Elevated TyG index outperforms TyG-BMI in predicting delirium among non-diabetic sepsis patients

Elevated TyG index outperforms TyG-BMI in predicting delirium among non-diabetic sepsis patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Elevated TyG index outperforms TyG-BMI in predicting delirium among non-diabetic sepsis patients Shuangmei Zhao, Fufu Wang, Guangdong Wang, Kaige Xuan, Chucheng Jiao, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6568553/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The Triglyceride Glucose (TyG) index and TyG-Body Mass Index (TyG-BMI), recognized as validated surrogate markers of insulin resistance (IR), have demonstrated prognostic utility in various metabolic disorders. However, their potential as predictive biomarkers for sepsis-associated delirium (SD) in non-diabetic populations remains unexplored. This study aims to systematically evaluate and compare the predictive performance of TyG and TyG-BMI indices for delirium incidence among sepsis patients without diabetes mellitus. Methods Our study included a comprehensive retrospective observational cohort analysis, utilizing an extensive dataset from the Critical Care Medical Information Market IV (MIMIC-IV 2.2). The study population was divided into quartiles based on triglyceride-glucose (TyG) index and TyG-body mass index (TyG-BMI). The primary outcome assessed was the incidence of delirium at 28 days, and the secondary outcome was length of ICU stay. To evaluate the relationship between the TyG index, TyG-BMI, and delirium, we employed a Cox proportional hazards regression model, supplemented by constrained cubic spline function (RCS) analysis to improve accuracy. In addition, the Kaplan-Meier (KM) method was used to estimate the survival probability, and the receiver operating characteristic (ROC) curve was plotted to compare the ability of the two indicators to predict delirium. Results A total of 2,665 non-diabetic sepsis patients were identified from the database. The Cox proportional hazards model revealed that the TyG index was independently associated with the 28-day incidence of delirium (hazard ratio [HR], 1.354; 95% confidence interval [CI], 1.225–1.496). Similarly, the TyG-BMI index also showed a significant correlation with the 28-day delirium incidence, with HRs (95% CI) of 1.009 (1.006–1.012), respectively. Kaplan-Meier (K-M) analysis demonstrated that the cumulative incidence of 28-day delirium increased with higher quartiles of the TyG index or TyG-BMI index. Based on the ROC curve analysis, the TyG index exhibited better predictive performance for the 28-day incidence of delirium (AUC: 0.589) compared to the TyG-BMI index (AUC: 0.566). The effect of the TyG index on delirium occurrence remained consistent across subgroups, with no significant interactions observed with randomization factors. Additionally, incorporating the TyG index into the base model for 28-day delirium prediction slightly improved its predictive capability (AUC: 0.708 for the base model vs. 0.715 for the base model + TyG index). Conclusion As a continuous variable, both measures showed a significant association with the 28-day risk of delirium in critically ill patients with non-diabetic sepsis, and the TyG index became the most promising indicator of risk stratification and prevention strategies in critically ill patients with non-diabetic sepsis, superior to the TyG-BMI index. Triglyceride glucose index (TyG) Triglyceride glucose-body mass index (TyG-BMI) sepsis delirium length of ICU stay Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Sepsis is a systemic inflammatory response syndrome (SIRS) triggered by infection that can lead to multiple organ dysfunction and even death in severe cases [ 1 – 2 ]. It is one of the most morbidity and mortality rates worldwide, particularly in intensive care units (ICUs), and is characterized by complex pathophysiological processes, often with multiple organ dysfunction and serious complications [ 3 – 5 ]. Delirium is an acute brain dysfunction that occurs in 50–80% of patients with sepsis. Not only does delirium significantly prolong hospital stays and increase healthcare costs, but it is also strongly associated with long-term cognitive impairment, decreased quality of life, and increased mortality in patients [ 6 ]. Although the exact pathogenesis of delirium is still not fully understood, studies have shown that metabolic disorders, insulin resistance (IR), systemic inflammatory responses, and oxidative stress play a key role in its development. Therefore, identifying biomarkers that can predict delirium early is important to improve outcomes in patients with sepsis [ 7 – 9 ]. In recent years, the triglyceride-glucose index (TyG index) and its derivative, the TyG-BMI index, have emerged as important tools for assessing insulin resistance and metabolic abnormalities. The TyG index, calculated based on fasting triglycerides (TG) and blood glucose (FPG), reflects the degree of insulin sensitivity and disturbances in glucose and lipid metabolism [ 10 – 15 ]. Due to its simplicity and cost-effectiveness, the TyG index has been widely adopted in clinical research. For instance, in the field of cardiovascular diseases, the TyG index has been demonstrated to be closely associated with atherosclerosis, the complexity of coronary artery lesions, and the prognosis of acute myocardial infarction. Studies have shown that an elevated TyG index can independently predict the anatomical complexity of coronary arteries (SYNTAX score > 22) in non-diabetic patients with chronic coronary syndrome, and it serves as an early warning indicator for disease progression in acute pancreatitis. Furthermore, the TyG-BMI index, which incorporates body mass index (BMI), enhances its predictive capability for metabolic syndrome and cardiovascular risks, particularly demonstrating higher sensitivity in evaluating obesity-related metabolic disorders [ 16 – 18 ]. However, despite the extensive research on the TyG index and TyG-BMI index in metabolic and cardiovascular diseases, their application in sepsis-associated delirium remains largely unexplored. The metabolic state of sepsis patients often undergoes dramatic changes due to hypercatabolism, heightened inflammatory responses, and insulin resistance. Studies suggest that insulin resistance may exacerbate oxidative stress, endothelial dysfunction, and neuroinflammation, thereby promoting the onset of delirium [ 19 – 20 ]. In addition, obesity, as a key component of metabolic syndrome, may further exacerbate the inflammatory response and metabolic derangement in patients with sepsis through the release of inflammatory factors from adipose tissue, thereby increasing the risk of delirium [ 21 – 23 ]. However, the value of the TyG-BMI index, which includes obesity parameters (BMI), in predicting delirium has not been validated. Based on this, this study used the MIMIC-IV database to explore for the first time the predictive value of TyG index and TyG-BMI index in delirium in critically ill patients with non-diabetic sepsis. By comparing the predictive performance of these two indicators, this study aims to provide a theoretical basis for the early identification of high-risk groups and the optimization of intervention strategies. These findings will not only reveal the critical role of metabolic derangement in sepsis-associated delirium, but will also validate whether the TyG-BMI index and its multidimensional metabolic assessment capabilities provide greater clinical applicability. This may provide new insights and approaches for the prevention and management of delirium associated with sepsis. Methods Source of data This study is a retrospective observational cohort study, utilizing data from the publicly accessible Medical Information Mart for Intensive Care-IV (MIMIC-IV-2.2) database. MIMIC-IV, developed by the Computational Physiology Laboratory at the Massachusetts Institute of Technology (MIT), is a widely used and publicly available medical database that includes clinical data from intensive care unit (ICU) patients at Beth Israel Deaconess Medical Center in Boston, Massachusetts, between 2008 and 2019. The database encompasses demographic information, vital signs, imaging reports, laboratory test results, and diagnoses coded according to the International Classification of Diseases, Ninth Revision (ICD-9) and Tenth Revision (ICD-10) [ 24 ]. To access the data, one of the authors (Shuangmei Zhao) completed the required training and obtained access credentials (Certification ID: 65512045), from which relevant variables for this study were extracted. As all patient health information in the database has been de-identified, additional patient consent was deemed unnecessary. Further details about this public database can be found at: https://mimic.mit.edu/ . No human or animal clinical trials were involved in this study. Clinical trial registration number: N/A. This study included adult patients diagnosed with sepsis based on the International Classification of Diseases, Ninth Revision (ICD-9) and Tenth Revision (ICD-10). The following exclusion criteria were applied: (1) individuals younger than 18 years at initial admission; (2) patients with an ICU stay shorter than 24 hours; (3) patients missing measurements of triglycerides, fasting blood glucose, height, or weight at ICU admission; (4) patients with multiple ICU admissions for sepsis, retaining only data from the first admission; (5) patients diagnosed with diabetes or acute pancreatitis; (6) patients admitted due to delirium, coma, or dementia; and (7) patients with a documented history of neurological disorders or a family history of such conditions. Data extraction For data retrieval from the database, PostgreSQL software (version 13.7.2) was deployed. The extraction procedure was facilitated by applying Structured Query Language (SQL). This process targeted the acquisition of data across five principal domains: (1) Demographic data including age, gender, height, weight, and body mass index (BMI). (2) Clinical severity indices include the Glasgow Coma Scale (GCS), Sequential Organ Failure Assessment (SOFA) score, Simplified Acute Physiology Score (SAPS)-II, Oxford Acute Illness Severity Score (OASIS), and Assessment of Acute Physiology and Chronic Health (APS)-III. (3) Physiological indicators, including systolic blood pressure (SBP), diastolic blood pressure (DBP), heart rate, and respiratory rate. (4) Hematological and biochemical markers, including hemoglobin concentration (Hb), red blood cell count (RBC), red blood cell distribution width (RDW), platelet count, white blood cell count (WBC), activated partial thromboplastin time (APTT), prothrombin time (PT), international normalized ratio (INR), serum sodium, serum potassium, serum chloride, anion gap, blood urea nitrogen, high-density lipoprotein (HDL), low-density lipoprotein (LDL), cholesterol, Alanine aminotransferase (ALT), aspartate aminotransferase (AST), and serum creatinine. (5) Existing comorbidities such as hypertension, acute respiratory distress syndrome (ARDS), heart failure (HF), chronic obstructive pulmonary disease (COPD), respiratory failure (RF), liver cirrhosis, pneumonia, hyperlipidemia, chronic kidney disease (CKD), acute renal failure (AKI), malignancy, myocardial infarction, etc., as well as those including mechanical ventilation, vasoactive drugs, continuous renal replacement therapy (CRRT), insulin, statins, Therapeutic interventions including sedative medications and antibiotic use. The observation period for each participant commenced at the time of hospital admission and continued until the onset of delirium. The analysis relied on laboratory values and scores indicative of disease severity, which were collected within the first 24 hours following ICU admission. To mitigate the impact of missing data, variables with an absence rate exceeding 10% were systematically excluded from the analysis. Calculation of TyG and TyG-BMI The TyG index was calculated as ln [fasting glucose (mg/ dl) ×fasting TG (mg/dl)]/2. BMI was calculated as body weight (Kg)/height2 (m). TyG-BMI index was determined based on the combination of TyG index and BMI. TyG-BMI index was computed according the equation: TyG index×BMI [ 25 ]. Clinical outcomes The start date of follow-up is the date of admission of the patient. The primary outcome was the incidence of delirium at 28 days, and the secondary outcome measure was length of ICU stay. Statistical analysis In this study, participants were divided into quartiles based on their TyG, TyG-BMI values, expressed as Q1 to Q4. Quantitative variables were reported either as the mean ± standard deviation (SD) or as the median and interquartile range (IQR), depending on the distribution of the data. Qualitative variables were expressed as counts and proportions. For continuous variables that followed a normal distribution, the t-test or analysis of variance (ANOVA) was utilized for analysis. Conversely, for variables that deviated from normal distribution, the Mann-Whitney U test or Kruskal-Wallis test was applied. Pearson's chi-square test was used to compare categorical variables in TyG, TyG-BMI quartiles. To determine the incidence of delirium at each quartile throughout the observation period. Furthermore, we utilized the Kaplan-Meier (KM) survival method to ascertain the incidence of within-group endpoints, as defined by TyG, TyG-BMI levels, and employed log-rank tests to determine statistical differences. A Cox proportional hazards regression model was used to assess the hazard ratio (HR) with 95% confidence intervals (95% CI) for the occurrence of an event. A baseline variable with a significance level of p < 0.05 between delirium and delirium not occurring was included in the multivariate model. In addition, multicollinearity was checked using variance expansion factor (VIF) to ensure variable independence in the study. As suggested by previous studies, the recommended maximum VIF value used in the study was 5. Model I was unadjusted for Age, Gender, HR, RR, BMI, whereas Model II was adjusted for Age, Gender, HR, RR, BMI, Hypertension, Heart failure, Insulin, Statin, Platelet, PT, APTT, ALT, AST, Potassium, RBC, WBC, Bun, Creatinine, Ventilation, CRRT, Sedative drug, Chloride, Liver disease, AKI. Subgroup analyses were performed to explore the correlation between the continuous TyG index and the incidence of delirium in different subgroups. In addition, we constructed a Cox proportional hazards model using restricted cubic splines (RCS), which allowed us to investigate the potential nonlinear relationship between TyG, TyG-BMI changes, and delirium incidence. Subject Operating Characteristic (ROC) curve analysis was then performed to compare the predictive power, sensitivity, and specificity of the two measures to assess the incidence of delirium. It was considered that the two-tailed P value of < 0.05 indicated statistical significance. Statistical analysis was performed using R software (version 4.4.2) alongside SPSS 22.0 (IBM SPSS Statistics, Armonk, NY, USA). Results Baseline characteristics After screening the data of patients with non-diabetic sepsis in the MIMICIV. database, 2665 patients who met the inclusion criteria were included in this study. Figure 1 illustrates the patient selection process. The baseline characteristics of the included patients were classified according to their 28-day delirium incidence. A total of 2665 patients were divided into delirium group (1187, 45%) and non-delirium group (1486, 55%). Non-survivors tend to be older than survivors. In addition, patients with AKI, respiratory failure, and liver disease have a higher incidence of delirium. The body weight, BMI, platelets, LDL, cholesterol, RBC, APSIII score, TyG, and TyG-BMI in the delirium group were significantly higher than those in the non-delirium group. The proportion of patients in the delirium group receiving insulin, vasoactive drugs, sedative drugs, antibiotics, and mechanical ventilation within 24 hours was significantly lower than that in the non-delirium group. Table 1 gives a detailed comparison of delirium and non-delirium. Table 1 Baseline characteristics between delirium and non-delirium populations Variable Names Overall (n = 2665) Non-delirium(n = 1468) Delirium(n = 1187) P-value Gender, N (%) 0.597 F 1073 (40.41) 835 (40.71) 238 (39.40) M 1582 (59.59) 1216 (59.29) 366 (60.60) Age (years) 62 (49–72) 62 (50–73) 61 (48–72) 0.057 Weight (kg) 80.5 (67.075–97.7) 79.8 (66.6–97.2) 83.383 (68.975-99) 0.002 Height (cm) 1.7 (1.63–1.78) 1.7 (1.63–1.78) 1.7 (1.63–1.78) 0.881 BMI 27.77 (23.86-32.905) 27.48 (23.56–32.74) 28.55 (24.668–33.853) 0.001 HR 93 (80–109) 93 (80–109) 93 (80–110) 0.656 SBP 118 (102-136.923) 119 (102–137) 116 (101–133) 0.021 DBP 68 (57–82) 68 (57–82) 67 (57.75-78) 0.068 RR 20 (16–25) 20 (16–24) 21 (17-25.25) 0.007 Hemoglobin 10.7 (8.9–12.6) 10.6 (8.9–12.5) 10.966 (8.9-12.925) 0.057 Platelet 186 (118–257) 184 (116.5–254) 194.5 (122.75-265.25) 0.257 RDW 14.7 (13.6–16.7) 14.8 (13.6–16.9) 14.4 (13.4–16.2) 0.002 RBC 3.57 (2.98–4.21) 3.55 (2.97–4.18) 3.69 (3-4.32) 0.025 WBC 12.2 (8.2-17.05) 12.2 (8.2–17.1) 11.921 (8.375-17) 0.931 Anion-gap 14 (12–18) 14 (12–18) 14 (12-17.25) 0.972 Calcium 8.2 (7.7–8.7) 8.2 (7.6–8.8) 8.2 (7.7–8.7) 0.809 Chloride 104 (100–108) 104 (100–108) 103 (99–107) 0.019 TyG 9.12 (8.64–9.68) 9.06 (8.585–9.61) 9.3 (8.8–9.91) < 0.001 TyG-BMI 254.02 (211.835-308.775) 249.81 (209.535-304.495) 269.575 (224.382-322.192) < 0.001 Potassium 4.1 (3.7–4.6) 4.1 (3.7–4.6) 4.1 (3.7–4.7) 0.067 Sodium 139 (135–142) 139 (135–142) 139 (135–141) 0.091 INR 1.3 (1.2–1.7) 1.3 (1.2–1.7) 1.3 (1.2–1.7) 0.655 PT 14.7 (12.8–18.5) 14.8 (12.9-18.45) 14.2 (12.5–18.6) 0.831 APTT 32 (27.6–41.1) 32 (27.5–40.6) 31.9 (28.2–42.7) 0.259 HDL 39 (28–52) 39 (28–52) 39 (29–53) 0.324 LDL 74 (49–103) 72.469 (49–100) 80 (54–109) 0.004 Cholesterol 143 (107-178.5) 141 (106–177) 149 (110-183.25) 0.039 ALT 33 (18–73) 33 (18–73) 33 (18.75-72) 0.710 AST 49 (27–116) 49 (26–117) 49 (29–113) 0.919 Anion-gap 14 (12–18) 14 (12–18) 14 (12-17.25) 0.969 Creatinine 1.1 (0.8–1.7) 1.1 (0.8–1.7) 1.1 (0.8–1.6) 0.229 Bun 21 (14–35) 21 (14–35) 20 (13–35) 0.091 SOFA 7 (4–10) 7 (4–10) 7 (4–10) 0.004 APSIII 53 (39–71) 52 (38.5–69) 55 (40–74) 0.046 SAPSII 40 (31–51) 40 (30.5–50) 41 (31–52) 0.063 OASIS 36 (30–42) 36 (30–42) 37 (31-42.25) 0.046 GCS 15 (14–15) 15 (14–15) 15 (14–15) 0.595 Statin, N (%) 0.359 0 1382 (52.05) 1078 (52.56) 304 (50.33) 1 1273 (47.95) 973 (47.44) 300 (49.67) Insulin, N (%) 0.734 0 795 (29.94) 618 (30.13) 177 (29.30) 1 1860 (70.06) 1433 (69.87) 427 (70.70) Vasopressor, N (%) < 0.001 0 556 (20.94) 493 (24.04) 63 (10.43) 1 2099 (79.06) 1558 (75.96) 541 (89.57) Sedative drug, N (%) < 0.001 0 209 (7.87) 196 (9.56) 13 (2.15) 1 2446 (92.13) 1855 (90.44) 591 (97.85) Antibiotics, N (%) 0.558 0 10 (0.38) 9 (0.44) 1 (0.17) 1 2645 (99.62) 2042 (99.56) 603 (99.83) CRRT, N (%) < 0.001 0 2182 (82.18) 1733 (84.50) 449 (74.34) 1 473 (17.82) 318 (15.50) 155 (25.66) Ventilation, N (%) < 0.001 0 170 (6.40) 159 (7.75) 11 (1.82) 1 2485 (93.60) 1892 (92.25) 593 (98.18) Paraplegia, N (%) 0.876 0 2389 (89.98) 1844 (89.91) 545 (90.23) 1 266 (10.02) 207 (10.09) 59 (9.77) ARDS, N (%) 0.714 0 2632 (99.13) 2032 (99.07) 600 (99.34) 1 23 (0.87) 19 (0.93) 4 (0.66) Hypertension, N (%) 0.921 0 1734 (65.31) 1338 (65.24) 396 (65.56) 1 921 (34.69) 713 (34.76) 208 (34.44) Hyperlipidemia, N (%) 0.224 0 1965 (74.01) 1530 (74.60) 435 (72.02) 1 690 (25.99) 521 (25.40) 169 (27.98) Myocardial Infarction, N (%) 0.344 0 2215 (83.43) 1703 (83.03) 512 (84.77) 1 440 (16.57) 348 (16.97) 92 (15.23) Heart Failure, N (%) 0.716 0 1912 (72.02) 1473 (71.82) 439 (72.68) 1 743 (27.98) 578 (28.18) 165 (27.32) Renal Failure, N (%) 0.718 0 2227 (83.88) 1717 (83.72) 510 (84.44) 1 428 (16.12) 334 (16.28) 94 (15.56) Malignant cancer, N (%) 0.746 0 2295 (86.44) 1770 (86.30) 525 (86.92) 1 360 (13.56) 281 (13.70) 79 (13.08) Liver disease, N (%) 0.013 0 2315 (87.19) 1770 (86.30) 545 (90.23) 1 340 (12.81) 281 (13.70) 59 (9.77) AKI, N (%) < 0.001 0 241 (9.08) 216 (10.53) 25 (4.14) 1 2414 (90.92) 1835 (89.47) 579 (95.86) Respiratory failure, N (%) < 0.001 0 1081 (40.72) 879 (42.86) 202 (33.44) 1 1574 (59.28) 1172 (57.14) 402 (66.56) Liver cirrhosis, N (%) 0.007 0 2287 (86.14) 1746 (85.13) 541 (89.57) 1 368 (13.86) 305 (14.87) 63 (10.43) Pneumonia, N (%) 0.095 0 1387 (52.24) 1090 (53.14) 297 (49.17) 1 1268 (47.76) 961 (46.86) 307 (50.83) CKD, N (%) 0.587 0 2314 (87.16) 1792 (87.37) 522 (86.42) 1 341 (12.84) 259 (12.63) 82 (13.58) COPD, N (%) 0.749 0 2264 (85.27) 1746 (85.13) 518 (85.76) 1 391 (14.73) 305 (14.87) 86 (14.24) Associations between the TyG index and delirium When the TyG index was treated as a continuous variable, Cox proportional hazards analysis showed a significant association between the incidence of delirium and the TyG index. This association was observed in both the unadjusted model (hazard ratio [HR] 1.352; 95% confidence interval [CI] 1.233–1.484) and the fully adjusted model (HR 1.354; 95% CI 1.225–1.496). Patients were then divided into four groups based on the quartiles of the TyG index: Q1 (TyG ≤ 8.64, N = 664), Q2 (TyG > 8.64, ≤ 9.12; N = 664), Q3 (> 9.12, ≤ 9.68; N = 663), and Q4 (> 9.68; N = 664). Cox proportional hazards analysis revealed that the highest quartile of the TyG index (Q4) was significantly associated with the incidence of delirium in both the unadjusted model (HR 1.973; 95% CI 1.561–2.493) and the adjusted models (Model 1: HR 1.880; 95% CI 1.480–2.389; Model 2: HR 1.950; 95% CI 1.525–2.495) (Table 2 ). Table 2 Association between IR related index and delirium (Cox regression) Index Groups Non-adjusted Model 1 Model 2 HR (95%Cl) P-Value HR (95%Cl) P-Value HR (95%Cl) P-Value TyG Continuous 1.352 (1.233–1.484) < 0.001 1.322 (1.201–1.455) < 0.001 1.354 (1.225–1.496) 8.64, ≤ 9.12; N = 664) 1.301 (1.012–1.674) 0.04 1.291 (1.004–1.661) 0.05 1.283 (0.996–1.653) 0.053 Q3(> 9.12, ≤ 9.68; N = 663) 1.580 (1.239–2.015) < 0.001 1.534 (1.200–1.961) < 0.001 1.572 (1.227–2.014) 9.68; N = 664) 1.973 (1.561–2.493) < 0.001 1.880 (1.480–2.389) < 0.001 1.950 (1.525–2.495) < 0.001 P for trend < 0.001 < 0.001 < 0.001 TyG-BMI Continuous 1.002 (1.001–1.003) < 0.001 1.008 (1.005–1.011) < 0.001 1.009 (1.006–1.012) 211.75, ≤ 254.02; N = 665) 1.146 (0.893–1.470) 0.28 1.198 (0.922–1.557) 0.17 1.192 (0.916–1.553) 0.19 Q3(> 254.02, ≤ 308.74; N = 656) 1.511 (1.193–1.914) < 0.001 1.640 (1.240–2.170) < 0.001 1.352 (1.233–1.484) 308.74; N = 656) 1.655 (1.312–2.087) < 0.001 1.949 (1.332–2.852) < 0.001 1.352 (1.233–1.484) < 0.001 P for trend < 0.001 < 0.001 < 0.001 Associations between the TyG-BMI and delirium Cox proportional hazards model analysis showed that when the TyG-BMI index was continuous, the TyG-BMI index was more effective in the unadjusted model (HR 1.002; 95% CI 1.001 ~ 1.003) and fully adjusted model (HR 1.009; 95% CI 1.006 ~ 1.012), TyG-BMI index was significantly correlated with the incidence of delirium. When TyG-BMI was a nominal variable (Quartile 1: ≤211.75; Q2 :211.75 ~ 254.02; Q3: 254.02 ~ 308.74; Q4: > 308.74) according to the unadjusted model (Q1 vs. Q2: HR 1.146; 95% CI 0. 893 ~ 1.470; Q3: HR 1.511; 95% CI 1.193 ~ 1.914; Q4: HR 1.655; 95% CI 1.312 ~ 2.087; Trend test P < 0.001) and model 1 (Q1 vs. Q2: HR, 1.198; 95% CI 0. 922 ~ 1.557; Q3: HR 1.640; 95% CI 1.240 ~ 2.170; Q4: HR 1.949; 95% CI 1.332 ~ 2.852; Trend test P < 0.001), and model II (Q1 vs. Q2: HR, 1.192; 95% CI 0. 916 ~ 1.553; Q3: HR 1.352; 95% CI 1.233 ~ 1.484; Q4: HR 1.352; 95% CI 1.233 ~ 1.484; Trend test P < 0.001). TyG-BMI index was also associated with a higher incidence of delirium, and there was an increasing trend with the increase of TyG-BMI index. The effect of TyG - BMI on ICU length of stay is shown in Schedules 1 and 2. The RCS model revealed a nonlinear relationship between TyG-BMI and the incidence of delirium, where TyG-BMI was a continuous variable (Fig. 3 C nonlinear P = 0.366). TyG-BMI Quartile The K-M curve for the occurrence of delirium at 28 days is shown in Fig. 2 C. The results showed that the cumulative incidence of delirium increased with the increase in the TyG-BMI (p = 0.00064) quartile. ROC curve analysis of TyG and TyG-BMI The ROC curves for the ability of the two indicators to predict the incidence of delirium in patients with non-diabetic sepsis are shown in Fig. 4 . The results showed that the Ty G index was superior to the Ty G - BMI [ 0.589 vs. 0.566] in predicting the incidence of delirium. We then performed subgroup analyses to assess the relationship between Ty G, TyG-BMI index and the incidence of delirium in different subgroups, with patients at quartile 4 consistently showing a higher risk of death in all subgroups defined by sex (male and female), presence or absence of hypertension, renal failure, myocardial infarction, liver disease, cerebrovascular disease, statin and mechanical ventilation use. This pattern held true with or without adjustment for covariates, and no significant interactions were found (Fig. 5 ). Finally, whether the IR index further improves the predictive power of the underlying model (including age, sex, HR, RR, BMI, hypertension, heart failure, insulin, statins, platelets, PT, APTT, ALT, AST, potassium, red blood cells, white blood cells, urea, creatinine, ventilation, CRRT, sedatives, chlorides, liver disease, AKI). The area under the curve (AUC) used for comparison is shown in Fig. 4 . Unfortunately, the results of this study suggest that the incremental predictive power of the two IR indices for the basic risk model in non-diabetic sepsis patients is not significant. Discussion To the best of our knowledge, this study is the first to explore the predictive value of the triglyceride-glucose index (TyG index) and its derivative, the TyG-BMI index, for delirium in critically ill non-diabetic sepsis patients, and to promote the further application of the TyG index and TyG-BMI index in the field of critical care medicine. The results showed that both TyG index and TyG-BMI index were significantly associated with the occurrence of delirium in sepsis patients, however, the two IR indices did not significantly improve the prediction performance of the basic risk model for delirium risk, but the TyG index seemed to be the most promising indicator for prevention and risk stratification in non-diabetic sepsis patients. This finding provides a new metabolic biomarker for the early identification and intervention of sepsis-related delirium, and provides a theoretical basis for optimizing the delirium risk assessment model in clinical practice. In the field of critical care, the relationship between BMI and patient prognosis has been extensively explored in previous studies. A retrospective observational cohort study based on the MIMIC-IV v2.2 and EICU collaborative research database found that BMI in patients with sepsis had an L-shaped relationship with ICU mortality. When the BMI is lower than a specific cut-off point, the ICU mortality rate increases significantly as the BMI decreases. Above the cut-off point, an increase in BMI also leads to an increase in mortality. This suggests that BMI can be used as an important indicator to assess the risk level and prognosis of patients with sepsis, but the study did not address the link between BMI and delirium [ 26 – 27 ]. In this study, the TyG-BMI index was introduced, and the BMI was combined with the TyG index reflecting insulin resistance, in an attempt to reveal its predictive effect on delirium in critically ill patients with non-diabetic sepsis, and provide a new idea for clinical evaluation. Research on insulin resistance-related indicators and outcomes in critically ill patients has also attracted much attention. Studies in critically ill patients with chronic heart failure (CHF) have shown that TyG index, as a surrogate indicator of insulin resistance, is independently associated with 5-year mortality and is superior to TyG-BMI and TG/HDL-C in predicting all-cause mortality at 5-year mortality [ 28 ]. However, these studies focused primarily on patients with CHF, differed in the pathophysiology of the disease from the non-diabetic sepsis critically ill patient population in this study, and focused on mortality rather than delirium. This study specifically focused on critically ill patients with non-diabetic sepsis, and studied the predictive value of TyG and TyG-BMI index on delirium, which is helpful to understand the risk factors for delirium in this specific patient group and provide a more targeted basis for clinical intervention. In terms of the prediction model of delirium in critically ill patients, some studies have developed delirium prediction models based on logistic regression, random forest and bidirectional long short-term memory (BiLSTM) algorithms using the eICU Collaborative Research Database (eICU-crd) and the Critical Care Medical Information Database Version III (MIMIC-III) databases. Among them, the BiLSTM model has the best performance and can effectively predict delirium to a certain extent under different prediction windows [ 29 – 30 ]. However, most of these models are based on a variety of complex clinical parameters, and this study focuses on TyG and TyG-BMI, two relatively concise and metabolically related indicators, which are easier to operate and easier to be applied clinically, providing a more targeted and practical method for the prediction of delirium. In addition, there are also studies on the relationship between the TyG index and delirium in older patients. A study in patients aged 65 years and older showed a direct correlation between the TyG index and the risk of delirium in the ICU. Through the analysis of MIMIC-IV and eICU-crd databases, it was found that the TyG index can be used as a reliable indicator to assess the risk of delirium in elderly ICU patients [ 31 ]. However, this study focused on critically ill patients with non-diabetic sepsis, further refined the study subjects, and explored the predictive value of TyG and TyG-BMI index in this specific population, which is different from previous studies. Firstly, this study validates the potential value of the TyG index in predicting sepsis-associated delirium. The TyG index has been widely used in the research of cardiovascular and metabolic diseases as a reliable indicator of insulin resistance (IR) and metabolic disorders [ 32 – 35 ]. However, its use in sepsis and its complications remains limited. This study found that an elevated TyG index was significantly associated with an increased risk of delirium in non-diabetic sepsis patients, which may be closely related to metabolic disturbances, oxidative stress, and neuroinflammation caused by insulin resistance [ 36 – 37 ]. Patients with sepsis often experience a hypercatabolic and enhanced inflammatory response, and insulin resistance may exacerbate endothelial dysfunction and neuroinflammation, thereby contributing to the onset of delirium. This finding is consistent with previous studies describing the role of insulin resistance in neurological complications [ 38 – 40 ]. Secondly, this study further examines the predictive performance of the TyG-BMI index. The TyG-BMI index not only reflects glucose and lipid metabolism, but also incorporates obesity parameters (BMI), allowing for a more comprehensive assessment of the interaction between neurological metabolism and inflammation [ 41 – 42 ]. Compared to traditional clinical scoring systems such as SOFA and APACHE II, the TyG index and TyG-BMI index offer the advantages of simplicity and cost-effectiveness, while also dynamically reflecting patients' metabolic status. Although traditional scoring systems can assess disease severity, they lack attention to metabolic indicators. This study suggests that incorporating the TyG index or TyG-BMI index into delirium risk assessment models may help identify high-risk patients earlier, thereby providing a basis for personalized interventions. For example, early interventions targeting insulin resistance and metabolic disturbances, such as nutritional support and blood glucose control, may help reduce the incidence of delirium. However, there are some limitations to this study that must be addressed. First, as a retrospective study, its results may be influenced by confounding factors. Although we adjusted for these factors by multivariate analysis, further validation through prospective studies is needed. Second, despite the abundance of data in the MIMIC-IV database, there may be cases where the data is missing, inaccurate, or incomplete during recording. These issues may interfere with the accuracy of the study results and lead to bias in assessing the predictive value of the TyG and TyG-BMI indices. In addition, data collected from a single center, due to the lack of standardization, lack of reference intervals in the general population and different subgroups, make it difficult to implement the results in clinical practice. These factors need to be further explored in future research. In summary, this study demonstrates for the first time the value of the TyG index and the TyG-BMI index in predicting delirium in critically ill patients with non-diabetic sepsis and highlights the potential advantages of the TyG-BMI index due to its multidimensional metabolic assessment capabilities. In addition, the findings provide new insights for the early identification and intervention of sepsis-related delirium and lay a foundation for future related research. By further optimizing the application of metabolic biomarkers in clinical practice, we can improve the prognosis of patients with sepsis, reduce the incidence of delirium, and thus improve the quality of life and long-term survival of patients. Conclusion To the best of our knowledge, this study is the first to compare the ability of two surrogate measures of IR, Ty G index and Ty G - BMI index, to predict the occurrence of delirium in non-diabetic sepsis patients. Despite the inclusion of additional risk variables, both the Ty G index and the Ty G - BMI showed a strong correlation with delirium. While the inclusion of these indicators in the basic risk model did not lead to an improvement in delirium prediction performance, the Ty G-index appears to be the most promising indicator for prevention and risk stratification in non-diabetic sepsis patients. Abbreviations TyG Triglyceride glucose index TyG-BMI Triglyceride glucose-body mass index BMI body mass index RR respiratory rate HR heart rate IQR interquartile range GCS Glasgow Coma Scale SOFA sequential organ failure assessment SAPS-II simplified acute physiological score II OASIS Oxford acute severity of illness score APS-III Acute Physiology and Chronic Health SBP systolic blood pressure DBP diastolic blood pressure RBC red blood cell RDW red blood cell distribution width LDL low-density lipoprotein HDL high-density lipoprotein AST Alanine aminotransferase ALT aspartate aminotransferase WBC white blood cell PLT platelet Hb hemoglobin APTT activated partial thromboplastin time PT Prothrombin time INR international normalized ratio HR hazard ratio CI confidence interval COPD chronic obstructive pulmonary disease AKI acute renal failure CKD chronic kidney disease ARDS acute respiratory distress syndrome CRRT continuous renal replacement therapy Declarations Supplementary Information The online version contains supplementary material available at the end of the document. Acknowledgments We thank the participants and the staff of the cohorts for their continuing dedication and efforts. Author contributions Shuangmei Zhao and Fuxu Wang designed the study. Guangdong Wang, Kaige Xuan and Chucheng Jiao extracted, collected, and analyzed this data. Shuang Mei Zhao compiled tables and figures. Liu Tao Sui and Zhi Maoreviewed the results, interpreted the information, and wrote the manuscript. All authors have made equal contributions to the manuscript and have been approved for submission. Funding The project was supported by the National Natural Science Foundation of China (No. 82171299). Availability of data and material Publicly available datasets were analyzed in this study. These data can be found at https://mimic.mit.edu/. Ethical approval The ethical approval and participation consent followed the Helsinki Declaration guidelines. Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center review committee approved using the MIMIC-III database. Given that the data is accessible to the public through the MIMIC-IV database, the need for ethical approval and informed consent was waived. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Author details 1 Department of Critical Care Medicine, The Affiliated Hospital of Qingdao University, Qingdao, China. 2 Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Xi’an Jiao tong University, Xi’an, Shanxi, China. 3 Department of Critical Care Medicine, The First Medical Center of PLA General Hospital, Beijing, China. 4 Department of Neurology, The Affiliated Hiser Hospital of Qingdao University, Qingdao, China. References Gong T, Liu YT, Fan J. 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Action tremor features discovery for essential tremor and Parkinson's disease with explainable multilayer BiLSTM. Comput Biol Med. 2024;180:108957. Liang D, Liu C, Wang Y. The association between triglyceride-glucose index and the likelihood of cardiovascular disease in the U.S. population of older adults aged ≥ 60 years: a population-based study. Cardiovasc Diabetol. 2024;23(1):151. Cui C, Liu L, Qi Y, Han N, Xu H, Wang Z, et al. Joint association of TyG index and high sensitivity C-reactive protein with cardiovascular disease: a national cohort study. Cardiovasc Diabetol. 2024;23(1):156. Zhou H, Ding X, Lan Y, Fang W, Yuan X, Tian Y, et al. Dual-trajectory of TyG levels and lifestyle scores and their associations with ischemic stroke in a non-diabetic population: a cohort study. Cardiovasc Diabetol. 2024;23(1):225. Zhang Y, Wang F, Tang J, Shen L, He J, Chen Y. 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Association of triglyceride glucose index with stroke: from two large cohort studies and Mendelian randomization analysis. Int J Surg. 2024;110(9):5409–16. Zhou H, Ding X, Lan Y, Fang W, Yuan X, Tian Y, et al. Dual-trajectory of TyG levels and lifestyle scores and their associations with ischemic stroke in a non-diabetic population: a cohort study. Cardiovasc Diabetol. 2024;23(1):225. Zhang R, Hong J, Wu Y, Lin L, Chen S, Xiao Y. Joint association of triglyceride glucose index (TyG) and a body shape index (ABSI) with stroke incidence: a nationwide prospective cohort study. Cardiovasc Diabetol. 2025;24(1):7. Jiang L, Zhu T, Song W, Zhai Y, Tang Y, Ruan F, et al. Assessment of six insulin resistance surrogate indexes for predicting stroke incidence in Chinese middle-aged and elderly populations with abnormal glucose metabolism: a nationwide prospective cohort study. Cardiovasc Diabetol. 2025;24(1):56. Ouyang Q, Xu L, Yu M. Associations of triglyceride glucose-body mass index with short-term mortality in critically ill patients with ischemic stroke. Cardiovasc Diabetol. 2025;24(1):91. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6568553","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":456188591,"identity":"657d8cd3-dec9-4f2d-8c82-fcb170ed1d0a","order_by":0,"name":"Shuangmei Zhao","email":"","orcid":"","institution":"The Affiliated Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Shuangmei","middleName":"","lastName":"Zhao","suffix":""},{"id":456188592,"identity":"7592b647-f2e6-498d-bc08-c53520c339e2","order_by":1,"name":"Fufu Wang","email":"","orcid":"","institution":"The Affiliated Hospital of Qingdao 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2","display":"","copyAsset":false,"role":"figure","size":642606,"visible":true,"origin":"","legend":"\u003cp\u003eK‒M survival analysis curves for delirium and length of ICU stay with non-diabetic sepsis patients. \u003cstrong\u003e(A)\u003c/strong\u003eTyG and\u003cstrong\u003e \u003c/strong\u003edelirium\u003cstrong\u003e.\u003c/strong\u003e \u003cstrong\u003e(B)\u003c/strong\u003e TyG and length of ICU stay.\u003cstrong\u003e (C)\u003c/strong\u003eTyG-BMI and\u003cstrong\u003e \u003c/strong\u003edelirium\u003cstrong\u003e. (D)\u003c/strong\u003e TyG-BMI and length of ICU stay.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6568553/v1/6cbfd7e0b8891db2f663ec6e.png"},{"id":82799743,"identity":"233f7a13-c82f-4a03-bd5f-4c728bf1610d","added_by":"auto","created_at":"2025-05-15 11:03:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":619913,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline curve for\u003cstrong\u003e (A)\u003c/strong\u003e TyG and delirium, \u003cstrong\u003e(B)\u003c/strong\u003e TyG and length of ICU stay,\u003cstrong\u003e(C)\u003c/strong\u003e TyG-BMI and delirium, and \u003cstrong\u003e(D)\u003c/strong\u003e TyG-BMI and length of ICU stay.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6568553/v1/0d80ce15e9b5268650b8d787.png"},{"id":82794890,"identity":"37291921-db3e-4ada-867b-89f022ed53eb","added_by":"auto","created_at":"2025-05-15 10:31:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1202458,"visible":true,"origin":"","legend":"\u003cp\u003eThe IR index was plotted as an ROC curve predicting delirium and\u003cstrong\u003e \u003c/strong\u003elength of ICU stay. \u003cstrong\u003e(A)\u003c/strong\u003eTyG versus TyG-BMI to predict delirium. \u003cstrong\u003e(B)\u003c/strong\u003e TyG versus TyG-BMI to predict length of ICU stay.\u003cstrong\u003e (C)\u003c/strong\u003e Basic risk model versus +TyG, +TyG-BMI to predict delirium. \u003cstrong\u003e(D)\u003c/strong\u003e Basic risk model versus +TyG, +TyG-BMI to predict length of ICU stay.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6568553/v1/3d3445453f7fe365da26fd87.png"},{"id":82799270,"identity":"68ab63ef-8b2b-407f-8c5c-283ff064ec08","added_by":"auto","created_at":"2025-05-15 10:55:35","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1895346,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of Ty G \u003cstrong\u003e(A) \u003c/strong\u003eand TyG-BMI \u003cstrong\u003e(B) \u003c/strong\u003ewith delirium in non-diabetic sepsis patients. HR: hazard ratio, CI: confidence interval\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6568553/v1/89ec78ca65a422d61ee910fc.png"},{"id":85200209,"identity":"9c546180-2d15-4eda-abd0-df81fd68b53e","added_by":"auto","created_at":"2025-06-23 10:16:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7018427,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6568553/v1/2bb96bb7-98a6-478c-8331-04fc098a3e4c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Elevated TyG index outperforms TyG-BMI in predicting delirium among non-diabetic sepsis patients","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSepsis is a systemic inflammatory response syndrome (SIRS) triggered by infection that can lead to multiple organ dysfunction and even death in severe cases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. It is one of the most morbidity and mortality rates worldwide, particularly in intensive care units (ICUs), and is characterized by complex pathophysiological processes, often with multiple organ dysfunction and serious complications [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Delirium is an acute brain dysfunction that occurs in 50\u0026ndash;80% of patients with sepsis. Not only does delirium significantly prolong hospital stays and increase healthcare costs, but it is also strongly associated with long-term cognitive impairment, decreased quality of life, and increased mortality in patients [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Although the exact pathogenesis of delirium is still not fully understood, studies have shown that metabolic disorders, insulin resistance (IR), systemic inflammatory responses, and oxidative stress play a key role in its development. Therefore, identifying biomarkers that can predict delirium early is important to improve outcomes in patients with sepsis [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn recent years, the triglyceride-glucose index (TyG index) and its derivative, the TyG-BMI index, have emerged as important tools for assessing insulin resistance and metabolic abnormalities. The TyG index, calculated based on fasting triglycerides (TG) and blood glucose (FPG), reflects the degree of insulin sensitivity and disturbances in glucose and lipid metabolism [\u003cspan additionalcitationids=\"CR11 CR12 CR13 CR14\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Due to its simplicity and cost-effectiveness, the TyG index has been widely adopted in clinical research. For instance, in the field of cardiovascular diseases, the TyG index has been demonstrated to be closely associated with atherosclerosis, the complexity of coronary artery lesions, and the prognosis of acute myocardial infarction. Studies have shown that an elevated TyG index can independently predict the anatomical complexity of coronary arteries (SYNTAX score\u0026thinsp;\u0026gt;\u0026thinsp;22) in non-diabetic patients with chronic coronary syndrome, and it serves as an early warning indicator for disease progression in acute pancreatitis. Furthermore, the TyG-BMI index, which incorporates body mass index (BMI), enhances its predictive capability for metabolic syndrome and cardiovascular risks, particularly demonstrating higher sensitivity in evaluating obesity-related metabolic disorders [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, despite the extensive research on the TyG index and TyG-BMI index in metabolic and cardiovascular diseases, their application in sepsis-associated delirium remains largely unexplored. The metabolic state of sepsis patients often undergoes dramatic changes due to hypercatabolism, heightened inflammatory responses, and insulin resistance. Studies suggest that insulin resistance may exacerbate oxidative stress, endothelial dysfunction, and neuroinflammation, thereby promoting the onset of delirium [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In addition, obesity, as a key component of metabolic syndrome, may further exacerbate the inflammatory response and metabolic derangement in patients with sepsis through the release of inflammatory factors from adipose tissue, thereby increasing the risk of delirium [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, the value of the TyG-BMI index, which includes obesity parameters (BMI), in predicting delirium has not been validated.\u003c/p\u003e \u003cp\u003eBased on this, this study used the MIMIC-IV database to explore for the first time the predictive value of TyG index and TyG-BMI index in delirium in critically ill patients with non-diabetic sepsis. By comparing the predictive performance of these two indicators, this study aims to provide a theoretical basis for the early identification of high-risk groups and the optimization of intervention strategies. These findings will not only reveal the critical role of metabolic derangement in sepsis-associated delirium, but will also validate whether the TyG-BMI index and its multidimensional metabolic assessment capabilities provide greater clinical applicability. This may provide new insights and approaches for the prevention and management of delirium associated with sepsis.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSource of data\u003c/h2\u003e \u003cp\u003eThis study is a retrospective observational cohort study, utilizing data from the publicly accessible Medical Information Mart for Intensive Care-IV (MIMIC-IV-2.2) database. MIMIC-IV, developed by the Computational Physiology Laboratory at the Massachusetts Institute of Technology (MIT), is a widely used and publicly available medical database that includes clinical data from intensive care unit (ICU) patients at Beth Israel Deaconess Medical Center in Boston, Massachusetts, between 2008 and 2019. The database encompasses demographic information, vital signs, imaging reports, laboratory test results, and diagnoses coded according to the International Classification of Diseases, Ninth Revision (ICD-9) and Tenth Revision (ICD-10) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. To access the data, one of the authors (Shuangmei Zhao) completed the required training and obtained access credentials (Certification ID: 65512045), from which relevant variables for this study were extracted. As all patient health information in the database has been de-identified, additional patient consent was deemed unnecessary. Further details about this public database can be found at: \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. No human or animal clinical trials were involved in this study. Clinical trial registration number: N/A.\u003c/p\u003e \u003cp\u003eThis study included adult patients diagnosed with sepsis based on the International Classification of Diseases, Ninth Revision (ICD-9) and Tenth Revision (ICD-10). The following exclusion criteria were applied: (1) individuals younger than 18 years at initial admission; (2) patients with an ICU stay shorter than 24 hours; (3) patients missing measurements of triglycerides, fasting blood glucose, height, or weight at ICU admission; (4) patients with multiple ICU admissions for sepsis, retaining only data from the first admission; (5) patients diagnosed with diabetes or acute pancreatitis; (6) patients admitted due to delirium, coma, or dementia; and (7) patients with a documented history of neurological disorders or a family history of such conditions.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData extraction\u003c/h3\u003e\n\u003cp\u003eFor data retrieval from the database, PostgreSQL software (version 13.7.2) was deployed. The extraction procedure was facilitated by applying Structured Query Language (SQL). This process targeted the acquisition of data across five principal domains: (1) Demographic data including age, gender, height, weight, and body mass index (BMI). (2) Clinical severity indices include the Glasgow Coma Scale (GCS), Sequential Organ Failure Assessment (SOFA) score, Simplified Acute Physiology Score (SAPS)-II, Oxford Acute Illness Severity Score (OASIS), and Assessment of Acute Physiology and Chronic Health (APS)-III. (3) Physiological indicators, including systolic blood pressure (SBP), diastolic blood pressure (DBP), heart rate, and respiratory rate. (4) Hematological and biochemical markers, including hemoglobin concentration (Hb), red blood cell count (RBC), red blood cell distribution width (RDW), platelet count, white blood cell count (WBC), activated partial thromboplastin time (APTT), prothrombin time (PT), international normalized ratio (INR), serum sodium, serum potassium, serum chloride, anion gap, blood urea nitrogen, high-density lipoprotein (HDL), low-density lipoprotein (LDL), cholesterol, Alanine aminotransferase (ALT), aspartate aminotransferase (AST), and serum creatinine. (5) Existing comorbidities such as hypertension, acute respiratory distress syndrome (ARDS), heart failure (HF), chronic obstructive pulmonary disease (COPD), respiratory failure (RF), liver cirrhosis, pneumonia, hyperlipidemia, chronic kidney disease (CKD), acute renal failure (AKI), malignancy, myocardial infarction, etc., as well as those including mechanical ventilation, vasoactive drugs, continuous renal replacement therapy (CRRT), insulin, statins, Therapeutic interventions including sedative medications and antibiotic use. The observation period for each participant commenced at the time of hospital admission and continued until the onset of delirium. The analysis relied on laboratory values and scores indicative of disease severity, which were collected within the first 24 hours following ICU admission. To mitigate the impact of missing data, variables with an absence rate exceeding 10% were systematically excluded from the analysis.\u003c/p\u003e\n\u003ch3\u003eCalculation of TyG and TyG-BMI\u003c/h3\u003e\n\u003cp\u003eThe TyG index was calculated as ln [fasting glucose (mg/ dl) \u0026times;fasting TG (mg/dl)]/2.\u003c/p\u003e \u003cp\u003eBMI was calculated as body weight (Kg)/height2 (m).\u003c/p\u003e \u003cp\u003eTyG-BMI index was determined based on the combination of TyG index and BMI.\u003c/p\u003e \u003cp\u003eTyG-BMI index was computed according the equation: TyG index\u0026times;BMI [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eClinical outcomes\u003c/h3\u003e\n\u003cp\u003eThe start date of follow-up is the date of admission of the patient. The primary outcome was the incidence of delirium at 28 days, and the secondary outcome measure was length of ICU stay.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eIn this study, participants were divided into quartiles based on their TyG, TyG-BMI values, expressed as Q1 to Q4. Quantitative variables were reported either as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or as the median and interquartile range (IQR), depending on the distribution of the data. Qualitative variables were expressed as counts and proportions. For continuous variables that followed a normal distribution, the t-test or analysis of variance (ANOVA) was utilized for analysis. Conversely, for variables that deviated from normal distribution, the Mann-Whitney U test or Kruskal-Wallis test was applied. Pearson's chi-square test was used to compare categorical variables in TyG, TyG-BMI quartiles. To determine the incidence of delirium at each quartile throughout the observation period.\u003c/p\u003e \u003cp\u003eFurthermore, we utilized the Kaplan-Meier (KM) survival method to ascertain the incidence of within-group endpoints, as defined by TyG, TyG-BMI levels, and employed log-rank tests to determine statistical differences. A Cox proportional hazards regression model was used to assess the hazard ratio (HR) with 95% confidence intervals (95% CI) for the occurrence of an event. A baseline variable with a significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 between delirium and delirium not occurring was included in the multivariate model. In addition, multicollinearity was checked using variance expansion factor (VIF) to ensure variable independence in the study. As suggested by previous studies, the recommended maximum VIF value used in the study was 5. Model I was unadjusted for Age, Gender, HR, RR, BMI, whereas Model II was adjusted for Age, Gender, HR, RR, BMI, Hypertension, Heart failure, Insulin, Statin, Platelet, PT, APTT, ALT, AST, Potassium, RBC, WBC, Bun, Creatinine, Ventilation, CRRT, Sedative drug, Chloride, Liver disease, AKI. Subgroup analyses were performed to explore the correlation between the continuous TyG index and the incidence of delirium in different subgroups. In addition, we constructed a Cox proportional hazards model using restricted cubic splines (RCS), which allowed us to investigate the potential nonlinear relationship between TyG, TyG-BMI changes, and delirium incidence. Subject Operating Characteristic (ROC) curve analysis was then performed to compare the predictive power, sensitivity, and specificity of the two measures to assess the incidence of delirium.\u003c/p\u003e \u003cp\u003eIt was considered that the two-tailed P value of \u0026lt;\u0026thinsp;0.05 indicated statistical significance. Statistical analysis was performed using R software (version 4.4.2) alongside SPSS 22.0 (IBM SPSS Statistics, Armonk, NY, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eAfter screening the data of patients with non-diabetic sepsis in the MIMICIV. database, 2665 patients who met the inclusion criteria were included in this study. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the patient selection process. The baseline characteristics of the included patients were classified according to their 28-day delirium incidence. A total of 2665 patients were divided into delirium group (1187, 45%) and non-delirium group (1486, 55%). Non-survivors tend to be older than survivors. In addition, patients with AKI, respiratory failure, and liver disease have a higher incidence of delirium. The body weight, BMI, platelets, LDL, cholesterol, RBC, APSIII score, TyG, and TyG-BMI in the delirium group were significantly higher than those in the non-delirium group. The proportion of patients in the delirium group receiving insulin, vasoactive drugs, sedative drugs, antibiotics, and mechanical ventilation within 24 hours was significantly lower than that in the non-delirium group. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e gives a detailed comparison of delirium and non-delirium.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics between delirium and non-delirium populations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable Names\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall (n\u0026thinsp;=\u0026thinsp;2665)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-delirium(n\u0026thinsp;=\u0026thinsp;1468)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDelirium(n\u0026thinsp;=\u0026thinsp;1187)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, 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 \u003cp\u003e0.597\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1073 (40.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e835 (40.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e238 (39.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1582 (59.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1216 (59.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e366 (60.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003e62 (49\u0026ndash;72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62 (50\u0026ndash;73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61 (48\u0026ndash;72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80.5 (67.075\u0026ndash;97.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79.8 (66.6\u0026ndash;97.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83.383 (68.975-99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.7 (1.63\u0026ndash;1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.7 (1.63\u0026ndash;1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.7 (1.63\u0026ndash;1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.881\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.77 (23.86-32.905)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.48 (23.56\u0026ndash;32.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.55 (24.668\u0026ndash;33.853)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93 (80\u0026ndash;109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93 (80\u0026ndash;109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93 (80\u0026ndash;110)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.656\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e118 (102-136.923)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119 (102\u0026ndash;137)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e116 (101\u0026ndash;133)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68 (57\u0026ndash;82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68 (57\u0026ndash;82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67 (57.75-78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (16\u0026ndash;25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (16\u0026ndash;24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (17-25.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.7 (8.9\u0026ndash;12.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.6 (8.9\u0026ndash;12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.966 (8.9-12.925)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e186 (118\u0026ndash;257)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e184 (116.5\u0026ndash;254)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e194.5 (122.75-265.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.7 (13.6\u0026ndash;16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.8 (13.6\u0026ndash;16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.4 (13.4\u0026ndash;16.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.57 (2.98\u0026ndash;4.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.55 (2.97\u0026ndash;4.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.69 (3-4.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.2 (8.2-17.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.2 (8.2\u0026ndash;17.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.921 (8.375-17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.931\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnion-gap\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (12\u0026ndash;18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (12\u0026ndash;18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (12-17.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.972\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.2 (7.7\u0026ndash;8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.2 (7.6\u0026ndash;8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.2 (7.7\u0026ndash;8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChloride\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104 (100\u0026ndash;108)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104 (100\u0026ndash;108)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e103 (99\u0026ndash;107)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.12 (8.64\u0026ndash;9.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.06 (8.585\u0026ndash;9.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.3 (8.8\u0026ndash;9.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003eTyG-BMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e254.02 (211.835-308.775)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e249.81 (209.535-304.495)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e269.575 (224.382-322.192)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003ePotassium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.1 (3.7\u0026ndash;4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.1 (3.7\u0026ndash;4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.1 (3.7\u0026ndash;4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e139 (135\u0026ndash;142)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e139 (135\u0026ndash;142)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e139 (135\u0026ndash;141)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.091\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\u003e1.3 (1.2\u0026ndash;1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3 (1.2\u0026ndash;1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3 (1.2\u0026ndash;1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.655\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.7 (12.8\u0026ndash;18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.8 (12.9-18.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.2 (12.5\u0026ndash;18.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.831\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (27.6\u0026ndash;41.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (27.5\u0026ndash;40.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.9 (28.2\u0026ndash;42.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.259\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (28\u0026ndash;52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (28\u0026ndash;52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (29\u0026ndash;53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74 (49\u0026ndash;103)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.469 (49\u0026ndash;100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80 (54\u0026ndash;109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e143 (107-178.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e141 (106\u0026ndash;177)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e149 (110-183.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (18\u0026ndash;73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (18\u0026ndash;73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (18.75-72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.710\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49 (27\u0026ndash;116)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49 (26\u0026ndash;117)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49 (29\u0026ndash;113)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.919\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnion-gap\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (12\u0026ndash;18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (12\u0026ndash;18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (12-17.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.969\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1 (0.8\u0026ndash;1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1 (0.8\u0026ndash;1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1 (0.8\u0026ndash;1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (14\u0026ndash;35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (14\u0026ndash;35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (13\u0026ndash;35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.091\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\u003e7 (4\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (4\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (4\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPSIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53 (39\u0026ndash;71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 (38.5\u0026ndash;69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55 (40\u0026ndash;74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAPSII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 (31\u0026ndash;51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (30.5\u0026ndash;50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41 (31\u0026ndash;52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.063\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\u003e36 (30\u0026ndash;42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (30\u0026ndash;42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (31-42.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (14\u0026ndash;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (14\u0026ndash;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (14\u0026ndash;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatin, 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 \u003cp\u003e0.359\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1382 (52.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1078 (52.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e304 (50.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1273 (47.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e973 (47.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e300 (49.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsulin, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\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 \u003cp\u003e0.734\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e795 (29.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e618 (30.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e177 (29.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1860 (70.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1433 (69.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e427 (70.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVasopressor, 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 \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e556 (20.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e493 (24.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63 (10.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2099 (79.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1558 (75.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e541 (89.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSedative drug, 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 \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e209 (7.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e196 (9.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (2.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2446 (92.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1855 (90.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e591 (97.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntibiotics, 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 \u003cp\u003e0.558\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (0.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2645 (99.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2042 (99.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e603 (99.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\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 \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2182 (82.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1733 (84.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e449 (74.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e473 (17.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e318 (15.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e155 (25.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVentilation, 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 \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e170 (6.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e159 (7.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (1.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2485 (93.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1892 (92.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e593 (98.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParaplegia, 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 \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2389 (89.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1844 (89.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e545 (90.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e266 (10.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e207 (10.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59 (9.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eARDS, 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 \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2632 (99.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2032 (99.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e600 (99.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\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 \u003cp\u003e0.921\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1734 (65.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1338 (65.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e396 (65.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e921 (34.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e713 (34.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e208 (34.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipidemia, 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 \u003cp\u003e0.224\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1965 (74.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1530 (74.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e435 (72.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e690 (25.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e521 (25.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e169 (27.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMyocardial Infarction, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.344\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2215 (83.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1703 (83.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e512 (84.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e440 (16.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e348 (16.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92 (15.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart Failure, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.716\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1912 (72.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1473 (71.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e439 (72.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e743 (27.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e578 (28.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e165 (27.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal Failure, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.718\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2227 (83.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1717 (83.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e510 (84.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e428 (16.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e334 (16.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94 (15.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\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 \u003cp\u003e0.746\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2295 (86.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1770 (86.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e525 (86.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e360 (13.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e281 (13.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79 (13.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver disease, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\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 \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2315 (87.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1770 (86.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e545 (90.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e340 (12.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e281 (13.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59 (9.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\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 \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e241 (9.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e216 (10.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (4.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2414 (90.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1835 (89.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e579 (95.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eRespiratory failure, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1081 (40.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e879 (42.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e202 (33.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1574 (59.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1172 (57.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e402 (66.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver cirrhosis, 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 \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2287 (86.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1746 (85.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e541 (89.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e368 (13.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e305 (14.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63 (10.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\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 \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1387 (52.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1090 (53.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e297 (49.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1268 (47.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e961 (46.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e307 (50.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKD, 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 \u003cp\u003e0.587\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2314 (87.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1792 (87.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e522 (86.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e341 (12.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e259 (12.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82 (13.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD, 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 \u003cp\u003e0.749\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2264 (85.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1746 (85.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e518 (85.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e391 (14.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e305 (14.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86 (14.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssociations between the TyG index and delirium\u003c/h3\u003e\n\u003cp\u003eWhen the TyG index was treated as a continuous variable, Cox proportional hazards analysis showed a significant association between the incidence of delirium and the TyG index. This association was observed in both the unadjusted model (hazard ratio [HR] 1.352; 95% confidence interval [CI] 1.233\u0026ndash;1.484) and the fully adjusted model (HR 1.354; 95% CI 1.225\u0026ndash;1.496). Patients were then divided into four groups based on the quartiles of the TyG index: Q1 (TyG\u0026thinsp;\u0026le;\u0026thinsp;8.64, N\u0026thinsp;=\u0026thinsp;664), Q2 (TyG\u0026thinsp;\u0026gt;\u0026thinsp;8.64, \u0026le; 9.12; N\u0026thinsp;=\u0026thinsp;664), Q3 (\u0026gt;\u0026thinsp;9.12, \u0026le; 9.68; N\u0026thinsp;=\u0026thinsp;663), and Q4 (\u0026gt;\u0026thinsp;9.68; N\u0026thinsp;=\u0026thinsp;664). Cox proportional hazards analysis revealed that the highest quartile of the TyG index (Q4) was significantly associated with the incidence of delirium in both the unadjusted model (HR 1.973; 95% CI 1.561\u0026ndash;2.493) and the adjusted models (Model 1: HR 1.880; 95% CI 1.480\u0026ndash;2.389; Model 2: HR 1.950; 95% CI 1.525\u0026ndash;2.495) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between IR related index and delirium (Cox regression)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroups\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-adjusted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR (95%Cl) P-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR (95%Cl) P-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR (95%Cl) P-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTyG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.352 (1.233\u0026ndash;1.484)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.322 (1.201\u0026ndash;1.455)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.354 (1.225\u0026ndash;1.496)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\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(\u0026le;\u0026thinsp;8.64; N\u0026thinsp;=\u0026thinsp;664)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\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\u003eQ2(\u0026gt;\u0026thinsp;8.64, \u0026le; 9.12; N\u0026thinsp;=\u0026thinsp;664)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.301 (1.012\u0026ndash;1.674) 0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.291 (1.004\u0026ndash;1.661) 0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.283 (0.996\u0026ndash;1.653) 0.053\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(\u0026gt;\u0026thinsp;9.12, \u0026le; 9.68; N\u0026thinsp;=\u0026thinsp;663)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.580 (1.239\u0026ndash;2.015)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.534 (1.200\u0026ndash;1.961)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.572 (1.227\u0026ndash;2.014)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\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(\u0026gt;\u0026thinsp;9.68; N\u0026thinsp;=\u0026thinsp;664)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.973 (1.561\u0026ndash;2.493)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.880 (1.480\u0026ndash;2.389)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.950 (1.525\u0026ndash;2.495)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\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\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTyG-BMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.002 (1.001\u0026ndash;1.003)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.008 (1.005\u0026ndash;1.011)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.009 (1.006\u0026ndash;1.012)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\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(\u0026le;\u0026thinsp;211.75; N\u0026thinsp;=\u0026thinsp;678)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\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\u003eQ2(\u0026gt;\u0026thinsp;211.75, \u0026le; 254.02; N\u0026thinsp;=\u0026thinsp;665)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.146 (0.893\u0026ndash;1.470) 0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.198 (0.922\u0026ndash;1.557) 0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.192 (0.916\u0026ndash;1.553) 0.19\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(\u0026gt;\u0026thinsp;254.02, \u0026le; 308.74; N\u0026thinsp;=\u0026thinsp;656)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.511 (1.193\u0026ndash;1.914)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.640 (1.240\u0026ndash;2.170)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.352 (1.233\u0026ndash;1.484)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\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(\u0026gt;\u0026thinsp;308.74; N\u0026thinsp;=\u0026thinsp;656)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.655 (1.312\u0026ndash;2.087)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.949 (1.332\u0026ndash;2.852)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.352 (1.233\u0026ndash;1.484)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\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\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAssociations between the TyG-BMI and delirium\u003c/h2\u003e \u003cp\u003eCox proportional hazards model analysis showed that when the TyG-BMI index was continuous, the TyG-BMI index was more effective in the unadjusted model (HR 1.002; 95% CI 1.001\u0026thinsp;~\u0026thinsp;1.003) and fully adjusted model (HR 1.009; 95% CI 1.006\u0026thinsp;~\u0026thinsp;1.012), TyG-BMI index was significantly correlated with the incidence of delirium. When TyG-BMI was a nominal variable (Quartile 1: \u0026le;211.75; Q2 :211.75\u0026thinsp;~\u0026thinsp;254.02; Q3: 254.02\u0026thinsp;~\u0026thinsp;308.74; Q4: \u0026gt; 308.74) according to the unadjusted model (Q1 vs. Q2: HR 1.146; 95% CI 0. 893\u0026thinsp;~\u0026thinsp;1.470; Q3: HR 1.511; 95% CI 1.193\u0026thinsp;~\u0026thinsp;1.914; Q4: HR 1.655; 95% CI 1.312\u0026thinsp;~\u0026thinsp;2.087; Trend test P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and model 1 (Q1 vs. Q2: HR, 1.198; 95% CI 0. 922\u0026thinsp;~\u0026thinsp;1.557; Q3: HR 1.640; 95% CI 1.240\u0026thinsp;~\u0026thinsp;2.170; Q4: HR 1.949; 95% CI 1.332\u0026thinsp;~\u0026thinsp;2.852; Trend test P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and model II (Q1 vs. Q2: HR, 1.192; 95% CI 0. 916\u0026thinsp;~\u0026thinsp;1.553; Q3: HR 1.352; 95% CI 1.233\u0026thinsp;~\u0026thinsp;1.484; Q4: HR 1.352; 95% CI 1.233\u0026thinsp;~\u0026thinsp;1.484; Trend test P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). TyG-BMI index was also associated with a higher incidence of delirium, and there was an increasing trend with the increase of TyG-BMI index. The effect of TyG - BMI on ICU length of stay is shown in Schedules 1 and 2. The RCS model revealed a nonlinear relationship between TyG-BMI and the incidence of delirium, where TyG-BMI was a continuous variable (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC nonlinear P\u0026thinsp;=\u0026thinsp;0.366). TyG-BMI Quartile The K-M curve for the occurrence of delirium at 28 days is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC. The results showed that the cumulative incidence of delirium increased with the increase in the TyG-BMI (p\u0026thinsp;=\u0026thinsp;0.00064) quartile.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eROC curve analysis of TyG and TyG-BMI\u003c/h2\u003e \u003cp\u003eThe ROC curves for the ability of the two indicators to predict the incidence of delirium in patients with non-diabetic sepsis are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The results showed that the Ty G index was superior to the Ty G - BMI [ 0.589 vs. 0.566] in predicting the incidence of delirium. We then performed subgroup analyses to assess the relationship between Ty G, TyG-BMI index and the incidence of delirium in different subgroups, with patients at quartile 4 consistently showing a higher risk of death in all subgroups defined by sex (male and female), presence or absence of hypertension, renal failure, myocardial infarction, liver disease, cerebrovascular disease, statin and mechanical ventilation use. This pattern held true with or without adjustment for covariates, and no significant interactions were found (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Finally, whether the IR index further improves the predictive power of the underlying model (including age, sex, HR, RR, BMI, hypertension, heart failure, insulin, statins, platelets, PT, APTT, ALT, AST, potassium, red blood cells, white blood cells, urea, creatinine, ventilation, CRRT, sedatives, chlorides, liver disease, AKI). The area under the curve (AUC) used for comparison is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Unfortunately, the results of this study suggest that the incremental predictive power of the two IR indices for the basic risk model in non-diabetic sepsis patients is not significant.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo the best of our knowledge, this study is the first to explore the predictive value of the triglyceride-glucose index (TyG index) and its derivative, the TyG-BMI index, for delirium in critically ill non-diabetic sepsis patients, and to promote the further application of the TyG index and TyG-BMI index in the field of critical care medicine. The results showed that both TyG index and TyG-BMI index were significantly associated with the occurrence of delirium in sepsis patients, however, the two IR indices did not significantly improve the prediction performance of the basic risk model for delirium risk, but the TyG index seemed to be the most promising indicator for prevention and risk stratification in non-diabetic sepsis patients. This finding provides a new metabolic biomarker for the early identification and intervention of sepsis-related delirium, and provides a theoretical basis for optimizing the delirium risk assessment model in clinical practice.\u003c/p\u003e \u003cp\u003eIn the field of critical care, the relationship between BMI and patient prognosis has been extensively explored in previous studies. A retrospective observational cohort study based on the MIMIC-IV v2.2 and EICU collaborative research database found that BMI in patients with sepsis had an L-shaped relationship with ICU mortality. When the BMI is lower than a specific cut-off point, the ICU mortality rate increases significantly as the BMI decreases. Above the cut-off point, an increase in BMI also leads to an increase in mortality. This suggests that BMI can be used as an important indicator to assess the risk level and prognosis of patients with sepsis, but the study did not address the link between BMI and delirium [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In this study, the TyG-BMI index was introduced, and the BMI was combined with the TyG index reflecting insulin resistance, in an attempt to reveal its predictive effect on delirium in critically ill patients with non-diabetic sepsis, and provide a new idea for clinical evaluation. Research on insulin resistance-related indicators and outcomes in critically ill patients has also attracted much attention. Studies in critically ill patients with chronic heart failure (CHF) have shown that TyG index, as a surrogate indicator of insulin resistance, is independently associated with 5-year mortality and is superior to TyG-BMI and TG/HDL-C in predicting all-cause mortality at 5-year mortality [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. However, these studies focused primarily on patients with CHF, differed in the pathophysiology of the disease from the non-diabetic sepsis critically ill patient population in this study, and focused on mortality rather than delirium. This study specifically focused on critically ill patients with non-diabetic sepsis, and studied the predictive value of TyG and TyG-BMI index on delirium, which is helpful to understand the risk factors for delirium in this specific patient group and provide a more targeted basis for clinical intervention.\u003c/p\u003e \u003cp\u003eIn terms of the prediction model of delirium in critically ill patients, some studies have developed delirium prediction models based on logistic regression, random forest and bidirectional long short-term memory (BiLSTM) algorithms using the eICU Collaborative Research Database (eICU-crd) and the Critical Care Medical Information Database Version III (MIMIC-III) databases. Among them, the BiLSTM model has the best performance and can effectively predict delirium to a certain extent under different prediction windows [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. However, most of these models are based on a variety of complex clinical parameters, and this study focuses on TyG and TyG-BMI, two relatively concise and metabolically related indicators, which are easier to operate and easier to be applied clinically, providing a more targeted and practical method for the prediction of delirium. In addition, there are also studies on the relationship between the TyG index and delirium in older patients. A study in patients aged 65 years and older showed a direct correlation between the TyG index and the risk of delirium in the ICU. Through the analysis of MIMIC-IV and eICU-crd databases, it was found that the TyG index can be used as a reliable indicator to assess the risk of delirium in elderly ICU patients [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. However, this study focused on critically ill patients with non-diabetic sepsis, further refined the study subjects, and explored the predictive value of TyG and TyG-BMI index in this specific population, which is different from previous studies.\u003c/p\u003e \u003cp\u003eFirstly, this study validates the potential value of the TyG index in predicting sepsis-associated delirium. The TyG index has been widely used in the research of cardiovascular and metabolic diseases as a reliable indicator of insulin resistance (IR) and metabolic disorders [\u003cspan additionalcitationids=\"CR33 CR34\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. However, its use in sepsis and its complications remains limited. This study found that an elevated TyG index was significantly associated with an increased risk of delirium in non-diabetic sepsis patients, which may be closely related to metabolic disturbances, oxidative stress, and neuroinflammation caused by insulin resistance [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Patients with sepsis often experience a hypercatabolic and enhanced inflammatory response, and insulin resistance may exacerbate endothelial dysfunction and neuroinflammation, thereby contributing to the onset of delirium. This finding is consistent with previous studies describing the role of insulin resistance in neurological complications [\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Secondly, this study further examines the predictive performance of the TyG-BMI index. The TyG-BMI index not only reflects glucose and lipid metabolism, but also incorporates obesity parameters (BMI), allowing for a more comprehensive assessment of the interaction between neurological metabolism and inflammation [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Compared to traditional clinical scoring systems such as SOFA and APACHE II, the TyG index and TyG-BMI index offer the advantages of simplicity and cost-effectiveness, while also dynamically reflecting patients' metabolic status. Although traditional scoring systems can assess disease severity, they lack attention to metabolic indicators. This study suggests that incorporating the TyG index or TyG-BMI index into delirium risk assessment models may help identify high-risk patients earlier, thereby providing a basis for personalized interventions. For example, early interventions targeting insulin resistance and metabolic disturbances, such as nutritional support and blood glucose control, may help reduce the incidence of delirium.\u003c/p\u003e \u003cp\u003eHowever, there are some limitations to this study that must be addressed. First, as a retrospective study, its results may be influenced by confounding factors. Although we adjusted for these factors by multivariate analysis, further validation through prospective studies is needed. Second, despite the abundance of data in the MIMIC-IV database, there may be cases where the data is missing, inaccurate, or incomplete during recording. These issues may interfere with the accuracy of the study results and lead to bias in assessing the predictive value of the TyG and TyG-BMI indices. In addition, data collected from a single center, due to the lack of standardization, lack of reference intervals in the general population and different subgroups, make it difficult to implement the results in clinical practice. These factors need to be further explored in future research.\u003c/p\u003e \u003cp\u003eIn summary, this study demonstrates for the first time the value of the TyG index and the TyG-BMI index in predicting delirium in critically ill patients with non-diabetic sepsis and highlights the potential advantages of the TyG-BMI index due to its multidimensional metabolic assessment capabilities. In addition, the findings provide new insights for the early identification and intervention of sepsis-related delirium and lay a foundation for future related research. By further optimizing the application of metabolic biomarkers in clinical practice, we can improve the prognosis of patients with sepsis, reduce the incidence of delirium, and thus improve the quality of life and long-term survival of patients.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eTo the best of our knowledge, this study is the first to compare the ability of two surrogate measures of IR, Ty G index and Ty G - BMI index, to predict the occurrence of delirium in non-diabetic sepsis patients. Despite the inclusion of additional risk variables, both the Ty G index and the Ty G - BMI showed a strong correlation with delirium. While the inclusion of these indicators in the basic risk model did not lead to an improvement in delirium prediction performance, the Ty G-index appears to be the most promising indicator for prevention and risk stratification in non-diabetic sepsis patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eTyG \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Triglyceride glucose index\u003c/p\u003e\n\u003cp\u003eTyG-BMI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Triglyceride glucose-body mass index\u003c/p\u003e\n\u003cp\u003eBMI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;body mass index\u003c/p\u003e\n\u003cp\u003eRR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; respiratory rate\u003c/p\u003e\n\u003cp\u003eHR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; heart rate\u003c/p\u003e\n\u003cp\u003eIQR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;interquartile range\u003c/p\u003e\n\u003cp\u003eGCS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Glasgow Coma Scale\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSOFA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; sequential organ failure assessment\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSAPS-II \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; simplified acute physiological score II\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOASIS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Oxford acute severity of illness score\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAPS-III \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Acute Physiology and Chronic Health\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSBP \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;systolic blood pressure\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDBP \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;diastolic blood pressure\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRBC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;red blood cell\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRDW \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; red blood cell distribution width\u003c/p\u003e\n\u003cp\u003eLDL \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;low-density lipoprotein\u003c/p\u003e\n\u003cp\u003eHDL \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;high-density lipoprotein\u003c/p\u003e\n\u003cp\u003eAST \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Alanine aminotransferase\u003c/p\u003e\n\u003cp\u003eALT \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;aspartate aminotransferase\u003c/p\u003e\n\u003cp\u003eWBC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; white blood cell\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePLT \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;platelet\u003c/p\u003e\n\u003cp\u003eHb \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; hemoglobin\u003c/p\u003e\n\u003cp\u003eAPTT \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;activated partial thromboplastin time\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePT \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Prothrombin time\u003c/p\u003e\n\u003cp\u003eINR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;international normalized ratio\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; hazard ratio\u003c/p\u003e\n\u003cp\u003eCI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; confidence interval\u003c/p\u003e\n\u003cp\u003eCOPD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;chronic obstructive pulmonary disease\u003c/p\u003e\n\u003cp\u003eAKI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;acute renal failure\u003c/p\u003e\n\u003cp\u003eCKD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; chronic kidney disease\u003c/p\u003e\n\u003cp\u003eARDS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;acute respiratory distress syndrome\u003c/p\u003e\n\u003cp\u003eCRRT \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;continuous renal replacement therapy\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eSupplementary Information\u003c/h2\u003e\n\u003cp\u003eThe online version contains supplementary material available at the end of the document.\u003c/p\u003e\n\u003ch2\u003eAcknowledgments\u003c/h2\u003e\n\u003cp\u003eWe thank the participants and the staff of the cohorts for their continuing dedication and efforts.\u003c/p\u003e\n\u003ch2\u003eAuthor contributions\u003c/h2\u003e\n\u003cp\u003eShuangmei Zhao and Fuxu Wang designed the study. Guangdong Wang, Kaige Xuan and Chucheng Jiao extracted, collected, and analyzed this data. Shuang Mei Zhao compiled tables and figures. Liu Tao Sui and Zhi Maoreviewed the results, interpreted the information, and wrote the manuscript. All authors have made equal contributions to the manuscript and have been approved for submission.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThe project was supported by the National Natural Science Foundation of China (No. 82171299).\u003c/p\u003e\n\u003ch2\u003eAvailability of data and material\u003c/h2\u003e\n\u003cp\u003ePublicly available datasets were analyzed in this study. These data can be found at https://mimic.mit.edu/.\u003c/p\u003e\n\u003ch2\u003eEthical approval\u003c/h2\u003e\n\u003cp\u003eThe ethical approval and participation consent followed the Helsinki Declaration guidelines. Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center review committee approved using the MIMIC-III database. Given that the data is accessible to the public through the MIMIC-IV database, the need for ethical approval and informed consent was waived.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eAuthor details\u003c/h2\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eDepartment of Critical Care Medicine, The Affiliated Hospital of Qingdao University, Qingdao, China.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eDepartment of Respiratory and Critical Care Medicine, First Affiliated Hospital of Xi\u0026rsquo;an Jiao tong University, Xi\u0026rsquo;an, Shanxi, China.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u003c/sup\u003eDepartment of Critical Care Medicine, The First Medical Center of PLA General Hospital, Beijing, China.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e4\u003c/sup\u003eDepartment of Neurology, The Affiliated Hiser Hospital of Qingdao University, Qingdao, China.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGong T, Liu YT, Fan J. 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Cardiovasc Diabetol. 2025;24(1):56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOuyang Q, Xu L, Yu M. Associations of triglyceride glucose-body mass index with short-term mortality in critically ill patients with ischemic stroke. Cardiovasc Diabetol. 2025;24(1):91.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Triglyceride glucose index (TyG), Triglyceride glucose-body mass index (TyG-BMI), sepsis, delirium, length of ICU stay","lastPublishedDoi":"10.21203/rs.3.rs-6568553/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6568553/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe Triglyceride Glucose (TyG) index and TyG-Body Mass Index (TyG-BMI), recognized as validated surrogate markers of insulin resistance (IR), have demonstrated prognostic utility in various metabolic disorders. However, their potential as predictive biomarkers for sepsis-associated delirium (SD) in non-diabetic populations remains unexplored. This study aims to systematically evaluate and compare the predictive performance of TyG and TyG-BMI indices for delirium incidence among sepsis patients without diabetes mellitus.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e Our study included a comprehensive retrospective observational cohort analysis, utilizing an extensive dataset from the Critical Care Medical Information Market IV (MIMIC-IV 2.2). The study population was divided into quartiles based on triglyceride-glucose (TyG) index and TyG-body mass index (TyG-BMI). The primary outcome assessed was the incidence of delirium at 28 days, and the secondary outcome was length of ICU stay. To evaluate the relationship between the TyG index, TyG-BMI, and delirium, we employed a Cox proportional hazards regression model, supplemented by constrained cubic spline function (RCS) analysis to improve accuracy. In addition, the Kaplan-Meier (KM) method was used to estimate the survival probability, and the receiver operating characteristic (ROC) curve was plotted to compare the ability of the two indicators to predict delirium.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 2,665 non-diabetic sepsis patients were identified from the database. The Cox proportional hazards model revealed that the TyG index was independently associated with the 28-day incidence of delirium (hazard ratio [HR], 1.354; 95% confidence interval [CI], 1.225\u0026ndash;1.496). Similarly, the TyG-BMI index also showed a significant correlation with the 28-day delirium incidence, with HRs (95% CI) of 1.009 (1.006\u0026ndash;1.012), respectively. Kaplan-Meier (K-M) analysis demonstrated that the cumulative incidence of 28-day delirium increased with higher quartiles of the TyG index or TyG-BMI index. Based on the ROC curve analysis, the TyG index exhibited better predictive performance for the 28-day incidence of delirium (AUC: 0.589) compared to the TyG-BMI index (AUC: 0.566). The effect of the TyG index on delirium occurrence remained consistent across subgroups, with no significant interactions observed with randomization factors. Additionally, incorporating the TyG index into the base model for 28-day delirium prediction slightly improved its predictive capability (AUC: 0.708 for the base model vs. 0.715 for the base model\u0026thinsp;+\u0026thinsp;TyG index).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eAs a continuous variable, both measures showed a significant association with the 28-day risk of delirium in critically ill patients with non-diabetic sepsis, and the TyG index became the most promising indicator of risk stratification and prevention strategies in critically ill patients with non-diabetic sepsis, superior to the TyG-BMI index.\u003c/p\u003e","manuscriptTitle":"Elevated TyG index outperforms TyG-BMI in predicting delirium among non-diabetic sepsis patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-15 10:31:30","doi":"10.21203/rs.3.rs-6568553/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b8fda7b1-6760-4900-bc4f-ff61490c8bf5","owner":[],"postedDate":"May 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-23T10:08:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-15 10:31:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6568553","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6568553","identity":"rs-6568553","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}