The Inflammation-Coagulation-Muscle Injury cascade: a clinically actionable nomogram for early mechanical ventilation prediction in tetanus
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Abstract Objective This study aimed to develop and internally validate a nomogram model for predicting the risk of mechanical ventilation (MV) requirement in adult patients with tetanus, based on routine clinical and laboratory indicators, to facilitate early identification of high-risk patients and optimize allocation of critical care resources. Methods A retrospective cohort of 227 adult tetanus patients admitted to two largest tertiary hospitals in Southern Jiangxi between January 2012 and December 2024 was included. Patients were stratified into a MV group and a non-mechanical ventilation (NMV) group based on MV implementation. Independent predictors of MV requirement were identified through LASSO regression and multivariate logistic regression analyses. A nomogram prediction model was subsequently constructed. Internal validation was performed using the Bootstrap method with 1,000 resamples and 10-fold cross-validation. Model performance was comprehensively evaluated through ROC curves, calibration plots, and Decision Curve Analysis (DCA) to assess discrimination, calibration, and clinical utility. Results Five independent predictors of MV requirement were identified: age (OR = 1.032, 95% CI :1.011–1.054), chronic kidney disease (CKD,OR = 4.939, 95% CI :1.621–18.681), neutrophil count (NEUT,OR = 1.187, 95% CI :1.078–1.321), D-dimer (DD,OR = 1.089, 95% CI :1.019–1.182), and creatine kinase-MB (CK-MB,OR = 1.034, 95% CI :1.015–1.057). The nomogram demonstrated robust predictive performance, with AUC of 0.813 (95% CI :0.757–0.868), excellent calibration (mean absolute error = 0.029), and clinical net benefit across threshold probabilities of 0.1–0.8 confirmed by DCA. Conclusion We successfully developed a nomogram incorporating five readily available clinical parameters to predict MV risk in adult tetanus patients. The model exhibited favorable discrimination, calibration, and clinical utility, offering a practical tool for early risk stratification and targeted management of critical care resources. Future multicenter external validation is warranted to promote its clinical application.
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The Inflammation-Coagulation-Muscle Injury cascade: a clinically actionable nomogram for early mechanical ventilation prediction in tetanus | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The Inflammation-Coagulation-Muscle Injury cascade: a clinically actionable nomogram for early mechanical ventilation prediction in tetanus Bo Zhang, Hui-Min Wang, Jie Kang, Jian -lu Liu, Xian-Fa Liu, Zhen Zhong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8786554/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Objective This study aimed to develop and internally validate a nomogram model for predicting the risk of mechanical ventilation (MV) requirement in adult patients with tetanus, based on routine clinical and laboratory indicators, to facilitate early identification of high-risk patients and optimize allocation of critical care resources. Methods A retrospective cohort of 227 adult tetanus patients admitted to two largest tertiary hospitals in Southern Jiangxi between January 2012 and December 2024 was included. Patients were stratified into a MV group and a non-mechanical ventilation (NMV) group based on MV implementation. Independent predictors of MV requirement were identified through LASSO regression and multivariate logistic regression analyses. A nomogram prediction model was subsequently constructed. Internal validation was performed using the Bootstrap method with 1,000 resamples and 10-fold cross-validation. Model performance was comprehensively evaluated through ROC curves, calibration plots, and Decision Curve Analysis (DCA) to assess discrimination, calibration, and clinical utility. Results Five independent predictors of MV requirement were identified: age (OR = 1.032, 95% CI :1.011–1.054), chronic kidney disease (CKD,OR = 4.939, 95% CI :1.621–18.681), neutrophil count (NEUT,OR = 1.187, 95% CI :1.078–1.321), D-dimer (DD,OR = 1.089, 95% CI :1.019–1.182), and creatine kinase-MB (CK-MB,OR = 1.034, 95% CI :1.015–1.057). The nomogram demonstrated robust predictive performance, with AUC of 0.813 (95% CI :0.757–0.868), excellent calibration (mean absolute error = 0.029), and clinical net benefit across threshold probabilities of 0.1–0.8 confirmed by DCA. Conclusion We successfully developed a nomogram incorporating five readily available clinical parameters to predict MV risk in adult tetanus patients. The model exhibited favorable discrimination, calibration, and clinical utility, offering a practical tool for early risk stratification and targeted management of critical care resources. Future multicenter external validation is warranted to promote its clinical application. Health sciences/Diseases Health sciences/Medical research Health sciences/Nephrology Health sciences/Risk factors Tetanus Mechanical Ventilation Chronic Kidney Disease Nomogram Risk prediction Figures Figure 1 Figure 2 Figure 3 Introduction Tetanus is a severe acute infectious disease caused by Clostridium tetani , characterized by generalized muscle rigidity and spasms. Respiratory failure due to respiratory muscle involvement remains a leading cause of mortality worldwide [ 1 ] . Although global initiatives have significantly reduced maternal and neonatal tetanus-related deaths, adult tetanus still poses a substantial burden in resource-limited settings. Even with intensive care unit (ICU) management, mortality rates for severe cases exceed 50% [ 2 , 3 ] . The disease course is highly aggressive, often complicated by autonomic dysfunction (e.g., hemodynamic instability, tachycardia), which further complicates clinical management. Respiratory failure is a common life-threatening complication, frequently necessitating MV for respiratory support. However, MV not only consumes substantial healthcare resources but is also independently associated with poor outcomes [ 4 ] . Currently, clinical decisions regarding MV initiation in tetanus patients primarily rely on subjective assessments of respiratory distress severity and severity scoring systems such as the Ablett classification [ 5 ] . However, these tools lack objective biomarkers and demonstrate limited accuracy in predicting respiratory failure, frequently resulting in delayed interventions or unnecessary ICU admissions [ 6 – 8 ] . The subjectivity inherent in the Ablett classification system for differentiating moderate-to-severe cases further highlights the urgent need for quantitative predictive tools [ 5 , 7 ] . Therefore, developing an early and objective instrument to identify high-risk patients requiring mechanical ventilation is critical. Such a tool could optimize ICU resource allocation, guide timely referrals to facilities with advanced critical care capabilities, and prevent complications associated with emergency intubation. Nomogram-based predictive models have gained increasing recognition in critical care medicine due to their ability to integrate multiple clinical variables into a user-friendly scoring system [ 9 ] . In conditions such as sepsis and acute respiratory distress syndrome, these models have demonstrated superior performance compared to traditional scoring systems, enabling earlier risk stratification and individualized treatment approaches.This study aimed to develop and internally validate a novel nomogram model for predicting MV requirement in adult tetanus patients, based on 12-year clinical data from two tertiary medical centers in Southern Jiangxi, China. We propose that this model could serve as a practical clinical decision-support tool, enabling early risk stratification and precision allocation of healthcare resources for tetanus patients. Participants and Methods 1. Study Participants This retrospective study enrolled patients aged ≥ 18 years diagnosed with tetanus at First Affiliated Hospital of Gannan Medical University and Ganzhou People's Hospital between January 2012 and December 2024. Cases were identified using the International Classification of Diseases, Tenth Revision (ICD-10) coding system. Inclusion criteria were: (1) clinical diagnosis of tetanus; (2) complete clinical records. Exclusion criteria were: (1) age < 18 years; (2) missing or incomplete critical medical records. A total of 227 eligible patients were included in the final analysis (Fig. 1 ). This study was approved by the Joint Ethics Committee of First Affiliated Hospital of Gannan Medical University and Ganzhou People's Hospital (NO: LLSL-2025397). 2. Study Methods Patients were stratified into a MV group (n = 123, 54.2%) and a NMV group (n = 104, 45.8%) based on MV implementation. Baseline data included: demographics: age, sex, lifestyle factors: smoking history, alcohol consumption; physiological parameters: respiratory rate, pulse, blood pressure, body mass index (BMI); clinical characteristics: comorbidities, incubation period, debridement status, initial presenting symptoms, injury site; laboratory biomarkers: complete blood count, liver function, renal function, myocardial enzymes, electrolytes, coagulation profile. 3. Statistical Methods Data were analyzed using R software (version 4.4.1). Continuous variables were presented as mean±standard deviation (SD) if normally distributed (tested by Shapiro-Wilk test) and compared using independent samples t-test; non-normally distributed variables were reported as median [interquartile range (IQR): P25, P75] and analyzed with Mann-Whitney U test. Categorical variables were summarized as n (%) and compared using χ² test or Fisher’s exact test when expected cell counts were < 5. Missing data (approximately 3.2% of variables) were handled using the 'missRanger' package in R, implementing a random forest-based multiple imputation approach with 100 iterations and a stopping criterion of 0.01. The proportion of missing data was below 5% for all variables, meeting the threshold for valid imputation. LASSO regression was performed using the glmnet package in R, with the optimal λ value selected via 10-fold cross-validation. The 'one-standard-error' rule (λ.1se = 0.07608) was applied to enhance model parsimony and clinical applicability, following established practices for clinical prediction models. Our sample of 227 patients with 123 events (MV requirement) yields an events-per-variable (EPV) ratio of 24.6:1, substantially exceeding the recommended minimum of 10:1 for logistic regression models. This adequate EPV ratio ensures model stability and reduces overfitting risk. 1. Baseline Characteristics Comparison A total of 227 patients were analyzed for baseline characteristics. No significant differences were observed between groups in sex, BMI, or blood pressure. However, the MV group exhibited significantly higher age (61.12 ± 15.77 vs 54.78 ± 17.30 years, p = 0.004), elevated pulse rate (median 88.0 vs 79.5 bpm, p < 0.001), and higher prevalence of comorbidities, including chronic obstructive pulmonary disease (COPD,16.3% vs 5.8%, p = 0.013), CKD (17.1% vs 3.8%, p = 0.002), and cerebrovascular disease (26.0% vs 10.6%, p = 0.003). Full baseline characteristics with p-values are detailed in Table 1 . Table 1 Baseline characteristics and P-values of 227 patients. Variables Total (N = 227) Non-Mechanical ventilation (N = 104) Mechanical ventilation(N = 123) p Gender(%) 0.122 Male 136(59.9) 68(65.4) 68(55.3) Female 91(40.1) 36(34.6) 55(44.7) Age, Mean ± SD 58.22 ± 16.75 54.78 ± 17.30 61.12 ± 15.77 0.004 Smoking History(%) 0.884 No 178(78.4) 82(78.8) 96(78) Yes 49(21.6) 22(21.2) 27(22) Alcohol History(%) 0.570 No 189(83.3) 85(81.7) 104(84.6) Yes 38(16.7) 19(18.3) 19(15.4) BMI 22.50(20.20,25.00) 22.50(20.20,25.00) 22.40(20.20,24.00) 0.827 Body Temperature 36.60(36.50,36.90) 36.60(36.50,36.80) 36.60(36.50,37.00) 0.521 Pulse Rate 85.00(74.00,100.50) 79.50(72.00,91.00) 88.00(79.00,105.50) < 0.001 Respiratory Rate 20.00(20.00,20.00) 20.00(20.00,20.00) 20.00(20.00,21.00) 0.815 SBP 132.00(119.50,148.00) 130.50(119.75,141.00) 132.00(119.50,153.00) 0.309 DBP 82.17 ± 14.51 82.50 ± 12.46 81.89 ± 16.09 0.755 STDT 4.00(2.00,7.00) 4.50(2.75,8.00) 4.00(2.00,7.00) 0.182 Incubation Period(%) 0.540 < 10 148(65.2) 70(67.3) 78(63.4) ≥ 10 79(34.8) 34(32.7) 45(36.6) Injury Site(%) 0.201 Multiple wounds 5(2.2) 0(0) 5(4.1) Upper Extremities 67(29.5) 35(33.7) 32(26) Head And Neck 27(11.9) 12(11.5) 15(12.2) Unknown 63(27.8) 26(25) 37(30.1) Lower extremity 65(28.6) 31(29.8) 34(27.6) Debridement(%) 0.551 No 217(95.6) 98(94.2) 119(96.7) Yes 10(4.4) 6(5.8) 4(3.3) Hypertension(%) 0.833 No 189(83.3) 86(82.7) 103(83.7) Yes 38(16.7) 18(17.3) 20(16.3) Diabetes Mellitus(%) 0.980 No 216(95.2) 99(95.2) 117(95.1) Yes 11(4.8) 5(4.8) 6(4.9) Coronary Heart Disease(%) 0.300 No 221(97.4) 103(99) 118(95.9) Yes 6(2.6) 1(1) 5(4.1) COPD(%) 0.013 No 201(88.5) 98(94.2) 103(83.7) Yes 26(11.5) 6(5.8) 20(16.3) CKD(%) 0.002 No 202(89) 100(96.2) 102(82.9) Yes 25(11) 4(3.8) 21(17.1) Cardiovascular And Cerebrovascular Diseases(%) 0.003 No 184(81.1) 93(89.4) 91(74) Yes 43(18.9) 11(10.6) 32(26) Liver Cirrhosis(%) 1.000 No 226(99.6) 104(100) 122(99.2) Yes 1(0.4) 0(0) 1(0.8) Active Tumor(%) 0.188 No 220(96.9) 103(99) 117(95.1) Yes 7(3.1) 1(1) 6(4.9) WBC(10⁹/L) 8.12(6.31,10.72) 7.17(5.75,8.91) 9.00(7.24,11.84) < 0.001 RBC(10 12 /L) 4.31 ± 0.73 4.45 ± 0.66 4.19 ± 0.77 0.009 HGB(g/L) 128.00(117.00,142.00) 132.50(119.75,144.25) 125.00(115.00,136.00) 0.007 PLT(10⁹/L) 242.00(198.50,295.00) 241.00(204.00,292.25) 247.00(195.50,298.00) 0.543 NEUT(10⁹/L) 6.08(4.42,8.59) 5.15(3.71,6.95) 7.14(5.49,9.86) < 0.001 LYMP(10⁹/L) 1.25(0.90,1.80) 1.40(1.04,2.08) 1.18(0.82,1.66) 0.018 MONO(10⁹/L) 0.48(0.36,0.61) 0.45(0.34,0.58) 0.50(0.37,0.70) 0.010 EO(10⁹/L) 0.02(0.00,0.08) 0.04(0.01,0.10) 0.01(0.00,0.06) < 0.001 BASO(10⁹/L) 0.02(0.01,0.03) 0.02(0.01,0.04) 0.02(0.01,0.03) 0.403 HCT(%) 38.70(35.25,41.50) 39.10(35.50,42.12) 37.80(34.90,40.85) 0.092 MCV(fL) 90.60(86.30,94.15) 89.80(85.08,93.45) 91.40(86.90,94.70) 0.064 MCH(pg) 30.40(29.15,31.40) 30.35(29.27,31.82) 30.40(29.10,31.30) 0.827 MCHC(g/L) 333.00(325.00,343.50) 334.50(326.00,346.25) 332.00(324.00,340.00) 0.047 PT(s) 11.60(10.90,12.55) 11.40(10.70,12.20) 11.80(11.15,12.85) 0.004 INR 1.01(0.95,1.08) 1.00(0.94,1.05) 1.04(0.97,1.10) 0.004 FIB(g/L) 3.15(2.53,3.96) 2.94(2.38,3.70) 3.30(2.70,4.25) 0.003 APTT(s) 25.90(24.10,27.90) 25.90(24.20,27.95) 25.90(23.95,27.70) 0.708 TT(s) 16.40(15.50,18.10) 16.85(15.95,18.57) 16.20(15.25,17.60) 0.004 AT3(%) 92.10 ± 17.23 92.82 ± 15.46 91.50 ± 18.62 0.560 PTA(%) 93.18 ± 17.98 96.31 ± 18.06 90.54 ± 17.56 0.016 DD(mg/L) 0.70(0.32,3.75) 0.42(0.24,1.36) 1.24(0.40,6.41) < 0.001 ALT(U/L) 19.99(15.00,29.00) 18.50(14.00,26.25) 21.00(16.00,30.50) 0.024 AST(U/L) 30.00(22.00,42.10) 25.53(20.00,37.55) 34.00(24.70,50.45) < 0.001 GGT(U/L) 18.30(13.00,30.88) 20.00(13.00,29.62) 18.00(13.00,33.00) 0.854 ALP(U/L) 69.00(57.50,90.55) 64.06(55.00,86.02) 74.00(58.00,93.74) 0.051 CHE(U/L) 7004.00(5491.00,8348.45) 7330.00(6065.75,8359.75) 6675.00(5065.00,8333.23) 0.043 TP(g/L) 65.80(60.90,71.80) 65.70(61.29,71.83) 66.00(59.70,71.55) 0.599 ALB(g/L) 39.50(36.55,42.85) 40.10(37.29,43.25) 38.60(35.25,42.23) 0.045 GLO(g/L) 26.30(22.45,30.50) 25.55(22.38,29.85) 27.40(22.75,32.11) 0.088 TBIL(µmol/L) 15.70(11.90,22.05) 15.30(11.90,20.93) 16.30(11.75,23.40) 0.531 DBIL(µmol/L) 4.60(2.81,6.41) 4.71(3.58,6.41) 4.10(2.55,6.45) 0.206 IBIL(µmol/L) 10.31(7.65,15.40) 9.95(7.27,13.95) 11.03(8.00,16.15) 0.369 TBA(µmol/L/L) 2.03(1.00,4.03) 2.39(1.38,4.83) 1.70(1.00,3.60) 0.022 UREA(mmol/L) 5.82(4.39,8.02) 5.13(4.01,6.46) 6.81(4.53,8.84) < 0.001 CRE(µmol/L) 71.00(59.35,83.05) 72.00(59.00,84.65) 69.00(59.85,82.00) 0.612 UA(µmol/L) 326.00(223.65,416.50) 326.00(221.22,398.25) 320.95(227.00,422.00) 0.878 K(mmol/L) 3.96(3.69,4.21) 3.96(3.67,4.20) 3.97(3.71,4.21) 0.956 Na(mmol/L) 140.40(138.51,142.65) 140.35(138.44,142.05) 140.70(138.61,143.10) 0.196 CL(mmol/L) 104.20 ± 4.70 103.53 ± 3.92 104.77 ± 5.21 0.047 Ca(mmol/L) 2.29 ± 0.17 2.32 ± 0.17 2.27 ± 0.16 0.041 Mg(mmol/L) 0.84(0.75,0.95) 0.84(0.75,0.95) 0.85(0.76,0.93) 0.853 P(mmol/L) 1.08(0.93,1.24) 1.07(0.94,1.21) 1.08(0.92,1.27) 0.787 LDH(U/L) 227.00(196.08,284.00) 209.00(185.75,252.12) 248.00(201.50,331.00) < 0.001 HBDH(U/L) 194.00(158.00,233.00) 182.00(151.21,227.00) 201.00(171.00,254.60) 0.008 CK(U/L) 276.18(161.30,713.50) 252.38(142.69,553.50) 328.00(213.50,735.45) 0.054 CKMB(U/L) 18.00(6.85,27.64) 11.22(4.08,20.25) 22.92(13.53,37.00) < 0.001 CRP(mg/L) 20.00(10.00,20.00) 20.00(10.00,20.00) 20.00(10.00,20.00) 0.257 hsCRP(mg/L) 2.75(0.93,10.00) 2.63(0.91,9.63) 3.52(0.95,10.00) 0.299 PCT(µg/L) 0.10(0.05,0.16) 0.10(0.05,0.15) 0.10(0.05,0.17) 0.703 MYO(µg/L) 93.00(41.61,230.25) 44.64(21.83,135.90) 113.00(73.27,306.65) < 0.001 Notes:ALB: Albumin; ALP: Alkaline Phosphatase; ALT: Alanine Aminotransferase; AP: Antithrombin III; APTT: Activated Partial Thromboplastin Time; AST: Aspartate Aminotransferase; BASO: Basophils; BMI: Body Mass Index; Ca: Calcium; CHE: Cholinesterase; CK: Creatine Kinase; CK-MB: Creatine Kinase-MB; CL: Chloride; COPD: Chronic Obstructive Pulmonary Disease; CRP: C-Reactive Protein; CRE: Creatinine; DBIL: Direct Bilirubin; DBP: Diastolic Blood Pressure; DD: D-Dimer; EO: Eosinophils; FIB: Fibrinogen; GGT: Gamma-Glutamyl Transferase; GLO: Globulin; HBDH: Alpha-Hydroxybutyrate Dehydrogenase; HCT: Hematocrit; HGB: Hemoglobin; hsCRP: High-Sensitivity C-Reactive Protein; IBIL: Indirect Bilirubin; INR: International Normalized Ratio; K: Potassium; LDH: Lactate Dehydrogenase; LYMP: Lymphocytes; MCH: Mean Corpuscular Hemoglobin; MCHC: Mean Corpuscular Hemoglobin Concentration; MCV: Mean Corpuscular Volume; Mg: Magnesium; MONO: Monocytes; MYO: Myoglobin; Na: Sodium; NEUT: Neutrophil Count; P: Phosphorus; PCT: Procalcitonin; PLT: Platelet Count; PTA: Prothrombin Activity; PT: Prothrombin Time; RBC: Red Blood Cell Count; SSP: Systolic Blood Pressure; STDT: Symptom-to-Door Time; TBIL: Total Bilirubin; TBA: Total Bile Acids; TP: Total Protein; TT: Thrombin Time; UA: Uric Acid; Unknown: Injury History with Indeterminate Causal Link; WBC: White Blood Cell Count. 2. Univariate Analysis and Feature Selection Variables showing intergroup differences ( p ≤ 0.10) were included in univariate logistic regression to identify 38 potential predictors (Supplementary Table S1 ). To reduce dimensionality and prevent overfitting, Least Absolute Shrinkage and Selection Operator (LASSO) regression was performed with 10-fold cross-validation to determine the optimal penalty coefficient ( λ ). The model was selected using the "one-standard-error rule" (lambda.1se), retaining 6 non-zero coefficients (Supplementary Figure S1 ). Final selected features included age, CKD, neutrophil count (NEUT), D-dimer (DD), blood urea nitrogen (UREA), and creatine kinase-MB (CK-MB) (lambda.1se = 0.07608; lambda. min = 0.02270) (Supplementary Table S2 ). 3. Multivariate Model, Nomogram, and Discrimination The LASSO-selected variables were incorporated into multivariate logistic regression using stepwise selection. Independent predictors for MV requirement were identified: age (OR = 1.032, 95% CI :1.011–1.054, p = 0.003), CKD (OR = 4.939, 95% CI :1.621–18.681, p = 0.009), NEUT (OR = 1.187, 95% CI :1.078–1.321, p = 0.001), DD (OR = 1.089, 95% CI :1.019–1.182, p = 0.022), and CK-MB (OR = 1.034, 95% CI :1.015–1.057, p = 0.001), with CKD as the highest risk factor followed by NEUT. Variance inflation factors (VIF) for all variables were < 1.1, indicating no multicollinearity (Supplementary Table S3). A nomogram was constructed to predict MV risk (Fig. 2 ). The model demonstrated excellent discrimination with an area under the AUC of 0.813 (95% CI :0.757–0.868) (Fig. 3 , Supplementary Table S4). Internal validation confirmed robustness: 10-fold cross-validation yielded an average AUC of 0.796 (Supplementary Figure S2 , Table S5), and Bootstrap validation (1,000 resamples) showed an AUC of 0.776 (Supplementary Figure S3, Table S6). Calibration analysis revealed strong agreement between predicted probabilities and actual outcomes (mean absolute error = 0.029) (Supplementary Figure S4). Furthermore, observed-to-predicted risk comparisons across risk strata enhanced model reliability (Supplementary Figure S5). The Hosmer-Lemeshow goodness-of-fit test was non-significant ( χ² =11.031; p = 0.200), indicating adequate calibration (Supplementary Table S4). Notes: CKD: Chronic Kidney Disease (0 = No CKD, 1 = With CKD); NEUT: Neutrophils count (×10⁹/L); DD: D - Dimer (µg/L); CK - MB: Creatine Kinase-MB (U/L); Risk of MV: The probability that the patient requires mechanical ventilation. 4. Clinical Utility Decision Curve Analysis (DCA) demonstrated that applying this prediction model to guide clinical decisions (i.e., determining readiness for MV based on model-predicted probabilities) yielded higher net benefits than either "treating all patients" or "treating no patients" across threshold probabilities of 0.1–0.8(Supplementary Figure S6). This confirms the model's robust clinical utility, enabling clinicians to early identify high-risk individuals and allocate healthcare resources efficiently, thereby optimizing clinical decision-making workflows. Discussion This study represents the development and internal validation of a nomogram model to predict MV requirements in adult tetanus patients, based on data from 227 cases across two tertiary hospitals in Southern Jiangxi, China (2012–2024). Given that tetanus patients frequently require urgent MV support due to respiratory muscle spasms and autonomic dysfunction, early identification of high-risk individuals may optimize ICU resource allocation and facilitate proactive airway management, potentially improving patient outcomes [ 6 , 7 ] . Compared to prior studies, this work uniquely focuses on developing a clinically actionable prediction tool rather than solely targeting mortality reduction. Historically, tetanus research has prioritized strategies to lower case-fatality rates, manage spasms, and optimize antitoxin administration timing [ 8 ] , yet lacked early risk stratification tools for MV requirements. For instance, although age, SpO₂, and heart rate were identified as MV risk factors in Vietnamese ICU populations, these findings were not integrated into a clinically applicable model with laboratory biomarkers [ 7 ] . Another study emphasized MV as a key to mortality reduction but failed to provide predictive methodologies [ 8 ] . While some attempts have been made to stratify overall mortality risk in tetanus patients using parameters such as the neutrophil-to-lymphocyte ratio (NLR) and aspartate aminotransferase (AST) levels [ 10 ] , tetanus-specific prediction models for MV requirements remain underdeveloped. By integrating routinely available laboratory markers with comorbidities, our model fills this critical gap with a bedside-ready scoring system. Furthermore, the multidimensional validation framework (including DCA) confirmed explicit net clinical benefits across threshold probabilities of 0.1–0.8, surpassing traditional statistical validation and directly substantiating its clinical decision-support value. The identified predictors in this study collectively elucidate the multidimensional pathophysiological mechanisms underlying MV requirements in tetanus patients. Elevated absolute NEUT reflects systemic inflammatory activation, potentially exacerbating autonomic dysfunction (e.g., sympathetic hyperactivity) through cytokine storms and increasing secondary infection risks (e.g., pneumonia), thereby significantly elevating respiratory muscle workload and oxygen demand [ 11 – 14 ] . Elevated DD levels indicate coagulation-fibrinolysis system activation and microthrombosis formation, likely linked to endothelial injury and tissue hypoxia. This hypercoagulable state may impair organ perfusion (particularly pulmonary circulation), accelerating respiratory failure progression [ 15 , 16 ] . CK-MB elevation not only signals myocardial injury but also likely originates from rhabdomyolysis induced by severe muscle spasms. This systemic stress response aggravates metabolic acidosis and electrolyte disturbances, reducing respiratory muscle functional reserve [ 17 ] . The presence of CKD amplifies risks through multiple pathways: impaired acid-base regulation, reduced drug clearance (e.g., sedatives), and volume overload-induced cardiogenic pulmonary edema collectively diminish compensatory capacity for respiratory failure [ 18 , 19 ] . Finally, age as an independent predictor reflects accumulated physiological frailty, comorbidity burden, and declining airway protective function, aligning with prior evidence of worse outcomes in elderly patients [ 6 , 7 , 20 ] . Collectively, this study unveils a pathological cascade involving "inflammation-coagulation-muscle injury-organ dysfunction," providing an integrative framework for understanding respiratory failure in tetanus. Based on the nomogram model, we propose an early stratification pathway for tetanus patients: Initial Assessment (within 2 hours of admission): Complete biochemical blood tests and calculate MV risk score using the nomogram. High-risk Patients (probability ≥ 0.5), implement a multidisciplinary protocol. Airway Preparation: Conduct immediate airway assessment, prepare sedatives (e.g., midazolam), neuromuscular blockers (e.g., vecuronium), and ventilator equipment. Prioritize ICU transfer to avoid hypoxia risks from emergent intubation. Intensified Medical Therapy: Ensure timely administration of human tetanus immune globulin and antibiotics (e.g., penicillin/metronidazole). Closely monitor vital signs for autonomic dysregulation (e.g., labile hypertension, tachycardia), and proactively use sedatives to stabilize clinical status. Precision Fluid Management: Avoid nephrotoxic agents and implement precise fluid regulation in patients with CKD to maintain end-organ perfusion while preventing pulmonary edema. Low-to-Moderate Risk Patients: Maintain vigilance with dynamic reassessment every 6–12 hours. Monitor trends in key biomarkers (NEUT, DD, CK-MB) to detect delayed respiratory deterioration. Although the nomogram demonstrates favorable predictive performance, its clinical implementation faces multiple challenges: First, regions with uneven distribution of healthcare resources may lack capacity to measure all biomarkers promptly. To address this, we propose developing a simplified scoring system incorporating only the most readily accessible indicators (e.g., age, neutrophil count, and clinical history). Second, model thresholds should be calibrated according to local ICU bed availability and medical standards. High-resource settings may adopt lower intervention thresholds (e.g., probability > 0.3) to maximize prevention of respiratory failure, whereas resource-constrained areas could implement higher thresholds (e.g., probability > 0.6) to prioritize critically ill patients. Third, integrating the model into clinical workflows requires multidisciplinary collaboration. We recommend establishing a tetanus-specific management pathway that combines predictive scoring with early warning systems and standardized treatment protocols, ensuring predictive results translate into actionable interventions. This study has several limitations. First, the retrospective dual-center design inevitably introduces selection and information biases. Although 12 years of data were included, excluding patients with short hospital stays (< 5 days) may create survival bias, underestimating early mortality risks. Second, MV decisions lacked standardized criteria, potentially influenced by non-clinical factors (e.g., physician experience, ICU bed availability). Important confounders such as Ablett classification, antitoxin administration timing, and sedative drug regimens were not recorded. Third, the model lacks external validation and simplified bedside scoring rules (e.g., converting continuous variables to categorical), limiting immediate implementation. Future studies should adopt multicenter prospective designs, integrate dynamic biomarker trends, and explore novel biomarkers (e.g., specific inflammatory/injury markers) to enhance predictive accuracy, generalizability, and clinical utility. Conclusion This study successfully developed and internally validated a nomogram model for early prediction of MV risk in adult tetanus patients, based on 227 cases from two Chinese medical centers (2012–2024). The model demonstrates excellent discrimination, robust calibration, and clear clinical net benefit, offering a practical tool for triage risk stratification and resource optimization. Future multicenter external validation and development of simplified bedside tools will further enable clinical translation of this model in tetanus critical care management. Abbreviations AUC Area Under the Curve BMI Body Mass Index CKD Chronic Kidney Disease CK-MB Creatine Kinase-MB COPD Chronic Obstructive Pulmonary Disease DD D-dimer DCA Decision Curve Analysis ICU Intensive Care Unit IQR Interquartile Range MV Mechanical Ventilation NMV Non-Mechanical Ventilation NEUT Neutrophil Count OR Odds Ratio SD Standard Deviation VIF Variance Inflation Factor Declarations Ethics approval and consent to participate: This study was approved by the Joint Ethics Committee of First Affiliated Hospital of Gannan Medical University and Ganzhou People's Hospital (NO: LLSL-2025397). The study protocol adhered to the principles of the Declaration of Helsinki. Due to the retrospective design, use of anonymized data, and minimal risk to participants, the requirement for informed consent was waived by the Ethics Committee. Competing Interests: The authors declare no competing interests. Authors' contributions Xian-fa Liu and Zhen Zhong designed the study. Bo Zhang and Hui-min Wang collected clinical data. Jie Kang and Jian-lu Liu performed data curation. Bo Zhang conducted statistical analyses. Hui-min Wang drafted the manuscript. Zhen Zhong critically revised the manuscript. All authors reviewed and approved the final version. Funding: This work was supported by the Key Laboratory of Prevention and Treatmentof Cardiovascular and Cerebrovascular Diseases, Ministry of Education (No.XN202023). Author Contribution Xian-fa Liu and Zhen Zhong designed the study. Bo Zhang and Hui-min Wang collected clinical data. Jie Kang and Jian-lu Liu performed data curation. Bo Zhang conducted statistical analyses. Hui-min Wang drafted the manuscript. Zhen Zhong critically revised the manuscript. All authors reviewed and approved the final version. Acknowledgement We are grateful to Professor Zhou Yuming for his strong support in data collection for our project. Data Availability The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. References Sudarshan, R. et al. Tetanus: recognition and management. The Lancet. Infectious diseases vol. 25,11 : e645-e657. (2025). 10.1016/S1473-3099(25)00292-0 Li, J. et al. Global epidemiology and burden of tetanus from 1990 to 2019: A systematic analysis for the Global Burden of Disease Study 2019. Int. J. Infect. diseases: IJID : official publication Int. Soc. Infect. Dis. 132 : 118–126. doi: 10.1016/j.ijid.2023.04.402 . (2023). Faria, A. P. V. et al. Tetanus vaccination in pregnant women: a systematic review and meta-analysis of the global literature. Public. health . 196 , 43–51. 10.1016/j.puhe.2021.04.019 (2021). Megighian, A. et al. Tetanus and tetanus neurotoxin: From peripheral uptake to central nervous tissue targets. J. neurochemistry vol . 158 , 1244–1253. 10.1111/jnc.15330 (2021). Chen, Z. et al. Mortality and risk factors in hospitalised adult patients with tetanus: a systematic review and meta-analysis. BMJ open 15,7 e101782. 28 Jul . 10.1136/bmjopen-2025-101782 (2025). Yen, L., Minh & Louise Thwaites, C. Tetanus. Lancet (London, England) vol. 393,10181 : 1657–1668. (2019). 10.1016/S0140-6736(18)33131-3 Davies-Foote, R. et al. Jun. Risk factors associated with mechanical ventilation, autonomic nervous dysfunction and physical outcome in Vietnamese adults with tetanus. Tropical medicine and health vol. 49,1 50. 21 (2021). 10.1186/s41182-021-00336-w Rao, Y. et al. Delirium in tetanus patients: a review of clinical features, pathophysiology, and integrated management strategies. Annals Med. vol . 57 (1), 2543980. 10.1080/07853890.2025.2543980 (2025). Shafiq, Y. et al. Predictive Accuracy of Infant Clinical Sign Algorithms for Mortality in Young Infants Aged 0 to 59 Days: A Systematic Review. Pediatrics 154 ,Suppl 1 : e2024066588E. (2024). 10.1542/peds.2024-066588E Wang, Y. & Zhang, L. Risk assessment of severe adult tetanus using the NLR and AST level and construction of a nomogram prediction model. Heliyon 10,1 e23487. 12 Dec. (2023). 10.1016/j.heliyon.2023.e23487 Pirazzini, M. et al. Toxicology and pharmacology of botulinum and tetanus neurotoxins: an update. Archives Toxicol. vol . 96 (6), 1521–1539. 10.1007/s00204-022-03271-9 (2022). Cheng, D. et al. Dec. Systemic immune inflammation index as a predictor of disease severity in tetanus patients: A retrospective observational study. PloS one vol. 19,12 e0316196. 31 (2024). 10.1371/journal.pone.0316196 Jarczak, D. & Nierhaus, A. Cytokine Storm-Definition, Causes, and Implications. International journal of molecular sciences vol. 23,19 11740. 3 Oct. (2022). 10.3390/ijms231911740 Maryke Spruyt, G. L. & Van Den, T. Heever. The treatment of autonomic dysfunction in tetanus. Southern African Journal of Critical Care (Online) 33.1 : 28–31. (2017). doi:org/10.7196/SAJCC.2017.v33i1.274 Nagasawa, H. et al. Oct. A case of iliopsoas hematoma as a complication of tetanus in a patient who did not receive anticoagulant therapy. BMC infectious diseases vol. 20,1 731. 7 (2020). 10.1186/s12879-020-05455-z Arumugham, V. Immunological mechanisms explaining the role of IgE, mast cells, histamine, elevating ferritin, IL-6, D-dimer, VEGF levels in COVID-19 and dengue, potential treatments such as mast cell stabilizers, antihistamines, Vitamin C, hydroxychloroquine, ivermectin and azithromycin. Apr. (2020). 10.2139/ssrn.3722710 Megighian, A. et al. Tetanus and tetanus neurotoxin: From peripheral uptake to central nervous tissue targets. J. neurochemistry vol . 158 , 1244–1253. 10.1111/jnc.15330 (2021). Pethő, Á. G. et al. May. Management of chronic kidney disease: The current novel and forgotten therapies. Journal of clinical & translational endocrinology vol. 36 100354. 22 (2024). 10.1016/j.jcte.2024.100354 Radkowski, P. et al. Jul. The Influence of Acid-Base Balance on Anesthetic Muscle Relaxants: A Comprehensive Review on Clinical Applications and Mechanisms. Medical science monitor: international medical journal of experimental and clinical research vol. 30 e944510. 1 (2024). 10.12659/MSM.944510 Huang, H. Y. et al. Apr. Prolonged Mechanical Ventilation: Outcomes and Management. Journal of clinical medicine vol. 11,9 2451. 27 (2022). 10.3390/jcm11092451 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8786554","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":590061084,"identity":"3942c1e3-d0fc-468f-bc97-c5030b3502a0","order_by":0,"name":"Bo Zhang","email":"","orcid":"","institution":"Gannan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Bo","middleName":"","lastName":"Zhang","suffix":""},{"id":590061085,"identity":"2c013fb1-0e86-435d-82e3-52d092afbd53","order_by":1,"name":"Hui-Min Wang","email":"","orcid":"","institution":"Gannan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hui-Min","middleName":"","lastName":"Wang","suffix":""},{"id":590061086,"identity":"d68eaa42-436f-4672-9185-45fbf53ff4e5","order_by":2,"name":"Jie Kang","email":"","orcid":"","institution":"Gannan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Kang","suffix":""},{"id":590061087,"identity":"3dd7b2f4-04b4-48bb-9970-e5d796bebf2b","order_by":3,"name":"Jian -lu Liu","email":"","orcid":"","institution":"Gannan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"-lu","lastName":"Liu","suffix":""},{"id":590061088,"identity":"e88cb716-fbad-405f-82f4-5a65a7e9d675","order_by":4,"name":"Xian-Fa Liu","email":"","orcid":"","institution":"First Affiliated Hospital of Gannan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xian-Fa","middleName":"","lastName":"Liu","suffix":""},{"id":590061089,"identity":"c750ad36-c2de-432a-8967-361b50c7d772","order_by":5,"name":"Zhen Zhong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIie3PIQsCMRTA8Y2DXXlofaKoyTwRxI+zIZhOMF4QHZx4Qa8r+CGMNs8yyzRf1G65ZhLtijubYb+08P7sPUIc5w8xPzre8wdO98focBHh2J6UQIvKivWoMrrPL0bbkzoGzSqwkKosaFWuM6/AYmBIBwA9ujQslIqRcrwQlluSVG4QmecnOpO7GkFz2lp+OYv0xhFej0EmDSMch5YEg7YCgQgYdEdy7hVKOhRS5PhKSLEE9ICuFQoOuo/CaLDe0ogjTXI1EdyPDvk9HNfLcfI9eQO/jTuO4zgfPQFlfElNvxpkjAAAAABJRU5ErkJggg==","orcid":"","institution":"First Affiliated Hospital of Gannan Medical University","correspondingAuthor":true,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Zhong","suffix":""}],"badges":[],"createdAt":"2026-02-04 12:38:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8786554/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8786554/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102759230,"identity":"f8404969-92ba-4479-8771-c5696e4f9b05","added_by":"auto","created_at":"2026-02-16 10:13:09","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":33309,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eParticipant Flowchart.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8786554/v1/ae31d4b53e5f6e2bccf61dd5.jpeg"},{"id":102759235,"identity":"bd74264b-f7e0-484e-ba46-0ed94e1108a0","added_by":"auto","created_at":"2026-02-16 10:13:10","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":182977,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNomogram for predicting the probability of mechanical ventilation in tetanus patients.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNotes: CKD: Chronic Kidney Disease (0 = No CKD, 1 = With CKD); NEUT: Neutrophils count (×10⁹/L); DD: D - Dimer (μg/L); CK - MB: Creatine Kinase-MB (U/L); Risk of MV: The probability that the patient requires mechanical ventilation.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8786554/v1/d40018ae56efb1c540aa9c96.jpeg"},{"id":103049186,"identity":"fc71014d-ce3f-4ae4-af25-e725c5bc389f","added_by":"auto","created_at":"2026-02-20 07:37:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2193788,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReceiver operating characteristic (ROC) curve for predicting mechanical ventilation in tetanus patients using nomogram model.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8786554/v1/88829d10b4e0da4873e1d9e7.png"},{"id":103050713,"identity":"c4fefa85-1671-469c-9b4a-1d79f208b04f","added_by":"auto","created_at":"2026-02-20 07:54:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1370484,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8786554/v1/f5838beb-9235-4a8d-8195-13f1af44d0ba.pdf"},{"id":102759205,"identity":"0bd30b18-edb5-4695-9ba8-f00f14b7fda6","added_by":"auto","created_at":"2026-02-16 10:13:05","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":44701,"visible":true,"origin":"","legend":"","description":"","filename":"file.docx","url":"https://assets-eu.researchsquare.com/files/rs-8786554/v1/3b78ed4e1440dced3f55ea0d.docx"},{"id":102759237,"identity":"561a8e0e-a6ef-4d0d-aa36-d03451203cbd","added_by":"auto","created_at":"2026-02-16 10:13:11","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":9698207,"visible":true,"origin":"","legend":"","description":"","filename":"file.docx","url":"https://assets-eu.researchsquare.com/files/rs-8786554/v1/905b1a2978c2231449c82e77.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Inflammation-Coagulation-Muscle Injury cascade: a clinically actionable nomogram for early mechanical ventilation prediction in tetanus","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTetanus is a severe acute infectious disease caused by \u003cem\u003eClostridium tetani\u003c/em\u003e, characterized by generalized muscle rigidity and spasms. Respiratory failure due to respiratory muscle involvement remains a leading cause of mortality worldwide \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Although global initiatives have significantly reduced maternal and neonatal tetanus-related deaths, adult tetanus still poses a substantial burden in resource-limited settings. Even with intensive care unit (ICU) management, mortality rates for severe cases exceed 50% \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. The disease course is highly aggressive, often complicated by autonomic dysfunction (e.g., hemodynamic instability, tachycardia), which further complicates clinical management. Respiratory failure is a common life-threatening complication, frequently necessitating MV for respiratory support. However, MV not only consumes substantial healthcare resources but is also independently associated with poor outcomes \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCurrently, clinical decisions regarding MV initiation in tetanus patients primarily rely on subjective assessments of respiratory distress severity and severity scoring systems such as the Ablett classification\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. However, these tools lack objective biomarkers and demonstrate limited accuracy in predicting respiratory failure, frequently resulting in delayed interventions or unnecessary ICU admissions \u003csup\u003e[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e–\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. The subjectivity inherent in the Ablett classification system for differentiating moderate-to-severe cases further highlights the urgent need for quantitative predictive tools\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Therefore, developing an early and objective instrument to identify high-risk patients requiring mechanical ventilation is critical. Such a tool could optimize ICU resource allocation, guide timely referrals to facilities with advanced critical care capabilities, and prevent complications associated with emergency intubation. Nomogram-based predictive models have gained increasing recognition in critical care medicine due to their ability to integrate multiple clinical variables into a user-friendly scoring system \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. In conditions such as sepsis and acute respiratory distress syndrome, these models have demonstrated superior performance compared to traditional scoring systems, enabling earlier risk stratification and individualized treatment approaches.This study aimed to develop and internally validate a novel nomogram model for predicting MV requirement in adult tetanus patients, based on 12-year clinical data from two tertiary medical centers in Southern Jiangxi, China. We propose that this model could serve as a practical clinical decision-support tool, enabling early risk stratification and precision allocation of healthcare resources for tetanus patients.\u003c/p\u003e \n\n \u003cp\u003e \u003c/p\u003e\n\n\n\n\n\n \u003cp\u003e \u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e\u003c/p\u003e\n\n\n\n \n\n"},{"header":"Participants and Methods","content":"\u003ch3\u003e1. Study Participants\u003c/h3\u003e\u003cp\u003eThis retrospective study enrolled patients aged ≥ 18 years diagnosed with tetanus at First Affiliated Hospital of Gannan Medical University and Ganzhou People's Hospital between January 2012 and December 2024. Cases were identified using the International Classification of Diseases, Tenth Revision (ICD-10) coding system. Inclusion criteria were: (1) clinical diagnosis of tetanus; (2) complete clinical records. Exclusion criteria were: (1) age \u0026lt; 18 years; (2) missing or incomplete critical medical records. A total of 227 eligible patients were included in the final analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This study was approved by the Joint Ethics Committee of First Affiliated Hospital of Gannan Medical University and Ganzhou People's Hospital (NO: LLSL-2025397).\u003c/p\u003e\u003ch3\u003e2. Study Methods\u003c/h3\u003e\u003cp\u003ePatients were stratified into a MV group (n = 123, 54.2%) and a NMV group (n = 104, 45.8%) based on MV implementation. Baseline data included: demographics: age, sex, lifestyle factors: smoking history, alcohol consumption; physiological parameters: respiratory rate, pulse, blood pressure, body mass index (BMI); clinical characteristics: comorbidities, incubation period, debridement status, initial presenting symptoms, injury site; laboratory biomarkers: complete blood count, liver function, renal function, myocardial enzymes, electrolytes, coagulation profile.\u003c/p\u003e\u003ch3\u003e3. Statistical Methods\u003c/h3\u003e\u003cp\u003eData were analyzed using R software (version 4.4.1). Continuous variables were presented as mean±standard deviation (SD) if normally distributed (tested by Shapiro-Wilk test) and compared using independent samples t-test; non-normally distributed variables were reported as median [interquartile range (IQR): P25, P75] and analyzed with Mann-Whitney U test. Categorical variables were summarized as n (%) and compared using \u003cem\u003eχ²\u003c/em\u003e test or Fisher’s exact test when expected cell counts were \u0026lt; 5. Missing data (approximately 3.2% of variables) were handled using the 'missRanger' package in R, implementing a random forest-based multiple imputation approach with 100 iterations and a stopping criterion of 0.01. The proportion of missing data was below 5% for all variables, meeting the threshold for valid imputation. LASSO regression was performed using the glmnet package in R, with the optimal λ value selected via 10-fold cross-validation. The 'one-standard-error' rule (λ.1se = 0.07608) was applied to enhance model parsimony and clinical applicability, following established practices for clinical prediction models. Our sample of 227 patients with 123 events (MV requirement) yields an events-per-variable (EPV) ratio of 24.6:1, substantially exceeding the recommended minimum of 10:1 for logistic regression models. This adequate EPV ratio ensures model stability and reduces overfitting risk.\u003c/p\u003e\u003ch3\u003e1. Baseline Characteristics Comparison\u003c/h3\u003e\u003cp\u003eA total of 227 patients were analyzed for baseline characteristics. No significant differences were observed between groups in sex, BMI, or blood pressure. However, the MV group exhibited significantly higher age (61.12 ± 15.77 vs 54.78 ± 17.30 years, \u003cem\u003ep\u003c/em\u003e = 0.004), elevated pulse rate (median 88.0 vs 79.5 bpm, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), and higher prevalence of comorbidities, including chronic obstructive pulmonary disease (COPD,16.3% vs 5.8%, \u003cem\u003ep\u003c/em\u003e = 0.013), CKD (17.1% vs 3.8%, \u003cem\u003ep\u003c/em\u003e = 0.002), and cerebrovascular disease (26.0% vs 10.6%, \u003cem\u003ep\u003c/em\u003e = 0.003). Full baseline characteristics with p-values are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cdiv class=\"gridtable\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\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 and P-values of 227 patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e \u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003e Variables\u003c/p\u003e \u003c/th\u003e\u003cth colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(N = 227)\u003c/p\u003e \u003c/th\u003e\u003cth colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003eNon-Mechanical ventilation\u003c/p\u003e \u003cp\u003e(N = 104)\u003c/p\u003e \u003c/th\u003e\u003cth colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003eMechanical ventilation(N = 123)\u003c/p\u003e \u003c/th\u003e\u003cth colname=\"c5\" style=\"text-align: left;\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eGender(%)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e136(59.9)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e68(65.4)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e68(55.3)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c5\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e91(40.1)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e36(34.6)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e55(44.7)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c5\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eAge, Mean ± SD\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e58.22 ± 16.75\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e54.78 ± 17.30\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e61.12 ± 15.77\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eSmoking History(%)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e178(78.4)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e82(78.8)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e96(78)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c5\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e49(21.6)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e22(21.2)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e27(22)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c5\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eAlcohol History(%)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.570\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e189(83.3)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e85(81.7)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e104(84.6)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c5\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e38(16.7)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e19(18.3)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e19(15.4)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c5\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e22.50(20.20,25.00)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e22.50(20.20,25.00)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e22.40(20.20,24.00)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.827\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eBody Temperature\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e36.60(36.50,36.90)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e36.60(36.50,36.80)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e36.60(36.50,37.00)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.521\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003ePulse Rate\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e85.00(74.00,100.50)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e79.50(72.00,91.00)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e88.00(79.00,105.50)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eRespiratory Rate\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e20.00(20.00,20.00)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e20.00(20.00,20.00)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e20.00(20.00,21.00)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.815\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eSBP\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e132.00(119.50,148.00)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e130.50(119.75,141.00)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e132.00(119.50,153.00)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.309\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eDBP\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e82.17 ± 14.51\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e82.50 ± 12.46\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e81.89 ± 16.09\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.755\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eSTDT\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e4.00(2.00,7.00)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e4.50(2.75,8.00)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e4.00(2.00,7.00)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eIncubation Period(%)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.540\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003e\u0026lt; 10\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e148(65.2)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e70(67.3)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e78(63.4)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c5\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003e≥ 10\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e79(34.8)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e34(32.7)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e45(36.6)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c5\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eInjury Site(%)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eMultiple wounds\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e5(2.2)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e5(4.1)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c5\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eUpper Extremities\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e67(29.5)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e35(33.7)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e32(26)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c5\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eHead And Neck\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e27(11.9)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e12(11.5)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e15(12.2)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c5\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e63(27.8)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e26(25)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e37(30.1)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c5\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eLower extremity\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e65(28.6)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e31(29.8)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e34(27.6)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c5\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eDebridement(%)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.551\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e217(95.6)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e98(94.2)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e119(96.7)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c5\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e10(4.4)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e6(5.8)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e4(3.3)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c5\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eHypertension(%)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e189(83.3)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e86(82.7)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e103(83.7)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c5\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e38(16.7)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e18(17.3)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e20(16.3)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c5\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eDiabetes Mellitus(%)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.980\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e216(95.2)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e99(95.2)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e117(95.1)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c5\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e11(4.8)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e5(4.8)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e6(4.9)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c5\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eCoronary Heart Disease(%)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.300\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e221(97.4)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e103(99)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e118(95.9)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c5\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e6(2.6)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e1(1)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e5(4.1)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c5\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eCOPD(%)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e201(88.5)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e98(94.2)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e103(83.7)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c5\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e26(11.5)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e6(5.8)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e20(16.3)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c5\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eCKD(%)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e202(89)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e100(96.2)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e102(82.9)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c5\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e25(11)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e4(3.8)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e21(17.1)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c5\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eCardiovascular And Cerebrovascular Diseases(%)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e184(81.1)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e93(89.4)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e91(74)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c5\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e43(18.9)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e11(10.6)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e32(26)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c5\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eLiver Cirrhosis(%)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e226(99.6)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e104(100)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e122(99.2)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c5\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e1(0.4)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e1(0.8)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c5\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eActive Tumor(%)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e220(96.9)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e103(99)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e117(95.1)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c5\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e7(3.1)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e1(1)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e6(4.9)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c5\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eWBC(10⁹/L)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e8.12(6.31,10.72)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e7.17(5.75,8.91)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e9.00(7.24,11.84)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eRBC(10\u003csup\u003e12\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e4.31 ± 0.73\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e4.45 ± 0.66\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e4.19 ± 0.77\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eHGB(g/L)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e128.00(117.00,142.00)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e132.50(119.75,144.25)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e125.00(115.00,136.00)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003ePLT(10⁹/L)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e242.00(198.50,295.00)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e241.00(204.00,292.25)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e247.00(195.50,298.00)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.543\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eNEUT(10⁹/L)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e6.08(4.42,8.59)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e5.15(3.71,6.95)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e7.14(5.49,9.86)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eLYMP(10⁹/L)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e1.25(0.90,1.80)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e1.40(1.04,2.08)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e1.18(0.82,1.66)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eMONO(10⁹/L)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e0.48(0.36,0.61)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e0.45(0.34,0.58)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e0.50(0.37,0.70)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eEO(10⁹/L)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e0.02(0.00,0.08)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e0.04(0.01,0.10)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e0.01(0.00,0.06)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eBASO(10⁹/L)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e0.02(0.01,0.03)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e0.02(0.01,0.04)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e0.02(0.01,0.03)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eHCT(%)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e38.70(35.25,41.50)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e39.10(35.50,42.12)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e37.80(34.90,40.85)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eMCV(fL)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e90.60(86.30,94.15)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e89.80(85.08,93.45)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e91.40(86.90,94.70)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eMCH(pg)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e30.40(29.15,31.40)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e30.35(29.27,31.82)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e30.40(29.10,31.30)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.827\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eMCHC(g/L)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e333.00(325.00,343.50)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e334.50(326.00,346.25)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e332.00(324.00,340.00)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003ePT(s)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e11.60(10.90,12.55)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e11.40(10.70,12.20)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e11.80(11.15,12.85)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eINR\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e1.01(0.95,1.08)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e1.00(0.94,1.05)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e1.04(0.97,1.10)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eFIB(g/L)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e3.15(2.53,3.96)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e2.94(2.38,3.70)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e3.30(2.70,4.25)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eAPTT(s)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e25.90(24.10,27.90)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e25.90(24.20,27.95)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e25.90(23.95,27.70)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.708\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eTT(s)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e16.40(15.50,18.10)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e16.85(15.95,18.57)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e16.20(15.25,17.60)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eAT3(%)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e92.10 ± 17.23\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e92.82 ± 15.46\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e91.50 ± 18.62\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.560\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003ePTA(%)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e93.18 ± 17.98\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e96.31 ± 18.06\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e90.54 ± 17.56\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eDD(mg/L)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e0.70(0.32,3.75)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e0.42(0.24,1.36)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e1.24(0.40,6.41)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eALT(U/L)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e19.99(15.00,29.00)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e18.50(14.00,26.25)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e21.00(16.00,30.50)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eAST(U/L)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e30.00(22.00,42.10)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e25.53(20.00,37.55)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e34.00(24.70,50.45)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eGGT(U/L)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e18.30(13.00,30.88)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e20.00(13.00,29.62)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e18.00(13.00,33.00)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.854\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eALP(U/L)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e69.00(57.50,90.55)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e64.06(55.00,86.02)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e74.00(58.00,93.74)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eCHE(U/L)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e7004.00(5491.00,8348.45)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e7330.00(6065.75,8359.75)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e6675.00(5065.00,8333.23)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eTP(g/L)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e65.80(60.90,71.80)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e65.70(61.29,71.83)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e66.00(59.70,71.55)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.599\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eALB(g/L)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e39.50(36.55,42.85)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e40.10(37.29,43.25)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e38.60(35.25,42.23)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eGLO(g/L)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e26.30(22.45,30.50)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e25.55(22.38,29.85)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e27.40(22.75,32.11)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eTBIL(µmol/L)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e15.70(11.90,22.05)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e15.30(11.90,20.93)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e16.30(11.75,23.40)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.531\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eDBIL(µmol/L)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e4.60(2.81,6.41)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e4.71(3.58,6.41)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e4.10(2.55,6.45)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eIBIL(µmol/L)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e10.31(7.65,15.40)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e9.95(7.27,13.95)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e11.03(8.00,16.15)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.369\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eTBA(µmol/L/L)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e2.03(1.00,4.03)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e2.39(1.38,4.83)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e1.70(1.00,3.60)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eUREA(mmol/L)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e5.82(4.39,8.02)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e5.13(4.01,6.46)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e6.81(4.53,8.84)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eCRE(µmol/L)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e71.00(59.35,83.05)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e72.00(59.00,84.65)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e69.00(59.85,82.00)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.612\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eUA(µmol/L)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e326.00(223.65,416.50)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e326.00(221.22,398.25)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e320.95(227.00,422.00)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.878\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eK(mmol/L)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e3.96(3.69,4.21)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e3.96(3.67,4.20)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e3.97(3.71,4.21)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.956\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eNa(mmol/L)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e140.40(138.51,142.65)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e140.35(138.44,142.05)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e140.70(138.61,143.10)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eCL(mmol/L)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e104.20 ± 4.70\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e103.53 ± 3.92\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e104.77 ± 5.21\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eCa(mmol/L)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e2.29 ± 0.17\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e2.32 ± 0.17\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e2.27 ± 0.16\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eMg(mmol/L)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e0.84(0.75,0.95)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e0.84(0.75,0.95)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e0.85(0.76,0.93)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.853\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eP(mmol/L)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e1.08(0.93,1.24)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e1.07(0.94,1.21)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e1.08(0.92,1.27)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.787\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eLDH(U/L)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e227.00(196.08,284.00)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e209.00(185.75,252.12)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e248.00(201.50,331.00)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eHBDH(U/L)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e194.00(158.00,233.00)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e182.00(151.21,227.00)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e201.00(171.00,254.60)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eCK(U/L)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e276.18(161.30,713.50)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e252.38(142.69,553.50)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e328.00(213.50,735.45)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eCKMB(U/L)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e18.00(6.85,27.64)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e11.22(4.08,20.25)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e22.92(13.53,37.00)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eCRP(mg/L)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e20.00(10.00,20.00)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e20.00(10.00,20.00)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e20.00(10.00,20.00)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003ehsCRP(mg/L)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e2.75(0.93,10.00)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e2.63(0.91,9.63)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e3.52(0.95,10.00)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.299\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003ePCT(µg/L)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e0.10(0.05,0.16)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e0.10(0.05,0.15)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e0.10(0.05,0.17)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.703\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colname=\"c1\" style=\"text-align: left;\"\u003e \u003cp\u003eMYO(µg/L)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c2\" style=\"text-align: left;\"\u003e \u003cp\u003e93.00(41.61,230.25)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c3\" style=\"text-align: left;\"\u003e \u003cp\u003e44.64(21.83,135.90)\u003c/p\u003e \u003c/td\u003e\u003ctd colname=\"c4\" style=\"text-align: left;\"\u003e \u003cp\u003e113.00(73.27,306.65)\u003c/p\u003e \u003c/td\u003e\u003ctd char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNotes:ALB: Albumin; ALP: Alkaline Phosphatase; ALT: Alanine Aminotransferase; AP: Antithrombin III; APTT: Activated Partial Thromboplastin Time; AST: Aspartate Aminotransferase; BASO: Basophils; BMI: Body Mass Index; Ca: Calcium; CHE: Cholinesterase; CK: Creatine Kinase; CK-MB: Creatine Kinase-MB; CL: Chloride; COPD: Chronic Obstructive Pulmonary Disease; CRP: C-Reactive Protein; CRE: Creatinine; DBIL: Direct Bilirubin; DBP: Diastolic Blood Pressure; DD: D-Dimer; EO: Eosinophils; FIB: Fibrinogen; GGT: Gamma-Glutamyl Transferase; GLO: Globulin; HBDH: Alpha-Hydroxybutyrate Dehydrogenase; HCT: Hematocrit; HGB: Hemoglobin; hsCRP: High-Sensitivity C-Reactive Protein; IBIL: Indirect Bilirubin; INR: International Normalized Ratio; K: Potassium; LDH: Lactate Dehydrogenase; LYMP: Lymphocytes; MCH: Mean Corpuscular Hemoglobin; MCHC: Mean Corpuscular Hemoglobin Concentration; MCV: Mean Corpuscular Volume; Mg: Magnesium; MONO: Monocytes; MYO: Myoglobin; Na: Sodium; NEUT: Neutrophil Count; P: Phosphorus; PCT: Procalcitonin; PLT: Platelet Count; PTA: Prothrombin Activity; PT: Prothrombin Time; RBC: Red Blood Cell Count; SSP: Systolic Blood Pressure; STDT: Symptom-to-Door Time; TBIL: Total Bilirubin; TBA: Total Bile Acids; TP: Total Protein; TT: Thrombin Time; UA: Uric Acid; Unknown: Injury History with Indeterminate Causal Link; WBC: White Blood Cell Count.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/div\u003e\u003ch3\u003e2. Univariate Analysis and Feature Selection\u003c/h3\u003e\u003cp\u003eVariables showing intergroup differences (\u003cem\u003ep\u003c/em\u003e ≤ 0.10) were included in univariate logistic regression to identify 38 potential predictors (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). To reduce dimensionality and prevent overfitting, Least Absolute Shrinkage and Selection Operator (LASSO) regression was performed with 10-fold cross-validation to determine the optimal penalty coefficient (\u003cem\u003eλ\u003c/em\u003e). The model was selected using the \"one-standard-error rule\" (lambda.1se), retaining 6 non-zero coefficients (Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Final selected features included age, CKD, neutrophil count (NEUT), D-dimer (DD), blood urea nitrogen (UREA), and creatine kinase-MB (CK-MB) (lambda.1se = 0.07608; lambda. min = 0.02270) (Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e\u003ch3\u003e3. Multivariate Model, Nomogram, and Discrimination\u003c/h3\u003e\u003cp\u003eThe LASSO-selected variables were incorporated into multivariate logistic regression using stepwise selection. Independent predictors for MV requirement were identified: age (OR = 1.032, 95%\u003cem\u003eCI\u003c/em\u003e:1.011–1.054, \u003cem\u003ep\u003c/em\u003e = 0.003), CKD (OR = 4.939, 95%\u003cem\u003eCI\u003c/em\u003e:1.621–18.681, \u003cem\u003ep\u003c/em\u003e = 0.009), NEUT (OR = 1.187, 95%\u003cem\u003eCI\u003c/em\u003e:1.078–1.321, \u003cem\u003ep\u003c/em\u003e = 0.001), DD (OR = 1.089, 95%\u003cem\u003eCI\u003c/em\u003e:1.019–1.182, \u003cem\u003ep\u003c/em\u003e = 0.022), and CK-MB (OR = 1.034, 95%\u003cem\u003eCI\u003c/em\u003e:1.015–1.057, \u003cem\u003ep\u003c/em\u003e = 0.001), with CKD as the highest risk factor followed by NEUT. Variance inflation factors (VIF) for all variables were \u0026lt; 1.1, indicating no multicollinearity (Supplementary Table S3). A nomogram was constructed to predict MV risk (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The model demonstrated excellent discrimination with an area under the AUC of 0.813 (95%\u003cem\u003eCI\u003c/em\u003e:0.757–0.868) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Supplementary Table S4). Internal validation confirmed robustness: 10-fold cross-validation yielded an average AUC of 0.796 (Supplementary Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e, Table S5), and Bootstrap validation (1,000 resamples) showed an AUC of 0.776 (Supplementary Figure S3, Table S6). Calibration analysis revealed strong agreement between predicted probabilities and actual outcomes (mean absolute error = 0.029) (Supplementary Figure S4). Furthermore, observed-to-predicted risk comparisons across risk strata enhanced model reliability (Supplementary Figure S5). The Hosmer-Lemeshow goodness-of-fit test was non-significant (\u003cem\u003eχ²\u003c/em\u003e=11.031; \u003cem\u003ep\u003c/em\u003e = 0.200), indicating adequate calibration (Supplementary Table S4).\u003c/p\u003e\u003cp\u003eNotes: CKD: Chronic Kidney Disease (0 = No CKD, 1 = With CKD); NEUT: Neutrophils count (×10⁹/L); DD: D - Dimer (µg/L); CK - MB: Creatine Kinase-MB (U/L); Risk of MV: The probability that the patient requires mechanical ventilation.\u003c/p\u003e\u003ch3\u003e4. Clinical Utility\u003c/h3\u003e\u003cp\u003eDecision Curve Analysis (DCA) demonstrated that applying this prediction model to guide clinical decisions (i.e., determining readiness for MV based on model-predicted probabilities) yielded higher net benefits than either \"treating all patients\" or \"treating no patients\" across threshold probabilities of 0.1–0.8(Supplementary Figure S6). This confirms the model's robust clinical utility, enabling clinicians to early identify high-risk individuals and allocate healthcare resources efficiently, thereby optimizing clinical decision-making workflows.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study represents the development and internal validation of a nomogram model to predict MV requirements in adult tetanus patients, based on data from 227 cases across two tertiary hospitals in Southern Jiangxi, China (2012\u0026ndash;2024). Given that tetanus patients frequently require urgent MV support due to respiratory muscle spasms and autonomic dysfunction, early identification of high-risk individuals may optimize ICU resource allocation and facilitate proactive airway management, potentially improving patient outcomes \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCompared to prior studies, this work uniquely focuses on developing a clinically actionable prediction tool rather than solely targeting mortality reduction. Historically, tetanus research has prioritized strategies to lower case-fatality rates, manage spasms, and optimize antitoxin administration timing \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e, yet lacked early risk stratification tools for MV requirements. For instance, although age, SpO₂, and heart rate were identified as MV risk factors in Vietnamese ICU populations, these findings were not integrated into a clinically applicable model with laboratory biomarkers \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Another study emphasized MV as a key to mortality reduction but failed to provide predictive methodologies \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. While some attempts have been made to stratify overall mortality risk in tetanus patients using parameters such as the neutrophil-to-lymphocyte ratio (NLR) and aspartate aminotransferase (AST) levels \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e, tetanus-specific prediction models for MV requirements remain underdeveloped. By integrating routinely available laboratory markers with comorbidities, our model fills this critical gap with a bedside-ready scoring system. Furthermore, the multidimensional validation framework (including DCA) confirmed explicit net clinical benefits across threshold probabilities of 0.1\u0026ndash;0.8, surpassing traditional statistical validation and directly substantiating its clinical decision-support value.\u003c/p\u003e \u003cp\u003eThe identified predictors in this study collectively elucidate the multidimensional pathophysiological mechanisms underlying MV requirements in tetanus patients. Elevated absolute NEUT reflects systemic inflammatory activation, potentially exacerbating autonomic dysfunction (e.g., sympathetic hyperactivity) through cytokine storms and increasing secondary infection risks (e.g., pneumonia), thereby significantly elevating respiratory muscle workload and oxygen demand \u003csup\u003e[\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Elevated DD levels indicate coagulation-fibrinolysis system activation and microthrombosis formation, likely linked to endothelial injury and tissue hypoxia. This hypercoagulable state may impair organ perfusion (particularly pulmonary circulation), accelerating respiratory failure progression \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. CK-MB elevation not only signals myocardial injury but also likely originates from rhabdomyolysis induced by severe muscle spasms. This systemic stress response aggravates metabolic acidosis and electrolyte disturbances, reducing respiratory muscle functional reserve \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. The presence of CKD amplifies risks through multiple pathways: impaired acid-base regulation, reduced drug clearance (e.g., sedatives), and volume overload-induced cardiogenic pulmonary edema collectively diminish compensatory capacity for respiratory failure \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Finally, age as an independent predictor reflects accumulated physiological frailty, comorbidity burden, and declining airway protective function, aligning with prior evidence of worse outcomes in elderly patients \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Collectively, this study unveils a pathological cascade involving \"inflammation-coagulation-muscle injury-organ dysfunction,\" providing an integrative framework for understanding respiratory failure in tetanus.\u003c/p\u003e \u003cp\u003eBased on the nomogram model, we propose an early stratification pathway for tetanus patients: Initial Assessment (within 2 hours of admission): Complete biochemical blood tests and calculate MV risk score using the nomogram. High-risk Patients (probability\u0026thinsp;\u0026ge;\u0026thinsp;0.5), implement a multidisciplinary protocol. Airway Preparation: Conduct immediate airway assessment, prepare sedatives (e.g., midazolam), neuromuscular blockers (e.g., vecuronium), and ventilator equipment. Prioritize ICU transfer to avoid hypoxia risks from emergent intubation. Intensified Medical Therapy: Ensure timely administration of human tetanus immune globulin and antibiotics (e.g., penicillin/metronidazole). Closely monitor vital signs for autonomic dysregulation (e.g., labile hypertension, tachycardia), and proactively use sedatives to stabilize clinical status. Precision Fluid Management: Avoid nephrotoxic agents and implement precise fluid regulation in patients with CKD to maintain end-organ perfusion while preventing pulmonary edema. Low-to-Moderate Risk Patients: Maintain vigilance with dynamic reassessment every 6\u0026ndash;12 hours. Monitor trends in key biomarkers (NEUT, DD, CK-MB) to detect delayed respiratory deterioration. Although the nomogram demonstrates favorable predictive performance, its clinical implementation faces multiple challenges: First, regions with uneven distribution of healthcare resources may lack capacity to measure all biomarkers promptly. To address this, we propose developing a simplified scoring system incorporating only the most readily accessible indicators (e.g., age, neutrophil count, and clinical history). Second, model thresholds should be calibrated according to local ICU bed availability and medical standards. High-resource settings may adopt lower intervention thresholds (e.g., probability\u0026thinsp;\u0026gt;\u0026thinsp;0.3) to maximize prevention of respiratory failure, whereas resource-constrained areas could implement higher thresholds (e.g., probability\u0026thinsp;\u0026gt;\u0026thinsp;0.6) to prioritize critically ill patients. Third, integrating the model into clinical workflows requires multidisciplinary collaboration. We recommend establishing a tetanus-specific management pathway that combines predictive scoring with early warning systems and standardized treatment protocols, ensuring predictive results translate into actionable interventions.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, the retrospective dual-center design inevitably introduces selection and information biases. Although 12 years of data were included, excluding patients with short hospital stays (\u0026lt;\u0026thinsp;5 days) may create survival bias, underestimating early mortality risks. Second, MV decisions lacked standardized criteria, potentially influenced by non-clinical factors (e.g., physician experience, ICU bed availability). Important confounders such as Ablett classification, antitoxin administration timing, and sedative drug regimens were not recorded. Third, the model lacks external validation and simplified bedside scoring rules (e.g., converting continuous variables to categorical), limiting immediate implementation. Future studies should adopt multicenter prospective designs, integrate dynamic biomarker trends, and explore novel biomarkers (e.g., specific inflammatory/injury markers) to enhance predictive accuracy, generalizability, and clinical utility.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study successfully developed and internally validated a nomogram model for early prediction of MV risk in adult tetanus patients, based on 227 cases from two Chinese medical centers (2012\u0026ndash;2024). The model demonstrates excellent discrimination, robust calibration, and clear clinical net benefit, offering a practical tool for triage risk stratification and resource optimization. Future multicenter external validation and development of simplified bedside tools will further enable clinical translation of this model in tetanus critical care management.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAUC Area Under the Curve\u003c/p\u003e\u003cp\u003eBMI Body Mass Index\u003c/p\u003e\u003cp\u003eCKD Chronic Kidney Disease\u003c/p\u003e\u003cp\u003eCK-MB Creatine Kinase-MB\u003c/p\u003e\u003cp\u003eCOPD Chronic Obstructive Pulmonary Disease\u003c/p\u003e\u003cp\u003eDD D-dimer\u003c/p\u003e\u003cp\u003eDCA Decision Curve Analysis\u003c/p\u003e\u003cp\u003eICU Intensive Care Unit\u003c/p\u003e\u003cp\u003eIQR Interquartile Range\u003c/p\u003e\u003cp\u003eMV Mechanical Ventilation\u003c/p\u003e\u003cp\u003eNMV Non-Mechanical Ventilation\u003c/p\u003e\u003cp\u003eNEUT Neutrophil Count\u003c/p\u003e\u003cp\u003eOR Odds Ratio\u003c/p\u003e\u003cp\u003eSD Standard Deviation\u003c/p\u003e\u003cp\u003eVIF Variance Inflation Factor\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eThis study was approved by the Joint Ethics Committee of First Affiliated Hospital of Gannan Medical University and Ganzhou People's Hospital (NO: LLSL-2025397). The study protocol adhered to the principles of the Declaration of Helsinki. Due to the retrospective design, use of anonymized data, and minimal risk to participants, the requirement for informed consent was waived by the Ethics Committee.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003ch2\u003eCompeting Interests:\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003ch2\u003e \u003cb\u003eAuthors' contributions\u003c/b\u003e \u003c/h2\u003e \u003cp\u003eXian-fa Liu and Zhen Zhong designed the study. Bo Zhang and Hui-min Wang collected clinical data. Jie Kang and Jian-lu Liu performed data curation. Bo Zhang conducted statistical analyses. Hui-min Wang drafted the manuscript. Zhen Zhong critically revised the manuscript. All authors reviewed and approved the final version.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis work was supported by the Key Laboratory of Prevention and Treatmentof Cardiovascular and Cerebrovascular Diseases, Ministry of Education (No.XN202023).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eXian-fa Liu and Zhen Zhong designed the study. Bo Zhang and Hui-min Wang collected clinical data. Jie Kang and Jian-lu Liu performed data curation. Bo Zhang conducted statistical analyses. Hui-min Wang drafted the manuscript. Zhen Zhong critically revised the manuscript. All authors reviewed and approved the final version.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe are grateful to Professor Zhou Yuming for his strong support in data collection for our project.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSudarshan, R. et al. Tetanus: recognition and management. The Lancet. Infectious diseases vol. 25,11 : e645-e657. (2025). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S1473-3099(25)00292-0\u003c/span\u003e\u003cspan address=\"10.1016/S1473-3099(25)00292-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, J. et al. 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(2017). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi:org/10.7196/SAJCC.2017.v33i1.274\u003c/span\u003e\u003cspan address=\"doi:10.7196/SAJCC.2017.v33i1.274\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNagasawa, H. et al. Oct. A case of iliopsoas hematoma as a complication of tetanus in a patient who did not receive anticoagulant therapy. BMC infectious diseases vol. 20,1 731. 7 (2020). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12879-020-05455-z\u003c/span\u003e\u003cspan address=\"10.1186/s12879-020-05455-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArumugham, V. Immunological mechanisms explaining the role of IgE, mast cells, histamine, elevating ferritin, IL-6, D-dimer, VEGF levels in COVID-19 and dengue, potential treatments such as mast cell stabilizers, antihistamines, Vitamin C, hydroxychloroquine, ivermectin and azithromycin. Apr. (2020). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2139/ssrn.3722710\u003c/span\u003e\u003cspan address=\"10.2139/ssrn.3722710\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMegighian, A. et al. 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Journal of clinical \u0026amp; translational endocrinology vol. 36 100354. 22 (2024). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jcte.2024.100354\u003c/span\u003e\u003cspan address=\"10.1016/j.jcte.2024.100354\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRadkowski, P. et al. Jul. The Influence of Acid-Base Balance on Anesthetic Muscle Relaxants: A Comprehensive Review on Clinical Applications and Mechanisms. Medical science monitor: international medical journal of experimental and clinical research vol. 30 e944510. 1 (2024). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.12659/MSM.944510\u003c/span\u003e\u003cspan address=\"10.12659/MSM.944510\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang, H. Y. et al. Apr. Prolonged Mechanical Ventilation: Outcomes and Management. Journal of clinical medicine vol. 11,9 2451. 27 (2022). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/jcm11092451\u003c/span\u003e\u003cspan address=\"10.3390/jcm11092451\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Tetanus, Mechanical Ventilation, Chronic Kidney Disease, Nomogram, Risk prediction","lastPublishedDoi":"10.21203/rs.3.rs-8786554/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8786554/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study aimed to develop and internally validate a nomogram model for predicting the risk of mechanical ventilation (MV) requirement in adult patients with tetanus, based on routine clinical and laboratory indicators, to facilitate early identification of high-risk patients and optimize allocation of critical care resources.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA retrospective cohort of 227 adult tetanus patients admitted to two largest tertiary hospitals in Southern Jiangxi between January 2012 and December 2024 was included. Patients were stratified into a MV group and a non-mechanical ventilation (NMV) group based on MV implementation. Independent predictors of MV requirement were identified through LASSO regression and multivariate logistic regression analyses. A nomogram prediction model was subsequently constructed. Internal validation was performed using the Bootstrap method with 1,000 resamples and 10-fold cross-validation. Model performance was comprehensively evaluated through ROC curves, calibration plots, and Decision Curve Analysis (DCA) to assess discrimination, calibration, and clinical utility.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFive independent predictors of MV requirement were identified: age (OR\u0026thinsp;=\u0026thinsp;1.032, 95%\u003cem\u003eCI\u003c/em\u003e:1.011\u0026ndash;1.054), chronic kidney disease (CKD,OR\u0026thinsp;=\u0026thinsp;4.939, 95%\u003cem\u003eCI\u003c/em\u003e:1.621\u0026ndash;18.681), neutrophil count (NEUT,OR\u0026thinsp;=\u0026thinsp;1.187, 95%\u003cem\u003eCI\u003c/em\u003e:1.078\u0026ndash;1.321), D-dimer (DD,OR\u0026thinsp;=\u0026thinsp;1.089, 95%\u003cem\u003eCI\u003c/em\u003e:1.019\u0026ndash;1.182), and creatine kinase-MB (CK-MB,OR\u0026thinsp;=\u0026thinsp;1.034, 95%\u003cem\u003eCI\u003c/em\u003e:1.015\u0026ndash;1.057). The nomogram demonstrated robust predictive performance, with AUC of 0.813 (95%\u003cem\u003eCI\u003c/em\u003e:0.757\u0026ndash;0.868), excellent calibration (mean absolute error\u0026thinsp;=\u0026thinsp;0.029), and clinical net benefit across threshold probabilities of 0.1\u0026ndash;0.8 confirmed by DCA.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eWe successfully developed a nomogram incorporating five readily available clinical parameters to predict MV risk in adult tetanus patients. The model exhibited favorable discrimination, calibration, and clinical utility, offering a practical tool for early risk stratification and targeted management of critical care resources. Future multicenter external validation is warranted to promote its clinical application.\u003c/p\u003e","manuscriptTitle":"The Inflammation-Coagulation-Muscle Injury cascade: a clinically actionable nomogram for early mechanical ventilation prediction in tetanus","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-16 10:12:45","doi":"10.21203/rs.3.rs-8786554/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"154924925468890867330312166705458052392","date":"2026-02-23T09:44:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-10T23:41:15+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-10T13:52:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-05T05:12:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-05T05:09:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-02-04T12:14:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"95640ae9-bf1b-46a9-88ca-a2c47d36594e","owner":[],"postedDate":"February 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":62785853,"name":"Health sciences/Diseases"},{"id":62785854,"name":"Health sciences/Medical research"},{"id":62785855,"name":"Health sciences/Nephrology"},{"id":62785856,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-02-16T10:12:45+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-16 10:12:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8786554","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8786554","identity":"rs-8786554","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}
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