Comparative Prognostic Value of SHR, TYG, and CHG for Predicting In-Hospital Cardiac Arrest in Critically ill Patients: A Dual-Center Cohort Study

Comparative Prognostic Value of SHR, TYG, and CHG for Predicting In-Hospital Cardiac Arrest in Critically ill Patients: A Dual-Center Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Comparative Prognostic Value of SHR, TYG, and CHG for Predicting In-Hospital Cardiac Arrest in Critically ill Patients: A Dual-Center Cohort Study zhitao zhong, Qiong Long This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8145693/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 Purpose In-hospital cardiac arrest (IHCA) is a high-mortality event requiring better early risk stratification. This study aimed to investigate the association between three accessible insulin resistance (IR) markers—the Stress Hyperglycemia Ratio (SHR), Triglyceride-Glucose Index (TYG), and Cholesterol, High-Density Lipoprotein, and Glucose index (CHG)—and the risk of IHCA in critically ill patients. Patients and Methods: This dual-center retrospective cohort study included adult patients from the MIMIC-IV (development) and NWICU (validation) databases. The associations between admission levels of SHR, TYG, and CHG and the primary outcome of IHCA, along with secondary outcomes (acute kidney injury [AKI] and sepsis), were assessed using multivariable logistic regression. We further explored dose-response relationships with restricted cubic splines (RCS) and threshold effect analysis. The robustness of findings was tested via subgroup analyses, and potential mechanisms were explored using mediation analysis. Predictive performance was compared using receiver operating characteristic (ROC) curves. Results A total of 3,059 patients from MIMIC-IV and 1,849 from NWICU were included. In the MIMIC-IV cohort, after full multivariable adjustment, elevated levels of SHR (OR 2.888, 95% CI 1.883–4.427), TYG (OR 1.446, 95% CI 1.075–1.946), and CHG (OR 1.580, 95% CI 1.050–2.378) were all independently associated with an increased risk of IHCA ( P < 0.05). Restricted cubic splines revealed a significant non-linear, dose-response relationship between SHR and IHCA ( P for non-linearity = 0.007), whereas the associations for TYG and CHG were linear ( P for non-linearity > 0.05). Mediation analysis indicated that the white blood cell (WBC) partially mediated these associations, accounting for 11.3%, 12.1%, and 14.5% of the total effect for SHR, TYG, and CHG, respectively. These findings, including significant associations with the secondary outcomes of AKI and sepsis, were successfully validated in the NWICU cohort. In predictive performance for IHCA, ROC analysis confirmed that SHR had the superior discriminatory ability (AUC = 0.763), outperforming both TYG (AUC = 0.624) and CHG (AUC = 0.639). Conclusion Elevated admission levels of SHR, TYG, and CHG are independent predictors of IHCA and other adverse outcomes in a broad population of critically ill patients. Among them, SHR demonstrated the strongest predictive ability. These readily available and inexpensive markers may serve as valuable tools for early bedside risk stratification to identify patients at high risk for circulatory collapse. In-Hospital Cardiac Arrest Stress Hyperglycemia Ratio Triglyceride-Glucose Index Cholesterol high-density lipoprotein glucose index Critical Care Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction In-hospital cardiac arrest (IHCA) represents a catastrophic event within the intensive care unit (ICU). In the United States alone, over 290,000 adults experience IHCA annually, and despite advances in resuscitation science, survival to hospital discharge remains dismally low at approximately 25%.[ 1 , 2 ] The success of treatment fundamentally depends on the early recognition of impending cardiac arrest and rapid intervention.[ 3 ] While established scoring systems, such as the National Early Warning Score (NEWS) or the Modified Early Warning Score (MEWS), exist to identify deteriorating patients, they often rely on a composite of physiological parameters and may lack optimal specificity.[ 4 , 5 ] Therefore, there remains a critical need for simple, accessible biomarkers that reflect underlying pathophysiological derangements preceding circulatory collapse. A central, yet often underappreciated, contributor to clinical deterioration in the critically ill is acute metabolic dysregulation, specifically insulin resistance (IR).[ 6 ] IR is a pathophysiological state characterized by a diminished cellular response to insulin, leading to an impaired ability of the hormone to effectively facilitate glucose uptake and utilization.[ 6 – 9 ] The gold standard for quantifying IR, the hyperglycemic clamp technique, is labor-intensive and unsuitable for routine clinical practice in the ICU setting.[ 10 ] This limitation has spurred the search for practical surrogate markers that can be derived from routinely collected laboratory data. In recent years, several glycolipid-based indices have emerged as powerful and convenient tools for assessing IR. These include the Triglyceride-Glucose Index (TYG),[ 11 ] the Stress Hyperglycemia Ratio (SHR) which adjusts admission glucose for chronic glycemic status using HbA1c,[ 12 ] and the more novel Cholesterol, High-Density Lipoprotein, and Glucose index (CHG).[ 13 ] While numerous studies have validated the prognostic utility of these markers for various adverse outcomes in critical care, the existing literature has several significant limitations. First, previous investigations have predominantly focused on mortality as the primary endpoint [ 14 – 17 ] While important, this outcome does not capture the risk of a specific, acute event like IHCA. Second, while one study has explored the link between SHR and mortality after a cardiac arrest has occurred, no research has yet investigated whether these IR markers can predict the initial occurrence of IHCA, representing a crucial knowledge gap.[ 18 ] Third, data is still scarce regarding the comparative prognostic performance of these three markers within a single, large cohort, and many findings originate from single-center studies, which may limit their generalizability.[ 19 ] These deficiencies underscore that the relationship between these accessible IR markers and the imminent risk of IHCA remains largely unexplored. Therefore, this study aims to address these critical gaps by utilizing two large, independent critical care databases. The primary objective of this study was to determine the association between admission levels of SHR, TYG, and CHG and the subsequent risk of IHCA in a broad population of critically ill patients. We hypothesized that elevated levels of these IR markers would be independently associated with an increased incidence of IHCA and other major adverse ICU outcomes. The findings may provide new insights for early risk stratification and could help clinicians identify high-risk patients who may benefit from heightened surveillance or targeted metabolic interventions. Methods Data source This retrospective cohort study was conducted using data from two independent, publicly available critical care databases. The development cohort was derived from the Medical Information Mart for Intensive Care IV (MIMIC-IV, v3.1), a large, single-center database containing de-identified health records for patients admitted to intensive care units (ICUs) at the Beth Israel Deaconess Medical Center (BIDMC) in Boston, Massachusetts, between 2008 and 2022. External validation was performed using the Northwestern ICU (NWICU, v0.1.0) database, which comprises de-identified data from patients admitted to ICUs at Northwestern Memorial Hospital (NMHC) between 2020 and 2022. For MIMIC-IV, this approval was granted by the institutional review board (IRB) of the Massachusetts Institute of Technology (MIT) and BIDMC. For the NWICU database, the Northwestern University (NU) IRB approved participation in the project, and the release of data from the Northwestern Medicine Enterprise Data Warehouse was additionally authorized by the NM Data Steward. The requirement for individual patient consent was waived for both databases because the research involved de-identified data, posed minimal risk to patients, did not impact clinical care, and obtaining specific consent was deemed impractical. One author (Z.Z., Certificate number: 47608458) obtained certified access to use the databases after completing the requisite training. Participant selection We included adult patients (aged ≥ 18 years) admitted to an ICU for the first time during their initial hospitalization. The exclusion criteria were as follows: (1) patients with an ICU length of stay less than 24 hours; and (2) patients with missing data for the components required to calculate the primary exposure variables (SHR, TYG, and CHG) on the first day of ICU admission. Clinical outcomes The primary outcome was the occurrence of IHCA. Secondary outcomes included the development of acute kidney injury (AKI) and sepsis during hospitalization. Sepsis was defined according to the Sepsis-3 criteria as life-threatening organ dysfunction caused by a dysregulated host response to infection, identified by an acute increase in the Sequential Organ Failure Assessment (SOFA) score of ≥ 2 points.[ 20 ] AKI was defined based on the Kidney Disease: Improving Global Outcomes (KDIGO) criteria: an increase in serum creatinine by ≥ 0.3 mg/dL within 48 hours, an increase in serum creatinine to ≥ 1.5 times baseline within 7 days, or a urine volume of < 0.5 mL/kg/h for 6 hours.[ 21 ] Data extraction Data were extracted for the first 24 hours of each patient's ICU stay using PostgreSQL with Structured Query Language (SQL). The extracted data included: (1) baseline demographics (age, gender, race, weight); (2) comorbidities (history of diabetes, heart failure, hypertension); (3) vital signs (heart rate [HR], systolic blood pressure [SBP], diastolic blood pressure [DBP], respiratory rate [RR], temperature, and pulse oxygen saturation [SpO₂]); and (4) a comprehensive panel of laboratory parameters. These parameters encompassed hematological tests (white blood cell [WBC] count, red blood cell [RBC] count, platelet [PLT] count, hemoglobin [Hb], hematocrit [Hct], red cell distribution width [RDW]), metabolic and renal function markers (glucose [Glu], blood urea nitrogen [BUN], creatinine, sodium, potassium, total calcium, chloride, phosphorus, magnesium), lipid profiles (triglycerides [TG], total cholesterol [TC], high-density lipoprotein cholesterol [HDL-C], low-density lipoprotein cholesterol [LDL-C]), coagulation status (prothrombin time [PT], partial thromboplastin time [PTT]), and markers of organ injury (alanine aminotransferase [ALT], aspartate aminotransferase [AST], creatine kinase [CK], total bilirubin), as well as glycated hemoglobin (HbA1c). The primary exposure variables, three insulin resistance (IR) markers, were calculated using the following formulas: Stress Hyperglycemia Ratio (SHR) = Glu (mg/dL) / (28.7 × HbA1c [%] − 46.7). Triglyceride-Glucose Index (TYG) = Ln [TG (mg/dL) × Glu (mg/dL) / 2]. Cholesterol, High-Density Lipoprotein, and Glucose index (CHG) = Ln [TC (mg/dL) × Glu (mg/dL) / (HDL-C (mg/dL) × 2)]. Statistical analysis Continuous variables were presented as median (interquartile range, IQR) due to their non-normal distribution, while categorical variables were expressed as numbers (percentages). Differences in baseline characteristics between patient groups (e.g., with vs. without IHCA) were compared using the Kruskal-Wallis test for continuous variables and the chi-square test or Fisher's exact test for categorical variables, as appropriate. To build the multivariable models, we first employed univariate binary logistic regression to screen for potential confounders associated with the clinical outcomes. Variables with a P -value < 0.1 in the univariate analysis,[ 22 ] along with those deemed clinically significant, were considered for inclusion in the multivariable models. To mitigate multicollinearity, we calculated the variance inflation factor (VIF) for all selected variables and ensured that all VIF values were less than 5.[ 23 ] Multivariable logistic regression analysis was performed to assess the independent association between the insulin resistance (IR) markers (SHR, TYG, and CHG) and the clinical outcomes (IHCA, AKI, and sepsis). The IR markers were analyzed both as continuous variables and as categorical variables based on tertiles. We constructed two adjusted models: Model Ⅰ, which adjusted for age, gender, race, weight, and heart failure; and Model Ⅱ, which included covariates from Model I plus SBP, WBC, total calcium, magnesium, and PTT. Results were presented as odds ratios (OR) with 95% confidence intervals (CI). To explore potential non-linear relationships between the continuous IR markers and outcomes, we utilized restricted cubic splines (RCS) with four knots. If a non-linear association was detected (P for non-linearity < 0.05), a two-piecewise linear regression model was applied to calculate the threshold effect and identify the inflection point. The predictive performance of SHR, TYG, and CHG for the outcomes was evaluated and compared using the area under the receiver operating characteristic curve (AUC). Subgroup analyses were conducted across various strata, including age, gender, race, diabetes, heart failure, and hypertension, to assess the robustness of our findings. P-values for interaction were calculated to examine whether the effects of the IR markers on outcomes differed across these subgroups. Finally, a mediation analysis was conducted to explore whether WBC mediated the relationship between the IR markers and the clinical outcomes. Variables with a missing rate higher than 50% were excluded from the analysis, and multiple imputation techniques were employed to address the remaining missing values.[ 24 ] All statistical analyses were conducted using R software (version 4.4.1). The P-value < 0.05 was considered statistically significant.[ 25 ] Results Baseline characteristics A total of 3,059 critically ill patients from the MIMIC-IV database and 1,849 from the NWICU database were ultimately enrolled in this study following a rigorous screening process (Fig. 1 ). In the MIMIC-IV cohort, the rates of IHCA, AKI, and sepsis were 2.8% (n = 85), 72.3% (n = 2,213), and 31.1% (n = 952), respectively. In the external validation NWICU cohort, the corresponding incidence rates were 1.8% (n = 34) for CA, 6.1% (n = 113) for AKI, and 2.4% (n = 45) for sepsis. In the MIMIC-IV cohort, patients who subsequently developed CA presented with a distinct and more severe clinical profile upon admission (Table 1 ). Notably, they were significantly younger (median 65 vs. 71 years, P = 0.003), had a higher body weight, and a greater proportion were male. Comorbidities were also markedly different, with a significantly higher prevalence of heart failure (51.8% vs. 23.9%, P < 0.001) but a lower prevalence of hypertension (35.3% vs. 54.8%, P < 0.001) in the CA group. Crucially, the CA group exhibited markedly elevated admission levels of all three insulin resistance markers: SHR (1.309 vs. 1.008), TYG (9.078 vs. 8.735), and CHG (5.632 vs. 5.344) (P < 0.001). This metabolic derangement was accompanied by evidence of profound systemic physiological distress, including significantly higher levels of inflammatory markers (WBC), markers of hepatic and myocardial injury (AST, ALT, CK), indicators of renal dysfunction (creatinine) (P < 0.001). Table 1 Baseline characteristics of patients stratified by in-hospital cardiac arrest Variables MIMIC (n = 3059) P NWICU (n = 1849) P No IHCA (n = 2974) IHCA (n = 85) No IHCA (n = 1815) IHCA (n = 34) Baseline characteristics Age (years) 71.000 (59.000, 82.000) 65.000 (57.000, 74.000) 0.003 66.000 (56.000, 76.000) 64.000 (58.250, 72.500) 0.293 Weight (kg) 78.200 (65.600, 93.175) 87.500 (74.200, 102.500) < 0.001 80.640 (67.210, 98.520) 92.890 (79.270, 111.835) 0.009 Gender 0.030 0.005 Female 1369 (46.032%) 29 (34.118%) 753 (41.488%) 6 (17.647%) Male 1605 (53.968%) 56 (65.882%) 1062 (58.512%) 28 (82.353%) Race 0.250 0.015 Other 1283 (43.141%) 42 (49.412%) 628 (34.601%) 5 (14.706%) White 1691 (56.859%) 43 (50.588%) 1187 (65.399%) 29 (85.294%) Comorbidities Diabetes 0.457 0.874 No 2105 (70.780%) 57 (67.059%) 1612 (88.815%) 31 (91.176%) Yes 869 (29.220%) 28 (32.941%) 203 (11.185%) 3 (8.824%) Heart failure < 0.001 0.244 No 2262 (76.059%) 41 (48.235%) 1674 (92.231%) 29 (85.294%) Yes 712 (23.941%) 44 (51.765%) 141 (7.769%) 5 (14.706%) Hypertension < 0.001 0.657 No 1343 (45.158%) 55 (64.706%) 1495 (82.369%) 29 (85.294%) Yes 1631 (54.842%) 30 (35.294%) 320 (17.631%) 5 (14.706%) Vital signs HR (bpm) 80.000 (69.000, 93.000) 85.000 (69.000, 101.000) 0.070 81.000 (70.000, 97.000) 82.500 (71.000, 98.500) 0.685 SBP (mmHg) 136.000 (120.000, 153.000) 124.000 (104.000, 139.000) < 0.001 141.000 (125.000, 159.000) 131.500 (122.250, 152.500) 0.148 DBP (mmHg) 76.500 (66.000, 89.000) 74.000 (64.000, 87.000) 0.176 76.000 (66.000, 88.000) 77.500 (70.000, 86.000) 0.644 RR (bpm) 18.000 (15.000, 21.750) 19.000 (16.000, 24.000) 0.023 20.000 (17.000, 24.000) 21.500 (18.250, 24.000) 0.080 SpO2 (%) 98.000 (96.000, 99.000) 98.000 (96.000, 100.000) 0.255 97.000 (95.000, 99.000) 98.000 (96.000, 99.000) 0.389 Temperature (°C) 36.780 (36.560, 37.000) 36.720 (36.440, 37.110) 0.837 36.722 (36.500, 37.000) 36.667 (36.389, 36.819) 0.035 Laboratory tests SHR 1.008 (0.872, 1.188) 1.309 (1.119, 1.567) < 0.001 1.093 (0.911, 1.307) 1.661 (1.277, 2.342) < 0.001 TYG 8.735 (8.360, 9.197) 9.078 (8.606, 9.530) < 0.001 8.868 (8.417, 9.386) 9.516 (9.185, 10.130) < 0.001 CHG 5.344 (5.045, 5.682) 5.632 (5.240, 5.947) < 0.001 5.505 (5.157, 5.917) 6.195 (5.742, 6.394) < 0.001 Glu (mg/dL) 122.000 (103.000, 151.000) 155.250 (127.500, 217.500) < 0.001 133.000 (108.000, 177.000) 241.500 (168.000, 317.750) < 0.001 HbA1c (%) 5.700 (5.400, 6.300) 5.700 (5.300, 6.300) 0.680 5.800 (5.500, 6.600) 5.800 (5.500, 6.650) 0.544 TG (mg/dL) 100.500 (73.000, 141.000) 100.000 (72.000, 147.000) 0.767 103.000 (73.000, 148.000) 125.000 (91.750, 190.000) 0.025 TC (mg/dL) 160.000 (130.250, 192.000) 145.000 (112.000, 182.000) 0.003 153.000 (126.000, 189.000) 165.500 (143.250, 186.250) 0.448 HDL-C (mg/dL) 46.750 (37.000, 58.000) 42.000 (35.000, 51.000) 0.002 42.000 (34.000, 52.000) 41.000 (31.000, 49.500) 0.453 LDL-C (mg/dL) 86.500 (63.000, 114.000) 71.000 (53.000, 103.000) 0.009 85.000 (60.000, 115.000) 89.000 (57.250, 105.500) 0.867 WBC (K/uL) 9.800 (7.500, 12.500) 14.060 (10.600, 17.750) < 0.001 9.300 (7.300, 12.200) 12.650 (10.050, 15.200) < 0.001 RBC (m/uL) 4.120 (3.670, 4.550) 4.070 (3.560, 4.630) 0.784 4.440 (3.770, 4.960) 4.665 (4.290, 5.100) 0.104 PLT (K/uL) 211.000 (169.330, 260.000) 211.500 (180.330, 269.500) 0.510 233.000 (188.000, 286.000) 236.500 (172.000, 292.250) 0.807 Hb (g/dL) 12.400 (11.000, 13.650) 12.400 (10.850, 13.800) 0.788 13.600 (12.000, 14.900) 14.300 (13.575, 15.275) 0.024 RDW (%) 13.700 (13.000, 14.600) 13.900 (13.050, 14.900) 0.232 13.400 (12.900, 14.500) 13.050 (12.625, 13.925) 0.042 Hct (%) 37.460 (33.500, 40.900) 37.400 (33.200, 41.270) 0.954 41.100 (36.750, 44.850) 44.500 (42.100, 46.575) < 0.001 Sodium (mEq/L) 139.000 (137.000, 141.500) 138.500 (135.750, 140.200) 0.003 138.000 (136.000, 140.000) 139.000 (135.250, 140.000) 0.269 Potassium (mEq/L) 4.000 (3.700, 4.300) 4.250 (3.900, 4.500) < 0.001 4.000 (3.700, 4.300) 3.850 (3.250, 4.100) 0.019 Total Calcium (mg/dL) 8.800 (8.400, 9.123) 8.450 (7.900, 8.830) < 0.001 9.100 (8.700, 9.500) 8.650 (8.200, 9.075) 0.001 Chloride (mEq/L) 104.000 (101.000, 106.500) 103.670 (100.500, 107.000) 0.649 103.000 (100.000, 106.000) 102.000 (99.250, 105.000) 0.386 Phosphorus (mg/dL) 3.300 (2.900, 3.800) 3.550 (3.000, 4.530) 0.001 3.200 (2.600, 3.900) 3.600 (3.100, 4.200) 0.012 Magnesium (mg/dL) 2.000 (1.830, 2.100) 2.120 (2.000, 2.330) < 0.001 1.900 (1.800, 2.100) 2.000 (1.825, 2.275) 0.064 PT (sec) 12.550 (11.700, 13.800) 13.300 (12.400, 15.900) < 0.001 12.200 (11.300, 13.600) 12.300 (11.550, 14.275) 0.324 PTT (sec) 29.500 (26.700, 35.188) 42.300 (29.300, 64.200) < 0.001 30.000 (27.250, 33.400) 32.250 (28.150, 37.825) 0.021 CK (IU/L) 137.670 (73.500, 361.000) 721.500 (152.000, 1775.000) < 0.001 130.000 (71.000, 372.000) 554.000 (141.500, 1481.500) < 0.001 Total Bilirubin (mg/dL) 0.600 (0.400, 0.800) 0.600 (0.450, 0.900) 0.050 0.600 (0.400, 0.800) 0.500 (0.400, 0.800) 0.617 ALT (IU/L) 20.000 (14.000, 32.000) 69.330 (30.000, 196.400) < 0.001 24.000 (15.000, 35.000) 60.000 (32.500, 139.750) < 0.001 AST (IU/L) 25.000 (18.000, 42.875) 128.000 (53.500, 296.000) < 0.001 23.000 (17.000, 36.000) 89.500 (36.250, 198.500) < 0.001 BUN (mg/dL) 16.000 (12.000, 22.500) 21.500 (15.500, 33.400) < 0.001 18.000 (13.000, 24.000) 17.500 (15.000, 20.000) 0.794 Creatinine (mg/dL) 0.900 (0.700, 1.150) 1.130 (0.800, 1.980) < 0.001 1.000 (0.810, 1.300) 1.245 (1.120, 1.422) < 0.001 length of hospitalization (day) 6.265 (3.600, 11.787) 8.620 (4.110, 16.450) 0.011 4.050 (2.170, 7.945) 7.585 (3.998, 15.558) < 0.001 length of ICU (day) 2.830 (1.760, 5.317) 5.290 (2.460, 9.110) < 0.001 1.900 (1.165, 3.295) 3.900 (1.995, 7.840) < 0.001 Notes: Data were presented as median (Q1, Q3), or n (%). Abbreviations: IHCA, in-hospital cardiac arrest; HR, heart rate; SBP, systolic blood pressure; DBP, diastolic blood pressure; SpO₂, pulse oxygen saturation; WBC, white blood cell; PLT, platelet count; RDW, red cell distribution width; Hb, hemoglobin; Hct, hematocrit; BUN, blood urea nitrogen; PT, prothrombin time; PTT, partial thromboplastin time; TG, triglycerides; TC, total cholesterol; HDL-C, high-density lipoprotein cholesterol; Glu, glucose; LDL-C, low-density lipoprotein cholesterol; ALT, alanine aminotransferase; AST, aspartate aminotransferase; CK, creatine kinase; HbA1c, glycated hemoglobin; ICU, intensive care unit; TYG, triglyceride-glucose index; CHG, cholesterol, high-density lipoprotein, and glucose index; SHR, stress hyperglycemia ratio. This pattern was largely corroborated in the NWICU validation cohort, where patients in the CA group also demonstrated significantly higher levels of SHR (1.661 vs. 1.093), TYG (9.516 vs. 8.868), and CHG (6.195 vs. 5.505) (P < 0.001). Furthermore, this trend was not confined to CA; patients who developed AKI or sepsis also presented with significantly higher baseline levels of these IR markers (SHR, TYG, and CHG) compared to their respective counterparts (P < 0.001), as detailed in Supplementary Tables S3 and S4 . Independent Association of IR Markers with Clinical Outcomes To build the multivariable logistic regression models, covariates were selected based on univariate analysis and were supplemented with clinically important demographic characteristics, such as age and gender ( Supplementary Tables S5 and S6 ). After full adjustment for potential confounders (Model II), SHR (OR 2.888, 95% CI 1.883–4.427, P < 0.001), TYG (OR 1.446, 95% CI 1.075–1.946, P = 0.015), and CHG (OR 1.580, 95% CI 1.050–2.378, P = 0.028) were all identified as significant independent risk factors for in-hospital CA in the MIMIC-IV cohort (Table 2 ). Furthermore, these markers were also independently associated with the secondary outcomes. For AKI, the adjusted ORs were significant for SHR (OR 2.484, 95% CI 1.752–3.521, P < 0.001), TYG (OR 1.151, 95% CI 1.010–1.311, P = 0.035), and CHG (OR 1.247, 95% CI 1.041–1.494, P = 0.016). Similarly, for sepsis, SHR (OR 1.776, 95% CI 1.340–2.354, P < 0.001), TYG (OR 1.250, 95% CI 1.104–1.416, P < 0.001), and CHG (OR 1.235, 95% CI 1.039–1.468, P = 0.016) remained significant predictors (Table 2 ). Table 2 Multivariable logistic regression analysis of the association between insulin resistance markers and clinical outcomes in MIMIC dataset Exposure Non-adjusted Model Ⅰ Model Ⅱ OR (95% CI) P OR (95% CI) P OR (95% CI) P Cardiac arrest SHR 5.025 (3.237 ~ 7.802) < 0.001 4.340 (2.840 ~ 6.631) < 0.001 2.888 (1.883 ~ 4.427) < 0.001 TYG 1.725 (1.328 ~ 2.241) < 0.001 1.552 (1.176 ~ 2.049) 0.002 1.446 (1.075 ~ 1.946) 0.015 CHG 2.439 (1.715 ~ 3.470) < 0.001 1.955 (1.332 ~ 2.867) < 0.001 1.580 (1.050 ~ 2.378) 0.028 AKI SHR 3.737 (2.706 ~ 5.160) < 0.001 3.438 (2.463 ~ 4.800) < 0.001 2.484 (1.752 ~ 3.521) < 0.001 TYG 1.329 (1.176 ~ 1.502) < 0.001 1.270 (1.119 ~ 1.443) < 0.001 1.151 (1.010 ~ 1.311) 0.035 CHG 1.582 (1.343 ~ 1.864) < 0.001 1.459 (1.226 ~ 1.736) < 0.001 1.247 (1.041 ~ 1.494) 0.016 Sepsis SHR 3.535 (2.717 ~ 4.599) < 0.001 3.090 (2.369 ~ 4.032) < 0.001 1.776 (1.340 ~ 2.354) < 0.001 TYG 1.410 (1.262 ~ 1.575) < 0.001 1.446 (1.287 ~ 1.624) < 0.001 1.250 (1.104 ~ 1.416) < 0.001 CHG 1.520 (1.310 ~ 1.762) < 0.001 1.560 (1.330 ~ 1.829) < 0.001 1.235 (1.039 ~ 1.468) 0.016 Notes : Non-adjusted models: None; Model Ⅰ adjusted for: gender, race, age, weight, heart failure (yes/no); Model Ⅱ adjusted for: confounders in the minimally adjusted (Model Ⅰ) + SBP, WBC, total calcium, magnesium, and PTT. Notes: Non-adjusted models: None; Model Ⅰ adjusted for: gender, race, age, weight, heart failure (yes/no); Model Ⅱ adjusted for: confounders in the minimally adjusted (Model Ⅰ) + SBP, WBC, total calcium, magnesium, and PTT. Abbreviations: OR, odds ratios; CI, confidence intervals; SBP, systolic blood pressure; WBC, white blood cell; PTT, partial thromboplastin time; TYG, triglyceride-glucose index; CHG, cholesterol, high-density lipoprotein, and glucose index; SHR, stress hyperglycemia ratio. Table 3 Multivariable logistic regression analysis of the association between insulin resistance markers and clinical outcomes in NWICU dataset Exposure Non-adjusted Model Ⅰ Model Ⅱ OR (95% CI) P OR (95% CI) P OR (95% CI) P Cardiac arrest SHR 2.555 (1.806 ~ 3.613) < 0.001 2.966 (2.042 ~ 4.308) < 0.001 2.636 (1.789 ~ 3.883) < 0.001 TYG 2.523 (1.759 ~ 3.618) < 0.001 2.772 (1.855 ~ 4.144) < 0.001 2.752 (1.800 ~ 4.206) < 0.001 CHG 2.356 (1.579 ~ 3.517) < 0.001 2.650 (1.668 ~ 4.209) < 0.001 2.499 (1.521 ~ 4.105) < 0.001 AKI SHR 2.300 (1.761 ~ 3.004) < 0.001 2.266 (1.723 ~ 2.979) < 0.001 1.996 (1.503 ~ 2.652) < 0.001 TYG 1.864 (1.492 ~ 2.329) < 0.001 1.968 (1.555 ~ 2.491) < 0.001 1.861 (1.450 ~ 2.388) < 0.001 CHG 2.192 (1.713 ~ 2.805) < 0.001 2.316 (1.774 ~ 3.023) < 0.001 2.151 (1.614 ~ 2.867) < 0.001 Sepsis SHR 2.120 (1.532 ~ 2.933) < 0.001 2.202 (1.583 ~ 3.063) < 0.001 1.884 (1.346 ~ 2.638) < 0.001 TYG 2.306 (1.671 ~ 3.181) < 0.001 2.475 (1.761 ~ 3.480) < 0.001 2.239 (1.558 ~ 3.218) < 0.001 CHG 2.638 (1.864 ~ 3.734) < 0.001 2.938 (2.020 ~ 4.272) < 0.001 2.570 (1.704 ~ 3.876) < 0.001 Notes : Non-adjusted models: None; Model Ⅰ adjusted for: gender, race, age, weight, heart failure(yes/no); Model Ⅱ adjusted for: confounders in the minimally adjusted (Model Ⅰ) + SBP, WBC, total calcium, magnesium, and PTT. Notes: Non-adjusted models: None; Model Ⅰ adjusted for: gender, race, age, weight, heart failure(yes/no); Model Ⅱ adjusted for: confounders in the minimally adjusted (Model Ⅰ) + SBP, WBC, total calcium, magnesium, and PTT. Abbreviations: OR, odds ratios; CI, confidence intervals; SBP, systolic blood pressure; WBC, white blood cell; PTT, partial thromboplastin time; TYG, triglyceride-glucose index; CHG, cholesterol, high-density lipoprotein, and glucose index; SHR, stress hyperglycemia ratio. These strong associations were consistently replicated in the NWICU validation cohort. For CA, the adjusted ORs were 2.636 (95% CI 1.789–3.883, P < 0.001) for SHR, 2.752 (95% CI 1.800–4.206, P < 0.001) for TYG, and 2.499 (95% CI 1.521–4.105, P < 0.001) for CHG. The markers also robustly predicted AKI and sepsis in this cohort (Table 2 ). When the markers were categorized into tertiles, a clear dose-dependent relationship was observed for SHR and TYG with CA risk in the MIMIC-IV cohort (P for trend < 0.001). Notably, while the highest tertile of CHG was associated with increased CA risk, this association did not exhibit a statistically significant linear trend (P for trend = 0.112). In contrast, all three markers showed a significant dose-dependent risk for CA in the NWICU cohort (P for trend < 0.05). For the secondary outcomes of AKI and sepsis, all three markers demonstrated a significant positive linear trend across tertiles in both databases (P for trend < 0.05) ( Supplementary Tables S7 and S8 ). Dose-Response Relationship and Threshold Effect Analysis The RCS plots revealed a generally positive association for all three markers in both the MIMIC-IV and NWICU databases, indicating that higher IR levels were associated with increased CA risk (Fig. 2 ). In the MIMIC-IV cohort, the relationship between SHR and CA was non-linear (P for non-linearity = 0.007). In contrast, the associations for TYG and CHG were significantly linear (P for non-linearity > 0.05). In the NWICU cohort, a significant non-linear relationship was observed for SHR (P for non-linearity = 0.008) and TYG (P for non-linearity = 0.022), while the relationship for CHG was linear (P for non-linearity = 0.082). To further explore these relationships and quantify potential threshold effects, we performed a two-piecewise linear regression analysis. For SHR, significant inflection points were identified at 1.436 in the MIMIC-IV cohort and 2.015 in the NWICU cohort (log-likelihood ratio test P < 0.001 for both). Beyond these thresholds, the risk of CA escalated more steeply. A similar threshold effect was detected for TYG in the NWICU cohort, with a significant inflection point at 8.887 (P < 0.001) ( Supplementary Table S9 ). The dose-response relationships for the secondary outcomes were also comprehensively analyzed. For AKI and sepsis, all three markers generally showed a positive association with risk. The relationships were predominantly linear in the MIMIC-IV cohort, while in the NWICU cohort, evidence of non-linearity was observed for the association between TYG and both AKI and sepsis (P for non-linearity < 0.05), as detailed in Supplementary Figures S1 and S2 . Subgroup and Sensitivity Analyses To assess the consistency and generalizability of our findings, we conducted a series of pre-specified subgroup analyses. In the MIMIC-IV cohort, the positive association between elevated levels of SHR, TYG, and CHG and the risk of in-hospital CA was broadly consistent across all examined subgroups, including those stratified by age ( 0.05), underscoring the robustness of these associations within this population (Fig. 3 A-C). In the NWICU validation cohort, the associations for TYG and CHG with CA risk also remained consistent across subgroups, with no evidence of interaction (P for interaction > 0.05). However, the predictive effect of SHR on CA was significantly modified by race (P for interaction < 0.001) and the presence of diabetes (P for interaction = 0.016), suggesting a stronger association among non-White patients and those without pre-existing diabetes (Fig. 3 D-F). Subgroup analyses for the secondary outcomes revealed more complex patterns of interaction ( Supplementary Figures S3 and S4 ). For AKI, the predictive value of the IR markers was frequently modified by age, gender, and race in both cohorts (P for interaction < 0.05). For sepsis, significant interactions were noted in the MIMIC-IV cohort between SHR and age, and between CHG and diabetes. In the NWICU cohort, the effect of SHR on sepsis risk was significantly modified by gender and race (P for interaction < 0.05). Predictive Performance of IR Markers for Clinical Outcomes In the MIMIC-IV cohort, SHR demonstrated the highest discriminatory ability for the primary outcome of in-hospital CA, with an Area Under the Curve (AUC) of 0.763 (95% CI, 0.710–0.817). This was markedly superior to the performance of both TYG (AUC = 0.624, 95% CI, 0.562–0.687) and CHG (AUC = 0.639, 95% CI, 0.580–0.699) (Fig. 4 A). A similar pattern of superiority for SHR was observed for predicting AKI (AUC = 0.600) and sepsis (AUC = 0.615) (Fig. 4 B-C). These findings were largely validated in the NWICU cohort. SHR again demonstrated the strongest predictive performance for CA (AUC = 0.792, 95% CI, 0.708–0.877) and sepsis (AUC = 0.779). For AKI, its predictive ability (AUC = 0.640) was marginally surpassed by that of CHG (AUC = 0.641) (Fig. 4 D-F). Mediation Analysis of Inflammation In both the MIMIC-IV and NWICU cohorts, the analysis revealed that WBC significantly and partially mediated the effects of all three IR markers on IHCA risk. In the MIMIC-IV cohort, the proportion of the total effect mediated by WBC was 11.3% for SHR, 12.1% for TYG, and 14.5% for CHG (Fig. 5 A-C). This mediating role was consistently replicated in the NWICU cohort, where WBC accounted for 8.9% of the SHR-IHCA association, 5.3% of the TYG-IHCA association, and 7.7% of the CHG-IHCA association (Fig. 5 D-F). Regarding the secondary outcomes, WBC was also a significant mediator for the association between all three IR markers and sepsis in both cohorts. For AKI, this mediation was significant in the MIMIC-IV cohort; however, it did not reach statistical significance in the NWICU cohort ( P > 0.05) ( Supplementary Figures S5 and S6 ). Discussion In this large, dual-center retrospective investigation, we revealed that elevated admission levels of three accessible insulin resistance (IR) markers (SHR, TYG, and CHG) are independently associated with an increased risk of IHCA in a diverse population of critically ill patients. This primary finding remained robust after comprehensive multivariable adjustment and was successfully validated in an independent external cohort, underscoring the generalizability of these metabolic biomarkers for acute risk stratification. Our findings substantially expand the existing literature by shifting the focus from chronic outcomes to an acute, catastrophic event. While previous studies have established links between IR markers and longer-term endpoints like mortality in ICU populations,[ 26 , 27 ] their utility in predicting the imminent onset of IHCA has been largely unexplored. Our work differs from prior research, such as the study by Yang et al., which associated SHR with mortality after a cardiac arrest had already occurred;[ 28 ] our results, in contrast, position these markers as pre-event risk predictors. Furthermore, while Liu et al. recently linked the TYG index to cardiac arrest specifically within the context of acute myocardial infarction,[ 24 ] our study considerably broadens this paradigm. We demonstrate that the association between severe metabolic dysregulation and circulatory collapse is not confined to a single cardiac diagnosis but represents a more fundamental pathophysiological process observable across a wide spectrum of critical illnesses. Moreover, all three IR markers also demonstrated independent associations with the prediction of AKI and sepsis. The pathophysiological rationale connecting severe IR to adverse outcomes is multifaceted, with systemic inflammation emerging as a key mechanistic link. Critical illness is a state of profound metabolic stress that frequently triggers stress hyperglycemia and acute IR.[ 29 ] Severe IR is known to promote a cascade of deleterious systemic effects, including endothelial dysfunction, mitochondrial damage, and a pro-thrombotic state.[ 6 , 7 ] Our mediation analysis supports this concept, revealing that WBC partially mediated the relationship between all three IR markers and IHCA. This finding is biologically plausible, as severe IR initiates a cascade of metabolic derangements, including hyperglycemia, compensatory hyperinsulinemia, dyslipidemia, and endothelial dysfunction, alongside elevated inflammatory markers and a prothrombotic state, all of which collectively contribute to progressive organ injury.[ 30 ] This inflammatory surge can directly destabilize the cardiovascular system by impairing myocardial contractility, promoting endothelial injury, and reducing the threshold for arrhythmias.[ 31 – 33 ] A key finding of our study was the superior predictive performance of SHR for IHCA, AKI, and sepsis compared to TYG and CHG. The unique strength of SHR lies in its formula, which incorporates HbA1c to adjust acute glucose levels for the patient's chronic glycemic background.[ 34 ] This allows SHR to more accurately quantify the relative glucose elevation attributable to acute physiological stress, thereby distinguishing true stress-induced hyperglycemia (SIH) from the chronic hyperglycemia of poorly controlled diabetes.[ 30 , 35 , 36 ] Our subgroup analyses further underscored the robustness of these markers, though they also revealed important nuances. The predictive effect of SHR on IHCA was significantly stronger in patients without pre-existing diabetes in the NWICU cohort. This is logical, as a high SHR in a non-diabetic individual represents a more profound acute deviation from their metabolic baseline, signaling a more severe systemic stress response and thus a higher imminent risk.[ 28 , 37 ] Notably, while SHR was the best overall predictor, the novel CHG index consistently demonstrated superior predictive efficacy for AKI compared to the traditional TYG index in both cohorts. This may suggest that the interplay of cholesterol metabolism, in addition to triglycerides and glucose, is particularly relevant to the pathophysiology of acute kidney injury in the critically ill. In contrast to traditional complex scoring systems, the IR markers we investigated are calculated from routine, inexpensive laboratory values, making them universally applicable tools for early, bedside risk stratification.[ 38 – 40 ] Their use could enable clinicians to identify a high-risk population for IHCA upon ICU admission. These patients might benefit from heightened surveillance, more intensive cardiac monitoring, or a lower threshold for escalating care. Importantly, these findings frame acute IR not just as a biomarker but as a potential therapeutic target. However, this study has limitations that must be acknowledged. First, its retrospective, observational design establishes association, not causation. Despite rigorous adjustment, residual confounding from unmeasured variables may persist. Second, we used a static snapshot of laboratory values from the first 24 hours, which may not capture the dynamic evolution of a patient's metabolic state. Third, we noted some inconsistencies between the MIMIC-IV and NWICU cohorts, particularly in certain secondary analyses. These discrepancies may stem from several factors, including the substantially smaller number of IHCA events in the NWICU cohort, which reduces statistical power and can make the detection of trends and interactions less stable. Furthermore, differences in patient populations, case-mix, and standards of care between the two hospital systems and time periods could also contribute to this variability. Looking forward, prospective studies are needed to validate these findings and to explore the dynamic trajectory of these IR markers over the course of an ICU stay. Ultimately, the most critical next step will be to conduct randomized controlled trials to determine whether early, targeted interventions aimed at ameliorating severe IR in high-risk patients can reduce the incidence of IHCA and improve outcomes. Conclusion In conclusion, elevated admission levels of the insulin resistance markers SHR, TYG, and CHG are independently associated with a significantly increased risk of IHCA in a broad population of critically ill patients. Among these, SHR demonstrated the strongest predictive performance. These readily available and inexpensive markers hold promise as valuable tools for early risk stratification, helping to identify patients who warrant heightened clinical surveillance and who may be candidates for future targeted metabolic interventions. Abbreviations AKI Acute Kidney Injury ALT Alanine Aminotransferase AST Aspartate Aminotransferase BUN Blood Urea Nitrogen CHG Cholesterol, High-Density Lipoprotein, and Glucose index CK Creatine Kinase DBP Diastolic Blood Pressure Glu Glucose Hb Hemoglobin HbA1c Glycated Hemoglobin Hct Hematocrit HDL-C High-Density Lipoprotein Cholesterol HR Heart Rate ICU Intensive Care Unit IHCA In-Hospital Cardiac Arrest IR Insulin Resistance IRB Institutional Review Board KDIGO Kidney Disease:Improving Global Outcomes LDL-C Low-Density Lipoprotein Cholesterol MEWS Modified Early Warning Score MIMIC-IV Medical Information Mart for Intensive Care IV NEWS National Early Warning Score NMHC Northwestern Memorial Hospital NWICU Northwestern ICU PLT Platelet PT Prothrombin Time PTT Partial Thromboplastin Time RBC Red Blood Cell RDW Red Cell Distribution Width RR Respiratory Rate SBP Systolic Blood Pressure SHR Stress Hyperglycemia Ratio SpO₂ Pulse Oxygen Saturation SQL Structured Query Language TC Total Cholesterol TG Triglycerides TYG Triglyceride-Glucose Index WBC White Blood Cell Declarations Availability of data and materials The datasets analyzed during the current study (MIMIC-IV and NWICU) are available in the PhysioNet repository, https://physionet.org/. Ethics approval and consent to participate This study was based on de-identified data from two large, publicly available databases. The collection of patient information for the MIMIC-IV database was reviewed by the IRB at the BIDMC and the MIT, who granted a waiver of informed consent. The NWICU database project was approved by the NU IRB also approved participation and granted a waiver of informed consent, as the project did not impact clinical care and all protected health information was de-identified. One author (Z.Z., Certificate number: 47608458) obtained certified access to use the databases after completing the requisite training and signing the data use agreements. Competing interests The authors declare that they have no competing interests in this section. Author Contributions Conceptualization, Z.Z. and Q.L.; Data analysis, Z.Z.; Writing – Original Draft, Z.Z.; Writing – Review & Editing, Z.Z. and Q.L. All authors have read and agreed to the published version of the manuscript. Acknowledgment We would like to express our gratitude to the MIT and the BIDMC for the creation and maintenance of the MIMIC-IV database. We also thank Northwestern University for providing the NWICU database. We acknowledge all the researchers and staff who have contributed to these valuable data resources. Clinical trial not applicable. Consent to Publish declaration not applicable. Funding This study was funded by Research project of Zigong City Science & Technology and Intellectual Property Right Bureau (2023-YGY-3-04). References Andersen LW, Holmberg MJ, Berg KM, Donnino MW, Granfeldt A: In-Hospital Cardiac Arrest: A Review. Jama 2019, 321(12):1200–1210. Nolan JP, Sandroni C, Böttiger BW, Cariou A, Cronberg T, Friberg H, Genbrugge C, Haywood K, Lilja G, Moulaert VRM et al : European Resuscitation Council and European Society of Intensive Care Medicine guidelines 2021: post-resuscitation care. Intensive care medicine 2021, 47(4):369–421. Kleinman ME, Perkins GD, Bhanji F, Billi JE, Bray JE, Callaway CW, de Caen A, Finn JC, Hazinski MF, Lim SH et al : ILCOR Scientific Knowledge Gaps and Clinical Research Priorities for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care: A Consensus Statement. Circulation 2018, 137(22):e802-e819. Smith ME, Chiovaro JC, O'Neil M, Kansagara D, Quiñones AR, Freeman M, Motu'apuaka ML, Slatore CG: Early warning system scores for clinical deterioration in hospitalized patients: a systematic review. Annals of the American Thoracic Society 2014, 11(9):1454–1465. Smith GB, Prytherch DR, Meredith P, Schmidt PE, Featherstone PI: The ability of the National Early Warning Score (NEWS) to discriminate patients at risk of early cardiac arrest, unanticipated intensive care unit admission, and death. Resuscitation 2013, 84(4):465–470. Pliszka M, Szablewski L: Severe Insulin Resistance Syndromes: Clinical Spectrum and Management. International journal of molecular sciences 2025, 26(12). Mir MM, Jeelani M, Alharthi MH, Rizvi SF, Sohail SK, Wani JI, Sabah ZU, BinAfif WF, Nandi P, Alshahrani AM et al : Unraveling the Mystery of Insulin Resistance: From Principle Mechanistic Insights and Consequences to Therapeutic Interventions. International journal of molecular sciences 2025, 26(6). Brie AD, Christodorescu RM, Popescu R, Adam O, Tîrziu A, Brie DM: Atherosclerosis and Insulin Resistance: Is There a Link Between Them? Biomedicines 2025, 13(6). Hajri T, Ouguerram K, Fungwe TV: Impact of Lipid Oxidation Products on Inflammation and Insulin Resistance: A Focus on Mechanisms of Action. Cell biochemistry and biophysics 2025. DeFronzo RA, Tobin JD, Andres R: Glucose clamp technique: a method for quantifying insulin secretion and resistance. The American journal of physiology 1979, 237(3):E214-223. Guerrero-Romero F, Simental-Mendía LE, González-Ortiz M, Martínez-Abundis E, Ramos-Zavala MG, Hernández-González SO, Jacques-Camarena O, Rodríguez-Morán M: The product of triglycerides and glucose, a simple measure of insulin sensitivity. Comparison with the euglycemic-hyperinsulinemic clamp. The Journal of clinical endocrinology and metabolism 2010, 95(7):3347–3351. Li L, Zhao M, Zhang Z, Zhou L, Zhang Z, Xiong Y, Hu Z, Yao Y: Prognostic significance of the stress hyperglycemia ratio in critically ill patients. Cardiovascular diabetology 2023, 22(1):275. Mo D, Zhang P, Zhang M, Dai H, Guan J: Cholesterol, high-density lipoprotein, and glucose index versus triglyceride-glucose index in predicting cardiovascular disease risk: a cohort study. Cardiovascular diabetology 2025, 24(1):116. Lv L, Xiong J, Huang Y, He T, Zhao J: Association between the Triglyceride Glucose Index and All-Cause Mortality in Critically Ill Patients with Acute Kidney Injury. Kidney diseases (Basel, Switzerland) 2024, 10(1):69–78. Ma C, Jiang W, Li J, Sun W, Zhang J, Xu P, Guo Y, Ning N, Li J, Zhao B et al : Association of Stress Hyperglycemia Ratio and in-Hospital Mortality in Patients with Sepsis: A Two Center Retrospective Cohort Study. Journal of inflammation research 2024, 17:7939–7950. Li B, Yang X, Wang JH, Chen W, Wang Q, Zhong L: Nonlinear association between triglyceride-glucose index and 28-day mortality in intensive care units: a multi-center retrospective cohort study. Frontiers in endocrinology 2025, 16:1545478. Lou J, Xiang Z, Zhu X, Fan Y, Song J, Cui S, Li J, Jin G, Huang N: A retrospective study utilized MIMIC-IV database to explore the potential association between triglyceride-glucose index and mortality in critically ill patients with sepsis. Scientific reports 2024, 14(1):24081. Lian LY, Xue WH, Lu JJ, Zheng RJ: Impact of stress hyperglycemia ratio on mortality in patients with cardiac arrest: insight from American MIMIC-IV database. Frontiers in endocrinology 2024, 15:1383993. Bellomo R, Warrillow SJ, Reade MC: Why we should be wary of single-center trials. Critical care medicine 2009, 37(12):3114–3119. Rhodes A, Evans LE, Alhazzani W, Levy MM, Antonelli M, Ferrer R, Kumar A, Sevransky JE, Sprung CL, Nunnally ME et al : Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock: 2016. Critical care medicine 2017, 45(3):486–552. Kellum JA, Lameire N: Diagnosis, evaluation, and management of acute kidney injury: a KDIGO summary (Part 1). Critical care (London, England) 2013, 17(1):204. Moon KM, Kim G, Baik SK, Choi E, Kim MY, Kim HA, Cho MY, Shin SY, Kim JM, Park HJ et al : Ultrasonographic scoring system score versus liver stiffness measurement in prediction of cirrhosis. Clinical and molecular hepatology 2013, 19(4):389–398. Akinwande MO, Dikko HG, Samson A: Variance Inflation Factor: As a Condition for the Inclusion of Suppressor Variable(s) in Regression Analysis. Open Journal of Statistics 2015, 05(7):754–767. Liu H, Wang L, Wang H, Hao X, Du Z, Li C, Hou X: Triglyceride-glucose index correlates with the incidences and prognoses of cardiac arrest following acute myocardial infarction: data from two large-scale cohorts. Cardiovascular diabetology 2025, 24(1):108. Liu X, Tong X, Zhu J, Tian L, Jie Z, Zou Y, Lin X, Liang H, Li W, Ju Y et al : Metagenome-genome-wide association studies reveal human genetic impact on the oral microbiome. Cell discovery 2021, 7(1):117. Li T, Yi Z, Huang Y, Tan Y, Gao S, Wang T, Guo S: Prognostic value of triglyceride-glucose index combined with stress hyperglycemia ratio for all-cause mortality in critically ill patients with stroke. Cardiovascular diabetology 2025, 24(1):337. Wang Y, Xu W, Wang J, Shu Y, Xin Y, Yang Y: Relationship between stress hyperglycemia ratio of one-year mortality in patients with heart failure: Analysis of the MIMIC-IV database. PloS one 2025, 20(8):e0328812. Lai W, Peng XY, Peng H, Chen Y: Stress Hyperglycemia Ratio Outperforms Glycemic Variability in Predicting Mortality Among Acute Myocardial Infarction Patients With Reduced Ejection Fraction: A Retrospective Cohort Study. Journal of diabetes 2025, 17(8):e70122. Marik PE, Bellomo R: Stress hyperglycemia: an essential survival response! Critical care medicine 2013, 41(6):e93-94. Mifsud S, Schembri EL, Gruppetta M: Stress-induced hyperglycaemia. British journal of hospital medicine (London, England : 2005) 2018, 79(11):634–639. Roberts BW, Trzeciak S: Systemic inflammatory response after cardiac arrest: potential target for therapy? Critical care medicine 2015, 43(6):1336–1337. Dri E, Lampas E, Lazaros G, Lazarou E, Theofilis P, Tsioufis C, Tousoulis D: Inflammatory Mediators of Endothelial Dysfunction. Life (Basel, Switzerland) 2023, 13(6). Simons SO, Heptinstall AB, Marjenberg Z, Marshall J, Mullerova H, Rogliani P, Nordon C, Hawkins NM: Temporal Dynamics of Cardiovascular Risk in Patients with Chronic Obstructive Pulmonary Disease During Stable Disease and Exacerbations: Review of the Mechanisms and Implications. International journal of chronic obstructive pulmonary disease 2024, 19:2259–2271. Song G, Liu X, Lu Z, Guan J, Chen X, Li Y, Liu G, Wang G, Ma F: Relationship between stress hyperglycaemic ratio (SHR) and critical illness: a systematic review. Cardiovascular diabetology 2025, 24(1):188. Plummer MP, Hermanides J, Deane AM: Is it time to personalise glucose targets during critical illness? Current opinion in clinical nutrition and metabolic care 2022, 25(5):364–369. Chen L, Zeng X, Zou W, Chen M, Fan Y, Huang P: Predictive performance of stress hyperglycemia ratio for poor prognosis in critically ill patients: a systematic review and dose-response meta-analysis. European journal of medical research 2025, 30(1):613. Wang F, Guo Y, Tang Y, Zhao S, Xuan K, Mao Z, Lu R, Hou R, Zhu X: Combined assessment of stress hyperglycemia ratio and glycemic variability to predict all-cause mortality in critically ill patients with atherosclerotic cardiovascular diseases across different glucose metabolic states: an observational cohort study with machine learning. Cardiovascular diabetology 2025, 24(1):199. Placzkowska S, Pawlik-Sobecka L, Kokot I, Piwowar A: Indirect insulin resistance detection: Current clinical trends and laboratory limitations. Biomedical papers of the Medical Faculty of the University Palacky, Olomouc, Czechoslovakia 2019, 163(3):187–199. Muniyappa R, Lee S, Chen H, Quon MJ: Current approaches for assessing insulin sensitivity and resistance in vivo: advantages, limitations, and appropriate usage. American journal of physiology Endocrinology and metabolism 2008, 294(1):E15-26. Lee HY, Kuo PC, Qian F, Li CH, Hu JR, Hsu WT, Jhou HJ, Chen PH, Lee CH, Su CH et al : Prediction of In-Hospital Cardiac Arrest in the Intensive Care Unit: Machine Learning-Based Multimodal Approach. JMIR medical informatics 2024, 12:e49142. Additional Declarations No competing interests reported. Supplementary Files Additionalfile.docx Additional file: Table S1. ICD-9 and ICD-10 Codes Used for the Diagnosis of Cardiac Arrest. Table S2. Missing Number (%) for Included Variables in Dataset. Table S3. Baseline Characteristics of Patients Stratified by the Presence or Absence of AKI. Table S4. Baseline Characteristics of Patients Stratified by the Presence or Absence of Sepsis. Table S5. Univariate logistic regression analysis. Table S6. The variance inflation factor test. Table S7. Multivariable Logistic Regression Analysis of the Association Between Three Biomarkers and Different Outcomes in the MIMIC Database. Table S8. Multivariable Logistic Regression Analysis of the Association Between Three Biomarkers and Different Outcomes in the NWICU Database. Table S9. Inflection Point Analysis of Stress Hyperglycemia Ratio (SHR) and Triglyceride-Glucose Index (TYG) for Clinical Outcomes. Figure S1. D Dose-Response Relationship between Three Biomarkers and AKI using Restricted Cubic Splines in the MIMIC and NWICU Databases. Figure S2. Dose-Response Relationship between Three Biomarkers and Sepsis using Restricted Cubic Splines in the MIMIC and NWICU Databases. Figure S3. Subgroup Analysis for the Association Between Three Biomarkers and the Risk of AKI in the MIMIC and NWICU Databases. Figure S4. Subgroup Analysis for the Association Between Three Biomarkers and the Risk of Sepsis in the MIMIC and NWICU Databases. Figure S5. Mediation Effect of WBC on the Association between Three Biomarkers and AKI in the MIMIC and NWICU Databases. Figure S6. Mediation Effect of WBC on the Association between Three Biomarkers and Sepsis in the MIMIC and NWICU Databases. 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-8145693","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":563499368,"identity":"bef350a0-e27b-4798-89f9-d4bb96c92606","order_by":0,"name":"zhitao zhong","email":"","orcid":"","institution":"Zigong Fourth People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"zhitao","middleName":"","lastName":"zhong","suffix":""},{"id":563499369,"identity":"9fb7718a-6baa-4aac-9905-48afc1c599c5","order_by":1,"name":"Qiong Long","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYBACPjBpAMTMjI0PPlRIyMkT0sIG18LOfNhwxhkLY8MGorSAAD9bmjRvW0UiwwFCWth7D794U2CTJ+/MYyDNO08igbGB+eGjG/i08JxLs5xjkFZseJjHwHDuNok8dgY2Y+McfFokcsyMeQwOJ25s5jFIeLtNopixgYdNmmgtB3jnSCQ2HCCsxfgxSMt8ZrbERt4GYrTwnDFjBPolcQMz82HGGcckjA2bCfiFn73H+MObPzaJ8/sPtv/4UFMnJ8/e/PAxPi1gt/EASYMDMD4zfuVgJR9AWuQbCKscBaNgFIyCEQoAZBhHXfW7NbkAAAAASUVORK5CYII=","orcid":"","institution":"Zigong Fourth People’s Hospital","correspondingAuthor":true,"prefix":"","firstName":"Qiong","middleName":"","lastName":"Long","suffix":""}],"badges":[],"createdAt":"2025-11-18 12:53:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8145693/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8145693/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":99190430,"identity":"6daa9d8b-bcfc-436c-a5ec-d5a6d0bfce1d","added_by":"auto","created_at":"2025-12-30 00:51:15","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":116963,"visible":true,"origin":"","legend":"","description":"","filename":"article.docx","url":"https://assets-eu.researchsquare.com/files/rs-8145693/v1/446dc7f5a7bc55c91adaa146.docx"},{"id":99190432,"identity":"afcc377d-f65e-400e-bc88-37f3324a7dff","added_by":"auto","created_at":"2025-12-30 00:51:15","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":38942,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.Baselinecharacteristicsofpatientsstratifiedbyinhospitalcardiacarrest.docx","url":"https://assets-eu.researchsquare.com/files/rs-8145693/v1/d27df85d90b17e5cf88e818a.docx"},{"id":99190444,"identity":"40231802-a0c7-4782-851b-2e35ba8399f1","added_by":"auto","created_at":"2025-12-30 00:51:15","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":22308,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.MultivariablelogisticregressionanalysisoftheassociationbetweeninsulinresistancemarkersandclinicaloutcomesinMIMICdataset.docx","url":"https://assets-eu.researchsquare.com/files/rs-8145693/v1/c7cda27b2e54ed0769d55d7d.docx"},{"id":99190438,"identity":"23acee13-304c-4d0e-bfe2-186858d8ed3a","added_by":"auto","created_at":"2025-12-30 00:51:15","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":22784,"visible":true,"origin":"","legend":"","description":"","filename":"Table3.MultivariablelogisticregressionanalysisoftheassociationbetweeninsulinresistancemarkersandclinicaloutcomesinNWICUdataset.docx","url":"https://assets-eu.researchsquare.com/files/rs-8145693/v1/a26901c84f03e2071bae8e9c.docx"},{"id":99190446,"identity":"d525ba46-28cb-4f59-9294-9d7fb712c20b","added_by":"auto","created_at":"2025-12-30 00:51:15","extension":"json","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5791,"visible":true,"origin":"","legend":"","description":"","filename":"7a55f150f79e4ab3a0753661bc4f872d.json","url":"https://assets-eu.researchsquare.com/files/rs-8145693/v1/7ec99fa3cac145136e0579c4.json"},{"id":99190434,"identity":"abae241e-2678-44d7-b0cd-7fc3fe0d9482","added_by":"auto","created_at":"2025-12-30 00:51:15","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":744300,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-8145693/v1/b05618ae12610c8e7a6c9f12.docx"},{"id":99190436,"identity":"9a10a89f-c3e8-4bfb-a02a-15ad30bfc9c3","added_by":"auto","created_at":"2025-12-30 00:51:15","extension":"xml","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":187474,"visible":true,"origin":"","legend":"","description":"","filename":"7a55f150f79e4ab3a0753661bc4f872d1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8145693/v1/8bfbcf2dd9904a529c5d7e38.xml"},{"id":99190435,"identity":"904b58f0-5515-4b7d-9529-a20e9cc901d6","added_by":"auto","created_at":"2025-12-30 00:51:15","extension":"pdf","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":103208,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1.Flowchartofpatientselection..pdf","url":"https://assets-eu.researchsquare.com/files/rs-8145693/v1/b0bffab2c221c8de63116315.pdf"},{"id":99316442,"identity":"21cc5698-2407-4fb1-87b1-21a6d38efafe","added_by":"auto","created_at":"2025-12-31 16:28:28","extension":"pdf","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":467082,"visible":true,"origin":"","legend":"","description":"","filename":"Figure2.Doseresponserelationshipbetweeninsulinresistancemarkersandtheriskofinhospitalcardiacarrest.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8145693/v1/04fb04dbe268ec5bfd3b7e9b.pdf"},{"id":99316714,"identity":"cbd52eb3-57bd-40ad-bd21-5ca7e1592e72","added_by":"auto","created_at":"2025-12-31 16:29:03","extension":"pdf","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1839320,"visible":true,"origin":"","legend":"","description":"","filename":"Figure3.Subgroupanalysisfortheassociationbetweeninsulinresistancemarkersandinhospitalcardiacarrest.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8145693/v1/c6d0cf974e7b4cfcdb0e10d8.pdf"},{"id":99190447,"identity":"c62bc69b-a62e-40de-abf8-2e3d02da5c50","added_by":"auto","created_at":"2025-12-30 00:51:15","extension":"pdf","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":800585,"visible":true,"origin":"","legend":"","description":"","filename":"Figure4.ROCcurvesforpredictingclinicaloutcomes.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8145693/v1/c67bede23d399f6a419eaf09.pdf"},{"id":99190437,"identity":"98b613b5-3770-4409-9d74-bd6fa6253ffe","added_by":"auto","created_at":"2025-12-30 00:51:15","extension":"pdf","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":89606,"visible":true,"origin":"","legend":"","description":"","filename":"Figure5.MediationanalysisoftheroleofWBCintheassociationbetweenmarkersandinhospitalcardiacarrest.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8145693/v1/e6087a490fa58fa42c1b8f2d.pdf"},{"id":99317061,"identity":"abd539cc-4215-49c1-8566-5d12c781f6e3","added_by":"auto","created_at":"2025-12-31 16:29:37","extension":"xml","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":188202,"visible":true,"origin":"","legend":"","description":"","filename":"7a55f150f79e4ab3a0753661bc4f872d1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8145693/v1/468008a4c9470d570ace18a9.xml"},{"id":99190448,"identity":"9a62616a-6fad-4fc7-8918-a6dabfba5251","added_by":"auto","created_at":"2025-12-30 00:51:16","extension":"html","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":207573,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8145693/v1/8f01e20d3dd5c1ae4744359a.html"},{"id":99190428,"identity":"1aefcae9-0504-451e-b1ba-874c1a60649b","added_by":"auto","created_at":"2025-12-30 00:51:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":32428,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of the research patient selection process.\u003cbr\u003e\n \u003cstrong\u003eAbbreviations:\u003c/strong\u003e IHCA, in-hospital cardiac arrest; ICU, intensive care unit; MIMIC, Medical Information Mart for Intensive Care; NWICU, Northwestern ICU.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8145693/v1/9dd9f44d4e433a7a48796892.png"},{"id":99190431,"identity":"1004a410-5998-47ec-98ee-dddcd90baf02","added_by":"auto","created_at":"2025-12-30 00:51:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":46013,"visible":true,"origin":"","legend":"\u003cp\u003eDose-response relationship between insulin resistance markers and the risk of in-hospital cardiac arrest. (A-C) Dose-response relationships for SHR, TYG, and CHG in the MIMIC-IV cohort. (D-F) Dose-response relationships for SHR, TYG, and CHG in the NWICU cohort.\u003cbr\u003e\n \u003cstrong\u003eAbbreviations:\u003c/strong\u003e CHG, cholesterol, high-density lipoprotein, and glucose index; CI, confidence interval; OR, odds ratio; SHR, stress hyperglycemia ratio; TYG, triglyceride-glucose index.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8145693/v1/d017e4c8958912712a844a3f.png"},{"id":99190429,"identity":"8549243a-f51e-4b10-8bde-4be9f37fdb3e","added_by":"auto","created_at":"2025-12-30 00:51:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":85087,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis for the association between insulin resistance markers and in-hospital cardiac arrest. (A-C) Subgroup analyses for SHR, TYG, and CHG in the MIMIC-IV cohort. (D-F) Subgroup analyses for SHR, TYG, and CHG in the NWICU cohort.\u003cbr\u003e\n \u003cstrong\u003eAbbreviations:\u003c/strong\u003e CHG, cholesterol, high-density lipoprotein, and glucose index; CI, confidence interval; OR, odds ratio; SHR, stress hyperglycemia ratio; TYG, triglyceride-glucose index.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8145693/v1/02346f03bf0c6571a930c7c2.png"},{"id":99190441,"identity":"1ec95e29-c708-478f-b3fc-71d6ec12956f","added_by":"auto","created_at":"2025-12-30 00:51:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":105870,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves for predicting clinical outcomes. (A-C) ROC curves for SHR, TYG, and CHG in predicting in-hospital cardiac arrest, AKI, and sepsis in the MIMIC-IV cohort. (D-F) ROC curves for SHR, TYG, and CHG in predicting in-hospital cardiac arrest, AKI, and sepsis in the NWICU cohort.\u003cbr\u003e\n \u003cstrong\u003eAbbreviations:\u003c/strong\u003e ROC, receiver operating characteristic; AKI, acute kidney injury; AUC, area under the curve; CHG, cholesterol, high-density lipoprotein, and glucose index; SHR, stress hyperglycemia ratio; TYG, triglyceride-glucose index.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8145693/v1/9cc4af33bede95ca391b47f1.png"},{"id":99190442,"identity":"3a72050a-ffb5-4d7b-aabe-b4ab0820dd9e","added_by":"auto","created_at":"2025-12-30 00:51:15","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":48098,"visible":true,"origin":"","legend":"\u003cp\u003eMediation analysis of the role of WBC in the association between insulin resistance markers and in-hospital cardiac arrest. (A-C) Mediation effects for SHR, TYG, and CHG in the MIMIC-IV cohort. (D-F) Mediation effects for SHR, TYG, and CHG in the NWICU cohort.\u003cbr\u003e\n \u003cstrong\u003eNote:\u003c/strong\u003e * indicates \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05.\u003cbr\u003e\n \u003cstrong\u003eAbbreviations:\u003c/strong\u003e IHCA, in-hospital cardiac arrest; CHG, cholesterol, high-density lipoprotein, and Glucose index; SHR, stress hyperglycemia ratio; TYG, triglyceride-glucose index; WBC, white blood cell.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-8145693/v1/15240340463bfc1b62bc09cf.png"},{"id":103637669,"identity":"6bf48aa1-e569-4e53-a0a3-093b7ce1011e","added_by":"auto","created_at":"2026-02-28 08:11:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2012244,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8145693/v1/e763b758-374b-40e9-91bc-c36c845f3352.pdf"},{"id":99190433,"identity":"cf7576ad-6239-4f34-acb3-135f4976bd88","added_by":"auto","created_at":"2025-12-30 00:51:15","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":744300,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S1.\u003c/strong\u003e ICD-9 and ICD-10 Codes Used for the Diagnosis of Cardiac Arrest.\u003cbr\u003e\n \u003cstrong\u003eTable S2.\u003c/strong\u003e Missing Number (%) for Included Variables in Dataset.\u003cbr\u003e\n \u003cstrong\u003eTable S3.\u003c/strong\u003e Baseline Characteristics of Patients Stratified by the Presence or Absence of AKI.\u003cbr\u003e\n \u003cstrong\u003eTable S4.\u003c/strong\u003e Baseline Characteristics of Patients Stratified by the Presence or Absence of Sepsis.\u003cbr\u003e\n \u003cstrong\u003eTable S5.\u003c/strong\u003e Univariate logistic regression analysis.\u003cbr\u003e\n \u003cstrong\u003eTable S6.\u003c/strong\u003e The variance inflation factor test.\u003cbr\u003e\n \u003cstrong\u003eTable S7.\u003c/strong\u003e Multivariable Logistic Regression Analysis of the Association Between Three Biomarkers and Different Outcomes in the MIMIC Database.\u003cbr\u003e\n \u003cstrong\u003eTable S8.\u003c/strong\u003e Multivariable Logistic Regression Analysis of the Association Between Three Biomarkers and Different Outcomes in the NWICU Database.\u003cbr\u003e\n \u003cstrong\u003eTable S9.\u003c/strong\u003e Inflection Point Analysis of Stress Hyperglycemia Ratio (SHR) and Triglyceride-Glucose Index (TYG) for Clinical Outcomes.\u003cbr\u003e\n \u003cstrong\u003eFigure S1.\u003c/strong\u003e D Dose-Response Relationship between Three Biomarkers and AKI using Restricted Cubic Splines in the MIMIC and NWICU Databases.\u003cbr\u003e\n \u003cstrong\u003eFigure S2.\u003c/strong\u003e Dose-Response Relationship between Three Biomarkers and Sepsis using Restricted Cubic Splines in the MIMIC and NWICU Databases.\u003cbr\u003e\n \u003cstrong\u003eFigure S3.\u003c/strong\u003e Subgroup Analysis for the Association Between Three Biomarkers and the Risk of AKI in the MIMIC and NWICU Databases.\u003cbr\u003e\n \u003cstrong\u003eFigure S4.\u003c/strong\u003e Subgroup Analysis for the Association Between Three Biomarkers and the Risk of Sepsis in the MIMIC and NWICU Databases.\u003cbr\u003e\n \u003cstrong\u003eFigure S5.\u003c/strong\u003e Mediation Effect of WBC on the Association between Three Biomarkers and AKI in the MIMIC and NWICU Databases.\u003cbr\u003e\n \u003cstrong\u003eFigure S6.\u003c/strong\u003e Mediation Effect of WBC on the Association between Three Biomarkers and Sepsis in the MIMIC and NWICU Databases.\u003c/p\u003e","description":"","filename":"Additionalfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-8145693/v1/14725a4cb2905658d6251fa6.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparative Prognostic Value of SHR, TYG, and CHG for Predicting In-Hospital Cardiac Arrest in Critically ill Patients: A Dual-Center Cohort Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn-hospital cardiac arrest (IHCA) represents a catastrophic event within the intensive care unit (ICU). In the United States alone, over 290,000 adults experience IHCA annually, and despite advances in resuscitation science, survival to hospital discharge remains dismally low at approximately 25%.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] The success of treatment fundamentally depends on the early recognition of impending cardiac arrest and rapid intervention.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] While established scoring systems, such as the National Early Warning Score (NEWS) or the Modified Early Warning Score (MEWS), exist to identify deteriorating patients, they often rely on a composite of physiological parameters and may lack optimal specificity.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] Therefore, there remains a critical need for simple, accessible biomarkers that reflect underlying pathophysiological derangements preceding circulatory collapse.\u003c/p\u003e \u003cp\u003eA central, yet often underappreciated, contributor to clinical deterioration in the critically ill is acute metabolic dysregulation, specifically insulin resistance (IR).[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] IR is a pathophysiological state characterized by a diminished cellular response to insulin, leading to an impaired ability of the hormone to effectively facilitate glucose uptake and utilization.[\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] The gold standard for quantifying IR, the hyperglycemic clamp technique, is labor-intensive and unsuitable for routine clinical practice in the ICU setting.[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] This limitation has spurred the search for practical surrogate markers that can be derived from routinely collected laboratory data.\u003c/p\u003e \u003cp\u003eIn recent years, several glycolipid-based indices have emerged as powerful and convenient tools for assessing IR. These include the Triglyceride-Glucose Index (TYG),[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] the Stress Hyperglycemia Ratio (SHR) which adjusts admission glucose for chronic glycemic status using HbA1c,[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] and the more novel Cholesterol, High-Density Lipoprotein, and Glucose index (CHG).[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] While numerous studies have validated the prognostic utility of these markers for various adverse outcomes in critical care, the existing literature has several significant limitations.\u003c/p\u003e \u003cp\u003eFirst, previous investigations have predominantly focused on mortality as the primary endpoint [\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] While important, this outcome does not capture the risk of a specific, acute event like IHCA. Second, while one study has explored the link between SHR and mortality after a cardiac arrest has occurred, no research has yet investigated whether these IR markers can predict the initial occurrence of IHCA, representing a crucial knowledge gap.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] Third, data is still scarce regarding the comparative prognostic performance of these three markers within a single, large cohort, and many findings originate from single-center studies, which may limit their generalizability.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] These deficiencies underscore that the relationship between these accessible IR markers and the imminent risk of IHCA remains largely unexplored.\u003c/p\u003e \u003cp\u003eTherefore, this study aims to address these critical gaps by utilizing two large, independent critical care databases. The primary objective of this study was to determine the association between admission levels of SHR, TYG, and CHG and the subsequent risk of IHCA in a broad population of critically ill patients. We hypothesized that elevated levels of these IR markers would be independently associated with an increased incidence of IHCA and other major adverse ICU outcomes. The findings may provide new insights for early risk stratification and could help clinicians identify high-risk patients who may benefit from heightened surveillance or targeted metabolic interventions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source\u003c/h2\u003e \u003cp\u003eThis retrospective cohort study was conducted using data from two independent, publicly available critical care databases. The development cohort was derived from the Medical Information Mart for Intensive Care IV (MIMIC-IV, v3.1), a large, single-center database containing de-identified health records for patients admitted to intensive care units (ICUs) at the Beth Israel Deaconess Medical Center (BIDMC) in Boston, Massachusetts, between 2008 and 2022. External validation was performed using the Northwestern ICU (NWICU, v0.1.0) database, which comprises de-identified data from patients admitted to ICUs at Northwestern Memorial Hospital (NMHC) between 2020 and 2022. For MIMIC-IV, this approval was granted by the institutional review board (IRB) of the Massachusetts Institute of Technology (MIT) and BIDMC. For the NWICU database, the Northwestern University (NU) IRB approved participation in the project, and the release of data from the Northwestern Medicine Enterprise Data Warehouse was additionally authorized by the NM Data Steward. The requirement for individual patient consent was waived for both databases because the research involved de-identified data, posed minimal risk to patients, did not impact clinical care, and obtaining specific consent was deemed impractical. One author (Z.Z., Certificate number: 47608458) obtained certified access to use the databases after completing the requisite training.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipant selection\u003c/h3\u003e\n\u003cp\u003eWe included adult patients (aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years) admitted to an ICU for the first time during their initial hospitalization. The exclusion criteria were as follows: (1) patients with an ICU length of stay less than 24 hours; and (2) patients with missing data for the components required to calculate the primary exposure variables (SHR, TYG, and CHG) on the first day of ICU admission.\u003c/p\u003e\n\u003ch3\u003eClinical outcomes\u003c/h3\u003e\n\u003cp\u003eThe primary outcome was the occurrence of IHCA. Secondary outcomes included the development of acute kidney injury (AKI) and sepsis during hospitalization. Sepsis was defined according to the Sepsis-3 criteria as life-threatening organ dysfunction caused by a dysregulated host response to infection, identified by an acute increase in the Sequential Organ Failure Assessment (SOFA) score of \u0026ge;\u0026thinsp;2 points.[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] AKI was defined based on the Kidney Disease: Improving Global Outcomes (KDIGO) criteria: an increase in serum creatinine by \u0026ge;\u0026thinsp;0.3 mg/dL within 48 hours, an increase in serum creatinine to \u0026ge;\u0026thinsp;1.5 times baseline within 7 days, or a urine volume of \u0026lt;\u0026thinsp;0.5 mL/kg/h for 6 hours.[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/p\u003e\n\u003ch3\u003eData extraction\u003c/h3\u003e\n\u003cp\u003eData were extracted for the first 24 hours of each patient's ICU stay using PostgreSQL with Structured Query Language (SQL). The extracted data included: (1) baseline demographics (age, gender, race, weight); (2) comorbidities (history of diabetes, heart failure, hypertension); (3) vital signs (heart rate [HR], systolic blood pressure [SBP], diastolic blood pressure [DBP], respiratory rate [RR], temperature, and pulse oxygen saturation [SpO₂]); and (4) a comprehensive panel of laboratory parameters. These parameters encompassed hematological tests (white blood cell [WBC] count, red blood cell [RBC] count, platelet [PLT] count, hemoglobin [Hb], hematocrit [Hct], red cell distribution width [RDW]), metabolic and renal function markers (glucose [Glu], blood urea nitrogen [BUN], creatinine, sodium, potassium, total calcium, chloride, phosphorus, magnesium), lipid profiles (triglycerides [TG], total cholesterol [TC], high-density lipoprotein cholesterol [HDL-C], low-density lipoprotein cholesterol [LDL-C]), coagulation status (prothrombin time [PT], partial thromboplastin time [PTT]), and markers of organ injury (alanine aminotransferase [ALT], aspartate aminotransferase [AST], creatine kinase [CK], total bilirubin), as well as glycated hemoglobin (HbA1c). The primary exposure variables, three insulin resistance (IR) markers, were calculated using the following formulas: Stress Hyperglycemia Ratio (SHR)\u0026thinsp;=\u0026thinsp;Glu (mg/dL) / (28.7 \u0026times; HbA1c [%]\u0026thinsp;\u0026minus;\u0026thinsp;46.7). Triglyceride-Glucose Index (TYG)\u0026thinsp;=\u0026thinsp;Ln [TG (mg/dL) \u0026times; Glu (mg/dL) / 2]. Cholesterol, High-Density Lipoprotein, and Glucose index (CHG)\u0026thinsp;=\u0026thinsp;Ln [TC (mg/dL) \u0026times; Glu (mg/dL) / (HDL-C (mg/dL) \u0026times; 2)].\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables were presented as median (interquartile range, IQR) due to their non-normal distribution, while categorical variables were expressed as numbers (percentages). Differences in baseline characteristics between patient groups (e.g., with vs. without IHCA) were compared using the Kruskal-Wallis test for continuous variables and the chi-square test or Fisher's exact test for categorical variables, as appropriate. To build the multivariable models, we first employed univariate binary logistic regression to screen for potential confounders associated with the clinical outcomes. Variables with a \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.1 in the univariate analysis,[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] along with those deemed clinically significant, were considered for inclusion in the multivariable models. To mitigate multicollinearity, we calculated the variance inflation factor (VIF) for all selected variables and ensured that all VIF values were less than 5.[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] Multivariable logistic regression analysis was performed to assess the independent association between the insulin resistance (IR) markers (SHR, TYG, and CHG) and the clinical outcomes (IHCA, AKI, and sepsis). The IR markers were analyzed both as continuous variables and as categorical variables based on tertiles. We constructed two adjusted models: Model Ⅰ, which adjusted for age, gender, race, weight, and heart failure; and Model Ⅱ, which included covariates from Model I plus SBP, WBC, total calcium, magnesium, and PTT. Results were presented as odds ratios (OR) with 95% confidence intervals (CI). To explore potential non-linear relationships between the continuous IR markers and outcomes, we utilized restricted cubic splines (RCS) with four knots. If a non-linear association was detected (P for non-linearity\u0026thinsp;\u0026lt;\u0026thinsp;0.05), a two-piecewise linear regression model was applied to calculate the threshold effect and identify the inflection point. The predictive performance of SHR, TYG, and CHG for the outcomes was evaluated and compared using the area under the receiver operating characteristic curve (AUC). Subgroup analyses were conducted across various strata, including age, gender, race, diabetes, heart failure, and hypertension, to assess the robustness of our findings. P-values for interaction were calculated to examine whether the effects of the IR markers on outcomes differed across these subgroups. Finally, a mediation analysis was conducted to explore whether WBC mediated the relationship between the IR markers and the clinical outcomes. Variables with a missing rate higher than 50% were excluded from the analysis, and multiple imputation techniques were employed to address the remaining missing values.[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] All statistical analyses were conducted using R software (version 4.4.1). The \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eA total of 3,059 critically ill patients from the MIMIC-IV database and 1,849 from the NWICU database were ultimately enrolled in this study following a rigorous screening process (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In the MIMIC-IV cohort, the rates of IHCA, AKI, and sepsis were 2.8% (n\u0026thinsp;=\u0026thinsp;85), 72.3% (n\u0026thinsp;=\u0026thinsp;2,213), and 31.1% (n\u0026thinsp;=\u0026thinsp;952), respectively. In the external validation NWICU cohort, the corresponding incidence rates were 1.8% (n\u0026thinsp;=\u0026thinsp;34) for CA, 6.1% (n\u0026thinsp;=\u0026thinsp;113) for AKI, and 2.4% (n\u0026thinsp;=\u0026thinsp;45) for sepsis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the MIMIC-IV cohort, patients who subsequently developed CA presented with a distinct and more severe clinical profile upon admission (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Notably, they were significantly younger (median 65 vs. 71 years, P\u0026thinsp;=\u0026thinsp;0.003), had a higher body weight, and a greater proportion were male. Comorbidities were also markedly different, with a significantly higher prevalence of heart failure (51.8% vs. 23.9%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) but a lower prevalence of hypertension (35.3% vs. 54.8%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in the CA group. Crucially, the CA group exhibited markedly elevated admission levels of all three insulin resistance markers: SHR (1.309 vs. 1.008), TYG (9.078 vs. 8.735), and CHG (5.632 vs. 5.344) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This metabolic derangement was accompanied by evidence of profound systemic physiological distress, including significantly higher levels of inflammatory markers (WBC), markers of hepatic and myocardial injury (AST, ALT, CK), indicators of renal dysfunction (creatinine) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of patients stratified by in-hospital cardiac arrest\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMIMIC (n\u0026thinsp;=\u0026thinsp;3059)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eNWICU (n\u0026thinsp;=\u0026thinsp;1849)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo IHCA (n\u0026thinsp;=\u0026thinsp;2974)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIHCA (n\u0026thinsp;=\u0026thinsp;85)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo IHCA (n\u0026thinsp;=\u0026thinsp;1815)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIHCA (n\u0026thinsp;=\u0026thinsp;34)\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\u003eBaseline characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71.000 (59.000, 82.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65.000 (57.000, 74.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e66.000 (56.000, 76.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e64.000 (58.250, 72.500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.293\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e78.200 (65.600, 93.175)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87.500 (74.200, 102.500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e80.640 (67.210, 98.520)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e92.890 (79.270, 111.835)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.030\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1369 (46.032%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29 (34.118%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e753 (41.488%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6 (17.647%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1605 (53.968%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56 (65.882%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1062 (58.512%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28 (82.353%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1283 (43.141%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42 (49.412%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e628 (34.601%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5 (14.706%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1691 (56.859%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43 (50.588%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1187 (65.399%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29 (85.294%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2105 (70.780%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57 (67.059%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1612 (88.815%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e31 (91.176%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e869 (29.220%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28 (32.941%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e203 (11.185%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3 (8.824%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart failure\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2262 (76.059%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41 (48.235%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1674 (92.231%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29 (85.294%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e712 (23.941%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44 (51.765%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e141 (7.769%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5 (14.706%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1343 (45.158%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55 (64.706%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1495 (82.369%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29 (85.294%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1631 (54.842%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30 (35.294%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e320 (17.631%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5 (14.706%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVital signs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR (bpm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80.000 (69.000, 93.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85.000 (69.000, 101.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e81.000 (70.000, 97.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e82.500 (71.000, 98.500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.685\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e136.000 (120.000, 153.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e124.000 (104.000, 139.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e141.000 (125.000, 159.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e131.500 (122.250, 152.500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e76.500 (66.000, 89.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74.000 (64.000, 87.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e76.000 (66.000, 88.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e77.500 (70.000, 86.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.644\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRR (bpm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.000 (15.000, 21.750)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.000 (16.000, 24.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.023\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.000 (17.000, 24.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21.500 (18.250, 24.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpO2 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e98.000 (96.000, 99.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e98.000 (96.000, 100.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e97.000 (95.000, 99.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e98.000 (96.000, 99.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.389\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36.780 (36.560, 37.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.720 (36.440, 37.110)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36.722 (36.500, 37.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e36.667 (36.389, 36.819)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.035\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory tests\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.008 (0.872, 1.188)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.309 (1.119, 1.567)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.093 (0.911, 1.307)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.661 (1.277, 2.342)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTYG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.735 (8.360, 9.197)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.078 (8.606, 9.530)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.868 (8.417, 9.386)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.516 (9.185, 10.130)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.344 (5.045, 5.682)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.632 (5.240, 5.947)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.505 (5.157, 5.917)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.195 (5.742, 6.394)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlu (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e122.000 (103.000, 151.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e155.250 (127.500, 217.500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e133.000 (108.000, 177.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e241.500 (168.000, 317.750)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.700 (5.400, 6.300)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.700 (5.300, 6.300)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.800 (5.500, 6.600)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.800 (5.500, 6.650)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.544\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e100.500 (73.000, 141.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100.000 (72.000, 147.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e103.000 (73.000, 148.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e125.000 (91.750, 190.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e160.000 (130.250, 192.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e145.000 (112.000, 182.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e153.000 (126.000, 189.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e165.500 (143.250, 186.250)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.448\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46.750 (37.000, 58.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42.000 (35.000, 51.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e42.000 (34.000, 52.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e41.000 (31.000, 49.500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.453\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e86.500 (63.000, 114.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71.000 (53.000, 103.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e85.000 (60.000, 115.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e89.000 (57.250, 105.500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC (K/uL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.800 (7.500, 12.500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.060 (10.600, 17.750)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.300 (7.300, 12.200)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.650 (10.050, 15.200)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBC (m/uL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.120 (3.670, 4.550)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.070 (3.560, 4.630)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.440 (3.770, 4.960)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.665 (4.290, 5.100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT (K/uL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e211.000 (169.330, 260.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e211.500 (180.330, 269.500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e233.000 (188.000, 286.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e236.500 (172.000, 292.250)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHb (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.400 (11.000, 13.650)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.400 (10.850, 13.800)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.600 (12.000, 14.900)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.300 (13.575, 15.275)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDW (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.700 (13.000, 14.600)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.900 (13.050, 14.900)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.400 (12.900, 14.500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.050 (12.625, 13.925)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.042\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHct (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37.460 (33.500, 40.900)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.400 (33.200, 41.270)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41.100 (36.750, 44.850)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e44.500 (42.100, 46.575)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium (mEq/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e139.000 (137.000, 141.500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e138.500 (135.750, 140.200)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e138.000 (136.000, 140.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e139.000 (135.250, 140.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.269\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium (mEq/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.000 (3.700, 4.300)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.250 (3.900, 4.500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.000 (3.700, 4.300)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.850 (3.250, 4.100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Calcium (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.800 (8.400, 9.123)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.450 (7.900, 8.830)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.100 (8.700, 9.500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.650 (8.200, 9.075)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChloride (mEq/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e104.000 (101.000, 106.500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e103.670 (100.500, 107.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e103.000 (100.000, 106.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e102.000 (99.250, 105.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.386\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphorus (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.300 (2.900, 3.800)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.550 (3.000, 4.530)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.200 (2.600, 3.900)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.600 (3.100, 4.200)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMagnesium (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.000 (1.830, 2.100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.120 (2.000, 2.330)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.900 (1.800, 2.100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.000 (1.825, 2.275)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePT (sec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.550 (11.700, 13.800)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.300 (12.400, 15.900)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.200 (11.300, 13.600)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.300 (11.550, 14.275)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTT (sec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29.500 (26.700, 35.188)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42.300 (29.300, 64.200)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.000 (27.250, 33.400)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e32.250 (28.150, 37.825)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCK (IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e137.670 (73.500, 361.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e721.500 (152.000, 1775.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e130.000 (71.000, 372.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e554.000 (141.500, 1481.500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Bilirubin (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.600 (0.400, 0.800)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.600 (0.450, 0.900)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.600 (0.400, 0.800)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.500 (0.400, 0.800)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.617\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT (IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.000 (14.000, 32.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69.330 (30.000, 196.400)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.000 (15.000, 35.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e60.000 (32.500, 139.750)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST (IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.000 (18.000, 42.875)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e128.000 (53.500, 296.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.000 (17.000, 36.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e89.500 (36.250, 198.500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.000 (12.000, 22.500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.500 (15.500, 33.400)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.000 (13.000, 24.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17.500 (15.000, 20.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.794\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.900 (0.700, 1.150)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.130 (0.800, 1.980)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000 (0.810, 1.300)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.245 (1.120, 1.422)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elength of hospitalization (day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.265 (3.600, 11.787)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.620 (4.110, 16.450)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.050 (2.170, 7.945)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.585 (3.998, 15.558)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elength of ICU (day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.830 (1.760, 5.317)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.290 (2.460, 9.110)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.900 (1.165, 3.295)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.900 (1.995, 7.840)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eNotes:\u003c/strong\u003e Data were presented as median (Q1, Q3), or n (%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e IHCA, in-hospital cardiac arrest; HR, heart rate; SBP, systolic blood pressure; DBP, diastolic blood pressure; SpO₂, pulse oxygen saturation; WBC, white blood cell; PLT, platelet count; RDW, red cell distribution width; Hb, hemoglobin; Hct, hematocrit; BUN, blood urea nitrogen; PT, prothrombin time; PTT, partial thromboplastin time; TG, triglycerides; TC, total cholesterol; HDL-C, high-density lipoprotein cholesterol; Glu, glucose; LDL-C, low-density lipoprotein cholesterol; ALT, alanine aminotransferase; AST, aspartate aminotransferase; CK, creatine kinase; HbA1c, glycated hemoglobin; ICU, intensive care unit; TYG, triglyceride-glucose index; CHG, cholesterol, high-density lipoprotein, and glucose index; SHR, stress hyperglycemia ratio.\u003c/p\u003e \u003cp\u003eThis pattern was largely corroborated in the NWICU validation cohort, where patients in the CA group also demonstrated significantly higher levels of SHR (1.661 vs. 1.093), TYG (9.516 vs. 8.868), and CHG (6.195 vs. 5.505) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Furthermore, this trend was not confined to CA; patients who developed AKI or sepsis also presented with significantly higher baseline levels of these IR markers (SHR, TYG, and CHG) compared to their respective counterparts (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as detailed in \u003cb\u003eSupplementary Tables S3 and S4\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIndependent Association of IR Markers with Clinical Outcomes\u003c/h3\u003e\n\u003cp\u003eTo build the multivariable logistic regression models, covariates were selected based on univariate analysis and were supplemented with clinically important demographic characteristics, such as age and gender (\u003cb\u003eSupplementary Tables S5 and S6\u003c/b\u003e). After full adjustment for potential confounders (Model II), SHR (OR 2.888, 95% CI 1.883\u0026ndash;4.427, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), TYG (OR 1.446, 95% CI 1.075\u0026ndash;1.946, P\u0026thinsp;=\u0026thinsp;0.015), and CHG (OR 1.580, 95% CI 1.050\u0026ndash;2.378, P\u0026thinsp;=\u0026thinsp;0.028) were all identified as significant independent risk factors for in-hospital CA in the MIMIC-IV cohort (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Furthermore, these markers were also independently associated with the secondary outcomes. For AKI, the adjusted ORs were significant for SHR (OR 2.484, 95% CI 1.752\u0026ndash;3.521, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), TYG (OR 1.151, 95% CI 1.010\u0026ndash;1.311, P\u0026thinsp;=\u0026thinsp;0.035), and CHG (OR 1.247, 95% CI 1.041\u0026ndash;1.494, P\u0026thinsp;=\u0026thinsp;0.016). Similarly, for sepsis, SHR (OR 1.776, 95% CI 1.340\u0026ndash;2.354, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), TYG (OR 1.250, 95% CI 1.104\u0026ndash;1.416, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and CHG (OR 1.235, 95% CI 1.039\u0026ndash;1.468, P\u0026thinsp;=\u0026thinsp;0.016) remained significant predictors (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\u003eMultivariable logistic regression analysis of the association between insulin resistance markers and clinical outcomes in MIMIC dataset\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNon-adjusted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eModel Ⅰ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eModel Ⅱ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCardiac arrest\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.025 (3.237\u0026thinsp;~\u0026thinsp;7.802)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.340 (2.840\u0026thinsp;~\u0026thinsp;6.631)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.888 (1.883\u0026thinsp;~\u0026thinsp;4.427)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTYG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.725 (1.328\u0026thinsp;~\u0026thinsp;2.241)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.552 (1.176\u0026thinsp;~\u0026thinsp;2.049)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.446 (1.075\u0026thinsp;~\u0026thinsp;1.946)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCHG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.439 (1.715\u0026thinsp;~\u0026thinsp;3.470)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.955 (1.332\u0026thinsp;~\u0026thinsp;2.867)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.580 (1.050\u0026thinsp;~\u0026thinsp;2.378)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.028\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAKI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.737 (2.706\u0026thinsp;~\u0026thinsp;5.160)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.438 (2.463\u0026thinsp;~\u0026thinsp;4.800)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.484 (1.752\u0026thinsp;~\u0026thinsp;3.521)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTYG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.329 (1.176\u0026thinsp;~\u0026thinsp;1.502)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.270 (1.119\u0026thinsp;~\u0026thinsp;1.443)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.151 (1.010\u0026thinsp;~\u0026thinsp;1.311)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.035\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCHG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.582 (1.343\u0026thinsp;~\u0026thinsp;1.864)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.459 (1.226\u0026thinsp;~\u0026thinsp;1.736)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.247 (1.041\u0026thinsp;~\u0026thinsp;1.494)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSepsis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.535 (2.717\u0026thinsp;~\u0026thinsp;4.599)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.090 (2.369\u0026thinsp;~\u0026thinsp;4.032)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.776 (1.340\u0026thinsp;~\u0026thinsp;2.354)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTYG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.410 (1.262\u0026thinsp;~\u0026thinsp;1.575)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.446 (1.287\u0026thinsp;~\u0026thinsp;1.624)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.250 (1.104\u0026thinsp;~\u0026thinsp;1.416)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCHG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.520 (1.310\u0026thinsp;~\u0026thinsp;1.762)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.560 (1.330\u0026thinsp;~\u0026thinsp;1.829)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.235 (1.039\u0026thinsp;~\u0026thinsp;1.468)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003cb\u003eNotes\u003c/b\u003e: Non-adjusted models: None; Model Ⅰ adjusted for: gender, race, age, weight, heart failure (yes/no); Model Ⅱ adjusted for: confounders in the minimally adjusted (Model Ⅰ)\u0026thinsp;+\u0026thinsp;SBP, WBC, total calcium, magnesium, and PTT.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u003cstrong\u003eNotes:\u003c/strong\u003e Non-adjusted models: None; Model\u0026nbsp;Ⅰ\u0026nbsp;adjusted for: gender, race, age, weight, heart failure (yes/no); Model\u0026nbsp;Ⅱ\u0026nbsp;adjusted for: confounders in the minimally adjusted (Model\u0026nbsp;Ⅰ) + SBP, WBC, total calcium, magnesium, and PTT.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e OR, odds ratios; CI, confidence intervals; SBP, systolic blood pressure; WBC, white blood cell; PTT, partial thromboplastin time; TYG, triglyceride-glucose index; CHG, cholesterol, high-density lipoprotein, and glucose index; SHR, stress hyperglycemia ratio.\u003c/p\u003e\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable logistic regression analysis of the association between insulin resistance markers and clinical outcomes in NWICU dataset\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNon-adjusted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eModel Ⅰ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eModel Ⅱ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCardiac arrest\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.555 (1.806\u0026thinsp;~\u0026thinsp;3.613)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.966 (2.042\u0026thinsp;~\u0026thinsp;4.308)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.636 (1.789\u0026thinsp;~\u0026thinsp;3.883)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTYG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.523 (1.759\u0026thinsp;~\u0026thinsp;3.618)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.772 (1.855\u0026thinsp;~\u0026thinsp;4.144)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.752 (1.800\u0026thinsp;~\u0026thinsp;4.206)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCHG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.356 (1.579\u0026thinsp;~\u0026thinsp;3.517)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.650 (1.668\u0026thinsp;~\u0026thinsp;4.209)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.499 (1.521\u0026thinsp;~\u0026thinsp;4.105)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAKI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.300 (1.761\u0026thinsp;~\u0026thinsp;3.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.266 (1.723\u0026thinsp;~\u0026thinsp;2.979)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.996 (1.503\u0026thinsp;~\u0026thinsp;2.652)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTYG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.864 (1.492\u0026thinsp;~\u0026thinsp;2.329)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.968 (1.555\u0026thinsp;~\u0026thinsp;2.491)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.861 (1.450\u0026thinsp;~\u0026thinsp;2.388)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCHG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.192 (1.713\u0026thinsp;~\u0026thinsp;2.805)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.316 (1.774\u0026thinsp;~\u0026thinsp;3.023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.151 (1.614\u0026thinsp;~\u0026thinsp;2.867)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSepsis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.120 (1.532\u0026thinsp;~\u0026thinsp;2.933)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.202 (1.583\u0026thinsp;~\u0026thinsp;3.063)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.884 (1.346\u0026thinsp;~\u0026thinsp;2.638)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTYG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.306 (1.671\u0026thinsp;~\u0026thinsp;3.181)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.475 (1.761\u0026thinsp;~\u0026thinsp;3.480)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.239 (1.558\u0026thinsp;~\u0026thinsp;3.218)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCHG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.638 (1.864\u0026thinsp;~\u0026thinsp;3.734)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.938 (2.020\u0026thinsp;~\u0026thinsp;4.272)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.570 (1.704\u0026thinsp;~\u0026thinsp;3.876)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003cb\u003eNotes\u003c/b\u003e: Non-adjusted models: None; Model Ⅰ adjusted for: gender, race, age, weight, heart failure(yes/no); Model Ⅱ adjusted for: confounders in the minimally adjusted (Model Ⅰ)\u0026thinsp;+\u0026thinsp;SBP, WBC, total calcium, magnesium, and PTT.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eNotes:\u003c/strong\u003e Non-adjusted models: None; Model Ⅰ adjusted for: gender, race, age, weight, heart failure(yes/no); Model Ⅱ adjusted for: confounders in the minimally adjusted (Model Ⅰ) + SBP, WBC, total calcium, magnesium, and PTT.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e OR, odds ratios; CI, confidence intervals; SBP, systolic blood pressure; WBC, white blood cell; PTT, partial thromboplastin time; TYG, triglyceride-glucose index; CHG, cholesterol, high-density lipoprotein, and glucose index; SHR, stress hyperglycemia ratio.\u003c/p\u003e \u003cp\u003eThese strong associations were consistently replicated in the NWICU validation cohort. For CA, the adjusted ORs were 2.636 (95% CI 1.789\u0026ndash;3.883, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for SHR, 2.752 (95% CI 1.800\u0026ndash;4.206, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for TYG, and 2.499 (95% CI 1.521\u0026ndash;4.105, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for CHG. The markers also robustly predicted AKI and sepsis in this cohort (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhen the markers were categorized into tertiles, a clear dose-dependent relationship was observed for SHR and TYG with CA risk in the MIMIC-IV cohort (P for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Notably, while the highest tertile of CHG was associated with increased CA risk, this association did not exhibit a statistically significant linear trend (P for trend\u0026thinsp;=\u0026thinsp;0.112). In contrast, all three markers showed a significant dose-dependent risk for CA in the NWICU cohort (P for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.05). For the secondary outcomes of AKI and sepsis, all three markers demonstrated a significant positive linear trend across tertiles in both databases (P for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (\u003cb\u003eSupplementary Tables S7 and S8\u003c/b\u003e).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDose-Response Relationship and Threshold Effect Analysis\u003c/h2\u003e \u003cp\u003eThe RCS plots revealed a generally positive association for all three markers in both the MIMIC-IV and NWICU databases, indicating that higher IR levels were associated with increased CA risk (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In the MIMIC-IV cohort, the relationship between SHR and CA was non-linear (P for non-linearity\u0026thinsp;=\u0026thinsp;0.007). In contrast, the associations for TYG and CHG were significantly linear (P for non-linearity\u0026thinsp;\u0026gt;\u0026thinsp;0.05). In the NWICU cohort, a significant non-linear relationship was observed for SHR (P for non-linearity\u0026thinsp;=\u0026thinsp;0.008) and TYG (P for non-linearity\u0026thinsp;=\u0026thinsp;0.022), while the relationship for CHG was linear (P for non-linearity\u0026thinsp;=\u0026thinsp;0.082).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further explore these relationships and quantify potential threshold effects, we performed a two-piecewise linear regression analysis. For SHR, significant inflection points were identified at 1.436 in the MIMIC-IV cohort and 2.015 in the NWICU cohort (log-likelihood ratio test P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for both). Beyond these thresholds, the risk of CA escalated more steeply. A similar threshold effect was detected for TYG in the NWICU cohort, with a significant inflection point at 8.887 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (\u003cb\u003eSupplementary Table S9\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eThe dose-response relationships for the secondary outcomes were also comprehensively analyzed. For AKI and sepsis, all three markers generally showed a positive association with risk. The relationships were predominantly linear in the MIMIC-IV cohort, while in the NWICU cohort, evidence of non-linearity was observed for the association between TYG and both AKI and sepsis (P for non-linearity\u0026thinsp;\u0026lt;\u0026thinsp;0.05), as detailed in \u003cb\u003eSupplementary Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and S2\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup and Sensitivity Analyses\u003c/h2\u003e \u003cp\u003eTo assess the consistency and generalizability of our findings, we conducted a series of pre-specified subgroup analyses. In the MIMIC-IV cohort, the positive association between elevated levels of SHR, TYG, and CHG and the risk of in-hospital CA was broadly consistent across all examined subgroups, including those stratified by age (\u0026lt;\u0026thinsp;65 vs. \u0026ge;65 years), gender, race, diabetes, heart failure, and hypertension. No significant interactions were detected for any of the three markers in this cohort (P for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05), underscoring the robustness of these associations within this population (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-C).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the NWICU validation cohort, the associations for TYG and CHG with CA risk also remained consistent across subgroups, with no evidence of interaction (P for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, the predictive effect of SHR on CA was significantly modified by race (P for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and the presence of diabetes (P for interaction\u0026thinsp;=\u0026thinsp;0.016), suggesting a stronger association among non-White patients and those without pre-existing diabetes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD-F).\u003c/p\u003e \u003cp\u003eSubgroup analyses for the secondary outcomes revealed more complex patterns of interaction (\u003cb\u003eSupplementary Figures S3 and S4\u003c/b\u003e). For AKI, the predictive value of the IR markers was frequently modified by age, gender, and race in both cohorts (P for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.05). For sepsis, significant interactions were noted in the MIMIC-IV cohort between SHR and age, and between CHG and diabetes. In the NWICU cohort, the effect of SHR on sepsis risk was significantly modified by gender and race (P for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePredictive Performance of IR Markers for Clinical Outcomes\u003c/h2\u003e \u003cp\u003eIn the MIMIC-IV cohort, SHR demonstrated the highest discriminatory ability for the primary outcome of in-hospital CA, with an Area Under the Curve (AUC) of 0.763 (95% CI, 0.710\u0026ndash;0.817). This was markedly superior to the performance of both TYG (AUC\u0026thinsp;=\u0026thinsp;0.624, 95% CI, 0.562\u0026ndash;0.687) and CHG (AUC\u0026thinsp;=\u0026thinsp;0.639, 95% CI, 0.580\u0026ndash;0.699) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). A similar pattern of superiority for SHR was observed for predicting AKI (AUC\u0026thinsp;=\u0026thinsp;0.600) and sepsis (AUC\u0026thinsp;=\u0026thinsp;0.615) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB-C).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThese findings were largely validated in the NWICU cohort. SHR again demonstrated the strongest predictive performance for CA (AUC\u0026thinsp;=\u0026thinsp;0.792, 95% CI, 0.708\u0026ndash;0.877) and sepsis (AUC\u0026thinsp;=\u0026thinsp;0.779). For AKI, its predictive ability (AUC\u0026thinsp;=\u0026thinsp;0.640) was marginally surpassed by that of CHG (AUC\u0026thinsp;=\u0026thinsp;0.641) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD-F).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMediation Analysis of Inflammation\u003c/h2\u003e \u003cp\u003eIn both the MIMIC-IV and NWICU cohorts, the analysis revealed that WBC significantly and partially mediated the effects of all three IR markers on IHCA risk. In the MIMIC-IV cohort, the proportion of the total effect mediated by WBC was 11.3% for SHR, 12.1% for TYG, and 14.5% for CHG (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-C). This mediating role was consistently replicated in the NWICU cohort, where WBC accounted for 8.9% of the SHR-IHCA association, 5.3% of the TYG-IHCA association, and 7.7% of the CHG-IHCA association (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD-F).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRegarding the secondary outcomes, WBC was also a significant mediator for the association between all three IR markers and sepsis in both cohorts. For AKI, this mediation was significant in the MIMIC-IV cohort; however, it did not reach statistical significance in the NWICU cohort (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (\u003cb\u003eSupplementary Figures S5 and S6\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this large, dual-center retrospective investigation, we revealed that elevated admission levels of three accessible insulin resistance (IR) markers (SHR, TYG, and CHG) are independently associated with an increased risk of IHCA in a diverse population of critically ill patients. This primary finding remained robust after comprehensive multivariable adjustment and was successfully validated in an independent external cohort, underscoring the generalizability of these metabolic biomarkers for acute risk stratification.\u003c/p\u003e \u003cp\u003eOur findings substantially expand the existing literature by shifting the focus from chronic outcomes to an acute, catastrophic event. While previous studies have established links between IR markers and longer-term endpoints like mortality in ICU populations,[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] their utility in predicting the imminent onset of IHCA has been largely unexplored. Our work differs from prior research, such as the study by Yang et al., which associated SHR with mortality after a cardiac arrest had already occurred;[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] our results, in contrast, position these markers as pre-event risk predictors. Furthermore, while Liu et al. recently linked the TYG index to cardiac arrest specifically within the context of acute myocardial infarction,[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] our study considerably broadens this paradigm. We demonstrate that the association between severe metabolic dysregulation and circulatory collapse is not confined to a single cardiac diagnosis but represents a more fundamental pathophysiological process observable across a wide spectrum of critical illnesses. Moreover, all three IR markers also demonstrated independent associations with the prediction of AKI and sepsis.\u003c/p\u003e \u003cp\u003eThe pathophysiological rationale connecting severe IR to adverse outcomes is multifaceted, with systemic inflammation emerging as a key mechanistic link. Critical illness is a state of profound metabolic stress that frequently triggers stress hyperglycemia and acute IR.[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] Severe IR is known to promote a cascade of deleterious systemic effects, including endothelial dysfunction, mitochondrial damage, and a pro-thrombotic state.[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] Our mediation analysis supports this concept, revealing that WBC partially mediated the relationship between all three IR markers and IHCA. This finding is biologically plausible, as severe IR initiates a cascade of metabolic derangements, including hyperglycemia, compensatory hyperinsulinemia, dyslipidemia, and endothelial dysfunction, alongside elevated inflammatory markers and a prothrombotic state, all of which collectively contribute to progressive organ injury.[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] This inflammatory surge can directly destabilize the cardiovascular system by impairing myocardial contractility, promoting endothelial injury, and reducing the threshold for arrhythmias.[\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eA key finding of our study was the superior predictive performance of SHR for IHCA, AKI, and sepsis compared to TYG and CHG. The unique strength of SHR lies in its formula, which incorporates HbA1c to adjust acute glucose levels for the patient's chronic glycemic background.[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] This allows SHR to more accurately quantify the relative glucose elevation attributable to acute physiological stress, thereby distinguishing true stress-induced hyperglycemia (SIH) from the chronic hyperglycemia of poorly controlled diabetes.[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] Our subgroup analyses further underscored the robustness of these markers, though they also revealed important nuances. The predictive effect of SHR on IHCA was significantly stronger in patients without pre-existing diabetes in the NWICU cohort. This is logical, as a high SHR in a non-diabetic individual represents a more profound acute deviation from their metabolic baseline, signaling a more severe systemic stress response and thus a higher imminent risk.[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] Notably, while SHR was the best overall predictor, the novel CHG index consistently demonstrated superior predictive efficacy for AKI compared to the traditional TYG index in both cohorts. This may suggest that the interplay of cholesterol metabolism, in addition to triglycerides and glucose, is particularly relevant to the pathophysiology of acute kidney injury in the critically ill.\u003c/p\u003e \u003cp\u003e In contrast to traditional complex scoring systems, the IR markers we investigated are calculated from routine, inexpensive laboratory values, making them universally applicable tools for early, bedside risk stratification.[\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] Their use could enable clinicians to identify a high-risk population for IHCA upon ICU admission. These patients might benefit from heightened surveillance, more intensive cardiac monitoring, or a lower threshold for escalating care. Importantly, these findings frame acute IR not just as a biomarker but as a potential therapeutic target.\u003c/p\u003e \u003cp\u003eHowever, this study has limitations that must be acknowledged. First, its retrospective, observational design establishes association, not causation. Despite rigorous adjustment, residual confounding from unmeasured variables may persist. Second, we used a static snapshot of laboratory values from the first 24 hours, which may not capture the dynamic evolution of a patient's metabolic state. Third, we noted some inconsistencies between the MIMIC-IV and NWICU cohorts, particularly in certain secondary analyses. These discrepancies may stem from several factors, including the substantially smaller number of IHCA events in the NWICU cohort, which reduces statistical power and can make the detection of trends and interactions less stable. Furthermore, differences in patient populations, case-mix, and standards of care between the two hospital systems and time periods could also contribute to this variability.\u003c/p\u003e \u003cp\u003eLooking forward, prospective studies are needed to validate these findings and to explore the dynamic trajectory of these IR markers over the course of an ICU stay. Ultimately, the most critical next step will be to conduct randomized controlled trials to determine whether early, targeted interventions aimed at ameliorating severe IR in high-risk patients can reduce the incidence of IHCA and improve outcomes.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, elevated admission levels of the insulin resistance markers SHR, TYG, and CHG are independently associated with a significantly increased risk of IHCA in a broad population of critically ill patients. Among these, SHR demonstrated the strongest predictive performance. These readily available and inexpensive markers hold promise as valuable tools for early risk stratification, helping to identify patients who warrant heightened clinical surveillance and who may be candidates for future targeted metabolic interventions.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAKI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAcute Kidney Injury\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eALT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAlanine Aminotransferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAspartate Aminotransferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBUN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBlood Urea Nitrogen\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCHG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCholesterol, High-Density Lipoprotein, and Glucose index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCK\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCreatine Kinase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiastolic Blood Pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGlu\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlucose\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHb\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHemoglobin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHbA1c\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlycated Hemoglobin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHct\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHematocrit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHDL-C\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh-Density Lipoprotein Cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHeart Rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntensive Care Unit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIHCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIn-Hospital Cardiac Arrest\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInsulin Resistance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIRB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInstitutional Review Board\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKDIGO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKidney Disease:Improving Global Outcomes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLDL-C\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow-Density Lipoprotein Cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMEWS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eModified Early Warning Score\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMIMIC-IV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMedical Information Mart for Intensive Care IV\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNEWS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Early Warning Score\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNMHC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNorthwestern Memorial Hospital\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNWICU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNorthwestern ICU\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePLT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePlatelet\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProthrombin Time\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePTT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePartial Thromboplastin Time\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRed Blood Cell\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRDW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRed Cell Distribution Width\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRespiratory Rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSystolic Blood Pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStress Hyperglycemia Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSpO₂\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePulse Oxygen Saturation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSQL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStructured Query Language\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTotal Cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTriglycerides\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTYG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTriglyceride-Glucose Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWhite Blood Cell\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study (MIMIC-IV and NWICU) are available in the PhysioNet repository, https://physionet.org/.\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThis study was based on de-identified data from two large, publicly available databases. The collection of patient information for the MIMIC-IV database was reviewed by the IRB at the BIDMC and the MIT, who granted a waiver of informed consent. The NWICU database project was approved by the NU IRB also approved participation and granted a waiver of informed consent, as the project did not impact clinical care and all protected health information was de-identified. One author (Z.Z., Certificate number: 47608458) obtained certified access to use the databases after completing the requisite training and signing the data use agreements.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests in this section.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eConceptualization, Z.Z. and Q.L.; Data analysis, Z.Z.; Writing \u0026ndash; Original Draft, Z.Z.; Writing \u0026ndash; Review \u0026amp; Editing, Z.Z. and Q.L. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgment\u003c/p\u003e\n\u003cp\u003eWe would like to express our gratitude to the MIT and the BIDMC for the creation and maintenance of the MIMIC-IV database. We also thank Northwestern University for providing the NWICU database. We acknowledge all the researchers and staff who have contributed to these valuable data resources.\u003c/p\u003e\n\u003cp\u003eClinical trial\u003c/p\u003e\n\u003cp\u003enot applicable.\u003c/p\u003e\n\u003cp\u003eConsent to Publish declaration\u003c/p\u003e\n\u003cp\u003enot applicable.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis study was funded by Research project of Zigong City Science \u0026amp; Technology and Intellectual Property Right Bureau (2023-YGY-3-04).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAndersen LW, Holmberg MJ, Berg KM, Donnino MW, Granfeldt A: In-Hospital Cardiac Arrest: A Review. \u003cem\u003eJama\u003c/em\u003e 2019, 321(12):1200\u0026ndash;1210.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNolan JP, Sandroni C, B\u0026ouml;ttiger BW, Cariou A, Cronberg T, Friberg H, Genbrugge C, Haywood K, Lilja G, Moulaert VRM \u003cem\u003eet al\u003c/em\u003e: European Resuscitation Council and European Society of Intensive Care Medicine guidelines 2021: post-resuscitation care. \u003cem\u003eIntensive care medicine\u003c/em\u003e 2021, 47(4):369\u0026ndash;421.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKleinman ME, Perkins GD, Bhanji F, Billi JE, Bray JE, Callaway CW, de Caen A, Finn JC, Hazinski MF, Lim SH \u003cem\u003eet al\u003c/em\u003e: ILCOR Scientific Knowledge Gaps and Clinical Research Priorities for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care: A Consensus Statement. \u003cem\u003eCirculation\u003c/em\u003e 2018, 137(22):e802-e819.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith ME, Chiovaro JC, O'Neil M, Kansagara D, Qui\u0026ntilde;ones AR, Freeman M, Motu'apuaka ML, Slatore CG: Early warning system scores for clinical deterioration in hospitalized patients: a systematic review. \u003cem\u003eAnnals of the American Thoracic Society\u003c/em\u003e 2014, 11(9):1454\u0026ndash;1465.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith GB, Prytherch DR, Meredith P, Schmidt PE, Featherstone PI: The ability of the National Early Warning Score (NEWS) to discriminate patients at risk of early cardiac arrest, unanticipated intensive care unit admission, and death. \u003cem\u003eResuscitation\u003c/em\u003e 2013, 84(4):465\u0026ndash;470.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePliszka M, Szablewski L: Severe Insulin Resistance Syndromes: Clinical Spectrum and Management. \u003cem\u003eInternational journal of molecular sciences\u003c/em\u003e 2025, 26(12).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMir MM, Jeelani M, Alharthi MH, Rizvi SF, Sohail SK, Wani JI, Sabah ZU, BinAfif WF, Nandi P, Alshahrani AM \u003cem\u003eet al\u003c/em\u003e: Unraveling the Mystery of Insulin Resistance: From Principle Mechanistic Insights and Consequences to Therapeutic Interventions. \u003cem\u003eInternational journal of molecular sciences\u003c/em\u003e 2025, 26(6).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrie AD, Christodorescu RM, Popescu R, Adam O, T\u0026icirc;rziu A, Brie DM: Atherosclerosis and Insulin Resistance: Is There a Link Between Them? \u003cem\u003eBiomedicines\u003c/em\u003e 2025, 13(6).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHajri T, Ouguerram K, Fungwe TV: Impact of Lipid Oxidation Products on Inflammation and Insulin Resistance: A Focus on Mechanisms of Action. \u003cem\u003eCell biochemistry and biophysics\u003c/em\u003e 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeFronzo RA, Tobin JD, Andres R: Glucose clamp technique: a method for quantifying insulin secretion and resistance. \u003cem\u003eThe American journal of physiology\u003c/em\u003e 1979, 237(3):E214-223.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuerrero-Romero F, Simental-Mend\u0026iacute;a LE, Gonz\u0026aacute;lez-Ortiz M, Mart\u0026iacute;nez-Abundis E, Ramos-Zavala MG, Hern\u0026aacute;ndez-Gonz\u0026aacute;lez SO, Jacques-Camarena O, Rodr\u0026iacute;guez-Mor\u0026aacute;n M: The product of triglycerides and glucose, a simple measure of insulin sensitivity. Comparison with the euglycemic-hyperinsulinemic clamp. \u003cem\u003eThe Journal of clinical endocrinology and metabolism\u003c/em\u003e 2010, 95(7):3347\u0026ndash;3351.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi L, Zhao M, Zhang Z, Zhou L, Zhang Z, Xiong Y, Hu Z, Yao Y: Prognostic significance of the stress hyperglycemia ratio in critically ill patients. \u003cem\u003eCardiovascular diabetology\u003c/em\u003e 2023, 22(1):275.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMo D, Zhang P, Zhang M, Dai H, Guan J: Cholesterol, high-density lipoprotein, and glucose index versus triglyceride-glucose index in predicting cardiovascular disease risk: a cohort study. \u003cem\u003eCardiovascular diabetology\u003c/em\u003e 2025, 24(1):116.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLv L, Xiong J, Huang Y, He T, Zhao J: Association between the Triglyceride Glucose Index and All-Cause Mortality in Critically Ill Patients with Acute Kidney Injury. \u003cem\u003eKidney diseases (Basel, Switzerland)\u003c/em\u003e 2024, 10(1):69\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa C, Jiang W, Li J, Sun W, Zhang J, Xu P, Guo Y, Ning N, Li J, Zhao B \u003cem\u003eet al\u003c/em\u003e: Association of Stress Hyperglycemia Ratio and in-Hospital Mortality in Patients with Sepsis: A Two Center Retrospective Cohort Study. \u003cem\u003eJournal of inflammation research\u003c/em\u003e 2024, 17:7939\u0026ndash;7950.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi B, Yang X, Wang JH, Chen W, Wang Q, Zhong L: Nonlinear association between triglyceride-glucose index and 28-day mortality in intensive care units: a multi-center retrospective cohort study. \u003cem\u003eFrontiers in endocrinology\u003c/em\u003e 2025, 16:1545478.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLou J, Xiang Z, Zhu X, Fan Y, Song J, Cui S, Li J, Jin G, Huang N: A retrospective study utilized MIMIC-IV database to explore the potential association between triglyceride-glucose index and mortality in critically ill patients with sepsis. \u003cem\u003eScientific reports\u003c/em\u003e 2024, 14(1):24081.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLian LY, Xue WH, Lu JJ, Zheng RJ: Impact of stress hyperglycemia ratio on mortality in patients with cardiac arrest: insight from American MIMIC-IV database. \u003cem\u003eFrontiers in endocrinology\u003c/em\u003e 2024, 15:1383993.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBellomo R, Warrillow SJ, Reade MC: Why we should be wary of single-center trials. \u003cem\u003eCritical care medicine\u003c/em\u003e 2009, 37(12):3114\u0026ndash;3119.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRhodes A, Evans LE, Alhazzani W, Levy MM, Antonelli M, Ferrer R, Kumar A, Sevransky JE, Sprung CL, Nunnally ME \u003cem\u003eet al\u003c/em\u003e: Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock: 2016. \u003cem\u003eCritical care medicine\u003c/em\u003e 2017, 45(3):486\u0026ndash;552.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKellum JA, Lameire N: Diagnosis, evaluation, and management of acute kidney injury: a KDIGO summary (Part 1). \u003cem\u003eCritical care (London, England)\u003c/em\u003e 2013, 17(1):204.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoon KM, Kim G, Baik SK, Choi E, Kim MY, Kim HA, Cho MY, Shin SY, Kim JM, Park HJ \u003cem\u003eet al\u003c/em\u003e: Ultrasonographic scoring system score versus liver stiffness measurement in prediction of cirrhosis. \u003cem\u003eClinical and molecular hepatology\u003c/em\u003e 2013, 19(4):389\u0026ndash;398.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkinwande MO, Dikko HG, Samson A: Variance Inflation Factor: As a Condition for the Inclusion of Suppressor Variable(s) in Regression Analysis. \u003cem\u003eOpen Journal of Statistics\u003c/em\u003e 2015, 05(7):754\u0026ndash;767.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu H, Wang L, Wang H, Hao X, Du Z, Li C, Hou X: Triglyceride-glucose index correlates with the incidences and prognoses of cardiac arrest following acute myocardial infarction: data from two large-scale cohorts. \u003cem\u003eCardiovascular diabetology\u003c/em\u003e 2025, 24(1):108.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu X, Tong X, Zhu J, Tian L, Jie Z, Zou Y, Lin X, Liang H, Li W, Ju Y \u003cem\u003eet al\u003c/em\u003e: Metagenome-genome-wide association studies reveal human genetic impact on the oral microbiome. \u003cem\u003eCell discovery\u003c/em\u003e 2021, 7(1):117.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi T, Yi Z, Huang Y, Tan Y, Gao S, Wang T, Guo S: Prognostic value of triglyceride-glucose index combined with stress hyperglycemia ratio for all-cause mortality in critically ill patients with stroke. \u003cem\u003eCardiovascular diabetology\u003c/em\u003e 2025, 24(1):337.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Y, Xu W, Wang J, Shu Y, Xin Y, Yang Y: Relationship between stress hyperglycemia ratio of one-year mortality in patients with heart failure: Analysis of the MIMIC-IV database. \u003cem\u003ePloS one\u003c/em\u003e 2025, 20(8):e0328812.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLai W, Peng XY, Peng H, Chen Y: Stress Hyperglycemia Ratio Outperforms Glycemic Variability in Predicting Mortality Among Acute Myocardial Infarction Patients With Reduced Ejection Fraction: A Retrospective Cohort Study. \u003cem\u003eJournal of diabetes\u003c/em\u003e 2025, 17(8):e70122.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarik PE, Bellomo R: Stress hyperglycemia: an essential survival response! \u003cem\u003eCritical care medicine\u003c/em\u003e 2013, 41(6):e93-94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMifsud S, Schembri EL, Gruppetta M: Stress-induced hyperglycaemia. \u003cem\u003eBritish journal of hospital medicine (London, England\u003c/em\u003e: 2005) 2018, 79(11):634\u0026ndash;639.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoberts BW, Trzeciak S: Systemic inflammatory response after cardiac arrest: potential target for therapy? \u003cem\u003eCritical care medicine\u003c/em\u003e 2015, 43(6):1336\u0026ndash;1337.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDri E, Lampas E, Lazaros G, Lazarou E, Theofilis P, Tsioufis C, Tousoulis D: Inflammatory Mediators of Endothelial Dysfunction. \u003cem\u003eLife (Basel, Switzerland)\u003c/em\u003e 2023, 13(6).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSimons SO, Heptinstall AB, Marjenberg Z, Marshall J, Mullerova H, Rogliani P, Nordon C, Hawkins NM: Temporal Dynamics of Cardiovascular Risk in Patients with Chronic Obstructive Pulmonary Disease During Stable Disease and Exacerbations: Review of the Mechanisms and Implications. \u003cem\u003eInternational journal of chronic obstructive pulmonary disease\u003c/em\u003e 2024, 19:2259\u0026ndash;2271.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong G, Liu X, Lu Z, Guan J, Chen X, Li Y, Liu G, Wang G, Ma F: Relationship between stress hyperglycaemic ratio (SHR) and critical illness: a systematic review. \u003cem\u003eCardiovascular diabetology\u003c/em\u003e 2025, 24(1):188.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePlummer MP, Hermanides J, Deane AM: Is it time to personalise glucose targets during critical illness? \u003cem\u003eCurrent opinion in clinical nutrition and metabolic care\u003c/em\u003e 2022, 25(5):364\u0026ndash;369.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen L, Zeng X, Zou W, Chen M, Fan Y, Huang P: Predictive performance of stress hyperglycemia ratio for poor prognosis in critically ill patients: a systematic review and dose-response meta-analysis. \u003cem\u003eEuropean journal of medical research\u003c/em\u003e 2025, 30(1):613.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang F, Guo Y, Tang Y, Zhao S, Xuan K, Mao Z, Lu R, Hou R, Zhu X: Combined assessment of stress hyperglycemia ratio and glycemic variability to predict all-cause mortality in critically ill patients with atherosclerotic cardiovascular diseases across different glucose metabolic states: an observational cohort study with machine learning. \u003cem\u003eCardiovascular diabetology\u003c/em\u003e 2025, 24(1):199.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePlaczkowska S, Pawlik-Sobecka L, Kokot I, Piwowar A: Indirect insulin resistance detection: Current clinical trends and laboratory limitations. \u003cem\u003eBiomedical papers of the Medical Faculty of the University Palacky, Olomouc, Czechoslovakia\u003c/em\u003e 2019, 163(3):187\u0026ndash;199.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuniyappa R, Lee S, Chen H, Quon MJ: Current approaches for assessing insulin sensitivity and resistance in vivo: advantages, limitations, and appropriate usage. \u003cem\u003eAmerican journal of physiology Endocrinology and metabolism\u003c/em\u003e 2008, 294(1):E15-26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee HY, Kuo PC, Qian F, Li CH, Hu JR, Hsu WT, Jhou HJ, Chen PH, Lee CH, Su CH \u003cem\u003eet al\u003c/em\u003e: Prediction of In-Hospital Cardiac Arrest in the Intensive Care Unit: Machine Learning-Based Multimodal Approach. \u003cem\u003eJMIR medical informatics\u003c/em\u003e 2024, 12:e49142.\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":"In-Hospital Cardiac Arrest, Stress Hyperglycemia Ratio, Triglyceride-Glucose Index, Cholesterol, high-density lipoprotein, glucose index, Critical Care","lastPublishedDoi":"10.21203/rs.3.rs-8145693/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8145693/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eIn-hospital cardiac arrest (IHCA) is a high-mortality event requiring better early risk stratification. This study aimed to investigate the association between three accessible insulin resistance (IR) markers\u0026mdash;the Stress Hyperglycemia Ratio (SHR), Triglyceride-Glucose Index (TYG), and Cholesterol, High-Density Lipoprotein, and Glucose index (CHG)\u0026mdash;and the risk of IHCA in critically ill patients.\u003c/p\u003e\u003ch2\u003ePatients and Methods:\u003c/h2\u003e \u003cp\u003eThis dual-center retrospective cohort study included adult patients from the MIMIC-IV (development) and NWICU (validation) databases. The associations between admission levels of SHR, TYG, and CHG and the primary outcome of IHCA, along with secondary outcomes (acute kidney injury [AKI] and sepsis), were assessed using multivariable logistic regression. We further explored dose-response relationships with restricted cubic splines (RCS) and threshold effect analysis. The robustness of findings was tested via subgroup analyses, and potential mechanisms were explored using mediation analysis. Predictive performance was compared using receiver operating characteristic (ROC) curves.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 3,059 patients from MIMIC-IV and 1,849 from NWICU were included. In the MIMIC-IV cohort, after full multivariable adjustment, elevated levels of SHR (OR 2.888, 95% CI 1.883\u0026ndash;4.427), TYG (OR 1.446, 95% CI 1.075\u0026ndash;1.946), and CHG (OR 1.580, 95% CI 1.050\u0026ndash;2.378) were all independently associated with an increased risk of IHCA (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Restricted cubic splines revealed a significant non-linear, dose-response relationship between SHR and IHCA (\u003cem\u003eP\u003c/em\u003e for non-linearity\u0026thinsp;=\u0026thinsp;0.007), whereas the associations for TYG and CHG were linear (\u003cem\u003eP\u003c/em\u003e for non-linearity\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Mediation analysis indicated that the white blood cell (WBC) partially mediated these associations, accounting for 11.3%, 12.1%, and 14.5% of the total effect for SHR, TYG, and CHG, respectively. These findings, including significant associations with the secondary outcomes of AKI and sepsis, were successfully validated in the NWICU cohort. In predictive performance for IHCA, ROC analysis confirmed that SHR had the superior discriminatory ability (AUC\u0026thinsp;=\u0026thinsp;0.763), outperforming both TYG (AUC\u0026thinsp;=\u0026thinsp;0.624) and CHG (AUC\u0026thinsp;=\u0026thinsp;0.639).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eElevated admission levels of SHR, TYG, and CHG are independent predictors of IHCA and other adverse outcomes in a broad population of critically ill patients. Among them, SHR demonstrated the strongest predictive ability. These readily available and inexpensive markers may serve as valuable tools for early bedside risk stratification to identify patients at high risk for circulatory collapse.\u003c/p\u003e","manuscriptTitle":"Comparative Prognostic Value of SHR, TYG, and CHG for Predicting In-Hospital Cardiac Arrest in Critically ill Patients: A Dual-Center Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-30 00:51:10","doi":"10.21203/rs.3.rs-8145693/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":"4f3fa4b9-1003-4c62-97f8-33232a0c8741","owner":[],"postedDate":"December 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-28T08:10:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-30 00:51:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8145693","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8145693","identity":"rs-8145693","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}