.83, 95% CI 0.59–1.17; P = 0.294). The likelihood ratio test confirmed that this two-piecewise model provided a significantly better fit than a standard linear model for both 30-day ( P = 0.019) and 1-year mortality ( P = 0.013), supporting the presence of a distinct threshold effect. Table 3 Threshold effect analysis of CV on mortality in critically ill trauma patients using the two-piecewise linear regression model Outcome HR (95%CI) P value 30-day mortality Fitting model by standard linear regression 1.49 (1.23–1.81) < 0.001 Fitting model by two-piecewise linear regression Inflection point 12.2% CV 12.2% 1.75 (1.39–2.21) < 0.001 P for likelihood ratio test 0.019 1-year mortality Fitting model by standard linear regression 1.28 (1.10–1.49) 0.001 Fitting model by two-piecewise linear regression Inflection point 12.2% CV 12.2% 1.49 (1.24–1.80) < 0.001 P for likelihood ratio test 0.013 CV: coefficient of variation (glycemic variability). HR: Hazard Ratio, CI: Confidence Interval. Adjusted for gender, age, race, vital signs (heart rate, SBP, DBP, MBP, respiratory rate, body temperature, SpO 2 ), laboratory tests (hemoglobin, WBC, platelets, creatinine, BUN, PT, APTT, INR, sodium, potassium, calcium), comorbidities (diabetes, myocardial infarction, congestive heart failure, cerebrovascular disease, chronic pulmonary disease, renal disease, malignant cancer), severity scores (Charlson Comorbidity Index, GCS, ISS, OASIS, SOFA), trauma variables (TBI, chest/abdominal/spinal/extremity/multiple trauma, trauma mechanism), and treatments (mechanical ventilation, vasoactive drugs, insulin, emergency surgery, blood transfusion). Subgroup analyses Prespecified subgroup analyses assessed whether the GV–mortality association differed across twelve clinically relevant strata (Fig. 4 ). Subgroups were defined by demographics (age < 60 vs ≥ 60 years; sex), injury profile (traumatic brain injury, multiple trauma, ISS ≥ 16), baseline health status (diabetes mellitus, hypoglycemia, Charlson Comorbidity Index ≥ 3), critical-care interventions (mechanical ventilation, vasoactive-drug infusion, blood transfusion) and complications (sepsis). For 30-day mortality, significant effect modification was detected for age, diabetes, hypoglycemia, Charlson score and vasoactive-drug use. The impact of glycemic variability was more than three-fold stronger in younger patients (HR 4.18, 95% CI 2.98–5.87) than in those ≥ 60 years (HR 1.53, 95% CI 1.28–1.83; P < 0.001 for interaction). A larger effect was also observed in participants without diabetes (HR 2.31, 95% CI 1.92–2.77) versus those with diabetes (HR 1.35, 95% CI 0.95–1.90; P = 0.007) and in patients who experienced hypoglycemia (HR 3.23, 95% CI 1.91–5.45 vs HR 1.86, 95% CI 1.57–2.21; P = 0.047). Lower Charlson scores and vasoactive-drug therapy likewise intensified risk (both P < 0.001). For 1-year mortality, the interaction pattern persisted. Log(CV) conferred a greater hazard in patients < 60 years (HR 3.19, 95% CI 2.35–4.32) than in older individuals (HR 1.37, 95% CI 1.19–1.58; P < 0.001) and in those receiving vasoactive drugs (HR 2.52, 95% CI 1.96–3.25 vs HR 1.41, 95% CI 1.22–1.64; P < 0.001). A lower comorbidity burden (Charlson < 3) was paradoxically associated with a steeper gradient ( P < 0.001). No significant interactions were noted for sex, mechanical ventilation, transfusion, sepsis, TBI, multiple trauma or ISS ≥ 16. Mediation analyses To probe pathways linking glycemic variability to death, we performed a mediation analysis to formally test the role of sepsis as an intermediate pathway. After adjusting for a comprehensive set of pre-specified covariates, our analysis revealed that sepsis was a critical mediator of the effect of GV on mortality at both time points. For the short-term outcome, sepsis significantly mediated the association with 30-day mortality, explaining 50.7% of the total effect ( P < 0.001). Notably, the mediating role of sepsis became substantially more pronounced for the long-term outcome. It accounted for 70.5% of the association between GV and 1-year mortality ( P < 0.001). The detailed results are summarized in Supplementary Table S1 and Fig. 5 . Sensitivity analyses Multiple sensitivity checks corroborated the primary findings. First, we recalculated the coefficient of variation over five alternative windows—3, 7, 14, and 21 days, as well as the entire hospital stay—and re-ran the fully adjusted Cox model. As shown in Fig. 6 , the association between log-CV and mortality was consistent across all intervals. For 30-day death, adjusted hazard ratios (HRs) ranged from 1.40 (95% CI 1.18–1.67) to 1.49 (1.23–1.81); for 1-year death, HRs ranged from 1.22 (1.07–1.40) to 1.29 (1.11–1.49). Every estimate remained statistically significant ( P ≤ 0.003), indicating that the prognostic value of glycemic variability is not driven by a particular sampling frame. Second, we evaluated three data-handling strategies—complete-case analysis, multiple imputation, and exclusion of extreme CV values (n = 36, 0.9%)—using the same covariate set. Effect estimates changed little: for 30-day death, HRs varied between 1.44 and 1.49; for 1-year death, between 1.22 and 1.28. Quartile-based trend tests remained significant throughout. Together, these analyses show that the observed CV–mortality relationship is robust to alternative exposure definitions, missing-data procedures, and outlier treatment. Discussion In this retrospective study utilizing the MIMIC-IV database, we demonstrated that glycemic variability during ICU stay was significantly associated with both short-term and long-term mortality in critically ill trauma patients. Our analysis of 4,009 patients revealed a robust, nonlinear J-shaped relationship between glycemic variability and mortality outcomes, with relatively stable risk at lower CV values followed by sharp increases beyond specific thresholds. Through restricted cubic spline analysis, we identified a clinically relevant cutoff point at a CV of 12.2% for both 30-day and 1-year mortality, beyond which patients faced substantially elevated risks. Moreover, subgroup analysis revealed that younger patients (< 60 years), those without diabetes, and individuals receiving vasoactive support were particularly vulnerable to the adverse effects of GV. In addition, mediation analysis identified sepsis as primary pathway linking GV to mortality. To our knowledge, this represents the largest dedicated analysis of glycemic variability across the general trauma population, which addresses an important gap in the literature that has predominantly focused on mixed ICU populations. Trauma patients exhibit distinct metabolic characteristics compared to general critically ill patients. The acute stress response following trauma is substantial, involving massive catecholamine release, cortisol elevation, and cytokine activation that creates a unique hypermetabolic state 6 , 20 . In patients with shock, plasma concentrations of epinephrine can increase 50-fold and norepinephrine levels increase 10-fold, with cortisol output increasing up to ten-fold with severe stress 21 , 22 . This differs considerably from medical ICU patients who may have a more gradual onset of critical illness. Trauma patients experience sudden, severe physiological disruption that we hypothesize leads to more dramatic glucose fluctuations 23 , 24 . It is also worth noting that trauma-specific interventions—major surgery, the inflammatory cascade, and massive stress hormone secretion—likely contribute to greater glycemic instability compared to other ICU populations 25 , 26 . When we compare our findings to previous research in mixed ICU populations, we observe both similarities and important differences. Earlier studies have consistently demonstrated associations between glycemic variability and mortality, though the magnitude of risk and optimal thresholds have varied considerably. Krinsley, for instance, reported that patients with CV > 20% had significantly higher mortality rates in a mixed ICU population 27 . Similarly, studies in septic patients identified CV > 31.5% as cutoff points for increased mortality risk 28 . What is noteworthy about our results is that the CV threshold we identified (12.2%) is considerably lower than what has been reported in mixed or septic populations. This suggests that trauma patients may be more sensitive to glycemic fluctuations than previously recognized. The J-shaped relationship we observed is consistent with previous findings in various ICU populations 29 , indicating that mortality risk remains relatively stable at lower CV values but increases sharply beyond specific thresholds. Our mediation analysis provides insights into the pathophysiological pathways, though we acknowledge some uncertainty remains regarding the underlying mechanisms. The literature indicates that glucose oscillations generate reactive oxygen species more potently than sustained hyperglycemia 30 . We suspect this oxidative stress burden may be particularly detrimental in trauma patients who are already managing significant inflammatory challenges. The finding that sepsis mediated 50.7% of the 30-day mortality association and an even more substantial 70.5% of the 1-year mortality association suggests that glycemic variability may predispose patients to infectious complications. Higher GV is independently associated with increased mortality in sepsis patients, even after adjusting for illness severity and hypoglycemia 31 . A meta-analysis of 10 studies further confirms that septic patients with higher GV have significantly higher mortality, with immune dysfunction, such as impaired phagocytosis, as a proposed mechanism 32 . Glucose fluctuations impair neutrophil and macrophage function, increasing susceptibility to bacterial infections, including nosocomial infections like ventilator-associated pneumonia 33 , 34 . Does glycemic variability precipitate sepsis through immune dysfunction, or does incipient sepsis drive glucose instability? Our retrospective design cannot definitively answer this question, though the temporal sequence—with elevated CV typically preceding sepsis diagnosis—suggests variability may be more than simply a marker of illness severity 13 . The differential impact of glycemic variability based on diabetes status represents a noteworthy finding. Non-diabetic patients demonstrated substantially higher mortality risk (HR 2.31, 95% CI 1.92–2.77) compared to diabetic patients (HR 1.35, 95% CI 0.95–1.90) when experiencing elevated CV. This paradoxical tolerance in diabetic patients, consistently reported in critical care literature 27 , 35 , 36 , may stem from chronic hyperglycemia conferring adaptive metabolic responses, such as downregulation of glucose transporters 37 . This has important implications for clinical practice, as non-diabetic patients—who, based on our data, represent approximately 80% of trauma admissions—may require more intensive glycemic monitoring, challenging a “one-size-fits-all” approach to glycemic control 27 . In addition, the age-related differences in vulnerability to glycemic variability warrant further discussion. Younger patients (age < 60) demonstrated markedly greater susceptibility to the harmful effects of GV (HR 4.18, 95% CI 2.98–5.87) compared to older patients (HR 1.53, 95% CI 1.28–1.83). This age-related pattern is consistent with findings from previous studies 38 , 39 . The proposed mechanism is that elderly patients have increased adaptability and tolerance to oxidative stress damage, because oxidative stress levels naturally increase with age 39 , 40 . The clinical application of these findings faces several challenges. While we identified specific CV thresholds associated with increased mortality, the optimal intervention strategy remains uncertain. Current glucose management protocols focus primarily on absolute glucose targets without considering variability 19 . Attempting to reduce CV through intensive insulin therapy carries significant risks, particularly hypoglycemia, which independently increases mortality in critically ill patients 41 . Furthermore, no prospective studies have demonstrated that specifically targeting glycemic variability improves patient outcomes. Most interventions that reduce variability also lower mean glucose levels, making it difficult to isolate the beneficial effects 8 . This raises fundamental questions about the nature of glycemic variability in trauma. Is elevated CV a modifiable risk factor that directly contributes to poor outcomes, or merely an epiphenomenon reflecting illness severity? The strong mediation through sepsis and organ dysfunction pathways suggests potential causality, but definitive evidence requires prospective intervention studies. Until such evidence emerges, CV should be considered a prognostic marker rather than a therapeutic target 42 . Strengths and Limitations This study has several notable strengths. First, we utilized a large, well-characterized trauma cohort from the validated MIMIC-IV database, which provided comprehensive clinical data and robust long-term follow-up. Second, our approach employed advanced statistical methods, including restricted cubic spline analysis to identify nonlinear relationships and threshold effects, as well as bootstrap-based mediation analysis to explore causal pathways. Third, we evaluated both short-term and long-term mortality outcomes with extensive covariate adjustment across multiple domains. Finally, this represents the first dedicated analysis of glycemic variability specifically in trauma patients, which addresses an important gap in the current literature that has predominantly focused on mixed ICU populations. However, several limitations merit consideration. First, due to the retrospective observational design, we cannot establish causal relationships, despite our efforts to adjust for potential confounders. Second, glucose monitoring frequency varied depending on clinical indication, which may have introduced ascertainment bias since sicker patients typically had more frequent measurements. We also lacked detailed information on insulin protocols, nutritional support timing, and corticosteroid administration—all factors that could significantly influence both glycemic patterns and patient outcomes 43 , 44 . Additionally, the single-center nature of the MIMIC-IV database may limit the generalizability of our findings, though the large sample size and comprehensive clinical data do strengthen the internal validity of our results. Conclusion In conclusion, glycemic variability emerges as an important prognostic factor in trauma patients, with particular relevance for young, non-diabetic individuals who show greatest vulnerability. While the association with mortality is clear, translating this knowledge into improved outcomes requires further research to establish causality and develop safe, effective interventions. Until then, clinicians should consider CV as one of several factors in comprehensive risk assessment, recognizing that our current ability to modify glycemic variability remains limited. Methods Data source This retrospective cohort study drew its data from the MIMIC-IV database, version 3.1 ( https://mimic.mit.edu/ ). MIMIC-IV is a publicly available, de-identified repository that contains granular clinical information on hospital and intensive-care admissions to Beth Israel Deaconess Medical Center (BIDMC; Boston, MA, USA) between 2008 and 2022 45 . The current release comprises more than 65,000 ICU admissions, and captures demographics, high-frequency vital signs, laboratory results, medications, imaging and procedure reports, and longitudinal mortality data. One author of the present study (LB) completed the Collaborative Institutional Training Initiative (CITI) “Data or Specimens Only Research” course, signed the data-use agreement, and was granted access to the database (certification number: 41254964). The use of de-identified data in MIMIC-IV has been approved by the Institutional Review Boards of BIDMC and the Massachusetts Institute of Technology, with a waiver of informed consent under the Health Insurance Portability and Accountability Act (HIPAA) safe-harbor provisions. Accordingly, no additional ethical approval was required for the present analysis. All study procedures adhered to the principles of the Declaration of Helsinki and the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines 46 . Study population Trauma cases were extracted from the MIMIC-IV database. To ensure a single baseline record per individual, we kept only each patient’s earliest hospital admission and the first ICU stay within that admission, yielding 65,366 distinct ICU encounters. Patients whose primary diagnosis was trauma, as defined by International Classification of Diseases (ICD)-9/10 codes, were screened for inclusion (n = 6,257). Exclusion criteria were: (1) age < 18 years (n = 0); discharge or death within 24 hours of ICU admission (n = 731); (3) fewer than three recorded blood glucose measurements during the ICU stay (n = 517). The final study cohort comprised 4,009 trauma patients (Fig. 1 ). These patients were subsequently stratified into quartiles of the glucose coefficient of variation (CV): Q1 (n = 1,005), Q2 (n = 1,001), Q3 (n = 1,001), and Q4 (n = 1,002). Data extraction Data were extracted from MIMIC-IV database with Structured Query Language (SQL) scripts run in Navicat 16.3.3 on the native PostgreSQL server and cleaned in R 4.5.0. We captured eight variable blocks: (1) Demographics: age, sex, race; (2) Vital signs: heart rate (HR), respiratory rate (RR), systolic, diastolic and mean arterial pressures (SBP, DBP, MAP), body temperature (BT), peripheral oxygen saturation (SpO₂); (3) Laboratory indices: hemoglobin (Hb), white-blood-cell count (WBC), platelet count (PLT), prothrombin time (PT), activated partial thromboplastin time (APTT), international normalized ratio (INR), creatinine (Cr), blood urea nitrogen (BUN), sodium (Na), potassium (K), chloride (Cl), calcium (Ca); (4) Comorbidities: diabetes mellitus (DM), myocardial infarction (MI), congestive heart failure (CHF), cerebrovascular disease (CVD), chronic pulmonary disease (COPD), chronic kidney disease (CKD), malignant cancer; (5) Trauma descriptors: anatomical injury flags for brain, chest, abdominal, spinal, extremity and multiple trauma, trauma-mechanism category, and Injury Severity Score (ISS) according ICD-9/10 code diagnosis; (6) Severity scores: Oxford Acute Severity of Illness Score (OASIS), Sequential Organ Failure Assessment (SOFA), Glasgow Coma Scale (GCS); (7) Therapeutic measures: mechanical ventilation (MV), vasoactive-drug infusion, continuous renal-replacement therapy (CRRT), insulin therapy, emergency surgery, blood transfusion; (8) Complications: sepsis (Sepsis-3), and acute kidney injury (AKI). At the same time, survival information was also collected. Vital signs and laboratory values were extracted from the first 24 hours after ICU admission. Glycemic variability was calculated using all glucose measurements throughout the ICU stay, obtained from bedside testing, serum chemistry, or whole-blood analyzers, for the entire stay and rolling 3-, 7-, 14-, and 21-day intervals. The minimum glucose value was used to identify hypoglycemia (glucose 20% missingness were excluded; those with ≤ 20% missing data were imputed via multiple imputation by chained equations (five iterations, mice package) to limit bias 48 . Glycemic variability GV was quantified with the coefficient of variation (CV), a dimensionless metric that is well suited to retrospective datasets in which the timing and number of glucose tests differ from patient to patient. For each individual, we collated every plasma, serum, or whole-blood glucose result obtained between ICU admission and the earlier of discharge or death. CV was then calculated as $$\:CV\left(\%\right)=\frac{standard\:deviation\:of\:glucose\:values}{mean\:glucose}\times\:100$$ Only patients who contributed at least three valid glucose measurements met the reliability threshold for inclusion in the analysis, ensuring that the CV captured true within-stay fluctuation rather than random assay noise. Study endpoint Two all-cause mortality endpoints were examined: short-term mortality, defined as death within 30 days of the index ICU admission, and long-term mortality, defined as death within 365 days of that admission. Survival time was measured from the moment of first ICU entry; patients still alive at the end of each window were censored at 30 days or 1 year, respectively. Mortality dates were taken from the state and hospital vital-status files embedded in MIMIC-IV, which capture deaths both in-hospital and after discharge and provide at least one year of follow-up for every patient. Statistical analysis The study population was divided into four groups based on CV quartiles (Q1–Q4). Normality was assessed with the Kolmogorov–Smirnov test. Continuous variables are reported as mean ± SD when normally distributed or median (interquartile range) otherwise; categorical variables are shown as n (%). Group comparisons used one-way ANOVA or the Kruskal–Wallis H test for continuous data and Pearson’s χ² or Fisher’s exact test for categorical data, as appropriate. The distribution of CV was found to be highly right-skewed ( Supplementary Fig. S1 ); consequently, it was natural-log–transformed (log-CV) for use in regression models and was also categorized into quartiles (Q1–Q4) for analysis. Linear trends across quartiles were assessed by treating quartile categories as ordinal variables in Cox regression models. The association between glycemic variability and mortality was examined with Cox proportional-hazards models for 30-day and 1-year all-cause death. The proportional hazards assumption was verified using Schoenfeld residuals. Four nested models were fitted: Model 1, unadjusted; Model 2, adjusted for age, sex and race; Model 3, further adjusted for vital signs, comorbidities and laboratory indices; and Model 4, additionally adjusted for trauma descriptors, severity scores, and therapeutic measures. Hazard ratios (HRs) and 95% confidence intervals (CIs) are reported, and multicollinearity was excluded by verifying variance-inflation factors < 5. Survival curves were generated with the Kaplan–Meier method and compared with the log-rank test. Model discrimination was quantified with time-dependent receiver-operating-characteristic (ROC) curves and corresponding areas under the curve (AUC). Potential non-linear relations between log-transformed CV and mortality were explored using restricted cubic splines (RCS) in multivariable-adjusted Cox proportional hazards models. Four knots were placed at the 5%, 35%, 65%, and 95% percentiles of each index’s distribution. Non-linearity was assessed using likelihood-ratio tests comparing spline and linear models. The value of log-CV at the nadir of the curve (point of lowest risk) was set as the reference (HR = 1). To formally test the threshold effect suggested by the RCS curve, we conducted a two-piecewise Cox regression analysis. The knot for this model was pre-specified at the nadir value. We then compared the hazard ratios across the two segments defined by this knot to quantify the change in association. Prespecified subgroup analyses evaluated effect modification by demographics (age < 60 vs ≥ 60 years, sex), trauma characteristics (traumatic brain injury, multiple trauma, ISS ≥ 16), clinical conditions (diabetes, hypoglycemia, Charlson comorbidity score ≥ 3), critical care interventions (mechanical ventilation, vasoactive drugs, blood transfusion), and complications (sepsis). Interaction p -values were derived from multiplicative interaction terms in Cox proportional hazards models. To explore potential causal pathways, bootstrap-based mediation analysis (1000 replicates) assessed whether sepsis mediated the GV–mortality relationship. All statistics were performed using the R programming environment (version 4.5.0, R Foundation for Statistical Computing, Vienna, Austria). Two-sided p -values < 0.05 were deemed statistically significant. Abbreviations GV glycemic variability CV coefficient of variation ICU intensive care unit MIMIC-IV Medical Information Mart for Intensive Care IV HRs Hazard ratios CIs confidence intervals ISS Injury Severity Score SOFA Sequential Organ Failure Assessment GCS Glasgow Coma Scale TBI traumatic brain injury DM diabetes mellitus AKI acute kidney injury MV mechanical ventilation RCS restricted cubic splines ROC receiver-operating-characteristic AUC areas under the curve CRRT continuous renal-replacement therapy CHF congestive heart failure MI myocardial infarction CVD cerebrovascular disease COPD chronic pulmonary disease CKD chronic kidney disease OASIS Oxford Acute Severity of Illness Score HR heart rate RR respiratory rate SBP systolic blood pressure DBP diastolic blood pressure MAP mean arterial pressure BT body temperature SpO₂ peripheral oxygen saturation Hb hemoglobin WBC white-blood-cell count PLT blood platelet count PT prothrombin time APTT activated partial thromboplastin time Cr creatinine BUN blood urea nitrogen Na sodium K potassium Cl chloride Ca calcium ICD International Classification of Diseases SD standard deviation STROBE Strengthening the Reporting of Observational Studies in Epidemiology AIC Akaike information criterion SQL Structured Query Language CITI Collaborative Institutional Training Initiative HIPAA Health Insurance Portability and Accountability Act. Declarations Acknowledgements We thank the contributors to the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database and acknowledge the efforts of all personnel who made this research possible. Ethics approval The MIMIC protocol was approved by the review boards of the Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center. As the data were publicly available, the study was exempt from the requirements of an ethics approval statement and informed consent. Availability of data and materials The datasets analyzed in the current study are available in the MIMIC-IV database. (https://physionet.org/content/mimiciv/3.1/) Declaration of interest The authors declare that they have no competing interests. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authorship contribution BL, GHJ, JHZ, CYS, JJM, XWL, JW, RCL, XMW and XNL contributed to the study conception and design. 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Tight Blood-Glucose Control without Early Parenteral Nutrition in the ICU. N. Engl. J. Med. 389 , 1180–1190 https://doi.org/10.1056/NEJMoa2304855. (2023). Johnson, A. E. W. et al. MIMIC-IV, a freely accessible electronic health record dataset. Sci. Data 10 , 1–9 https://doi.org/10.1038/s41597-022-01899-x. (2023). von Elm, E. et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet (lond. Engl.) 370 , 1453–1457 https://doi.org/10.1016/S0140-6736(07)61602-X. (2007). Clark, D. E. & Black, A. W. ICDPIC-R Version 1.0.0. Injury 52 , 3545 https://doi.org/10.1016/j.injury.2021.02.079. (2021). White, I. R., Royston, P. & Wood, A. M. Multiple imputation using chained equations: Issues and guidance for practice. Stat. Med. 30 , 377–399 https://doi.org/10.1002/sim.4067. (2011). Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx FigS1.tif FigS2.tif FigS3.tif Cite Share Download PDF Status: Published Journal Publication published 21 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 10 Nov, 2025 Reviews received at journal 07 Nov, 2025 Reviewers agreed at journal 05 Nov, 2025 Reviewers agreed at journal 04 Nov, 2025 Reviews received at journal 24 Oct, 2025 Reviewers agreed at journal 10 Oct, 2025 Reviewers invited by journal 19 Aug, 2025 Editor assigned by journal 19 Aug, 2025 Editor invited by journal 01 Aug, 2025 Submission checks completed at journal 31 Jul, 2025 First submitted to journal 31 Jul, 2025 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7246103","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":504633443,"identity":"dda3d791-7f09-4033-9521-3ec3f566e845","order_by":0,"name":"Bing Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYDCCA2DSgodBgrGBgaFCQk6eCC0gpRJQLWcsjA0biNQCRgyMbRWJUHtxA77jzc8ffNwjIWNwu7nt4dd5EgmMDcwPH93Ao0XyzDHDxhnPJHgM7hxsN5bdJpHHzsBmbJyDR4vBjRzGZp4DQC03EtukJbdJFDM28LBJk6BljkRiwwFStEh+bCBCC8gvM2cAtUjeOdgmzXBMwtiwmYBfgCH24MOHAzb2fLfbn0n+qKmTk2dvfvgYnxYUwMwDJolVDgKMP0hRPQpGwSgYBSMGAAAYV05s8EcYVAAAAABJRU5ErkJggg==","orcid":"","institution":"Hospital of PLA 81st Group Army","correspondingAuthor":true,"prefix":"","firstName":"Bing","middleName":"","lastName":"Liu","suffix":""},{"id":504633444,"identity":"3cccd103-1173-46d8-b9a8-7e0a3c008880","order_by":1,"name":"Jianghua Zhang","email":"","orcid":"","institution":"Hospital of PLA 81st Group Army","correspondingAuthor":false,"prefix":"","firstName":"Jianghua","middleName":"","lastName":"Zhang","suffix":""},{"id":504633445,"identity":"6afae839-41a7-46cd-be8e-9be8309667d7","order_by":2,"name":"Chuangye Song","email":"","orcid":"","institution":"Hospital of PLA 81st Group Army","correspondingAuthor":false,"prefix":"","firstName":"Chuangye","middleName":"","lastName":"Song","suffix":""},{"id":504633447,"identity":"ccda47e4-2ce7-4313-92f4-d0fe2d33aaa1","order_by":3,"name":"Jianjun Miao","email":"","orcid":"","institution":"Hospital of PLA 81st Group Army","correspondingAuthor":false,"prefix":"","firstName":"Jianjun","middleName":"","lastName":"Miao","suffix":""},{"id":504633448,"identity":"3c05d8ed-3b46-4e42-a6f2-fa6f09e62d14","order_by":4,"name":"Xiaowu Li","email":"","orcid":"","institution":"Hospital of PLA 81st Group Army","correspondingAuthor":false,"prefix":"","firstName":"Xiaowu","middleName":"","lastName":"Li","suffix":""},{"id":504633449,"identity":"f35875af-81ea-4efb-b92f-15bca8e8ba27","order_by":5,"name":"Jin Wang","email":"","orcid":"","institution":"Hospital of PLA 81st Group Army","correspondingAuthor":false,"prefix":"","firstName":"Jin","middleName":"","lastName":"Wang","suffix":""},{"id":504633450,"identity":"21cafa3b-3e9a-47d9-a05f-684e9e894411","order_by":6,"name":"Ruichang Lv","email":"","orcid":"","institution":"Hospital of PLA 81st Group Army","correspondingAuthor":false,"prefix":"","firstName":"Ruichang","middleName":"","lastName":"Lv","suffix":""},{"id":504633451,"identity":"65a03332-950b-414b-ab01-6e3910083a91","order_by":7,"name":"Xiaomei Wang","email":"","orcid":"","institution":"Hospital of PLA 81st Group Army","correspondingAuthor":false,"prefix":"","firstName":"Xiaomei","middleName":"","lastName":"Wang","suffix":""},{"id":504633452,"identity":"ad5485df-de1f-4720-a3c8-db3a2b7f3a08","order_by":8,"name":"Xiaoning Liu","email":"","orcid":"","institution":"Hospital of PLA 81st Group Army","correspondingAuthor":false,"prefix":"","firstName":"Xiaoning","middleName":"","lastName":"Liu","suffix":""},{"id":504633453,"identity":"7c79746a-f14f-4c2a-bdb3-9882af9bd638","order_by":9,"name":"Guohong Jia","email":"","orcid":"","institution":"Hospital of PLA 81st Group Army","correspondingAuthor":false,"prefix":"","firstName":"Guohong","middleName":"","lastName":"Jia","suffix":""}],"badges":[],"createdAt":"2025-07-29 18:38:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7246103/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7246103/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-32464-2","type":"published","date":"2025-12-21T15:57:58+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89998332,"identity":"59ba15cb-99a0-40aa-acd9-86db10bfa7e1","added_by":"auto","created_at":"2025-08-27 08:25:09","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":479886,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of patient selection.\u003c/strong\u003e ICU (intensive care unit), MIMIC-IV (Medical Information Mart for Intensive Care IV).\u003c/p\u003e","description":"","filename":"Fig1.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7246103/v1/926492e032c00426b420b07f.jpg"},{"id":89996909,"identity":"76b1507d-c4aa-4198-9676-ece94dc0874c","added_by":"auto","created_at":"2025-08-27 08:09:14","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2380952,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier analysis of 30-day (A) and 1-year (B) mortality in critically ill trauma patients, stratified by glycemic variability quartiles.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig2.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7246103/v1/666ccad1079a8bcc43fdf35b.jpg"},{"id":89996886,"identity":"b80c95fb-67a9-41f7-b7e3-f7ea3c5f13a3","added_by":"auto","created_at":"2025-08-27 08:09:12","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":730432,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDose-response association of log(CV) with 30-day and 1-year mortality in critically ill trauma patients, analyzed using restricted cubic splines (RCS). \u003c/strong\u003eThe RCS models used 4 knots, placed at the 5th, 35th, 65th, and 95th percentiles of the log(CV) distribution. The models were adjusted for demographics, vital signs, laboratory results, comorbidities, severity scores, trauma variables, and treatments. Reference values for estimation were set at the inflection point of lowest risk. The solid lines show adjusted hazard ratios, shaded areas indicate 95% confidence intervals, and the vertical dashed line indicates the inflection point. (A) RCS curve for 30-day mortality. (B) RCS curve for 1-year mortality.\u003c/p\u003e","description":"","filename":"Fig3.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7246103/v1/c2e9a653d36b8081bf4b6eda.jpg"},{"id":89996892,"identity":"bd5a2e5c-6f79-468c-9771-cb5a94c4e3de","added_by":"auto","created_at":"2025-08-27 08:09:13","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2243910,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSubgroup analyses of the association between glycemic variability and mortality in critically ill trauma patients. \u003c/strong\u003eForest plots display the adjusted hazard ratios (HRs) for 30-day (A) and 1-year (B) all-cause mortality. The HRs and 95% confidence intervals (CIs) represent the risk associated with a one-unit increase in the log-transformed coefficient of variation (log-CV). The \u003cem\u003eP\u003c/em\u003efor interaction was calculated to assess for significant effect modification across the strata of each subgroup. TBI (traumatic brain injury), ISS (Injury Severity Score).\u003c/p\u003e","description":"","filename":"Fig4.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7246103/v1/a93fc3a71e59b6dafd4f1294.jpg"},{"id":89996894,"identity":"17f2577b-ad1c-4a38-a5bf-c8c40a6c13e2","added_by":"auto","created_at":"2025-08-27 08:09:13","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":429463,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePath diagrams illustrating the mediating effect of sepsis on the association between glycemic variability and mortality. \u003c/strong\u003eGlycemic variability was quantified as the log-transformed coefficient of variation (log-CV). Panel (A) shows the model for 30-day mortality, and Panel (B) shows the model for 1-year mortality. All effects are presented as Hazard Ratios (HRs) with their corresponding 95% confidence intervals (CIs) and \u003cem\u003eP\u003c/em\u003e-values, derived from the multivariable-adjusted Cox mediation model.\u003c/p\u003e","description":"","filename":"Fig5.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7246103/v1/433ef847421d3a6eba5050b1.jpg"},{"id":89996935,"identity":"ad78ddb0-747e-41df-a47c-5604b22afec8","added_by":"auto","created_at":"2025-08-27 08:09:14","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":637751,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSensitivity analysis of the association between glycemic variability and mortality in critically ill trauma patients, using different time windows. \u003c/strong\u003eThe plots show the fully adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for 30-day (A) and 1-year (B) all-cause mortality. The coefficient of variation (CV) was calculated over the first 3, 7, 14, and 21 days of the ICU stay, as well as for the entire stay.\u003c/p\u003e","description":"","filename":"Fig6.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7246103/v1/62070d450e5b5cfa007972c6.jpg"},{"id":98814088,"identity":"0ed45c0b-eec9-4848-951f-3d7bdddae3f1","added_by":"auto","created_at":"2025-12-22 16:11:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8776913,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7246103/v1/62b9bd74-27a2-48d2-af20-96a55eef6b4a.pdf"},{"id":89997325,"identity":"fa875c49-30b9-4660-8e77-bf85bba8dfac","added_by":"auto","created_at":"2025-08-27 08:17:15","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":5093196,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7246103/v1/b4147fe3335c631a43b90b05.docx"},{"id":89997322,"identity":"d1a01c02-2caf-4abc-a3f1-baca33adbc49","added_by":"auto","created_at":"2025-08-27 08:17:12","extension":"tif","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":777572,"visible":true,"origin":"","legend":"","description":"","filename":"FigS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-7246103/v1/bca99a129c7dc2dc163c1902.tif"},{"id":89996876,"identity":"c0a21ee7-98f5-4d9f-a069-fb11d254c04e","added_by":"auto","created_at":"2025-08-27 08:09:11","extension":"tif","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":615064,"visible":true,"origin":"","legend":"","description":"","filename":"FigS2.tif","url":"https://assets-eu.researchsquare.com/files/rs-7246103/v1/59f197343ff3c8bebd175c7a.tif"},{"id":89996867,"identity":"b77f768c-ea4f-429e-a6c1-1efe9188ebed","added_by":"auto","created_at":"2025-08-27 08:09:10","extension":"tif","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":249021,"visible":true,"origin":"","legend":"","description":"","filename":"FigS3.tif","url":"https://assets-eu.researchsquare.com/files/rs-7246103/v1/1bac328e1dc7307d42c40603.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of glycemic variability with short and long-term mortality among critically ill trauma patients: A retrospective study from the MIMIC-IV database","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTrauma remains a dominant contributor to global morbidity and mortality, causing more than five million deaths each year and standing as the leading cause of death among people younger than 45 years\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Critically injured patients who require admission to an intensive care unit (ICU) experience particularly high case-fatality rates, owing to the interplay of hemodynamic instability, systemic inflammation and multi-organ dysfunction\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Although modern pre-hospital systems, damage-control resuscitation and advanced critical-care techniques have improved short-term survival, overall mortality in severe trauma has plateaued\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Furthermore, trauma patients demonstrate persistently elevated long-term mortality rates that exceed those of age-matched populations, indicating that acute care improvements have not addressed the broader impact of traumatic injury on survival\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. This underscores the need to identify modifiable physiological targets that could further enhance outcomes\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eGlycemic variability (GV)\u0026mdash;the degree of fluctuation in blood-glucose concentrations over time\u0026mdash;has emerged as one such target. In the critically ill, sympathetic over-activation, counter-regulatory hormone surges (glucagon, catecholamines, growth hormone, cortisol) and stress-induced insulin resistance combine to destabilize glucose homeostasis\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Among the several indices proposed to quantify GV, the coefficient of variation (CV) is favored for its simplicity, unit-free nature and proven prognostic value\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAccumulating evidence suggests that GV is more deleterious than sustained hyperglycemia alone. Across diverse ICU cohorts\u0026mdash;sepsis, heart failure, stroke and acute kidney injury\u0026mdash;high GV independently predicts mortality, nosocomial infection and organ failure\u003csup\u003e\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Mechanistic studies indicate that oscillating glucose levels amplify endothelial dysfunction, oxidative stress and inflammatory cytokine release beyond that seen with stable hyperglycemia\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Clinically, greater GV has also been linked to malignant arrhythmias, septic complications and progression to multi-organ failure\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWhether these observations hold true in trauma, however, remains unclear. The metabolic milieu of trauma is unique, characterized by massive catecholamine surges, acute blood-loss anemia, hyper-coagulability and a pronounced systemic inflammatory response that may alter glucose handling relative to other critical-illness states\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Hyperglycemia in trauma patients appears to be 'an entity of its own' with distinct pathophysiology compared to other ICU populations\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Moreover, current glycemic-control recommendations for trauma patients are extrapolated largely from mixed ICU populations, despite wide heterogeneity of injury patterns, resuscitation strategies and operative interventions in trauma care\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAgainst this backdrop we undertook a retrospective cohort study using the Medical Information Mart for Intensive Care IV (MIMIC-IV) database to evaluate the relationship between GV and both short-term (30-day) and long-term (1-year) all-cause mortality in adult ICU trauma patients. We hypothesized that higher CV would be independently associated with increased mortality and that a portion of this risk would be mediated through downstream complications such as sepsis. Clarifying these associations could inform evidence-based glucose-management protocols and help clinicians identify high-risk trauma patients who may benefit from tighter glycemic monitoring and targeted intervention.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eBaseline characteristics\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe final cohort consisted of 4,009 critically ill trauma patients (mean age\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, 61.84\u0026thinsp;\u0026plusmn;\u0026thinsp;21.75 years), of whom 2,529 (63.08%) were male. Overall, 616 (15.37%) patients died within 30 days and 937 (23.37%) died within 1 year of ICU admission. Patients were stratified into quartiles based on their glucose coefficient of variation (CV) (Q1: n\u0026thinsp;=\u0026thinsp;1,005; Q2: n\u0026thinsp;=\u0026thinsp;1,001; Q3: n\u0026thinsp;=\u0026thinsp;1,001; Q4: n\u0026thinsp;=\u0026thinsp;1,002). As detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, baseline characteristics differed markedly across these CV quartiles.\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 of critically ill trauma patients according to quartile of glycemic variability.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall (n\u0026thinsp;=\u0026thinsp;4009)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQ1 (n\u0026thinsp;=\u0026thinsp;1005)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQ2 (n\u0026thinsp;=\u0026thinsp;1001)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eQ3 (n\u0026thinsp;=\u0026thinsp;1001)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eQ4 (n\u0026thinsp;=\u0026thinsp;1002)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eDemographics\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61.84\u0026thinsp;\u0026plusmn;\u0026thinsp;21.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58.16\u0026thinsp;\u0026plusmn;\u0026thinsp;22.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60.52\u0026thinsp;\u0026plusmn;\u0026thinsp;22.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e63.11\u0026thinsp;\u0026plusmn;\u0026thinsp;21.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e65.59\u0026thinsp;\u0026plusmn;\u0026thinsp;20.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2529 (63.08%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e661 (65.77%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e671 (67.03%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e611 (61.04%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e586 (58.48%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.759\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWHITE, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2444 (60.96%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e612 (60.90%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e616 (61.54%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e608 (60.74%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e608 (60.68%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBLACK, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e199 (4.96%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47 (4.68%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46 (4.60%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e47 (4.70%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e59 (5.89%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHISPANIC/LATINO, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e136 (3.39%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34 (3.38%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35 (3.50%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e29 (2.90%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e38 (3.79%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eASIAN, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e78 (1.95%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19 (1.89%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16 (1.60%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17 (1.70%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e26 (2.59%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOTHERS/UNKOWN, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1152 (28.74%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e293 (29.15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e288 (28.77%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e300 (29.97%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e271 (27.05%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eVital signs\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart rate, bpm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e84.31\u0026thinsp;\u0026plusmn;\u0026thinsp;15.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e83.31\u0026thinsp;\u0026plusmn;\u0026thinsp;15.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e85.13\u0026thinsp;\u0026plusmn;\u0026thinsp;15.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e83.85\u0026thinsp;\u0026plusmn;\u0026thinsp;16.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e84.95\u0026thinsp;\u0026plusmn;\u0026thinsp;16.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.027\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\u003e122.94\u0026thinsp;\u0026plusmn;\u0026thinsp;14.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e124.30\u0026thinsp;\u0026plusmn;\u0026thinsp;14.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e123.24\u0026thinsp;\u0026plusmn;\u0026thinsp;14.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e122.95\u0026thinsp;\u0026plusmn;\u0026thinsp;14.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e121.26\u0026thinsp;\u0026plusmn;\u0026thinsp;14.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBP, mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64.93\u0026thinsp;\u0026plusmn;\u0026thinsp;10.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e67.01\u0026thinsp;\u0026plusmn;\u0026thinsp;10.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64.98\u0026thinsp;\u0026plusmn;\u0026thinsp;10.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e64.80\u0026thinsp;\u0026plusmn;\u0026thinsp;10.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e62.91\u0026thinsp;\u0026plusmn;\u0026thinsp;10.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMBP, mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81.08\u0026thinsp;\u0026plusmn;\u0026thinsp;10.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82.55\u0026thinsp;\u0026plusmn;\u0026thinsp;10.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e81.32\u0026thinsp;\u0026plusmn;\u0026thinsp;10.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e81.21\u0026thinsp;\u0026plusmn;\u0026thinsp;10.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e79.25\u0026thinsp;\u0026plusmn;\u0026thinsp;10.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRespiratory rate, /min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18.0 (16.2\u0026ndash;20.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.8 (16.0\u0026ndash;19.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.0 (16.3\u0026ndash;20.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18.0 (16.3\u0026ndash;20.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e18.2 (16.2\u0026ndash;20.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTemperature, \u0026deg;C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37.0 (36.7\u0026ndash;37.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37.0 (36.8\u0026ndash;37.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37.0 (36.8\u0026ndash;37.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e37.0 (36.7\u0026ndash;37.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e36.9 (36.6\u0026ndash;37.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpO₂, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e97.6 (96.2\u0026ndash;98.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e97.3 (96.0\u0026ndash;98.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e97.8 (96.3\u0026ndash;99.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e97.8 (96.3\u0026ndash;99.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e97.6 (96.3\u0026ndash;98.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eLaboratory measurements\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin, g/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.52\u0026thinsp;\u0026plusmn;\u0026thinsp;2.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.92\u0026thinsp;\u0026plusmn;\u0026thinsp;2.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.58\u0026thinsp;\u0026plusmn;\u0026thinsp;2.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.43\u0026thinsp;\u0026plusmn;\u0026thinsp;2.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.16\u0026thinsp;\u0026plusmn;\u0026thinsp;2.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC, 10⁹/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.2 (9.7\u0026ndash;17.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.9 (9.5\u0026ndash;17.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.5 (10.2\u0026ndash;17.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.3 (9.8\u0026ndash;17.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13.0 (9.7\u0026ndash;17.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.040\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelets, 10⁹/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e172 (129\u0026ndash;220)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e178(142\u0026ndash;224)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e172 (131\u0026ndash;217)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e170 (124\u0026ndash;218)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e165 (120\u0026ndash;220)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePT, sec\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.1 (12.0\u0026ndash;14.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.8 (11.8\u0026ndash;14.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.1 (12.0\u0026ndash;14.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.3 (12.2\u0026ndash;15.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13.4 (12.1\u0026ndash;15.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePTT, sec\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28.6 (26.0\u0026ndash;32.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.3 (25.8\u0026ndash;31.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28.4 (25.8\u0026ndash;32.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e28.8 (26.1\u0026ndash;32.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e29.2 (26.3\u0026ndash;34.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.20 (1.10\u0026ndash;1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.20 (1.10\u0026ndash;1.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.20 (1.10\u0026ndash;1.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.20 (1.10\u0026ndash;1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.20 (1.10\u0026ndash;1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum creatinine, mg/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (0.80\u0026ndash;1.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.90 (0.80\u0026ndash;1.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00 (0.80\u0026ndash;1.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.90 (0.80\u0026ndash;1.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.10 (0.80\u0026ndash;1.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBUN, mg/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18 (13\u0026ndash;25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (12\u0026ndash;22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17 (13\u0026ndash;24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18 (13\u0026ndash;24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21 (15\u0026ndash;30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSodium, mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e138 (135\u0026ndash;140)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e138 (136\u0026ndash;140)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e138 (136\u0026ndash;140)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e138 (135\u0026ndash;140)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e137 (135\u0026ndash;140)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.011\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\u003e3.90 (3.60\u0026ndash;4.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.90 (3.60\u0026ndash;4.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.90 (3.60\u0026ndash;4.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.80 (3.60\u0026ndash;4.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.90 (3.50\u0026ndash;4.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.267\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalcium, mg/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.20 (7.70\u0026ndash;8.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.30 (7.80\u0026ndash;8.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.10 (7.70\u0026ndash;8.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.20 (7.70\u0026ndash;8.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.10 (7.50\u0026ndash;8.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdmission glucose, mg/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e132 (110\u0026ndash;164)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e121 (106\u0026ndash;138)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e131 (112\u0026ndash;156)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e137 (111\u0026ndash;169)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e155 (113\u0026ndash;219)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean glucose, mg/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e128.1 (113.8\u0026ndash;148.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e118.3 (105.1\u0026ndash;131.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e127.3 (114.8\u0026ndash;143.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e130.8 (116.3\u0026ndash;148.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e143.0 (122.0\u0026ndash;174.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eTrauma characteristics\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eISS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16 (9\u0026ndash;25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (9\u0026ndash;25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16 (10\u0026ndash;25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16 (9\u0026ndash;25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e16 (9\u0026ndash;25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTBI, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2409 (60.09%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e612 (60.90%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e624 (62.34%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e612 (61.14%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e561 (55.99%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChest trauma, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1613 (40.23%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e393 (39.10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e432 (43.16%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e384 (38.36%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e404 (40.32%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.136\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbdominal trauma, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e844 (21.05%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e232 (23.08%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e208 (20.78%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e190 (18.98%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e214 (21.36%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.159\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpinal trauma, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1304 (32.53%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e322 (32.04%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e315 (31.47%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e330 (32.97%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e337 (33.63%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.737\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExtremity trauma, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1423 (35.50%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e347 (34.53%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e383 (38.26%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e330 (32.97%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e363 (36.23%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.079\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMultiple trauma, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1388 (34.62%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e355 (35.32%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e370 (36.96%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e324 (32.37%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e339 (33.83%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.160\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eTrauma mechanism, n (%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlunt, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1178 (29.38%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e321 (31.94%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e341 (34.07%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e275 (27.47%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e241 (24.05%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFall, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1653 (41.23%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e415 (41.29%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e399 (39.86%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e423 (42.26%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e416 (41.52%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePenetrating, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e112 (2.79%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33 (3.28%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26 (2.60%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e28 (2.80%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e25 (2.50%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1066 (26.59%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e236 (23.48%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e235 (23.48%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e275 (27.47%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e320 (31.94%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eContinued\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVariables\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eOverall (n\u0026thinsp;=\u0026thinsp;4009)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eQ1 (n\u0026thinsp;=\u0026thinsp;1005)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eQ2 (n\u0026thinsp;=\u0026thinsp;1001)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eQ3 (n\u0026thinsp;=\u0026thinsp;1001)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eQ4 (n\u0026thinsp;=\u0026thinsp;1002)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eP\u003c/b\u003e \u003cb\u003evalue\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eComorbidities\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharlson Comorbidity Index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.39\u0026thinsp;\u0026plusmn;\u0026thinsp;2.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.71\u0026thinsp;\u0026plusmn;\u0026thinsp;2.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.16\u0026thinsp;\u0026plusmn;\u0026thinsp;2.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.48\u0026thinsp;\u0026plusmn;\u0026thinsp;2.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.22\u0026thinsp;\u0026plusmn;\u0026thinsp;3.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e786 (19.61%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e81 (8.06%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e145 (14.49%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e183 (18.28%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e377 (37.62%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMyocardial infarction, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e280 (6.98%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49 (4.88%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e62 (6.19%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e67 (6.69%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e102 (10.18%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCongestive heart failure, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e535 (13.34%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95 (9.45%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e114 (11.39%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e142 (14.19%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e184 (18.36%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCerebrovascular disease, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e364 (9.08%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56 (5.57%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e93 (9.29%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e111 (11.09%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e104 (10.38%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic pulmonary disease, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e611 (15.24%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e136 (13.53%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e161 (16.08%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e136 (13.59%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e178 (17.76%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRenal disease, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e408 (10.18%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55 (5.47%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e81 (8.09%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e94 (9.39%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e178 (17.76%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMalignant cancer, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e142 (3.54%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31 (3.08%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39 (3.90%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e32 (3.20%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e40 (3.99%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.588\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eTreatments\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMechanical ventilation, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3365 (83.94%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e783 (77.91%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e874 (87.31%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e862 (86.11%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e846 (84.43%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVasoactive drugs, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e956 (23.85%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e115 (11.44%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e240 (23.98%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e284 (28.37%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e317 (31.64%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCRRT, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52 (1.30%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (0.20%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6 (0.60%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8 (0.80%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e36 (3.59%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInsulin therapy, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1226 (30.58%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e197 (19.60%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e306 (30.57%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e349 (34.87%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e374 (37.33%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmergency surgery, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2602 (64.90%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e597 (59.40%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e670 (66.93%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e659 (65.83%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e676 (67.47%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood transfusion, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1334 (33.28%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e230 (22.89%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e336 (33.57%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e372 (37.16%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e396 (39.52%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eComplications\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSepsis, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1834 (45.75%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e295 (29.35%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e511 (51.05%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e501 (50.05%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e527 (52.59%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAKI, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2928 (73.04%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e613 (61.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e753 (75.22%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e771 (77.02%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e791 (78.94%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePneumonia, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e377 (9.40%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57 (5.67%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e111 (11.09%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e113 (11.29%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e96 (9.58%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eSeverity scores\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSAPS II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32.58\u0026thinsp;\u0026plusmn;\u0026thinsp;12.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.48\u0026thinsp;\u0026plusmn;\u0026thinsp;11.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.94\u0026thinsp;\u0026plusmn;\u0026thinsp;11.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e33.89\u0026thinsp;\u0026plusmn;\u0026thinsp;12.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e36.01\u0026thinsp;\u0026plusmn;\u0026thinsp;13.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAPS III\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36 (28\u0026ndash;47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32 (24\u0026ndash;41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36 (28\u0026ndash;46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e38 (29\u0026ndash;49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e42 (32\u0026ndash;55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOASIS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31.59\u0026thinsp;\u0026plusmn;\u0026thinsp;7.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.43\u0026thinsp;\u0026plusmn;\u0026thinsp;7.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.85\u0026thinsp;\u0026plusmn;\u0026thinsp;6.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e32.37\u0026thinsp;\u0026plusmn;\u0026thinsp;7.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e32.73\u0026thinsp;\u0026plusmn;\u0026thinsp;7.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14 (13\u0026ndash;15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (13\u0026ndash;15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14 (13\u0026ndash;15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14 (13\u0026ndash;15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15 (13\u0026ndash;15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.110\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\u003e4 (2\u0026ndash;6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (2\u0026ndash;5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (3\u0026ndash;6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5 (3\u0026ndash;7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5 (3\u0026ndash;7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eOutcomes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eICU LOS, days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.93 (1.82\u0026ndash;5.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.13 (1.53\u0026ndash;3.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.36 (1.91\u0026ndash;7.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.77 (2.08\u0026ndash;7.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.10 (1.89\u0026ndash;6.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHospital LOS, days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.15 (4.89\u0026ndash;14.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.60 (4.18\u0026ndash;10.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.17 (5.46\u0026ndash;16.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.82 (5.45\u0026ndash;16.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.29 (4.88\u0026ndash;14.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eICU mortality, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e319 (7.96%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38 (3.78%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56 (5.59%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e82 (8.19%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e143 (14.27%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIn-hospital mortality, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e420 (10.48%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58 (5.77%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e80 (7.99%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e110 (10.99%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e172 (17.17%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30-day mortality, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e616 (15.37%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e97 (9.65%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e128 (12.79%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e163 (16.28%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e228 (22.75%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1-year mortality, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e937 (23.37%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e161 (16.02%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e202 (20.18%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e259 (25.87%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e315 (31.44%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eContinuous numerical variables are expressed as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations or medians (interquartile range), and categorical variables are presented as numbers (percentages). ICU (intensive care unit), SBP (systolic blood pressure), DBP (diastolic blood pressure), MBP (mean blood pressure), SpO₂ (oxygen saturation), WBC (white blood cell), PT (prothrombin time), PTT (partial thromboplastin time), INR (international normalized ratio), BUN (blood urea nitrogen), ISS (Injury Severity Score), TBI (traumatic brain injury), CRRT (continuous renal replacement therapy), AKI (acute kidney injury), SAPS II (Simplified Acute Physiology Score II), APS III (Acute Physiology Score III), OASIS (Oxford Acute Severity of Illness Score), GCS (Glasgow Coma Scale), SOFA (Sequential Organ Failure Assessment), LOS (length of stay).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eCompared with Q1, individuals in Q4 were older (65.59 \u003cem\u003evs\u003c/em\u003e 58.16 years) and displayed greater physiological derangement, reflected by higher mean scores for OASIS (32.73 vs. 29.43), median SOFA (5.00 [IQR 3.00\u0026ndash;7.00] \u003cem\u003evs\u003c/em\u003e 3.00 [IQR 2.00\u0026ndash;5.00]) and Charlson Comorbidity Index (4.22 \u003cem\u003evs\u003c/em\u003e 2.71) (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The highest-CV quartile also had lower arterial pressures, lower hemoglobin concentrations (10.16 \u003cem\u003evs\u003c/em\u003e 10.92 g/dL), and worse renal biochemistry\u0026mdash;creatinine 1.10 mg/dL (IQR 0.80\u0026ndash;1.50) versus 0.90 mg/dL (IQR 0.80\u0026ndash;1.10) and BUN 21.00 mg/dL (IQR 15.00\u0026ndash;30.00) versus 16.00 mg/dL (IQR 12.00\u0026ndash;22.00) (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eComorbid illness was more prevalent in Q4, particularly diabetes mellitus (37.62% \u003cem\u003evs\u003c/em\u003e 8.06%), myocardial infarction (10.18% \u003cem\u003evs\u003c/em\u003e 4.88%) and congestive heart failure (18.36% \u003cem\u003evs\u003c/em\u003e 9.45%). Correspondingly, high-CV patients required more intensive therapeutic support: vasoactive drugs (31.64% \u003cem\u003evs\u003c/em\u003e 11.44%), insulin therapy (37.33% \u003cem\u003evs\u003c/em\u003e 19.60%) and blood transfusion (39.52% \u003cem\u003evs\u003c/em\u003e 22.89%) (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Complication rates followed a similar pattern, with sepsis present in 52.59% of Q4 versus 29.35% of Q1 and acute kidney injury in 78.94% versus 61.00% (both \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eMortality increased stepwise across quartiles: 30-day death rose from 9.65% in Q1 to 22.75% in Q4, and 1-year death from 16.02\u0026ndash;31.44% (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for trend), underscoring a strong unadjusted association between higher glycemic variability and adverse outcomes.\u003c/p\u003e\u003cp\u003e\u003cb\u003eThe relationship between glycemic variability and mortality\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCox proportional-hazards models confirmed that higher glycemic variability was independently associated with both early and late death (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In the crude model, each one-unit increase in log-transformed CV doubled the hazard of 30-day mortality (HR 2.07, 95% CI 1.77\u0026ndash;2.43; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and raised the hazard of 1-year mortality by 79% (HR 1.79, 95% CI 1.57\u0026ndash;2.03; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). After comprehensive adjustment for demographic factors, physiology, comorbidities, laboratory indices, severity scores, trauma burden, and therapeutic interventions, the associations remained robust: HR 1.49 (95% CI 1.23\u0026ndash;1.81; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for 30-day death and HR 1.28 (95% CI 1.11\u0026ndash;1.49; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for 1-year death.\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\u003eThe associations of glycemic variability with all-cause mortality in critically ill trauma patients\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eOutcome\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003eModel 4\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\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\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eHR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30-day mortality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePer unit increase in log(CV)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.07 (1.77\u0026ndash;2.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.93 (1.64\u0026ndash;2.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.67 (1.39\u0026ndash;1.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.49 (1.23\u0026ndash;1.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ1 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\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\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.34 (1.03\u0026ndash;1.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.032\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.27 (0.97\u0026ndash;1.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.06 (0.82\u0026ndash;1.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.646\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.86 (0.66\u0026ndash;1.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.286\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.74 (1.35\u0026ndash;2.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.50 (1.17\u0026ndash;1.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.19 (0.92\u0026ndash;1.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.92 (0.70\u0026ndash;1.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.522\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.55 (2.01\u0026ndash;3.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.17 (1.71\u0026ndash;2.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.67 (1.29\u0026ndash;2.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.35 (1.04\u0026ndash;1.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\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\u003e1-year mortality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePer unit increase in log(CV)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.79 (1.57\u0026ndash;2.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.59 (1.40\u0026ndash;1.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.41 (1.22\u0026ndash;1.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.28 (1.11\u0026ndash;1.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ1 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\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\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.28 (1.04\u0026ndash;1.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.21 (0.99\u0026ndash;1.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.07 (0.86\u0026ndash;1.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.553\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.92 (0.74\u0026ndash;1.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.445\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.71 (1.40\u0026ndash;2.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.47 (1.20\u0026ndash;1.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.26 (1.03\u0026ndash;1.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.03 (0.84\u0026ndash;1.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.764\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.19 (1.81\u0026ndash;2.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.79 (1.48\u0026ndash;2.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.47 (1.20\u0026ndash;1.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.26 (1.02\u0026ndash;1.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.030\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eCV: coefficient of variation (glycemic variability). HR: hazard ratio. CI: confidence interval.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eModel1: Crude.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eModel2: Adjusted for gender, age, race.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eModel 3: Model 2\u0026thinsp;+\u0026thinsp;vital signs (heart rate, SBP, respiratory rate, body temperature, SpO\u003csub\u003e2\u003c/sub\u003e), laboratory tests (hemoglobin, WBC, platelets, creatinine, BUN, sodium, potassium, calcium), comorbidities (diabetes, myocardial infarction, congestive heart failure, cerebrovascular disease, chronic pulmonary disease, renal disease, malignant cancer).\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eModel 4: Model 3\u0026thinsp;+\u0026thinsp;GCS score, trauma variables (ISS score, TBI, chest/abdominal/spinal/extremity/multiple trauma, trauma mechanism), treatments (mechanical ventilation, vasoactive drugs, insulin, emergency surgery, blood transfusion), complications (sepsis, AKI, pneumonia).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTreating CV as a categorical variable revealed a clear dose\u0026ndash;response pattern. Relative to the lowest quartile (Q1), fully adjusted hazards for 30-day death were 0.80 (95% CI 0.61\u0026ndash;1.05) in Q2, 0.90 (95% CI 0.69\u0026ndash;1.17) in Q3 and 1.33 (95% CI 1.02\u0026ndash;1.73) in Q4 (\u003cem\u003eP\u003c/em\u003e for trend\u0026thinsp;=\u0026thinsp;0.002). For 1-year death, the corresponding HRs were 0.88 (95% CI 0.71\u0026ndash;1.09) in Q2, 1.01 (95% CI 0.82\u0026ndash;1.24) in Q3 and 1.25 (95% CI 1.02\u0026ndash;1.54) in Q4, respectively (\u003cem\u003eP\u003c/em\u003e for trend\u0026thinsp;=\u0026thinsp;0.004).\u003c/p\u003e\u003cp\u003eKaplan\u0026ndash;Meier curves stratified by quartile corroborated these findings (log-rank \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for both time horizons). Survival probabilities diverged early and remained separated, with the greatest gap between Q1 and Q4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eModel discrimination improved markedly with covariate adjustment. For 30-day death, the area under the ROC curve (AUC) increased from 0.614 (95% CI 0.516\u0026ndash;0.712) in the unadjusted model to 0.845 (95% CI 0.747\u0026ndash;0.943) in the fully adjusted model - a 37.6% relative gain (\u003cb\u003eSupplementary Fig. S2A\u003c/b\u003e). For 1-year death, the AUC rose from 0.597 (95% CI 0.499\u0026ndash;0.695) to 0.831 (95% CI 0.733\u0026ndash;0.929), representing a 39.4% improvement (\u003cb\u003eSupplementary Fig. S2B\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eRestricted cubic-spline and threshold analyses\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo investigate the shape of the association between glycemic variability and mortality, we fitted a series of multivariable Restricted Cubic Splines (RCS) with progressively greater adjustment (\u003cb\u003eSupplementary Fig. S3\u003c/b\u003e). Interestingly, the J-shaped relationship only became evident after comprehensive multivariable adjustment. In the crude and partially adjusted models (Models 1\u0026ndash;3), the association appeared consistent with linearity (all \u003cem\u003eP\u003c/em\u003e for non-linearity\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, in the fully adjusted model (Model 4), a significant non-linear, J-shaped relationship emerged for both 30-day (\u003cem\u003eP\u003c/em\u003e for non-linearity\u0026thinsp;=\u0026thinsp;0.004) and 1-year mortality (\u003cem\u003eP\u003c/em\u003e for non-linearity\u0026thinsp;=\u0026thinsp;0.010) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The spline curves revealed that mortality risk was lowest at a log(CV) of 2.498, corresponding to a CV of approximately 12.2%, and increased sharply at values above this nadir.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eMoreover, based on the nadir identified by the RCS analysis, we formally tested this threshold effect using a pre-specified two-piecewise Cox regression model with a knot at a CV of 12.2% (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For 30-day mortality, no significant association was found below this threshold (HR 0.82, 95% CI 0.50\u0026ndash;1.33; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.420). However, above a CV of 12.2%, higher glycemic variability was strongly associated with increased mortality (HR 1.75, 95% CI 1.39\u0026ndash;2.21; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A nearly identical pattern was observed for 1-year mortality, with a significant increase in risk only above the 12.2% threshold (HR 1.49, 95% CI 1.24\u0026ndash;1.80; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while the association below it was non-significant (HR 0.83, 95% CI 0.59\u0026ndash;1.17; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.294). The likelihood ratio test confirmed that this two-piecewise model provided a significantly better fit than a standard linear model for both 30-day (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019) and 1-year mortality (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013), supporting the presence of a distinct threshold effect.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThreshold effect analysis of CV on mortality in critically ill trauma patients using the two-piecewise linear regression model\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOutcome\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e30-day mortality\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFitting model by standard linear regression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.49 (1.23\u0026ndash;1.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\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\u003eFitting model by two-piecewise linear regression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInflection point\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCV\u0026thinsp;\u0026lt;\u0026thinsp;12.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.82 (0.50\u0026ndash;1.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.420\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCV\u0026thinsp;\u0026gt;\u0026thinsp;12.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.75 (1.39\u0026ndash;2.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\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\u003e\u003cem\u003eP\u003c/em\u003e for likelihood ratio test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e1-year mortality\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFitting model by standard linear regression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.28 (1.10\u0026ndash;1.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFitting model by two-piecewise linear regression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInflection point\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCV\u0026thinsp;\u0026lt;\u0026thinsp;12.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.83 (0.59\u0026ndash;1.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.294\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCV\u0026thinsp;\u0026gt;\u0026thinsp;12.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.49 (1.24\u0026ndash;1.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\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\u003e\u003cem\u003eP\u003c/em\u003e for likelihood ratio test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eCV: coefficient of variation (glycemic variability). HR: Hazard Ratio, CI: Confidence Interval.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eAdjusted for gender, age, race, vital signs (heart rate, SBP, DBP, MBP, respiratory rate, body temperature, SpO\u003csub\u003e2\u003c/sub\u003e), laboratory tests (hemoglobin, WBC, platelets, creatinine, BUN, PT, APTT, INR, sodium, potassium, calcium), comorbidities (diabetes, myocardial infarction, congestive heart failure, cerebrovascular disease, chronic pulmonary disease, renal disease, malignant cancer), severity scores (Charlson Comorbidity Index, GCS, ISS, OASIS, SOFA), trauma variables (TBI, chest/abdominal/spinal/extremity/multiple trauma, trauma mechanism), and treatments (mechanical ventilation, vasoactive drugs, insulin, emergency surgery, blood transfusion).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSubgroup analyses\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePrespecified subgroup analyses assessed whether the GV\u0026ndash;mortality association differed across twelve clinically relevant strata (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Subgroups were defined by demographics (age\u0026thinsp;\u0026lt;\u0026thinsp;60 vs\u0026thinsp;\u0026ge;\u0026thinsp;60 years; sex), injury profile (traumatic brain injury, multiple trauma, ISS\u0026thinsp;\u0026ge;\u0026thinsp;16), baseline health status (diabetes mellitus, hypoglycemia, Charlson Comorbidity Index\u0026thinsp;\u0026ge;\u0026thinsp;3), critical-care interventions (mechanical ventilation, vasoactive-drug infusion, blood transfusion) and complications (sepsis).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFor 30-day mortality, significant effect modification was detected for age, diabetes, hypoglycemia, Charlson score and vasoactive-drug use. The impact of glycemic variability was more than three-fold stronger in younger patients (HR 4.18, 95% CI 2.98\u0026ndash;5.87) than in those\u0026thinsp;\u0026ge;\u0026thinsp;60 years (HR 1.53, 95% CI 1.28\u0026ndash;1.83; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for interaction). A larger effect was also observed in participants without diabetes (HR 2.31, 95% CI 1.92\u0026ndash;2.77) versus those with diabetes (HR 1.35, 95% CI 0.95\u0026ndash;1.90; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007) and in patients who experienced hypoglycemia (HR 3.23, 95% CI 1.91\u0026ndash;5.45 \u003cem\u003evs\u003c/em\u003e HR 1.86, 95% CI 1.57\u0026ndash;2.21; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.047). Lower Charlson scores and vasoactive-drug therapy likewise intensified risk (both \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eFor 1-year mortality, the interaction pattern persisted. Log(CV) conferred a greater hazard in patients\u0026thinsp;\u0026lt;\u0026thinsp;60 years (HR 3.19, 95% CI 2.35\u0026ndash;4.32) than in older individuals (HR 1.37, 95% CI 1.19\u0026ndash;1.58; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and in those receiving vasoactive drugs (HR 2.52, 95% CI 1.96\u0026ndash;3.25 \u003cem\u003evs\u003c/em\u003e HR 1.41, 95% CI 1.22\u0026ndash;1.64; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A lower comorbidity burden (Charlson\u0026thinsp;\u0026lt;\u0026thinsp;3) was paradoxically associated with a steeper gradient (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No significant interactions were noted for sex, mechanical ventilation, transfusion, sepsis, TBI, multiple trauma or ISS\u0026thinsp;\u0026ge;\u0026thinsp;16.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMediation analyses\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo probe pathways linking glycemic variability to death, we performed a mediation analysis to formally test the role of sepsis as an intermediate pathway. After adjusting for a comprehensive set of pre-specified covariates, our analysis revealed that sepsis was a critical mediator of the effect of GV on mortality at both time points. For the short-term outcome, sepsis significantly mediated the association with 30-day mortality, explaining 50.7% of the total effect (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Notably, the mediating role of sepsis became substantially more pronounced for the long-term outcome. It accounted for 70.5% of the association between GV and 1-year mortality (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The detailed results are summarized in \u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSensitivity analyses\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMultiple sensitivity checks corroborated the primary findings. First, we recalculated the coefficient of variation over five alternative windows\u0026mdash;3, 7, 14, and 21 days, as well as the entire hospital stay\u0026mdash;and re-ran the fully adjusted Cox model. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the association between log-CV and mortality was consistent across all intervals. For 30-day death, adjusted hazard ratios (HRs) ranged from 1.40 (95% CI 1.18\u0026ndash;1.67) to 1.49 (1.23\u0026ndash;1.81); for 1-year death, HRs ranged from 1.22 (1.07\u0026ndash;1.40) to 1.29 (1.11\u0026ndash;1.49). Every estimate remained statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.003), indicating that the prognostic value of glycemic variability is not driven by a particular sampling frame.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSecond, we evaluated three data-handling strategies\u0026mdash;complete-case analysis, multiple imputation, and exclusion of extreme CV values (n\u0026thinsp;=\u0026thinsp;36, 0.9%)\u0026mdash;using the same covariate set. Effect estimates changed little: for 30-day death, HRs varied between 1.44 and 1.49; for 1-year death, between 1.22 and 1.28. Quartile-based trend tests remained significant throughout. Together, these analyses show that the observed CV\u0026ndash;mortality relationship is robust to alternative exposure definitions, missing-data procedures, and outlier treatment.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this retrospective study utilizing the MIMIC-IV database, we demonstrated that glycemic variability during ICU stay was significantly associated with both short-term and long-term mortality in critically ill trauma patients. Our analysis of 4,009 patients revealed a robust, nonlinear J-shaped relationship between glycemic variability and mortality outcomes, with relatively stable risk at lower CV values followed by sharp increases beyond specific thresholds. Through restricted cubic spline analysis, we identified a clinically relevant cutoff point at a CV of 12.2% for both 30-day and 1-year mortality, beyond which patients faced substantially elevated risks. Moreover, subgroup analysis revealed that younger patients (\u0026lt;\u0026thinsp;60 years), those without diabetes, and individuals receiving vasoactive support were particularly vulnerable to the adverse effects of GV. In addition, mediation analysis identified sepsis as primary pathway linking GV to mortality.\u003c/p\u003e\u003cp\u003eTo our knowledge, this represents the largest dedicated analysis of glycemic variability across the general trauma population, which addresses an important gap in the literature that has predominantly focused on mixed ICU populations. Trauma patients exhibit distinct metabolic characteristics compared to general critically ill patients. The acute stress response following trauma is substantial, involving massive catecholamine release, cortisol elevation, and cytokine activation that creates a unique hypermetabolic state \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. In patients with shock, plasma concentrations of epinephrine can increase 50-fold and norepinephrine levels increase 10-fold, with cortisol output increasing up to ten-fold with severe stress \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. This differs considerably from medical ICU patients who may have a more gradual onset of critical illness. Trauma patients experience sudden, severe physiological disruption that we hypothesize leads to more dramatic glucose fluctuations \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. It is also worth noting that trauma-specific interventions\u0026mdash;major surgery, the inflammatory cascade, and massive stress hormone secretion\u0026mdash;likely contribute to greater glycemic instability compared to other ICU populations \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWhen we compare our findings to previous research in mixed ICU populations, we observe both similarities and important differences. Earlier studies have consistently demonstrated associations between glycemic variability and mortality, though the magnitude of risk and optimal thresholds have varied considerably. Krinsley, for instance, reported that patients with CV\u0026thinsp;\u0026gt;\u0026thinsp;20% had significantly higher mortality rates in a mixed ICU population \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Similarly, studies in septic patients identified CV\u0026thinsp;\u0026gt;\u0026thinsp;31.5% as cutoff points for increased mortality risk \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. What is noteworthy about our results is that the CV threshold we identified (12.2%) is considerably lower than what has been reported in mixed or septic populations. This suggests that trauma patients may be more sensitive to glycemic fluctuations than previously recognized. The J-shaped relationship we observed is consistent with previous findings in various ICU populations \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, indicating that mortality risk remains relatively stable at lower CV values but increases sharply beyond specific thresholds.\u003c/p\u003e\u003cp\u003eOur mediation analysis provides insights into the pathophysiological pathways, though we acknowledge some uncertainty remains regarding the underlying mechanisms. The literature indicates that glucose oscillations generate reactive oxygen species more potently than sustained hyperglycemia \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. We suspect this oxidative stress burden may be particularly detrimental in trauma patients who are already managing significant inflammatory challenges. The finding that sepsis mediated 50.7% of the 30-day mortality association and an even more substantial 70.5% of the 1-year mortality association suggests that glycemic variability may predispose patients to infectious complications. Higher GV is independently associated with increased mortality in sepsis patients, even after adjusting for illness severity and hypoglycemia \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. A meta-analysis of 10 studies further confirms that septic patients with higher GV have significantly higher mortality, with immune dysfunction, such as impaired phagocytosis, as a proposed mechanism \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Glucose fluctuations impair neutrophil and macrophage function, increasing susceptibility to bacterial infections, including nosocomial infections like ventilator-associated pneumonia \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Does glycemic variability precipitate sepsis through immune dysfunction, or does incipient sepsis drive glucose instability? Our retrospective design cannot definitively answer this question, though the temporal sequence\u0026mdash;with elevated CV typically preceding sepsis diagnosis\u0026mdash;suggests variability may be more than simply a marker of illness severity \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe differential impact of glycemic variability based on diabetes status represents a noteworthy finding. Non-diabetic patients demonstrated substantially higher mortality risk (HR 2.31, 95% CI 1.92\u0026ndash;2.77) compared to diabetic patients (HR 1.35, 95% CI 0.95\u0026ndash;1.90) when experiencing elevated CV. This paradoxical tolerance in diabetic patients, consistently reported in critical care literature \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, may stem from chronic hyperglycemia conferring adaptive metabolic responses, such as downregulation of glucose transporters \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. This has important implications for clinical practice, as non-diabetic patients\u0026mdash;who, based on our data, represent approximately 80% of trauma admissions\u0026mdash;may require more intensive glycemic monitoring, challenging a \u0026ldquo;one-size-fits-all\u0026rdquo; approach to glycemic control \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. In addition, the age-related differences in vulnerability to glycemic variability warrant further discussion. Younger patients (age\u0026thinsp;\u0026lt;\u0026thinsp;60) demonstrated markedly greater susceptibility to the harmful effects of GV (HR 4.18, 95% CI 2.98\u0026ndash;5.87) compared to older patients (HR 1.53, 95% CI 1.28\u0026ndash;1.83). This age-related pattern is consistent with findings from previous studies \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. The proposed mechanism is that elderly patients have increased adaptability and tolerance to oxidative stress damage, because oxidative stress levels naturally increase with age\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe clinical application of these findings faces several challenges. While we identified specific CV thresholds associated with increased mortality, the optimal intervention strategy remains uncertain. Current glucose management protocols focus primarily on absolute glucose targets without considering variability \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Attempting to reduce CV through intensive insulin therapy carries significant risks, particularly hypoglycemia, which independently increases mortality in critically ill patients \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Furthermore, no prospective studies have demonstrated that specifically targeting glycemic variability improves patient outcomes. Most interventions that reduce variability also lower mean glucose levels, making it difficult to isolate the beneficial effects \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThis raises fundamental questions about the nature of glycemic variability in trauma. Is elevated CV a modifiable risk factor that directly contributes to poor outcomes, or merely an epiphenomenon reflecting illness severity? The strong mediation through sepsis and organ dysfunction pathways suggests potential causality, but definitive evidence requires prospective intervention studies. Until such evidence emerges, CV should be considered a prognostic marker rather than a therapeutic target \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStrengths and Limitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study has several notable strengths. First, we utilized a large, well-characterized trauma cohort from the validated MIMIC-IV database, which provided comprehensive clinical data and robust long-term follow-up. Second, our approach employed advanced statistical methods, including restricted cubic spline analysis to identify nonlinear relationships and threshold effects, as well as bootstrap-based mediation analysis to explore causal pathways. Third, we evaluated both short-term and long-term mortality outcomes with extensive covariate adjustment across multiple domains. Finally, this represents the first dedicated analysis of glycemic variability specifically in trauma patients, which addresses an important gap in the current literature that has predominantly focused on mixed ICU populations.\u003c/p\u003e\u003cp\u003eHowever, several limitations merit consideration. First, due to the retrospective observational design, we cannot establish causal relationships, despite our efforts to adjust for potential confounders. Second, glucose monitoring frequency varied depending on clinical indication, which may have introduced ascertainment bias since sicker patients typically had more frequent measurements. We also lacked detailed information on insulin protocols, nutritional support timing, and corticosteroid administration\u0026mdash;all factors that could significantly influence both glycemic patterns and patient outcomes \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Additionally, the single-center nature of the MIMIC-IV database may limit the generalizability of our findings, though the large sample size and comprehensive clinical data do strengthen the internal validity of our results.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, glycemic variability emerges as an important prognostic factor in trauma patients, with particular relevance for young, non-diabetic individuals who show greatest vulnerability. While the association with mortality is clear, translating this knowledge into improved outcomes requires further research to establish causality and develop safe, effective interventions. Until then, clinicians should consider CV as one of several factors in comprehensive risk assessment, recognizing that our current ability to modify glycemic variability remains limited.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eData source\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis retrospective cohort study drew its data from the MIMIC-IV database, version 3.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mimic.mit.edu/\u003c/span\u003e\u003cspan address=\"https://mimic.mit.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). MIMIC-IV is a publicly available, de-identified repository that contains granular clinical information on hospital and intensive-care admissions to Beth Israel Deaconess Medical Center (BIDMC; Boston, MA, USA) between 2008 and 2022\u003csup\u003e45\u003c/sup\u003e. The current release comprises more than 65,000 ICU admissions, and captures demographics, high-frequency vital signs, laboratory results, medications, imaging and procedure reports, and longitudinal mortality data. One author of the present study (LB) completed the Collaborative Institutional Training Initiative (CITI) “Data or Specimens Only Research” course, signed the data-use agreement, and was granted access to the database (certification number: 41254964). The use of de-identified data in MIMIC-IV has been approved by the Institutional Review Boards of BIDMC and the Massachusetts Institute of Technology, with a waiver of informed consent under the Health Insurance Portability and Accountability Act (HIPAA) safe-harbor provisions. Accordingly, no additional ethical approval was required for the present analysis. All study procedures adhered to the principles of the Declaration of Helsinki and the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStudy population\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTrauma cases were extracted from the MIMIC-IV database. To ensure a single baseline record per individual, we kept only each patient’s earliest hospital admission and the first ICU stay within that admission, yielding 65,366 distinct ICU encounters. Patients whose primary diagnosis was trauma, as defined by International Classification of Diseases (ICD)-9/10 codes, were screened for inclusion (n = 6,257). Exclusion criteria were: (1) age \u0026lt; 18 years (n = 0); discharge or death within 24 hours of ICU admission (n = 731); (3) fewer than three recorded blood glucose measurements during the ICU stay (n = 517). The final study cohort comprised 4,009 trauma patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These patients were subsequently stratified into quartiles of the glucose coefficient of variation (CV): Q1 (n = 1,005), Q2 (n = 1,001), Q3 (n = 1,001), and Q4 (n = 1,002).\u003c/p\u003e\u003cp\u003e\u003cb\u003eData extraction\u003c/b\u003e\u003c/p\u003e\u003cp\u003eData were extracted from MIMIC-IV database with Structured Query Language (SQL) scripts run in Navicat 16.3.3 on the native PostgreSQL server and cleaned in R 4.5.0. We captured eight variable blocks: (1) Demographics: age, sex, race; (2) Vital signs: heart rate (HR), respiratory rate (RR), systolic, diastolic and mean arterial pressures (SBP, DBP, MAP), body temperature (BT), peripheral oxygen saturation (SpO₂); (3) Laboratory indices: hemoglobin (Hb), white-blood-cell count (WBC), platelet count (PLT), prothrombin time (PT), activated partial thromboplastin time (APTT), international normalized ratio (INR), creatinine (Cr), blood urea nitrogen (BUN), sodium (Na), potassium (K), chloride (Cl), calcium (Ca); (4) Comorbidities: diabetes mellitus (DM), myocardial infarction (MI), congestive heart failure (CHF), cerebrovascular disease (CVD), chronic pulmonary disease (COPD), chronic kidney disease (CKD), malignant cancer; (5) Trauma descriptors: anatomical injury flags for brain, chest, abdominal, spinal, extremity and multiple trauma, trauma-mechanism category, and Injury Severity Score (ISS) according ICD-9/10 code diagnosis; (6) Severity scores: Oxford Acute Severity of Illness Score (OASIS), Sequential Organ Failure Assessment (SOFA), Glasgow Coma Scale (GCS); (7) Therapeutic measures: mechanical ventilation (MV), vasoactive-drug infusion, continuous renal-replacement therapy (CRRT), insulin therapy, emergency surgery, blood transfusion; (8) Complications: sepsis (Sepsis-3), and acute kidney injury (AKI). At the same time, survival information was also collected.\u003c/p\u003e\u003cp\u003eVital signs and laboratory values were extracted from the first 24 hours after ICU admission. Glycemic variability was calculated using all glucose measurements throughout the ICU stay, obtained from bedside testing, serum chemistry, or whole-blood analyzers, for the entire stay and rolling 3-, 7-, 14-, and 21-day intervals. The minimum glucose value was used to identify hypoglycemia (glucose \u0026lt; 70 mg/dL). Trauma features were parsed from ICD-9/10 codes, and ISS was approximated with the \u003cem\u003eicdpicr\u003c/em\u003e package\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Variables with \u0026gt; 20% missingness were excluded; those with ≤ 20% missing data were imputed via multiple imputation by chained equations (five iterations, \u003cem\u003emice\u003c/em\u003e package) to limit bias\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGlycemic variability\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGV was quantified with the coefficient of variation (CV), a dimensionless metric that is well suited to retrospective datasets in which the timing and number of glucose tests differ from patient to patient. For each individual, we collated every plasma, serum, or whole-blood glucose result obtained between ICU admission and the earlier of discharge or death. CV was then calculated as\u003c/p\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:CV\\left(\\%\\right)=\\frac{standard\\:deviation\\:of\\:glucose\\:values}{mean\\:glucose}\\times\\:100$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003eOnly patients who contributed at least three valid glucose measurements met the reliability threshold for inclusion in the analysis, ensuring that the CV captured true within-stay fluctuation rather than random assay noise.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStudy endpoint\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTwo all-cause mortality endpoints were examined: short-term mortality, defined as death within 30 days of the index ICU admission, and long-term mortality, defined as death within 365 days of that admission. Survival time was measured from the moment of first ICU entry; patients still alive at the end of each window were censored at 30 days or 1 year, respectively. Mortality dates were taken from the state and hospital vital-status files embedded in MIMIC-IV, which capture deaths both in-hospital and after discharge and provide at least one year of follow-up for every patient.\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eThe study population was divided into four groups based on CV quartiles (Q1–Q4). Normality was assessed with the Kolmogorov–Smirnov test. Continuous variables are reported as mean ± SD when normally distributed or median (interquartile range) otherwise; categorical variables are shown as n (%). Group comparisons used one-way ANOVA or the Kruskal–Wallis H test for continuous data and Pearson’s χ² or Fisher’s exact test for categorical data, as appropriate. The distribution of CV was found to be highly right-skewed (\u003cb\u003eSupplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e); consequently, it was natural-log–transformed (log-CV) for use in regression models and was also categorized into quartiles (Q1–Q4) for analysis. Linear trends across quartiles were assessed by treating quartile categories as ordinal variables in Cox regression models.\u003c/p\u003e\u003cp\u003eThe association between glycemic variability and mortality was examined with Cox proportional-hazards models for 30-day and 1-year all-cause death. The proportional hazards assumption was verified using Schoenfeld residuals. Four nested models were fitted: Model 1, unadjusted; Model 2, adjusted for age, sex and race; Model 3, further adjusted for vital signs, comorbidities and laboratory indices; and Model 4, additionally adjusted for trauma descriptors, severity scores, and therapeutic measures. Hazard ratios (HRs) and 95% confidence intervals (CIs) are reported, and multicollinearity was excluded by verifying variance-inflation factors \u0026lt; 5. Survival curves were generated with the Kaplan–Meier method and compared with the log-rank test. Model discrimination was quantified with time-dependent receiver-operating-characteristic (ROC) curves and corresponding areas under the curve (AUC).\u003c/p\u003e\u003cp\u003ePotential non-linear relations between log-transformed CV and mortality were explored using restricted cubic splines (RCS) in multivariable-adjusted Cox proportional hazards models. Four knots were placed at the 5%, 35%, 65%, and 95% percentiles of each index’s distribution. Non-linearity was assessed using likelihood-ratio tests comparing spline and linear models. The value of log-CV at the nadir of the curve (point of lowest risk) was set as the reference (HR = 1). To formally test the threshold effect suggested by the RCS curve, we conducted a two-piecewise Cox regression analysis. The knot for this model was pre-specified at the nadir value. We then compared the hazard ratios across the two segments defined by this knot to quantify the change in association.\u003c/p\u003e\u003cp\u003ePrespecified subgroup analyses evaluated effect modification by demographics (age \u0026lt; 60 vs ≥ 60 years, sex), trauma characteristics (traumatic brain injury, multiple trauma, ISS ≥ 16), clinical conditions (diabetes, hypoglycemia, Charlson comorbidity score ≥ 3), critical care interventions (mechanical ventilation, vasoactive drugs, blood transfusion), and complications (sepsis). Interaction \u003cem\u003ep\u003c/em\u003e-values were derived from multiplicative interaction terms in Cox proportional hazards models. To explore potential causal pathways, bootstrap-based mediation analysis (1000 replicates) assessed whether sepsis mediated the GV–mortality relationship.\u003c/p\u003e\u003cp\u003eAll statistics were performed using the R programming environment (version 4.5.0, R Foundation for Statistical Computing, Vienna, Austria). Two-sided \u003cem\u003ep\u003c/em\u003e-values \u0026lt; 0.05 were deemed statistically significant.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGV\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eglycemic variability\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCV\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ecoefficient of variation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eICU\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eintensive care unit\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMIMIC-IV\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMedical Information Mart for Intensive Care IV\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHRs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHazard ratios\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCIs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003econfidence intervals\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eISS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInjury Severity Score\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSOFA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSequential Organ Failure Assessment\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGCS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGlasgow Coma Scale\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTBI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003etraumatic brain injury\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ediabetes mellitus\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAKI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eacute kidney injury\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMV\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003emechanical ventilation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRCS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003erestricted cubic splines\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ereceiver-operating-characteristic\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eareas under the curve\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCRRT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003econtinuous renal-replacement therapy\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCHF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003econgestive heart failure\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003emyocardial infarction\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCVD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ecerebrovascular disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCOPD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003echronic pulmonary disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCKD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003echronic kidney disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOASIS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOxford Acute Severity of Illness Score\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eheart rate\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003erespiratory rate\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSBP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003esystolic blood pressure\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDBP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ediastolic blood pressure\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMAP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003emean arterial pressure\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ebody temperature\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSpO₂\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eperipheral oxygen saturation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHb\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ehemoglobin\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eWBC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ewhite-blood-cell count\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePLT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eblood platelet count\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eprothrombin time\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAPTT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eactivated partial thromboplastin time\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCr\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ecreatinine\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBUN\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eblood urea nitrogen\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNa\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003esodium\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eK\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003epotassium\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCl\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003echloride\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCa\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ecalcium\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eICD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInternational Classification of Diseases\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003estandard deviation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSTROBE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStrengthening the Reporting of Observational Studies in Epidemiology\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAIC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAkaike information criterion\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSQL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStructured Query Language\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCITI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCollaborative Institutional Training Initiative\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHIPAA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHealth Insurance Portability and Accountability Act.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the contributors to the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database and acknowledge the efforts of all personnel who made this research possible.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe MIMIC protocol was approved by the review boards of the Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center. As the data were publicly available, the study was exempt from the requirements of an ethics approval statement and informed consent. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed in the current study are available in the MIMIC-IV database. \u0026nbsp; (https://physionet.org/content/mimiciv/3.1/) \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interest \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthorship contribution\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBL, GHJ, JHZ, CYS, JJM, XWL, JW, RCL, XMW and XNL contributed to the study conception and design. Material preparation, data collection and analysis were performed by BL, JHZ, CYS and JW. Data visualization was conducted by BL, JHZ, CYS and RCL. The first draft of the manuscript was written by BL, JHZ, CYS and RCL, and all authors commented on previous versions of the manuscript. Project administration and supervision were provided by GHJ, JJM and XWL. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization. Injuries and violence. https://www.who.int/news-room/fact-sheets/detail/injuries-and-violence.\u003c/li\u003e\n\u003cli\u003evan Breugel, J. M. M. \u003cem\u003eet al.\u003c/em\u003e Global changes in mortality rates in polytrauma patients admitted to the ICU\u0026mdash;a systematic review. \u003cem\u003eWorld J Emerg Surg\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 55 https://doi.org/10.1186/s13017-020-00330-3. (2020).\u003c/li\u003e\n\u003cli\u003eFrydrych, L. 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(2011).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"trauma, glycemic variability, critical care, mortality, MIMIC-IV database","lastPublishedDoi":"10.21203/rs.3.rs-7246103/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7246103/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe relationship between glycemic variability (GV) and mortality in critically ill trauma patients is not well-established. We aimed to evaluate the association between GV, quantified by the coefficient of variation (CV), and both short- and long-term mortality in this population. A cohort of patients was established from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Cox proportional-hazards models, Kaplan-Meier analysis, restricted cubic splines (RCS), and subgroup analyses were used to investigate the association between GV and mortality. A mediation model was constructed to determine the mediating role of sepsis. This study included 4,009 critically ill trauma patients. Higher GV was independently associated with increased 30-day (Adjusted HR 1.49, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 1-year mortality (Adjusted HR 1.28, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Nonlinear analyses revealed a J-shaped relationship, with mortality risk increasing sharply above a CV of 12.2%. The association was more pronounced in younger patients and those without diabetes. Mediation analysis revealed that sepsis significantly mediated this association, with proportions of 50.7% for 30-day and 70.5% for 1-year mortality. Higher glycemic variability is an independent predictor of both short- and long-term mortality in critically ill trauma patients. The risk appears to have a threshold effect, and sepsis is a major mediating pathway.\u003c/p\u003e","manuscriptTitle":"Association of glycemic variability with short and long-term mortality among critically ill trauma patients: A retrospective study from the MIMIC-IV database","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-27 08:08:35","doi":"10.21203/rs.3.rs-7246103/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-10T10:33:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-07T06:33:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"71481283419670194043363821343169199139","date":"2025-11-06T00:08:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"174065131849137915351425326738009842250","date":"2025-11-04T06:44:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-24T14:39:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"152735200807756645906078264506740098565","date":"2025-10-10T08:20:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-19T04:20:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-19T04:17:48+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-01T11:48:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-31T11:36:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-07-31T11:10:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3ff84ac4-4b1d-4d49-80c5-605c3a1769ed","owner":[],"postedDate":"August 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":53611134,"name":"Health sciences/Biomarkers"},{"id":53611135,"name":"Health sciences/Diseases"},{"id":53611136,"name":"Health sciences/Endocrinology"},{"id":53611137,"name":"Health sciences/Medical research"},{"id":53611138,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-12-22T16:05:11+00:00","versionOfRecord":{"articleIdentity":"rs-7246103","link":"https://doi.org/10.1038/s41598-025-32464-2","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-12-21 15:57:58","publishedOnDateReadable":"December 21st, 2025"},"versionCreatedAt":"2025-08-27 08:08:35","video":"","vorDoi":"10.1038/s41598-025-32464-2","vorDoiUrl":"https://doi.org/10.1038/s41598-025-32464-2","workflowStages":[]},"version":"v1","identity":"rs-7246103","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7246103","identity":"rs-7246103","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}