Early Prediction Model for In-Hospital all-cause Death in Patients with Acute ST-Elevation Myocardial Infarction After Primary Coronary Artery Stenting

Early Prediction Model for In-Hospital all-cause Death in Patients with Acute ST-Elevation Myocardial Infarction After Primary Coronary Artery Stenting | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Early Prediction Model for In-Hospital all-cause Death in Patients with Acute ST-Elevation Myocardial Infarction After Primary Coronary Artery Stenting Zhou Ying, Dong Fei, Zhao Yufei, Song Yu, Zhang Yunqiang, Liang Haiqing, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7398337/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background To establish an early prediction model for in-hospital all-cause death in patients with acute ST-segment elevation myocardial infarction (STEMI) after primary coronary artery stenting. Methods This retrospective study analyzed 3,916 STEMI patients undergoing primary coronary stenting within 24 hours of symptom onset at TEDA International Cardiovascular Hospital (2014–2022). We collected demographic, clinical, and procedural data, along with 48-hour laboratory results (including echocardiography and Holter monitoring). The primary outcome was in-hospital all-cause mortality. Eighty clinical parameters were compared between survivors and non-survivors to identify risk factors and develop an early prediction model. Result In a cohort of 3916 patients, 54 experienced in-hospital all-cause death. Comparison of 80 clinical variables between groups, followed by univariate logistic regression and least absolute shrinkage and selection operator (LASSO) regression, identified nine risk factors. Multicollinearity analysis confirmed no significant interactions. Multivariate logistic regression revealed six independent predictors: B-type natriuretic peptide (BNP) (per 200 pg/mL), creatine kinase-MB (CK-MB) (per 100 ng/mL), blood urea nitrogen (BUN) (per 1 mmol/L), lactic acid (LAC) (per 1 mmol/L), Holter mean heart rate (MHR) (per 10 bpm), and Holter total atrial beats (TAB) (per 1000 beats). Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) demonstrated superior net benefit of the combined model over individual predictors. Conclusion The combination of BNP, CK-MB, BUN, LAC, Holter MHR, and Holter TAB can effectively predicts in-hospital all-cause death in STEMI patients undergoing primary coronary artery stenting, offering potential clinical utility. ST-segment elevation myocardial infarction primary coronary artery stenting In-Hospital all-cause death Early prediction model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Cardiovascular diseases (CVDs) continue to exhibit rising prevalence and mortality rates, standing as a leading cause of death and disability worldwide. In China, CVDs account for the highest proportion of resident mortality. In 2021, the mortality rate of acute myocardial infarction (AMI) reached 63.25 per 100,000 in urban populations and 83.26 per 100,000 in rural populations [ 1 ]. Acute ST-elevation myocardial infarction (STEMI), one of the most dangerous manifestations of coronary artery disease, has become a major health concern. Despite significant advancements in treatment, particularly the widespread adoption of primary percutaneous coronary intervention (PPCI), the in-hospital mortality rate for STEMI patients remains as high as 8.6% [ 2 ]. Myocardial infarction causes impaired energy metabolism, inflammatory responses, oxidative stress, ischemia-reperfusion injury, myocardial hypertrophy, and fibrosis in cardiomyocytes, leading to abnormal myocardial remodeling and electrical instability [ 3 ]. Delayed hospital arrival, patient refusal of PPCI due to financial or physical constraints, or complications such as heart failure, cardiogenic shock, malignant arrhythmias, mechanical complications, reinfarction, bleeding, stroke, or infection may ultimately lead to in-hospital death. Early identification of high-risk features for in-hospital mortality is therefore critical to improving STEMI outcomes. Currently, there is a lack of early predictive models for in-hospital mortality among STEMI patients undergoing primary coronary stenting. This study aims to evaluate the predictive value of early clinical indicators for in-hospital all-cause mortality in this population. Methods Subjects This study retrospectively analyzed 3916 patients diagnosed with STEMI and treated with primary coronary stenting at TEDA International Cardiovascular Hospital between October 2014 and December 2022. The STEMI diagnosis criteria followed the 2013 American College of Cardiology Foundation (ACCF)/American Heart Association (AHA) STEMI Management Guidelines and the 2017 European Society of Cardiology (ESC) STEMI Diagnosis and Treatment Guidelines [ 4 – 5 ]. Upon arrival at our chest pain center emergency department and confirmation of STEMI diagnosis, all patients received a preprocedural loading dose of 300 mg aspirin plus either 300 mg clopidogrel or 180 mg ticagrelor, followed by maintenance doses post-procedure. Drug-eluting stents (DES) were implanted in all cases, with the coronary stenting procedures performed according to the 2014 ESC/European Association for Cardio-Thoracic Surgery (EACTS) Guidelines on Myocardial Revascularization and the 2021 American College of Cardiology (ACC)/AHA/Society for Cardiovascular Angiography and Interventions (SCAI) Coronary Artery Revascularization Guidelines [ 6 – 7 ]. Postoperative care adhered to standardized protocols as outlined in the guidelines [ 4 – 5 ].A flowchart of patient enrollment is presented in Fig. 1 . The study was approved by the ethics committee of the Tianjin TEDA International Cardiovascular Hospital (ethical approval number: [2023]-0310-1). Data Collection General information: gender, age, body mass index (BMI). Medical history: Information regarding the onset time of the disease, history of hypertension, diabetes mellitus, smoking status, and alcohol consumption was collected. Initial vital signs: Temperature, heart rate (HR), respiratory rate (RR), blood pressure (BP), and peripheral oxygen saturation were recorded. Killip classification at admission. Intraoperative data: Door-to-wire time (D-to-W time), infarct-related artery (IRA), IRA pre-PCI thrombolysis in myocardial infarction (TIMI) flow grade, IRA post-PCI TIMI flow grade and the number of stents implanted were collected. Laboratory tests: cardiac biomarkers [ high-sensitivity cardiac troponin I (cTNI), myoglobin (MYO), creatine kinase-MB isoenzyme (CK-MB)], brain natriuretic peptide (BNP), lactate (LAC), full blood count, renal and liver function tests, electrolytes, lipid profiles, random blood glucose (RBG), C-reactive protein (CRP), thyroid function tests, bedside echocardiography, and 24-hour Holter monitoring. All these tests (except the Holter) were completed within 24 hours of hospital admission, with cardiac biomarkers recorded at their peak within this period. The Holter was performed within 48 hours. The estimated glomerular filtration rate (eGFR) was calculated using the modified simplified modification of diet in renal disease(MDRD) formula [ 8 ]. Clinical Outcomes The primary clinical outcome was in-hospital all-cause mortality. Patients were categorized into either the in-hospital survival group or death group. Comparative analysis of clinical parameters between these two groups was performed to identify early independent predictors of in-hospital all-cause mortality among STEMI patients undergoing primary coronary stenting. Subsequently, an early prediction model was developed and its predictive performance was rigorously evaluated. Statistical Analysis Data collection spanned from January 2024 to December 2022. Given that missing values occurred completely at random with a missing rate < 40%, multiple imputation was employed to handle these missing data points. The imputation model incorporated clinically relevant variables including age, sex, history of hypertension, diabetes mellitus, smoking status, and clinical outcomes - all of which showed correlation with the missing variables. The distribution characteristics of variables were examined through Kolmogorov-Smirnov test(α = 0.05, p ≧ 0.05), with normally distributed parameters reported as mean ± standard deviations and non-normal data expressed as medians and quartiles (P25, P75). Categorical measures were quantified as proportional values (%). Comparative analyses between groups employed appropriate statistical methods: independent t-tests for normally distributed continuous variables, Mann-Whitney U tests for nonparametric continuous data, and either χ 2 test or rank-sum tests for categorical comparisons. Potential risk factors were initially screened using univariate logistic regression and least absolute shrinkage and selection operator (LASSO) regression. The screened variables were assessed for multicollinearity, and after confirming the absence of significant collinearity, they were incorporated into a multivariate logistic regression model using the forward-LR method to develop the predictive model, with the results presented in a forest plot. The model's performance was evaluated through receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA). All statistical analyses were performed using SPSS 27 (IBM Corporation, Armonk, NY, USA) and R4.3 (R Foundation for Statistical Computing, Vienna, Austria; utilizing glmnet, rmda, and forestplot packages). A p-value < 0.05 was considered statistically significant. Results Basic Data Among the 3916 patients (median age 61 years [IQR 52–69]; 3000 males [76.6%] and 916 females [23.4%]), 54 experienced in-hospital mortality, with causes including mechanical complications (n = 21), cardiogenic shock (n = 14), malignant arrhythmias (n = 4), multiorgan failure (n = 13), and septic shock (n = 2). Compared with survivors, non-survivors demonstrated significantly higher values ( p < 0.05) in age, female proportion, initial HR, emergency hemodynamic instability (%), intra-aortic balloon pump (IABP) and temporary pacemaker (TPM) use intraoperative(%), and multiple biomarkers (BNP, cTnI, MYO, CK-MB, creatine kinase [CK], lactate dehydrogenase[LDH], white blood cell [WBC], neutrophils percentage [N%], CRP, alanine minotransferase [ALT], aspartate aminotransferase [AST], RBG, creatinine [Cr], blood urea nitrogen [BUN], uric acid [UA], K + , thyroid-stimulating hormone [TSH], LAC, Holter parameters [mean heart rate [MHR], Max HR, total ventricular beats[TVB], total atrial beats[TAB]), while showing lower values ( p < 0.05) in BMI, initial blood pressures (systolic blood pressure [SBP], diastolic blood pressure [DBP], mean arterial pressure [MAP], SpO₂, β-blocker use within 24h of admission(%), left ventricular ejection fraction (LVEF), red blood cells (RBC), hemoglobin (HB), hematocrit (HCT), eGFR, CL - , albumin (ALB), and triglyceride (TG). The mortality group had higher proportions of left main coronary artery (LM)/left anterior descending branch (LAD) as infarct-related artery (IRA), post-procedural TIMI flow ≤ 2, and Killip class ≥ 2 (all p < 0.05). No statistically significant differences were observed in: smoking/alcohol history, medical history (hypertension, diabetes mellitus, stroke, atrial fibrillation, gastrointestinal diseases, renal diseases, gallbladder diseases, pulmonary diseases, thyroid disorders, gout, peripheral vascular disease, previous myocardial infarction, prior PCI, coronary artery bypass grafting [CABG], or coronary artery disease), onset time, RR, D-to-W time, IRA pre-PCI TIMI flow grade, number of implanted stents, intraprocedural use of glycoprotein (GP) IIb/IIIa inhibitors or vasoactive agents(%), Angiotensin Converting Enzyme Inhibitors (ACEI)/Angiotensin Ⅱ Receptor Blocker (ARB)/Angiotensin Receptor-Neprilysin Inhibitor (ARNI) use within 24h of admission(%), platelet count (PLT), alkaline phosphatase (ALP), K + , unconjugated bilirubin (UCB), total protein (TP), globulin (GLO), total cholesterol (TCHOL), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), or the longest RR interval on Holter monitoring. For detailed data, refer to Table 1 . Table 1 Comparison of baseline characteristics of patients Variable Total (n = 3916) Survival group (n = 3862) Death group (n = 54) p Age (years) 61 (52, 69) 61 (52,69) 75 (61, 80) < 0.001 Women (%) 916 (23.4) 896 (23.2) 20 (37.0) 0.017 BMI (kg/m 2 ) 25.2 (23.2, 27.2) 25.2 (23.2, 27.2) 23.5 (21.0, 25.6) < 0.001 Current smoking (%) 2178 (55.6) 2154 (55.8) 24 (44.4) 0.096 Drinking history (%) 1139 (29.1) 1123 (29.1) 16 (29.6) 0.929 Medical history Hypertension (%) 2217(56.6) 2185(56.6) 32(59.3) 0.693 Diabetes (%) 906(23.1) 889(23.0) 17(31.5) 0.143 Stroke (%) 417(10.6) 412(10.7) 5(9.3) 0.739 Atrial Fibrillation (%) 56(1.4) 54(1.4) 2(3.6) 0.156 Gastrointestinal Diseases (%) 258(6.6) 253(6.6) 5(9.3) 0.426 Kidney Disease (%) 66(1.7) 64(1.7) 2(3.7) 0.246 Gallbladder Disease(%) 66(1.7) 64(1.7) 2(3.7) 0.246 Lung Disease (%) 63(1.6) 62(1.6) 1(1.9) 0.886 Thyroid Disease (%) 46(1.2) 46(1.2) 0(0) 0.420 Gout (%) 34(0.9) 33(0.9) 1(1.9) 0.433 Peripheral Vascular Disease (%) 43(1.1) 42(1.1) 1(1.9) 0.592 Prior Myocardial Infarction (%) 222(5.7) 219(5.7) 3(5.6) 0.971 PCI (%) 321(8.2) 317(8.2) 4(7.4) 0.831 CABG (%) 11(0.3) 11(0.3) 0(0) 0.695 Coronary Heart Disease (%) 376(9.6) 370(9.6) 6(11.1) 0.705 Onset time (h) 3.0(2.0,5.5) 3.0(2.0,5.5) 3.0(2.0,6.0) 0.890 Initial vital signs HR (bpm) 73(62,82) 73(62,82) 80(68,97) 0.013 RR (times/ minute) 18(18,19) 18(18,19) 18(18,22) 0.051 SBP (mmHg) 138(120,154) 139(121,154) 120(97,141) < 0.001 DBP (mmHg) 83(70,93) 83(70,93) 70(60,90) < 0.001 MAP (mmHg) 102(90,113) 102(90,113) 88(73,107) < 0.001 SpO 2 (%) 98(97,99) 98(97,99) 98(94,99) < 0.001 Emergency hemodynamic instability (%) 282(7.2) 264(6.8) 18(33.3) < 0.001 D-to-W time (min) 54(44,66) 54(44,66) 54(47,65) 0.706 IRA LM (%) 31(0.8) 25(0.8) 6(11.1) < 0.001 LAD (%) 1806(46.1) 1778(46.0) 28(51.9) LCX (%) 432(11.0) 428(11.1) 4(7.4) RCA (%) 1644(42.0) 1628(42.2) 16(29.6) Intermediate branch (%) 3(0.1) 3(0.1) 0(0) IRA pre-PCI TIMI flow grade Class 0 (%) 774(19.8) 769(19.9) 5(9.3) 0.071 Class 1 (%) 257(6.6) 254(6.6) 3(5.6) Class 2 (%) 341(8.7) 339(8.8) 2(3.7) Class 3 (%) 2544(65.0) 2500(64.7) 44(81.5) IRA Pos-PCI TIMI flow grade Class ≦ 2 (%) 74(1.9) 71(1.8) 3(5.6) 0.046 Class 3 (%) 3842(98.1) 3791(98.2) 51(94.4) Number of stents implanted 1 (%) 3130(79.9) 3090(80.0) 40(74.1) 0.237 2 (%) 719(18.4) 705(18.3) 14(25.9) ≥ 3 (%) 67(1.7) 67(1.7) 0(0) Intraoperative use IABP (%) 79(2.0) 69(1.8) 10(18.5) < 0.001 TPM (%) 42(1.1) 37(1.0) 5(9.3) < 0.001 GP IIb/IIIa inhibitors (%) 528(13.5) 520(13.5) 8(14.8) 0.773 Vasoactive agents (%) 290(7.4) 284(7.4) 6(11.1) 0.295 ACEI/ARB/ARNI use within 24h of admission (%) 631(16.1) 623(16.1) 8(14.8) 0.794 β-B use within 24h of admission (%) 1166(29.8) 1158(30.0) 8(14.8) 0.015 LVEF (%) 55(50,60) 55(50,60) 46(35,53) < 0.001 BNP (pg/ml) 66(23,168) 65(23,166) 174(55,642) < 0.001 cTnI (pg/ml) 26.8 (13.5,56.2) 26.6(13.3,55.1) 71.9(27.9,81.0) < 0.001 MYO (ng/ml) 95(46,216) 93(46,206) 747(345,1311) < 0.001 CK-MB (ng/ml) 114.1(52.1,201.0) 112.8(51.4,199.2) 216.9(165.2,334.0) < 0.001 CK (U/L) 1113.1(300.0,1807.5) 1104.0(293.3,1784.8) 2081.3(1333.7,3539.8) < 0.001 LDH (U/L) 480(343,652) 477(341,645) 825(558,1202) < 0.001 WBC (10 9 /L) 9.6(7.9,11.4) 9.6(7.9,11.4) 11.5(10.1,16.4) < 0.001 N% 73.6(67.9,79.0) 73.4(67.8,78.9) 83(77.1,86.4) < 0.001 RBC (10 12 /L) 4.4(4.1,4.8) 4.4(4.1,4.8) 4.2(3.8,4.6) 0.010 HB (g/L) 137(126,147) 137(126,147) 131(119,142) 0.011 HCT (%) 40.2(37.3,42.9) 40.2(37.3,42.9) 38.2(35.3,40.7) 0.008 PLT (109/L) 216(184,254) 216(184,254) 211(184,252) 0.604 CRP (mg/L) 19.4(8.8,31.1) 19.4(8.7,31.0) 26.1(14.8,42.7) 0.002 ALP (U/L) 70(58,84) 69(58,84) 74(59,84) 0.627 ALT (U/L) 42(27,66) 41(27,65) 101(64,149) < 0.001 AST (U/L) 154(77,261) 151(77,256) 384(280,557) < 0.001 RBG (mmol/L) 8.0(6.7,10.4) 8.0(6.7,10.3) 11.5(7.5,14.1) < 0.001 Cr (umol/L) 68(59,80) 68(59,79) 96(80,127) < 0.001 BUN (mmol/L) 5.8(4.7,7.1) 5.8(4.7,7.0) 9.3(7.7,12.8) < 0.001 UA (umol/L) 331(275,395) 330(274,394) 422(336,507) < 0.001 eGFR [mL/(min·1.73m 2 )] 100.2(78.1,123.8) 100.6(78.9,124.1) 52.9(38.3,85.7) < 0.001 K + (mmol/L) 3.9(3.7,4.1) 3.9(3.7,4.1) 4.3(4.0,4.5) < 0.001 NA + (mmol/L) 140(138,141) 140(138,141) 141(137,142) 0.252 CL − (mmol/L) 104(101,107) 104(101,107) 102(99,106) 0.002 UCB (umol/L) 8.7(6.3,11.6) 8.7(6.3,11.6) 8.3(6.2,11.9) 0.542 TP (g/L) 65(62,69) 65(62,69) 65(61,67) 0.312 ALB (g/L) 40(38,42) 40(38,42) 38(34,40) < 0.001 GLO (g/L) 26(23,28) 26(23,28) 26(24,28) 0.294 TCHOL (mmol/L) 4.5(3.9,5.1) 4.5(3.9,5.1) 4.4 (3.4,5.4) 0.517 TG (mmol/L) 1.4(0.9,2.0) 1.4(0.9,2.0) 1.0(0.6,1.7) 0.001 LDL-C (mmol/L) 2.9(2.4,3.4) 2.9(2.4,3.4) 2.7(2.0,3.4) 0.188 HDL-C (mmol/L) 1.0(0.9,1.2) 1.0(0.9,1.2) 1.1(0.9,1.3) 0.071 TSH (mIU/L) 1.5(0.8,2.9) 1.5(0.8,2.9) 3.2(1.2,4.5) < 0.001 LAC (mmol/L) 1.7 (1.2,2.3) 1.7(1.2,2.3) 2.8(1.8,5.8) < 0.001 Holter monitor MHR (bpm) 70(64,78) 70(64,78) 85(75,98) < 0.001 Max HR (bpm) 103(95,113) 103(95,112) 119(105,142) < 0.001 TVB (times) 35(4,387) 38(8,259) 889(41,2571) < 0.001 TAB (times) 39(9,271) 34(4,371) 932(15,2054) < 0.001 Max RR (s) 1.4(1.2,1.6) 1.4(1.2,1.6) 1.3(1.0,1.6) 0.094 Killip classification at admission Killip Ⅰ (%) 3605(92.1) 3577(92.6) 28(51.9) < 0.001 Killip Ⅱ (%) 220(5.6) 215(5.6) 5(9.3) Killip Ⅲ (%) 27(0.7) 21(0.5) 6(22.2) Killip Ⅳ (%) 64(1.6) 49(1.3) 15(27.8) Note: BMI, body mass index; PCI, percutaneous coronary intervention; CABG, coronary artery bypass grafting; HR, heart rate; bpm, beats per minute; RR, respiratory rate; SBP, systolic blood pressure; DBP, diastolic blood pressure; MAP, mean arterial pressure; SpO 2 , blood oxygen saturation; D-to-W, door-to-wire; IRA, infarct-related artery; LM, left main artery; LAD, left anterior descending artery; LCX, left circumflex artery; RCA, right coronary artery; TIMI, thrombolysis in myocardial infarction; IABP, intra-aortic balloon pump; TPM, temporary pacemaker; GP, glycoprotein; ACEI, angiotensin-converting enzyme inhibitors; ARB, angiotensin II receptor blockers; ARNI, angiotensin receptor-erucinase inhibitors; β-B β-receptor blockers; LVEF, left ventricular ejection fraction; BNP, Beta-natriuretic peptide; cTnI, high-sensitivity cardiac troponin I; MYO, myoglobin; CK-MB, creatine kinase-MB isoenzyme; CK, creatine kinase; LDH, lactate dehydrogenase; WBC, white blood cells; N%, neutrophil percentage; RBC, red blood cells; HB, hemoglobin; HCT, hematocrit; PLT, platelet count; CRP, c-reactive protein; ALP, alkaline phosphatase; ALT, alanine aminotransferase; AST, aspartate aminotransferase; RBG, random blood glucose; Cr, creatinine; BUN, blood urea nitrogen; UA, uric acid; eGFR, estimated glomerular filtration rate; UCB, unconjugated bilirubin; TP, total protein; ALB, albumin; GLO, globulin; TCHOL, total cholesterol; TG, triglyceride; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TSH: thyroid-stimulating hormone; LAC, lactate; MHR, mean heart rate; TVB, total ventricular beats; TAB: total atrial beats. Univariate Analysis Univariate logistic regression analysis identified the following factors as significantly associated with in-hospital all-cause mortality (all p < 0.05): age, female gender, BMI, initial HR, initial SBP, initial DBP, initial MAP, SpO₂, emergency hemodynamic instability, IRA (LM&LAD), IABP use intraoperative, TPM use intraoperative, β-B use within 24h of admission, LVEF, BNP, cTnI, MYO, CK-MB, CK, LDH, WBC, N%, RBC, HB, CRP, ALT, AST, RBG, Cr, BUN, UA, eGFR, K + , CL − , ALB, TG, LAC, Holter MHR, Holter Max HR, Holter TVB, Holter TAB, Killip class (at admission)≧Ⅱ. The results are presented in Table 2 . Table 2 One-way logistic regression analysis. Variable Wald χ 2 β SE OR 95% CI p Age (per 5 years) 36.608 0.409 0.068 1.505 1.318–1.718 < 0.001 Women (%) 5.492 0.666 0.284 1.947 1.115-3.400 0.019 BMI (per IQR kg/m 2 ) 13.053 -0.589 0.163 0.555 0.403–0.764 < 0.001 Initial vital signs HR (per 10 bpm) 13.690 0.230 0.062 1.258 1.114–1.421 0.001 SBP ( per10 mmHg) 35.214 -0.255 0.043 0.775 0.713–0.843 < 0.001 DBP ( per10 mmHg) 20.166 -0.333 0.074 0.717 0.620–0.829 < 0.001 MAP (per10 mmHg) 30.148 -0.332 0.060 0.718 0.638–0.808 < 0.001 SpO 2 (per 5%) 9.126 -0.14 0.046 0.87 0.795–0.952 0.003 Emergency hemodynamic instability 42.137 1.919 0.296 6.814 3.818–12.164 < 0.001 IRA (LM&LAD) 5.470 0.663 0.284 1.941 1.113–3.385 0.019 IRA Pos-PCI TIMI flow grade < 3 3.566 1.144 0.606 3.141 0.958–10.302 0.059 IABP use intraoperative 46.381 2.525 0.371 12.493 6.040–25.840 < 0.001 TPM use intraoperative 22.410 2.356 0.498 10.549 3.977–27.979 < 0.001 β-B use within 24h of admission 5.488 -0.901 0.385 0.406 0.191–0.863 0.019 LVEF (per 5%) 68.806 -0.577 0.070 0.561 0.490–0.643 < 0.001 BNP (per 200 pg/ml) 61.876 0.356 0.045 1.428 1.307–1.560 < 0.001 cTnI (per 10 pg/ml) 28.733 0.231 0.043 1.259 1.158–1.370 < 0.001 MYO (per 500 ng/ml) 83.246 0.724 0.079 2.062 1.765–2.409 < 0.001 CK-MB (per 100 ng/ml) 70.004 0.811 0.097 2.249 1.860–2.719 < 0.001 CK (per 1000 U/L) 24.694 0.385 0.065 1.470 1.293–1.671 < 0.001 LDH (per 200 U/L) 82.642 0.480 0.053 1.617 1.458–1.793 < 0.001 WBC (per 5×10 9 /L) 43.904 1.084 0.164 2.956 2.145–4.074 < 0.001 N% (per 5%) 62.291 0.798 0.101 2.222 1.822–2.709 < 0.001 RBC (per 0.5×10 12 /L) 7.474 -0.336 0.123 0.715 0.562–0.909 0.006 HB (per 10 g/L) 6.810 -0.203 0.078 0.816 0.701–0.951 0.009 HCT(per 5%) 2.914 -0.243 0.143 0.784 0.593–1.037 0.088 CRP(per 10 mg/L) 26.328 0.162 0.032 1.176 1.106–1.252 < 0.001 ALT(per 100 U/L) 6.000 0.091 0.037 1.095 1.018–1.178 0.014 AST(per 200 U/L) 19.913 0.334 0.075 1.396 1.206–1.616 < 0.001 RBG(per 5 mmol/L) 27.565 0.605 0.115 1.831 1.461–2.295 < 0.001 Cr(per 10 umol/L) 48.417 0.175 0.025 1.191 1.134–1.251 < 0.001 BUN(per 1 mmol/L) 128.934 0.406 0.036 1.501 1.399–1.609 < 0.001 UA(per 100 umol/L) 50.687 0.786 0.110 2.196 1.768–2.726 < 0.001 eGFR[per 10 mL/(min·1.73m 2 )] 64.874 -0.424 0.053 0.654 0.590–0.726 < 0.001 K + (per 1 mmol/L) 28.597 1.408 0.263 4.086 2.439–6.845 < 0.001 CL − (per 3 mmol/L) 8.226 -0.304 0.106 0.738 0.599–0.908 0.004 ALB( per 5 g/L) 26.423 -0.829 0.161 0.436 0.318–0.599 < 0.001 TG(per 1 mmol/L) 7.735 -0.495 0.178 0.610 0.430–0.864 0.005 TSH(per 1 mIU/L) 2.897 0.030 0.017 1.030 0.995–1.066 0.089 LAC(per 1 mmol/L) 86.681 0.453 0.049 1.573 1.430–1.730 < 0.001 Holter monitor MHR(per 10 bpm) 85.551 0.763 0.083 2.146 1.825–2.523 < 0.001 Max HR (per 10 bpm) 64.294 0.423 0.053 1.526 1.376–1.692 < 0.001 TVB (per 1000 times) 7.110 0.078 0.029 1.081 1.021–1.145 0.008 TAB (per1000 times) 17.477 0.102 0.024 1.107 1.056–1.162 < 0.001 Killip Class(at admission)≧ Ⅱ 77.348 2.456 0.279 11.654 6.742–20.145 < 0.001 Note: SE, Standard Error; OR, Odds Ratio; CI, confidence interval; BMI, body mass index; IQR, interquartile range; HR, heart rate; bpm, beats per minute; SBP, systolic blood pressure; DBP, diastolic blood pressure; MAP, mean arterial pressure; SpO2, blood oxygen saturation; IRA, infarct-related artery; LM, left main coronary artery; LAD, left anterior descending artery; PCI, percutaneous coronary intervention; TIMI, thrombolysis in myocardial infarction; IABP, intra-aortic balloon pump; TPM, temporary pacing; β-B, β-receptor blockers; LVEF, left ventricular ejection fraction; BNP, Beta-natriuretic peptide; cTnI, high-sensitivity cardiac troponin I; MYO, myoglobin; CK-MB, creatine kinase-MB isoenzyme; CK, creatine kinase; LDH, lactate dehydrogenase; WBC, white blood cells; N%, neutrophil percentage; RBC, red blood cells; HB, hemoglobin; HCT, hematocrit; CRP, c-reactive protein; ALT, alanine aminotransferase; AST, aspartate aminotransferase; RBG, random blood glucose; Cr, Creatinine; BUN, blood urea nitrogen; UA, uric acid; eGFR, estimated glomerular filtration rate; ALB, albumin; TG, triglycerides; TSH, thyroid-stimulating hormone; LAC, lactate; MHR, mean heart rate; TVB, total ventricular beats; TAB, total atrial rhythm. LASSO Regression The 42 statistically significant variables were incorporated into LASSO regression analysis, with additional adjustment for history of hypertension, diabetes mellitus, smoking, and alcohol consumption. At the minimum mean squared error (λ = 0.002), corresponding to the optimal penalty value indicated by the right vertical dotted line, the analysis identified 9 non-zero coefficient variables: BNP (per200 pg/ml)、MYO (per500 ng/ml)、CK-MB (per100 ng/ml)、LDH (per200 U/L)、BUN (per 1 mmol/L)、LAC (per 1 mmol/L)、Holter MHR (per10 bpm)、Holter TAB (per1000 times) and Killip class (at admission) ≥Ⅱ. This model configuration demonstrated optimal fitting performance in the LASSO regression, as illustrated in Fig. 2 , 3 . Multicollinearity Analysis Collinearity analysis was performed for BNP (per200 pg/ml)、MYO (per500 ng/ml)、CK-MB (per100 ng/ml)、LDH (per200 U/L)、BUN (per 1 mmol/L)、LAC (per 1 mmol/L)、Holter MHR (per10 bpm)、Holter TAB (per1000 times) and Killip class (at admission) ≥Ⅱ, demonstrating tolerance (Tol) values all > 0.1 and variance inflation factors (VIF) all < 2, indicating no significant multicollinearity among these variables (see Table 3 ). Table 3 Multicollinearity Analysis Variable Tol VIF BNP (per 200 pg/ml) 0.862 1.160 MYO (per 500 ng/ml) 0.818 1.222 CK-MB (per 100 ng/ml) 0.639 1.565 LDH (per 200 U/L) 0.632 1.583 BUN (per 1 mmol/L) 0.828 1.208 LAC (per 1 mmol/L) 0.872 1.147 MHR (per 10 bpm) 0.901 1.110 TAB (per 1000 exposures) 0.976 1.024 Killip class ≥Ⅱ 0.886 1.129 Note: Tol, tolerance; VIF, variance inflation factor; BNP, B-natriuretic peptide; MYO, myoglobin; CK-MB, creatine kinase-MB isoenzyme; LDH, lactate dehydrogenase; BUN, blood urea nitrogen; LAC, lactate; MHR, mean heart rate; bpm, beats per minute; TAB, atrial rhythm count Multivariate Logistics Analysis The nine variables were simultaneously incorporated into a multivariate logistic regression model, which BNP (per 200 pg/mL), CK-MB (per 100 ng/mL), BUN (per 1 mmol/L), LAC (per 1 mmol/L), Holter MHR (per 10 bpm), and Holter TAB (per 1000 beats) as early independent risk factors for in-hospital all-cause death in STEMI patients undergoing primary coronary stenting, with the results visualized in a forest plot. The Hosmer-Lemeshow test demonstrated good model fit (χ²=5.610, df = 8, p = 0.691), as detailed in Table 4 and Fig. 4 . Table 4 Multivariate regression analysis of Logistics Variable Wald χ 2 β SE OR 95% CI p BNP (per 200 pg/ml) 6.388 0.145 0.057 1.156 1.033–1.293 0.011 CK-MB (per 100 ng/ml) 18.911 0.513 0.118 1.671 1.326–2.105 < 0.001 BUN (per 1 mmol/L) 42.942 0.299 0.046 1.348 1.233–1.474 < 0.001 LAC (per 2 mmol/L) 15.018 0.230 0.059 1.258 1.120–1.413 < 0.001 Holter MHR (per 10 bpm) 21.644 0.471 0.101 1.602 1.313–1.954 < 0.001 Holter TAB (per1000 times) 7.347 0.080 0.030 1.084 1.022–1.148 0.007 Note: SE, standard error; OR, odds ratio; CI, confidence interval; BNP, B-natriuretic peptide; CK-MB, creatine kinase-MB isoenzyme; BUN, blood urea nitrogen; LAC, lactate; MHR, mean heart rate; bpm, beats per minute; TAB, atrial rhythm count. Predictive Efficacy Tested by ROC Curve The combined predictive model incorporating BNP (per 200 pg/mL), CK-MB (per 100 ng/mL), BUN (per 1 mmol/L), LAC (per 1 mmol/L), Holter MHR (per 10 bpm), and Holter TAB (per 1000 times) demonstrated excellent discriminative ability for in-hospital all-cause death in STEMI patients undergoing primary coronary stenting, with an area under the curve (AUC) of 0.911 (95% CI: 0.863–0.959, P < 0.001), achieving 85% sensitivity and 87% specificity - all performance metrics surpassing those of any individual parameter (see Fig. 5 and Table 5 ). Table 5 AUC by ROC analysis. Variable AUC SE 95% CI Sensitivity (%) Specificity (%) p 6 factors 0.911 0.025 0.862–0.959 0.852 0.868 < 0.001 BNP (per 200 pg/ml) 0.664 0.044 0.577–0.751 0.481 0.797 < 0.001 CK-MB (per 100 ng/ml) 0.733 0.037 0.660–0.805 0.566 0.752 < 0.001 BUN (per 1 mmol/L) 0.855 0.029 0.797–0.912 0.870 0.737 < 0.001 LAC (per 2 mmol/L) 0.744 0.039 0.668–0.821 0.463 0.892 < 0.001 Holter MHR (per 10 bpm) 0.771 0.033 0.712–0.842 0.630 0.770 < 0.001 Holter TAB (per1000 times) 0.656 0.044 0.571–0.741 0.440 846 < 0.001 Note: AUC, area under the curve; ROC, receiver operating characteristic; SE, standard error; CI, confidence interval; BNP, B-natriuretic peptide; CK-MB, creatine kinase-MB isoenzyme; BUN, blood urea nitrogen; LAC, lactate; MHR, mean heart rate (bpm); bpm, beats per minute; TAB, atrial rhythm count. Predictive Efficacy Tested by by DCA DCA demonstrated that the purple solid line representing the net benefit of the six-marker combined prediction model consistently remained above both the grey solid line (indicating benefit when all subjects received intervention) and the black horizontal line (representing benefit when no subjects received intervention) across the threshold probability range where it intersected with the two reference lines (purple dashed lines). The model showed superior net benefit compared to any individual biomarker when the threshold probability ranged from approximately 0.010 to 0.710. This wide probability range indicates substantial clinical utility for the prediction model, as illustrated in Fig. 6 . Discussion This study analyzed comprehensive clinical data from STEMI patients undergoing primary coronary stenting, identifying elevated levels of BNP, CK-MB, BUN, LAC, Holter MHR, and Holter TAB as independent predictors of in-hospital all-cause death, with the combined predictive performance of these six markers surpassing that of any individual parameter. Our investigation incorporated 80 clinically relevant parameters for mortality prediction, including demographic characteristics, personal and medical history, symptom duration, initial vital signs, D-to-W time, IRA characteristics, pre-pci and post-pci TIMI flow grades, number of implanted stents, intraprocedural device assistance and medication use, 24-hour post-admission medication administration, LVEF, cardiac biomarkers, complete blood count, renal and hepatic function tests, RBG, electrolytes, lipid profiles, LAC, Holter monitoring results, and Killip classification at admission - with all laboratory values (except 24-hour Holter data) obtained within the first 24 hours of hospitalization. Compared to existing prediction models, this comprehensive early-assessment framework demonstrates superior inclusiveness by systematically incorporating objective clinical parameters available during the critical initial hospitalization period, while maintaining innovative predictive capability through multidimensional data integration. This study enrolled 3916 STEMI patients, including 54 in-hospital deaths (incorporating terminal status patients and those discharged automatically), yielding a mortality rate of 1.38% that was lower than most reported mortality rates for STEMI patients undergoing PPCI. PCI procedures comprised two approaches: percutaneous transluminal coronary angioplasty (PTCA) and coronary stenting, with international consensus favoring stenting due to its superior outcomes compared to PTCA alone, which is associated with higher risks of early ischemia/reinfarction, acute target vessel reocclusion, and all-cause mortality [ 9 – 10 ]. However, current guidelines recommend PTCA over stenting in specific clinical scenarios including hemodynamic instability/cardiogenic shock requiring reduced procedure time, high bleeding risk, anatomical constraints (small vessel diameter, diffuse disease, or severe calcification), early post-thrombolysis reperfusion, heavy thrombus burden, in-stent restenosis, or multi-organ failure with limited life expectancy [ 11 – 12 ]. The observed lower mortality in our cohort may be attributed to the exclusive inclusion of stented patients rather than those receiving PTCA alone. Furthermore, as a regional cardiovascular specialty center, our patient population demonstrates clearer disease specificity with fewer complex cases compared to general hospitals, coupled with our interventional cardiologists and coronary care unit (CCU) team's extensive experience, collectively contributing to reduced mortality rates. BNP, synthesized and secreted by ventricular cardiomyocytes, exerts diuretic, vasodilatory, and anti-fibrotic effects, with its release triggered by cardiac pressure or stretch (e.g., increased ventricular wall tension). Elevated BNP levels demonstrate strong correlations with heart failure and adverse cardiovascular outcomes [ 13 ]. BUN, a byproduct of protein metabolism, undergoes conversion to urea in the liver via the ornithine cycle before renal glomerular filtration and urinary excretion, with its concentration reflecting the balance between urea production and renal clearance. Previous studies have established that elevated BUN levels significantly correlate with increased risks of major adverse cardiovascular events (MACE) in STEMI patients post-PCI, contributing to prolonged hospitalization and higher mortality [ 14 ]. LAC, an intermediate glycolytic metabolite, accumulates when severe tissue hypoxia limits oxidative phosphorylation and reduces mitochondrial adenosine triphosphate (ATP) production, leading to pyruvate reduction to lactate by cytoplasmic LDH - thus serving as a byproduct of hypoperfusion or oxygen deprivation. As a key diagnostic marker for cardiogenic shock, elevated LAC positively associates with poor cardiovascular prognosis [ 15 ]. These established mechanisms align with our findings that elevated BNP, BUN, and LAC levels serve as early predictors of in-hospital all-cause mortality in STEMI patients. CK-MB, the myocardial-specific isoenzyme of creatine kinase composed of M and B subunits, is predominantly located in cardiomyocytes (accounting for 15%-25% of total cardiac CK) with minimal skeletal muscle presence (< 5%). Although traditionally used as a myocardial injury biomarker, CK-MB has been largely superseded by troponin in AMI diagnosis due to inferior specificity and sensitivity. However, beyond diagnostic utility, CK-MB remains valuable for predicting left ventricular remodeling and mortality in AMI patients, with peak levels strongly correlating with infarct size, wall motion abnormalities, left ventricular end-systolic volume index, and fatal outcomes [ 16 ]. Emerging evidence suggests CK-MB may outperform troponin for prognostic stratification, as demonstrated by a 2023 study showing CK-MB's superior quantitative value for outcome prediction in STEMI patients [ 17 ]. Our univariate logistic analysis identified both cTnI and CK-MB as significant mortality predictors, but multivariate modeling retained only CK-MB (per 100 ng/mL increase elevating in-hospital death risk by 1.5-fold) as having independent prognostic value. Heart rate, as a fundamental physiological parameter, demonstrates well-established associations between tachycardia and increased mortality in both healthy individuals and cardiovascular patients (coronary artery disease, hypertension, heart failure). In AMI, elevated HR exacerbate infarct expansion, myocardial ischemia, sympathetic overactivation, cardiac dysfunction, oxygen demand, and atherosclerotic progression - underpinning guideline recommendations for early β-blocker therapy targeting ~ 60 bpm resting rates [ 18 ]. While prior studies predominantly examined random heart rate measurements (admission, in-hospital, discharge), our focus on 24-hour MHR via Holter monitoring captures integrated neurohormonal regulation, with Shen J et al.'s congruent findings confirming Holter-derived MHR's superior predictive value over spot measurements [ 19 ]. Post-revascularization atrial arrhythmias (premature contractions, tachycardia, atrial fibrillation [AF], flutter) frequently complicate STEMI, with > 20% of AMI patients having AF history and 5% developing new-onset AF - the latter associated with markedly worse outcomes after PPCI [ 20 ]. Our study quantified this risk precisely, demonstrating that each 1000-beat increment in 24-hour atrial ectopy increased in-hospital mortality by 8.4% ( p = 0.006), establishing Holter-detected atrial arrhythmia burden as a statistically significant mortality predictor. Limitations of the study This single-center study has certain limitations that should be acknowledged, including a relatively small number of mortality cases (n = 54), which necessitates further validation through multicenter investigations. Additionally, the study population was restricted to STEMI patients undergoing emergency coronary stenting, excluding those receiving percutaneous transluminal coronary angioplasty (PTCA) alone - a selection bias that our research team intends to address in future studies by expanding patient enrollment criteria. Furthermore, the absence of post-discharge follow-up data represents another limitation that will be remedied in subsequent research through systematic longitudinal outcome tracking. Conclusions Our findings robustly demonstrate that elevated levels of BNP, CK-MB, BUN, LAC, Holter MHR, and Holter TAB serve as independent predictors of in-hospital all-cause death in STEMI patients. The combined use of these six biomarkers significantly enhances early mortality prediction accuracy, enabling timely risk stratification and proactive clinical interventions to reduce fatality rates. This multivariable approach holds substantial potential for clinical implementation by improving early identification of high-risk patients who may benefit from intensified therapeutic strategies. Declarations Ethics Approval and Consent to Participate The study was carried out in accordance with the guidelines of the Declaration of Helsinki and approved by the ethics committee of the TEDA International Cardiovascular Disease Hospital (ethical approval number: [2023]-0310-1). Due to the retrospective design of the study, the requirement for written informed consent from patients was waived. Consent to publish Not applicable. This study did not include any human participants. Conflict of Interest The authors declare no conflict of interest. Funding 1. National Key Clinical Specialty Construction Project - Comprehensive Treatment of Cardiovascular Diseases; 2. Tianjin Key Medical Discipline Construction Project (Grant No.TJYXZDXK-3-035C); 3. Demonstration Project for the Reform and High - quality Development of Public Hospitals in Tianjin Binhai New Area; 4. Science and Technology Project of Tianjin Binhai New Area Health Commission (2022BWKQ006). Author Contribution Ying Zhou, Fei Dong, Yu Song and Rui Jing designed the research study. Ying Zhou, Fei Dong and Yufei Zhao performed data collection. Ying Zhou, Yunqiang Zhang and Mu Guo analyzed the data. Ying Zhou, Fei Dong, and Rui Jing drafted the manuscript. All authors contributed to critical revision of the manuscript for important intellectual content. All authors read and approved the final manuscript. All authors have participated sufficiently in the work and agreed to be accountable for all aspects of the work. Availability of Data and Materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. 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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-7398337","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":525107999,"identity":"738d2a29-1ebb-45d6-a245-7873c8226eb5","order_by":0,"name":"Zhou Ying","email":"","orcid":"","institution":"TEDA International Cardiovascular Hospital, Tianjin University","correspondingAuthor":false,"prefix":"","firstName":"Zhou","middleName":"","lastName":"Ying","suffix":""},{"id":525108000,"identity":"60d75855-5863-4e07-b0ae-2ef51a29e03b","order_by":1,"name":"Dong Fei","email":"","orcid":"","institution":"TEDA International Cardiovascular 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University","correspondingAuthor":false,"prefix":"","firstName":"Zhang","middleName":"","lastName":"Yunqiang","suffix":""},{"id":525108008,"identity":"c35845cf-dd17-41b7-8a4a-6d4a77ea0d4c","order_by":5,"name":"Liang Haiqing","email":"","orcid":"","institution":"TEDA International Cardiovascular Hospital, Tianjin University","correspondingAuthor":false,"prefix":"","firstName":"Liang","middleName":"","lastName":"Haiqing","suffix":""},{"id":525108009,"identity":"b219678c-894e-468d-8e0d-e3e12a9e44b2","order_by":6,"name":"Guo Mu","email":"","orcid":"","institution":"Shanghai Fourth People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Guo","middleName":"","lastName":"Mu","suffix":""},{"id":525108011,"identity":"6a1e59e1-70e4-49fd-b7b6-3f2c590b58e0","order_by":7,"name":"Jing Rui","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYDACHoYEhoSK/3L8zMyHHxCv5cEZZmPJdrY0A2K1MDA+bGNO3HCeR0GCKB3mPAeefUhsY0vcfJiHwYChxiaaoBbL3obkGQnneIy3HeY98IDhWFpuAyEtBucZkhkSyiRktx3mSzBgbDhMrBY2A8bNzTwGEsRpOdsA1NKWoLiBmWgtZw4AtZw5YCxxGBjICUT55UxOMuOPigNy/P2HDz/4UGNDWAswYhIQ7ARcilAB+wHi1I2CUTAKRsHIBQAj4UF0FRqqHQAAAABJRU5ErkJggg==","orcid":"","institution":"TEDA International Cardiovascular Hospital, Tianjin University","correspondingAuthor":true,"prefix":"","firstName":"Jing","middleName":"","lastName":"Rui","suffix":""}],"badges":[],"createdAt":"2025-08-18 09:53:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7398337/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7398337/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93245488,"identity":"ff9653db-e993-4f34-b604-3be355287acc","added_by":"auto","created_at":"2025-10-10 15:09:10","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":278343,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.docx","url":"https://assets-eu.researchsquare.com/files/rs-7398337/v1/7acdcadbce5ea4ff091e295b.docx"},{"id":93245485,"identity":"9f835d31-3c0a-4fb2-923e-0340529ed568","added_by":"auto","created_at":"2025-10-10 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1","display":"","copyAsset":false,"role":"figure","size":42591,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow diagram of patient enrollment.\u003c/strong\u003e STEMI, ST-segment elevation myocardial infarction.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7398337/v1/b90ccc3578df28b8e2c9485e.jpg"},{"id":93245498,"identity":"f1159540-77c7-4006-b270-05a4117fbdbb","added_by":"auto","created_at":"2025-10-10 15:09:10","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":66133,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLASSO regression coefficient relationship\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7398337/v1/915f62df7d58743a2409036c.jpg"},{"id":93245480,"identity":"99933d31-ecae-4894-b386-55f97db87da4","added_by":"auto","created_at":"2025-10-10 15:09:09","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":64850,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLASSO regression coefficient relationship\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7398337/v1/7f92af6ef692cb1d036b0557.jpg"},{"id":93245471,"identity":"5cdd298c-8095-4def-83fd-9b7516d36510","added_by":"auto","created_at":"2025-10-10 15:09:08","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":67356,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEarly Prediction Model with Forest Plot for In-Hospital Death in STEMI Patients Undergoing primary Coronary Stenting. \u003c/strong\u003eSTEMI, ST-segment elevation myocardial infarction; OR, odds ratio; CI, confidence interval; BNP, B-natriuretic peptide; CK-MB, creatine kinase-MB isoenzyme; BUN, blood urea nitrogen; LAC, lactate; MHR, mean heart rate; bpm, beats per minute; TAB, atrial rhythm count.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7398337/v1/907d87d48e53f3dbc88187e1.jpg"},{"id":93245519,"identity":"b111abb4-5686-49f7-9469-c8cd7025a005","added_by":"auto","created_at":"2025-10-10 15:09:11","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":89381,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curve analysis of BNP, CK-MB, BUN, LAC, Holter MHR, Holter TAB alone and in combination for predictive modeling. \u003c/strong\u003eROC, receiver operating characteristic; BNP, B-natriuretic peptide; CK-MB, creatine kinase-MB isoenzyme; BUN, blood urea nitrogen; LAC, lactate; MHR, mean heart rate (bpm); bpm, beats per minute; TAB, atrial rhythm count.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7398337/v1/f5dedf586a38b8f4780611f3.jpg"},{"id":93245478,"identity":"d238caec-5c63-4ff5-949b-19db52056072","added_by":"auto","created_at":"2025-10-10 15:09:09","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":88858,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDecision curves of BNP, CK-MB, BUN, LAC, Holter MHR, Holter TAB alone and in combination to predict in-hospital all-cause death in STEMI patients undergoing primary coronary stenting. \u003c/strong\u003eSTEMI, ST-segment elevation myocardial infarction; BNP, B-natriuretic peptide; CK-MB, creatine kinase-MB isoenzyme; BUN, blood urea nitrogen; LAC, lactate; MHR, mean heart rate (bpm); bpm, beats per minute; TAB, atrial rhythm count.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7398337/v1/83d0132fc26e329e7b6c200b.jpg"},{"id":96044216,"identity":"e36d5988-15df-4e4c-bc94-9bc4ae84d474","added_by":"auto","created_at":"2025-11-17 05:08:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2063122,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7398337/v1/a967b344-cb50-4603-934d-b3493ee1a89e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Early Prediction Model for In-Hospital all-cause Death in Patients with Acute ST-Elevation Myocardial Infarction After Primary Coronary Artery Stenting","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCardiovascular diseases (CVDs) continue to exhibit rising prevalence and mortality rates, standing as a leading cause of death and disability worldwide. In China, CVDs account for the highest proportion of resident mortality. In 2021, the mortality rate of acute myocardial infarction (AMI) reached 63.25 per 100,000 in urban populations and 83.26 per 100,000 in rural populations [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Acute ST-elevation myocardial infarction (STEMI), one of the most dangerous manifestations of coronary artery disease, has become a major health concern. Despite significant advancements in treatment, particularly the widespread adoption of primary percutaneous coronary intervention (PPCI), the in-hospital mortality rate for STEMI patients remains as high as 8.6% [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMyocardial infarction causes impaired energy metabolism, inflammatory responses, oxidative stress, ischemia-reperfusion injury, myocardial hypertrophy, and fibrosis in cardiomyocytes, leading to abnormal myocardial remodeling and electrical instability [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Delayed hospital arrival, patient refusal of PPCI due to financial or physical constraints, or complications such as heart failure, cardiogenic shock, malignant arrhythmias, mechanical complications, reinfarction, bleeding, stroke, or infection may ultimately lead to in-hospital death. Early identification of high-risk features for in-hospital mortality is therefore critical to improving STEMI outcomes.\u003c/p\u003e\u003cp\u003eCurrently, there is a lack of early predictive models for in-hospital mortality among STEMI patients undergoing primary coronary stenting. This study aims to evaluate the predictive value of early clinical indicators for in-hospital all-cause mortality in this population.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eSubjects\u003c/h2\u003e\u003cp\u003eThis study retrospectively analyzed 3916 patients diagnosed with STEMI and treated with primary coronary stenting at TEDA International Cardiovascular Hospital between October 2014 and December 2022. The STEMI diagnosis criteria followed the 2013 American College of Cardiology Foundation (ACCF)/American Heart Association (AHA) STEMI Management Guidelines and the 2017 European Society of Cardiology (ESC) STEMI Diagnosis and Treatment Guidelines [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eUpon arrival at our chest pain center emergency department and confirmation of STEMI diagnosis, all patients received a preprocedural loading dose of 300 mg aspirin plus either 300 mg clopidogrel or 180 mg ticagrelor, followed by maintenance doses post-procedure. Drug-eluting stents (DES) were implanted in all cases, with the coronary stenting procedures performed according to the 2014 ESC/European Association for Cardio-Thoracic Surgery (EACTS) Guidelines on Myocardial Revascularization and the 2021 American College of Cardiology (ACC)/AHA/Society for Cardiovascular Angiography and Interventions (SCAI) Coronary Artery Revascularization Guidelines [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Postoperative care adhered to standardized protocols as outlined in the guidelines [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].A flowchart of patient enrollment is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e The study was approved by the ethics committee of the Tianjin TEDA International Cardiovascular Hospital (ethical approval number: [2023]-0310-1).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eGeneral information: gender, age, body mass index (BMI).\u003c/p\u003e\u003cp\u003eMedical history: Information regarding the onset time of the disease, history of hypertension, diabetes mellitus, smoking status, and alcohol consumption was collected.\u003c/p\u003e\u003cp\u003eInitial vital signs: Temperature, heart rate (HR), respiratory rate (RR), blood pressure (BP), and peripheral oxygen saturation were recorded.\u003c/p\u003e\u003cp\u003eKillip classification at admission.\u003c/p\u003e\u003cp\u003eIntraoperative data: Door-to-wire time (D-to-W time), infarct-related artery (IRA), IRA pre-PCI thrombolysis in myocardial infarction (TIMI) flow grade, IRA post-PCI TIMI flow grade and the number of stents implanted were collected.\u003c/p\u003e\u003cp\u003eLaboratory tests: cardiac biomarkers [ high-sensitivity cardiac troponin I (cTNI), myoglobin (MYO), creatine kinase-MB isoenzyme (CK-MB)], brain natriuretic peptide (BNP), lactate (LAC), full blood count, renal and liver function tests, electrolytes, lipid profiles, random blood glucose (RBG), C-reactive protein (CRP), thyroid function tests, bedside echocardiography, and 24-hour Holter monitoring. All these tests (except the Holter) were completed within 24 hours of hospital admission, with cardiac biomarkers recorded at their peak within this period. The Holter was performed within 48 hours. The estimated glomerular filtration rate (eGFR) was calculated using the modified simplified modification of diet in renal disease(MDRD) formula [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eClinical Outcomes\u003c/h3\u003e\n\u003cp\u003eThe primary clinical outcome was in-hospital all-cause mortality. Patients were categorized into either the in-hospital survival group or death group. Comparative analysis of clinical parameters between these two groups was performed to identify early independent predictors of in-hospital all-cause mortality among STEMI patients undergoing primary coronary stenting. Subsequently, an early prediction model was developed and its predictive performance was rigorously evaluated.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eData collection spanned from January 2024 to December 2022. Given that missing values occurred completely at random with a missing rate\u0026thinsp;\u0026lt;\u0026thinsp;40%, multiple imputation was employed to handle these missing data points. The imputation model incorporated clinically relevant variables including age, sex, history of hypertension, diabetes mellitus, smoking status, and clinical outcomes - all of which showed correlation with the missing variables.\u003c/p\u003e\u003cp\u003eThe distribution characteristics of variables were examined through Kolmogorov-Smirnov test(α\u0026thinsp;=\u0026thinsp;0.05, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;≧\u0026thinsp;0.05), with normally distributed parameters reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations and non-normal data expressed as medians and quartiles (P25, P75). Categorical measures were quantified as proportional values (%). Comparative analyses between groups employed appropriate statistical methods: independent t-tests for normally distributed continuous variables, Mann-Whitney U tests for nonparametric continuous data, and either χ\u003csup\u003e2\u003c/sup\u003e test or rank-sum tests for categorical comparisons.\u003c/p\u003e\u003cp\u003ePotential risk factors were initially screened using univariate logistic regression and least absolute shrinkage and selection operator (LASSO) regression. The screened variables were assessed for multicollinearity, and after confirming the absence of significant collinearity, they were incorporated into a multivariate logistic regression model using the forward-LR method to develop the predictive model, with the results presented in a forest plot. The model's performance was evaluated through receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA). All statistical analyses were performed using SPSS 27 (IBM Corporation, Armonk, NY, USA) and R4.3 (R Foundation for Statistical Computing, Vienna, Austria; utilizing glmnet, rmda, and forestplot packages). A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eBasic Data\u003c/h2\u003e\u003cp\u003eAmong the 3916 patients (median age 61 years [IQR 52\u0026ndash;69]; 3000 males [76.6%] and 916 females [23.4%]), 54 experienced in-hospital mortality, with causes including mechanical complications (n\u0026thinsp;=\u0026thinsp;21), cardiogenic shock (n\u0026thinsp;=\u0026thinsp;14), malignant arrhythmias (n\u0026thinsp;=\u0026thinsp;4), multiorgan failure (n\u0026thinsp;=\u0026thinsp;13), and septic shock (n\u0026thinsp;=\u0026thinsp;2). Compared with survivors, non-survivors demonstrated significantly higher values (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in age, female proportion, initial HR, emergency hemodynamic instability (%), intra-aortic balloon pump (IABP) and temporary pacemaker (TPM) use intraoperative(%), and multiple biomarkers (BNP, cTnI, MYO, CK-MB, creatine kinase [CK], lactate dehydrogenase[LDH], white blood cell [WBC], neutrophils percentage [N%], CRP, alanine minotransferase [ALT], aspartate aminotransferase [AST], RBG, creatinine [Cr], blood urea nitrogen [BUN], uric acid [UA], K\u003csup\u003e+\u003c/sup\u003e, thyroid-stimulating hormone [TSH], LAC, Holter parameters [mean heart rate [MHR], Max HR, total ventricular beats[TVB], total atrial beats[TAB]), while showing lower values (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in BMI, initial blood pressures (systolic blood pressure [SBP], diastolic blood pressure [DBP], mean arterial pressure [MAP], SpO₂, β-blocker use within 24h of admission(%), left ventricular ejection fraction (LVEF), red blood cells (RBC), hemoglobin (HB), hematocrit (HCT), eGFR, CL\u003csup\u003e-\u003c/sup\u003e, albumin (ALB), and triglyceride (TG). The mortality group had higher proportions of left main coronary artery (LM)/left anterior descending branch (LAD) as infarct-related artery (IRA), post-procedural TIMI flow\u0026thinsp;\u0026le;\u0026thinsp;2, and Killip class\u0026thinsp;\u0026ge;\u0026thinsp;2 (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). No statistically significant differences were observed in: smoking/alcohol history, medical history (hypertension, diabetes mellitus, stroke, atrial fibrillation, gastrointestinal diseases, renal diseases, gallbladder diseases, pulmonary diseases, thyroid disorders, gout, peripheral vascular disease, previous myocardial infarction, prior PCI, coronary artery bypass grafting [CABG], or coronary artery disease), onset time, RR, D-to-W time, IRA pre-PCI TIMI flow grade, number of implanted stents, intraprocedural use of glycoprotein (GP) IIb/IIIa inhibitors or vasoactive agents(%), Angiotensin Converting Enzyme Inhibitors (ACEI)/Angiotensin Ⅱ Receptor Blocker (ARB)/Angiotensin Receptor-Neprilysin Inhibitor (ARNI) use within 24h of admission(%), platelet count (PLT), alkaline phosphatase (ALP), K\u003csup\u003e+\u003c/sup\u003e, unconjugated bilirubin (UCB), total protein (TP), globulin (GLO), total cholesterol (TCHOL), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), or the longest RR interval on Holter monitoring. For detailed data, refer to Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of baseline characteristics of patients\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;3916)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSurvival group (n\u0026thinsp;=\u0026thinsp;3862)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDeath group (n\u0026thinsp;=\u0026thinsp;54)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61 (52, 69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e61 (52,69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e75 (61, 80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eWomen (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e916 (23.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e896 (23.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20 (37.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.2 (23.2, 27.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25.2 (23.2, 27.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23.5 (21.0, 25.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eCurrent smoking (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2178 (55.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2154 (55.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24 (44.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.096\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eDrinking history (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1139 (29.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1123 (29.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16 (29.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.929\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMedical history\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHypertension (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2217(56.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2185(56.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e32(59.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.693\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDiabetes (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e906(23.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e889(23.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17(31.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.143\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStroke (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e417(10.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e412(10.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5(9.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.739\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAtrial Fibrillation (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56(1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e54(1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2(3.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.156\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGastrointestinal Diseases (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e258(6.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e253(6.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5(9.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.426\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKidney Disease (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66(1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64(1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2(3.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.246\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGallbladder Disease(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66(1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64(1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2(3.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.246\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLung Disease (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63(1.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e62(1.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1(1.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.886\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThyroid Disease (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46(1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46(1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0(0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.420\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGout (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34(0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33(0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1(1.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.433\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePeripheral Vascular Disease (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43(1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42(1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1(1.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.592\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrior Myocardial Infarction (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e222(5.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e219(5.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3(5.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.971\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePCI (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e321(8.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e317(8.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4(7.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.831\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCABG (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11(0.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11(0.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0(0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.695\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoronary Heart Disease (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e376(9.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e370(9.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6(11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.705\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eOnset time (h)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.0(2.0,5.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.0(2.0,5.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.0(2.0,6.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.890\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eInitial vital signs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR (bpm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73(62,82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e73(62,82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e80(68,97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRR (times/ minute)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18(18,19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18(18,19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18(18,22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSBP (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e138(120,154)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e139(121,154)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e120(97,141)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDBP (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e83(70,93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e83(70,93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e70(60,90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMAP (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e102(90,113)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e102(90,113)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e88(73,107)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpO\u003csub\u003e2\u003c/sub\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e98(97,99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e98(97,99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e98(94,99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEmergency hemodynamic instability (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e282(7.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e264(6.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18(33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eD-to-W time (min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54(44,66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e54(44,66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e54(47,65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.706\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eIRA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLM (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31(0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25(0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6(11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLAD (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1806(46.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1778(46.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e28(51.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLCX (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e432(11.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e428(11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4(7.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRCA (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1644(42.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1628(42.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16(29.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIntermediate branch (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3(0.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3(0.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0(0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eIRA pre-PCI TIMI flow grade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClass 0 (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e774(19.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e769(19.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5(9.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.071\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClass 1 (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e257(6.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e254(6.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3(5.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClass 2 (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e341(8.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e339(8.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2(3.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClass 3 (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2544(65.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2500(64.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e44(81.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eIRA Pos-PCI TIMI flow grade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClass\u0026thinsp;≦\u0026thinsp;2 (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74(1.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e71(1.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3(5.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClass 3 (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3842(98.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3791(98.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e51(94.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eNumber of stents implanted\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3130(79.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3090(80.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e40(74.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.237\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e719(18.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e705(18.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14(25.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;3 (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e67(1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e67(1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0(0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eIntraoperative use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIABP (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e79(2.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e69(1.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10(18.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTPM (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42(1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37(1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5(9.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGP IIb/IIIa inhibitors (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e528(13.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e520(13.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8(14.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.773\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVasoactive agents (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e290(7.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e284(7.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6(11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.295\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eACEI/ARB/ARNI use within 24h of admission (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e631(16.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e623(16.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8(14.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.794\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eβ-B use within 24h of admission (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1166(29.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1158(30.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8(14.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eLVEF (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55(50,60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55(50,60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e46(35,53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eBNP (pg/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66(23,168)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e65(23,166)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e174(55,642)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ecTnI (pg/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.8 (13.5,56.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.6(13.3,55.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e71.9(27.9,81.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMYO (ng/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95(46,216)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e93(46,206)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e747(345,1311)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eCK-MB (ng/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e114.1(52.1,201.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e112.8(51.4,199.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e216.9(165.2,334.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eCK (U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1113.1(300.0,1807.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1104.0(293.3,1784.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2081.3(1333.7,3539.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eLDH (U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e480(343,652)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e477(341,645)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e825(558,1202)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eWBC (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.6(7.9,11.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.6(7.9,11.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.5(10.1,16.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eN%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73.6(67.9,79.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e73.4(67.8,78.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e83(77.1,86.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eRBC (10\u003csup\u003e12\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.4(4.1,4.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.4(4.1,4.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.2(3.8,4.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHB (g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e137(126,147)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e137(126,147)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e131(119,142)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHCT (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40.2(37.3,42.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40.2(37.3,42.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e38.2(35.3,40.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePLT (109/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e216(184,254)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e216(184,254)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e211(184,252)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.604\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eCRP (mg/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.4(8.8,31.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.4(8.7,31.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26.1(14.8,42.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eALP (U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70(58,84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e69(58,84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e74(59,84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.627\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eALT (U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42(27,66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41(27,65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e101(64,149)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAST (U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e154(77,261)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e151(77,256)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e384(280,557)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eRBG (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.0(6.7,10.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.0(6.7,10.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.5(7.5,14.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eCr (umol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68(59,80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e68(59,79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e96(80,127)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eBUN (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.8(4.7,7.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.8(4.7,7.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.3(7.7,12.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eUA (umol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e331(275,395)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e330(274,394)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e422(336,507)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eeGFR [mL/(min\u0026middot;1.73m\u003csup\u003e2\u003c/sup\u003e)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100.2(78.1,123.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100.6(78.9,124.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e52.9(38.3,85.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eK\u003csup\u003e+\u003c/sup\u003e (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.9(3.7,4.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.9(3.7,4.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.3(4.0,4.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eNA\u003csup\u003e+\u003c/sup\u003e (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e140(138,141)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e140(138,141)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e141(137,142)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.252\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eCL\u003csup\u003e\u0026minus;\u003c/sup\u003e (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e104(101,107)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e104(101,107)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e102(99,106)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eUCB (umol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.7(6.3,11.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.7(6.3,11.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.3(6.2,11.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.542\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eTP (g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65(62,69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e65(62,69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e65(61,67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.312\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eALB (g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40(38,42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40(38,42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e38(34,40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eGLO (g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26(23,28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26(23,28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26(24,28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.294\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eTCHOL (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.5(3.9,5.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.5(3.9,5.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.4 (3.4,5.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.517\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eTG (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.4(0.9,2.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.4(0.9,2.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.0(0.6,1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.9(2.4,3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.9(2.4,3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.7(2.0,3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.188\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.0(0.9,1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.0(0.9,1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.1(0.9,1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.071\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eTSH (mIU/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.5(0.8,2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.5(0.8,2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.2(1.2,4.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eLAC (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.7 (1.2,2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.7(1.2,2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.8(1.8,5.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHolter monitor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMHR (bpm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70(64,78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e70(64,78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e85(75,98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMax HR (bpm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e103(95,113)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e103(95,112)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e119(105,142)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTVB (times)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35(4,387)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38(8,259)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e889(41,2571)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTAB (times)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39(9,271)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34(4,371)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e932(15,2054)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMax RR (s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.4(1.2,1.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.4(1.2,1.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.3(1.0,1.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.094\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eKillip classification at admission\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKillip Ⅰ (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3605(92.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3577(92.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e28(51.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKillip Ⅱ (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e220(5.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e215(5.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5(9.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKillip Ⅲ (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27(0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21(0.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6(22.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKillip Ⅳ (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64(1.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49(1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15(27.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eNote: BMI, body mass index; PCI, percutaneous coronary intervention; CABG, coronary artery bypass grafting; HR, heart rate; bpm, beats per minute; RR, respiratory rate; SBP, systolic blood pressure; DBP, diastolic blood pressure; MAP, mean arterial pressure; SpO\u003csub\u003e2\u003c/sub\u003e, blood oxygen saturation; D-to-W, door-to-wire; IRA, infarct-related artery; LM, left main artery; LAD, left anterior descending artery; LCX, left circumflex artery; RCA, right coronary artery; TIMI, thrombolysis in myocardial infarction; IABP, intra-aortic balloon pump; TPM, temporary pacemaker; GP, glycoprotein; ACEI, angiotensin-converting enzyme inhibitors; ARB, angiotensin II receptor blockers; ARNI, angiotensin receptor-erucinase inhibitors; β-B β-receptor blockers; LVEF, left ventricular ejection fraction; BNP, Beta-natriuretic peptide; cTnI, high-sensitivity cardiac troponin I; MYO, myoglobin; CK-MB, creatine kinase-MB isoenzyme; CK, creatine kinase; LDH, lactate dehydrogenase; WBC, white blood cells; N%, neutrophil percentage; RBC, red blood cells; HB, hemoglobin; HCT, hematocrit; PLT, platelet count; CRP, c-reactive protein; ALP, alkaline phosphatase; ALT, alanine aminotransferase; AST, aspartate aminotransferase; RBG, random blood glucose; Cr, creatinine; BUN, blood urea nitrogen; UA, uric acid; eGFR, estimated glomerular filtration rate; UCB, unconjugated bilirubin; TP, total protein; ALB, albumin; GLO, globulin; TCHOL, total cholesterol; TG, triglyceride; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TSH: thyroid-stimulating hormone; LAC, lactate; MHR, mean heart rate; TVB, total ventricular beats; TAB: total atrial beats.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eUnivariate Analysis\u003c/h3\u003e\n\u003cp\u003eUnivariate logistic regression analysis identified the following factors as significantly associated with in-hospital all-cause mortality (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05): age, female gender, BMI, initial HR, initial SBP, initial DBP, initial MAP, SpO₂, emergency hemodynamic instability, IRA (LM\u0026amp;LAD), IABP use intraoperative, TPM use intraoperative, β-B use within 24h of admission, LVEF, BNP, cTnI, MYO, CK-MB, CK, LDH, WBC, N%, RBC, HB, CRP, ALT, AST, RBG, Cr, BUN, UA, eGFR, K\u003csup\u003e+\u003c/sup\u003e, CL\u003csup\u003e\u0026minus;\u003c/sup\u003e, ALB, TG, LAC, Holter MHR, Holter Max HR, Holter TVB, Holter TAB, Killip class (at admission)≧Ⅱ. The results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOne-way logistic regression analysis.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eWald\u003c/em\u003e χ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003e95%\u003c/em\u003eCI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\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 align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAge (per 5 years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36.608\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.409\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.505\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.318\u0026ndash;1.718\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eWomen (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.492\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.666\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.284\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.947\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.115-3.400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eBMI (per IQR kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.589\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.555\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.403\u0026ndash;0.764\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eInitial vital signs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR (per 10 bpm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.690\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.230\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.114\u0026ndash;1.421\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSBP ( per10 mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35.214\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.255\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.775\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.713\u0026ndash;0.843\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDBP ( per10 mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.166\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.717\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.620\u0026ndash;0.829\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMAP (per10 mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.332\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.060\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.718\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.638\u0026ndash;0.808\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpO\u003csub\u003e2\u003c/sub\u003e (per 5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.795\u0026ndash;0.952\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEmergency hemodynamic instability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42.137\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.919\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.296\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.814\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.818\u0026ndash;12.164\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eIRA (LM\u0026amp;LAD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.470\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.663\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.284\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.941\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.113\u0026ndash;3.385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eIRA Pos-PCI TIMI flow grade\u0026thinsp;\u0026lt;\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.566\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.606\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.958\u0026ndash;10.302\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eIABP use intraoperative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46.381\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.525\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.371\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.493\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.040\u0026ndash;25.840\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eTPM use intraoperative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.410\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.356\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.498\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.549\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.977\u0026ndash;27.979\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eβ-B use within 24h of admission\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.488\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.901\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.406\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.191\u0026ndash;0.863\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eLVEF (per 5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68.806\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.577\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.070\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.561\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.490\u0026ndash;0.643\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eBNP (per 200 pg/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61.876\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.356\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.428\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.307\u0026ndash;1.560\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ecTnI (per 10 pg/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.733\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.259\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.158\u0026ndash;1.370\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMYO (per 500 ng/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e83.246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.724\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.765\u0026ndash;2.409\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eCK-MB (per 100 ng/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.811\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.249\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.860\u0026ndash;2.719\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eCK (per 1000 U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.694\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.470\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.293\u0026ndash;1.671\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eLDH (per 200 U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82.642\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.480\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.617\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.458\u0026ndash;1.793\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eWBC (per 5\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43.904\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.164\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.956\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.145\u0026ndash;4.074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eN% (per 5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62.291\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.798\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.222\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.822\u0026ndash;2.709\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eRBC (per 0.5\u0026times;10\u003csup\u003e12\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.474\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.336\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.715\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.562\u0026ndash;0.909\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHB (per 10 g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.810\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.816\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.701\u0026ndash;0.951\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHCT(per 5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.914\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.243\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.784\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.593\u0026ndash;1.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.088\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eCRP(per 10 mg/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.328\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.162\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.176\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.106\u0026ndash;1.252\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eALT(per 100 U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.018\u0026ndash;1.178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAST(per 200 U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.913\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.396\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.206\u0026ndash;1.616\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eRBG(per 5 mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.565\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.605\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.831\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.461\u0026ndash;2.295\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eCr(per 10 umol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48.417\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.191\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.134\u0026ndash;1.251\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eBUN(per 1 mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e128.934\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.406\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.501\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.399\u0026ndash;1.609\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eUA(per 100 umol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50.687\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.786\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.196\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.768\u0026ndash;2.726\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eeGFR[per 10 mL/(min\u0026middot;1.73m\u003csup\u003e2\u003c/sup\u003e)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64.874\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.424\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.654\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.590\u0026ndash;0.726\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eK\u003csup\u003e+\u003c/sup\u003e (per 1 mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.597\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.408\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.263\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.439\u0026ndash;6.845\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eCL\u003csup\u003e\u0026minus;\u003c/sup\u003e (per 3 mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.226\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.304\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.738\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.599\u0026ndash;0.908\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eALB( per 5 g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.423\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.829\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.161\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.436\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.318\u0026ndash;0.599\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eTG(per 1 mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.735\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.495\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.610\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.430\u0026ndash;0.864\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eTSH(per 1 mIU/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.897\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.995\u0026ndash;1.066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.089\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eLAC(per 1 mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e86.681\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.453\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.573\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.430\u0026ndash;1.730\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHolter monitor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMHR(per 10 bpm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e85.551\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.763\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.146\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.825\u0026ndash;2.523\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMax HR (per 10 bpm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64.294\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.423\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.526\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.376\u0026ndash;1.692\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTVB (per 1000 times)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.021\u0026ndash;1.145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTAB (per1000 times)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.477\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.056\u0026ndash;1.162\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eKillip Class(at admission)≧ Ⅱ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e77.348\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.456\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.279\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.654\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.742\u0026ndash;20.145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eNote: SE, Standard Error; OR, Odds Ratio; CI, confidence interval; BMI, body mass index; IQR, interquartile range; HR, heart rate; bpm, beats per minute; SBP, systolic blood pressure; DBP, diastolic blood pressure; MAP, mean arterial pressure; SpO2, blood oxygen saturation; IRA, infarct-related artery; LM, left main coronary artery; LAD, left anterior descending artery; PCI, percutaneous coronary intervention; TIMI, thrombolysis in myocardial infarction; IABP, intra-aortic balloon pump; TPM, temporary pacing; β-B, β-receptor blockers; LVEF, left ventricular ejection fraction; BNP, Beta-natriuretic peptide; cTnI, high-sensitivity cardiac troponin I; MYO, myoglobin; CK-MB, creatine kinase-MB isoenzyme; CK, creatine kinase; LDH, lactate dehydrogenase; WBC, white blood cells; N%, neutrophil percentage; RBC, red blood cells; HB, hemoglobin; HCT, hematocrit; CRP, c-reactive protein; ALT, alanine aminotransferase; AST, aspartate aminotransferase; RBG, random blood glucose; Cr, Creatinine; BUN, blood urea nitrogen; UA, uric acid; eGFR, estimated glomerular filtration rate; ALB, albumin; TG, triglycerides; TSH, thyroid-stimulating hormone; LAC, lactate; MHR, mean heart rate; TVB, total ventricular beats; TAB, total atrial rhythm.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eLASSO Regression\u003c/h3\u003e\n\u003cp\u003eThe 42 statistically significant variables were incorporated into LASSO regression analysis, with additional adjustment for history of hypertension, diabetes mellitus, smoking, and alcohol consumption. At the minimum mean squared error (λ\u0026thinsp;=\u0026thinsp;0.002), corresponding to the optimal penalty value indicated by the right vertical dotted line, the analysis identified 9 non-zero coefficient variables: BNP (per200 pg/ml)、MYO (per500 ng/ml)、CK-MB (per100 ng/ml)、LDH (per200 U/L)、BUN (per 1 mmol/L)、LAC (per 1 mmol/L)、Holter MHR (per10 bpm)、Holter TAB (per1000 times) and Killip class (at admission) \u0026ge;Ⅱ. This model configuration demonstrated optimal fitting performance in the LASSO regression, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e,\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eMulticollinearity Analysis\u003c/h2\u003e\u003cp\u003eCollinearity analysis was performed for BNP (per200 pg/ml)、MYO (per500 ng/ml)、CK-MB (per100 ng/ml)、LDH (per200 U/L)、BUN (per 1 mmol/L)、LAC (per 1 mmol/L)、Holter MHR (per10 bpm)、Holter TAB (per1000 times) and Killip class (at admission) \u0026ge;Ⅱ, demonstrating tolerance (Tol) values all \u0026gt;\u0026thinsp;0.1 and variance inflation factors (VIF) all \u0026lt;\u0026thinsp;2, indicating no significant multicollinearity among these variables (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMulticollinearity Analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTol\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVIF\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBNP (per 200 pg/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.862\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.160\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMYO (per 500 ng/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.818\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.222\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCK-MB (per 100 ng/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.639\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.565\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDH (per 200 U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.632\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.583\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBUN (per 1 mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.828\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.208\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLAC (per 1 mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.872\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.147\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMHR (per 10 bpm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.901\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.110\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTAB (per 1000 exposures)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.976\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.024\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKillip class \u0026ge;Ⅱ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.886\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.129\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eNote: Tol, tolerance; VIF, variance inflation factor; BNP, B-natriuretic peptide; MYO, myoglobin; CK-MB, creatine kinase-MB isoenzyme; LDH, lactate dehydrogenase; BUN, blood urea nitrogen; LAC, lactate; MHR, mean heart rate; bpm, beats per minute; TAB, atrial rhythm count\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eMultivariate Logistics Analysis\u003c/h2\u003e\u003cp\u003eThe nine variables were simultaneously incorporated into a multivariate logistic regression model, which BNP (per 200 pg/mL), CK-MB (per 100 ng/mL), BUN (per 1 mmol/L), LAC (per 1 mmol/L), Holter MHR (per 10 bpm), and Holter TAB (per 1000 beats) as early independent risk factors for in-hospital all-cause death in STEMI patients undergoing primary coronary stenting, with the results visualized in a forest plot. The Hosmer-Lemeshow test demonstrated good model fit (χ\u0026sup2;=5.610, df\u0026thinsp;=\u0026thinsp;8, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.691), as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariate regression analysis of Logistics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eWald\u003c/em\u003e χ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003e95%\u003c/em\u003eCI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\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 align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBNP (per 200 pg/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.388\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.033\u0026ndash;1.293\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCK-MB (per 100 ng/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18.911\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.513\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.671\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.326\u0026ndash;2.105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBUN (per 1 mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42.942\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.299\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.348\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.233\u0026ndash;1.474\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLAC (per 2 mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.230\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.120\u0026ndash;1.413\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHolter MHR (per 10 bpm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21.644\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.471\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.602\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.313\u0026ndash;1.954\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHolter TAB (per1000 times)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.347\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.022\u0026ndash;1.148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eNote: SE, standard error; OR, odds ratio; CI, confidence interval; BNP, B-natriuretic peptide; CK-MB, creatine kinase-MB isoenzyme; BUN, blood urea nitrogen; LAC, lactate; MHR, mean heart rate; bpm, beats per minute; TAB, atrial rhythm count.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003ePredictive Efficacy Tested by ROC Curve\u003c/h2\u003e\u003cp\u003eThe combined predictive model incorporating BNP (per 200 pg/mL), CK-MB (per 100 ng/mL), BUN (per 1 mmol/L), LAC (per 1 mmol/L), Holter MHR (per 10 bpm), and Holter TAB (per 1000 times) demonstrated excellent discriminative ability for in-hospital all-cause death in STEMI patients undergoing primary coronary stenting, with an area under the curve (AUC) of 0.911 (95% CI: 0.863\u0026ndash;0.959, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), achieving 85% sensitivity and 87% specificity - all performance metrics surpassing those of any individual parameter (see Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAUC by ROC analysis.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAUC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSensitivity (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSpecificity (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\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 align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6 factors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.911\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.862\u0026ndash;0.959\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.852\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.868\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBNP (per 200 pg/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.664\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.577\u0026ndash;0.751\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.481\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.797\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCK-MB (per 100 ng/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.733\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.660\u0026ndash;0.805\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.566\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.752\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBUN (per 1 mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.855\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.797\u0026ndash;0.912\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.870\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.737\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLAC (per 2 mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.744\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.668\u0026ndash;0.821\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.463\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.892\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHolter MHR (per 10 bpm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.771\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.712\u0026ndash;0.842\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.630\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.770\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHolter TAB (per1000 times)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.656\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.571\u0026ndash;0.741\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.440\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e846\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eNote: AUC, area under the curve; ROC, receiver operating characteristic; SE, standard error; CI, confidence interval; BNP, B-natriuretic peptide; CK-MB, creatine kinase-MB isoenzyme; BUN, blood urea nitrogen; LAC, lactate; MHR, mean heart rate (bpm); bpm, beats per minute; TAB, atrial rhythm count.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003ePredictive Efficacy Tested by by DCA\u003c/h2\u003e\u003cp\u003eDCA demonstrated that the purple solid line representing the net benefit of the six-marker combined prediction model consistently remained above both the grey solid line (indicating benefit when all subjects received intervention) and the black horizontal line (representing benefit when no subjects received intervention) across the threshold probability range where it intersected with the two reference lines (purple dashed lines). The model showed superior net benefit compared to any individual biomarker when the threshold probability ranged from approximately 0.010 to 0.710. This wide probability range indicates substantial clinical utility for the prediction model, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study analyzed comprehensive clinical data from STEMI patients undergoing primary coronary stenting, identifying elevated levels of BNP, CK-MB, BUN, LAC, Holter MHR, and Holter TAB as independent predictors of in-hospital all-cause death, with the combined predictive performance of these six markers surpassing that of any individual parameter.\u003c/p\u003e\u003cp\u003eOur investigation incorporated 80 clinically relevant parameters for mortality prediction, including demographic characteristics, personal and medical history, symptom duration, initial vital signs, D-to-W time, IRA characteristics, pre-pci and post-pci TIMI flow grades, number of implanted stents, intraprocedural device assistance and medication use, 24-hour post-admission medication administration, LVEF, cardiac biomarkers, complete blood count, renal and hepatic function tests, RBG, electrolytes, lipid profiles, LAC, Holter monitoring results, and Killip classification at admission - with all laboratory values (except 24-hour Holter data) obtained within the first 24 hours of hospitalization. Compared to existing prediction models, this comprehensive early-assessment framework demonstrates superior inclusiveness by systematically incorporating objective clinical parameters available during the critical initial hospitalization period, while maintaining innovative predictive capability through multidimensional data integration.\u003c/p\u003e\u003cp\u003eThis study enrolled 3916 STEMI patients, including 54 in-hospital deaths (incorporating terminal status patients and those discharged automatically), yielding a mortality rate of 1.38% that was lower than most reported mortality rates for STEMI patients undergoing PPCI. PCI procedures comprised two approaches: percutaneous transluminal coronary angioplasty (PTCA) and coronary stenting, with international consensus favoring stenting due to its superior outcomes compared to PTCA alone, which is associated with higher risks of early ischemia/reinfarction, acute target vessel reocclusion, and all-cause mortality [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, current guidelines recommend PTCA over stenting in specific clinical scenarios including hemodynamic instability/cardiogenic shock requiring reduced procedure time, high bleeding risk, anatomical constraints (small vessel diameter, diffuse disease, or severe calcification), early post-thrombolysis reperfusion, heavy thrombus burden, in-stent restenosis, or multi-organ failure with limited life expectancy [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The observed lower mortality in our cohort may be attributed to the exclusive inclusion of stented patients rather than those receiving PTCA alone. Furthermore, as a regional cardiovascular specialty center, our patient population demonstrates clearer disease specificity with fewer complex cases compared to general hospitals, coupled with our interventional cardiologists and coronary care unit (CCU) team's extensive experience, collectively contributing to reduced mortality rates.\u003c/p\u003e\u003cp\u003eBNP, synthesized and secreted by ventricular cardiomyocytes, exerts diuretic, vasodilatory, and anti-fibrotic effects, with its release triggered by cardiac pressure or stretch (e.g., increased ventricular wall tension). Elevated BNP levels demonstrate strong correlations with heart failure and adverse cardiovascular outcomes [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. BUN, a byproduct of protein metabolism, undergoes conversion to urea in the liver via the ornithine cycle before renal glomerular filtration and urinary excretion, with its concentration reflecting the balance between urea production and renal clearance. Previous studies have established that elevated BUN levels significantly correlate with increased risks of major adverse cardiovascular events (MACE) in STEMI patients post-PCI, contributing to prolonged hospitalization and higher mortality [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. LAC, an intermediate glycolytic metabolite, accumulates when severe tissue hypoxia limits oxidative phosphorylation and reduces mitochondrial adenosine triphosphate (ATP) production, leading to pyruvate reduction to lactate by cytoplasmic LDH - thus serving as a byproduct of hypoperfusion or oxygen deprivation. As a key diagnostic marker for cardiogenic shock, elevated LAC positively associates with poor cardiovascular prognosis [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. These established mechanisms align with our findings that elevated BNP, BUN, and LAC levels serve as early predictors of in-hospital all-cause mortality in STEMI patients.\u003c/p\u003e\u003cp\u003eCK-MB, the myocardial-specific isoenzyme of creatine kinase composed of M and B subunits, is predominantly located in cardiomyocytes (accounting for 15%-25% of total cardiac CK) with minimal skeletal muscle presence (\u0026lt;\u0026thinsp;5%). Although traditionally used as a myocardial injury biomarker, CK-MB has been largely superseded by troponin in AMI diagnosis due to inferior specificity and sensitivity. However, beyond diagnostic utility, CK-MB remains valuable for predicting left ventricular remodeling and mortality in AMI patients, with peak levels strongly correlating with infarct size, wall motion abnormalities, left ventricular end-systolic volume index, and fatal outcomes [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Emerging evidence suggests CK-MB may outperform troponin for prognostic stratification, as demonstrated by a 2023 study showing CK-MB's superior quantitative value for outcome prediction in STEMI patients [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Our univariate logistic analysis identified both cTnI and CK-MB as significant mortality predictors, but multivariate modeling retained only CK-MB (per 100 ng/mL increase elevating in-hospital death risk by 1.5-fold) as having independent prognostic value.\u003c/p\u003e\u003cp\u003eHeart rate, as a fundamental physiological parameter, demonstrates well-established associations between tachycardia and increased mortality in both healthy individuals and cardiovascular patients (coronary artery disease, hypertension, heart failure). In AMI, elevated HR exacerbate infarct expansion, myocardial ischemia, sympathetic overactivation, cardiac dysfunction, oxygen demand, and atherosclerotic progression - underpinning guideline recommendations for early β-blocker therapy targeting\u0026thinsp;~\u0026thinsp;60 bpm resting rates [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. While prior studies predominantly examined random heart rate measurements (admission, in-hospital, discharge), our focus on 24-hour MHR via Holter monitoring captures integrated neurohormonal regulation, with Shen J et al.'s congruent findings confirming Holter-derived MHR's superior predictive value over spot measurements [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePost-revascularization atrial arrhythmias (premature contractions, tachycardia, atrial fibrillation [AF], flutter) frequently complicate STEMI, with \u0026gt;\u0026thinsp;20% of AMI patients having AF history and 5% developing new-onset AF - the latter associated with markedly worse outcomes after PPCI [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Our study quantified this risk precisely, demonstrating that each 1000-beat increment in 24-hour atrial ectopy increased in-hospital mortality by 8.4% (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006), establishing Holter-detected atrial arrhythmia burden as a statistically significant mortality predictor.\u003c/p\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eLimitations of the study\u003c/h2\u003e\u003cp\u003eThis single-center study has certain limitations that should be acknowledged, including a relatively small number of mortality cases (n\u0026thinsp;=\u0026thinsp;54), which necessitates further validation through multicenter investigations. Additionally, the study population was restricted to STEMI patients undergoing emergency coronary stenting, excluding those receiving percutaneous transluminal coronary angioplasty (PTCA) alone - a selection bias that our research team intends to address in future studies by expanding patient enrollment criteria. Furthermore, the absence of post-discharge follow-up data represents another limitation that will be remedied in subsequent research through systematic longitudinal outcome tracking.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur findings robustly demonstrate that elevated levels of BNP, CK-MB, BUN, LAC, Holter MHR, and Holter TAB serve as independent predictors of in-hospital all-cause death in STEMI patients. The combined use of these six biomarkers significantly enhances early mortality prediction accuracy, enabling timely risk stratification and proactive clinical interventions to reduce fatality rates. This multivariable approach holds substantial potential for clinical implementation by improving early identification of high-risk patients who may benefit from intensified therapeutic strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eEthics Approval and Consent to Participate\u003c/h2\u003e\u003cp\u003e The study was carried out in accordance with the guidelines of the Declaration of Helsinki and approved by the ethics committee of the TEDA International Cardiovascular Disease Hospital (ethical approval number: [2023]-0310-1). Due to the retrospective design of the study, the requirement for written informed consent from patients was waived.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003cp\u003eNot applicable. This study did not include any human participants.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eConflict of Interest\u003c/h2\u003e\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003e1. National Key Clinical Specialty Construction Project - Comprehensive Treatment of Cardiovascular Diseases;\u003c/p\u003e\u003cp\u003e2. Tianjin Key Medical Discipline Construction Project (Grant No.TJYXZDXK-3-035C);\u003c/p\u003e\u003cp\u003e3. Demonstration Project for the Reform and High - quality Development of Public Hospitals in Tianjin Binhai New Area;\u003c/p\u003e\u003cp\u003e4. Science and Technology Project of Tianjin Binhai New Area Health Commission (2022BWKQ006).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYing Zhou, Fei Dong, Yu Song and Rui Jing designed the research study. Ying Zhou, Fei Dong and Yufei Zhao performed data collection. Ying Zhou, Yunqiang Zhang and Mu Guo analyzed the data. Ying Zhou, Fei Dong, and Rui Jing drafted the manuscript. All authors contributed to critical revision of the manuscript for important intellectual content. All authors read and approved the final manuscript. All authors have participated sufficiently in the work and agreed to be accountable for all aspects of the work.\u003c/p\u003e\u003ch2\u003eAvailability of Data and Materials\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col class=\"decimal_type\"\u003e\n\u003cli\u003eNational Center for Cardiovascular Diseases, Report on Cardiovascular Health and Diseases in China Writing Committee, Shengshou Hu. Summary of the Report on Cardiovascular Health and Diseases in China 2023 [J]. 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DOI: 10.1093/europace/euz163.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"ST-segment elevation myocardial infarction, primary coronary artery stenting, In-Hospital all-cause death, Early prediction model","lastPublishedDoi":"10.21203/rs.3.rs-7398337/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7398337/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eTo establish an early prediction model for in-hospital all-cause death in patients with acute ST-segment elevation myocardial infarction (STEMI) after primary coronary artery stenting.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis retrospective study analyzed 3,916 STEMI patients undergoing primary coronary stenting within 24 hours of symptom onset at TEDA International Cardiovascular Hospital (2014\u0026ndash;2022). We collected demographic, clinical, and procedural data, along with 48-hour laboratory results (including echocardiography and Holter monitoring). The primary outcome was in-hospital all-cause mortality. Eighty clinical parameters were compared between survivors and non-survivors to identify risk factors and develop an early prediction model.\u003c/p\u003e\u003ch2\u003eResult\u003c/h2\u003e\u003cp\u003eIn a cohort of 3916 patients, 54 experienced in-hospital all-cause death. Comparison of 80 clinical variables between groups, followed by univariate logistic regression and least absolute shrinkage and selection operator (LASSO) regression, identified nine risk factors. Multicollinearity analysis confirmed no significant interactions. Multivariate logistic regression revealed six independent predictors: B-type natriuretic peptide (BNP) (per 200 pg/mL), creatine kinase-MB (CK-MB) (per 100 ng/mL), blood urea nitrogen (BUN) (per 1 mmol/L), lactic acid (LAC) (per 1 mmol/L), Holter mean heart rate (MHR) (per 10 bpm), and Holter total atrial beats (TAB) (per 1000 beats). Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) demonstrated superior net benefit of the combined model over individual predictors.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe combination of BNP, CK-MB, BUN, LAC, Holter MHR, and Holter TAB can effectively predicts in-hospital all-cause death in STEMI patients undergoing primary coronary artery stenting, offering potential clinical utility.\u003c/p\u003e","manuscriptTitle":"Early Prediction Model for In-Hospital all-cause Death in Patients with Acute ST-Elevation Myocardial Infarction After Primary Coronary Artery Stenting","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-10 15:08:54","doi":"10.21203/rs.3.rs-7398337/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"785c57e6-b512-473e-9869-744437600b20","owner":[],"postedDate":"October 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-17T05:08:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-10 15:08:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7398337","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7398337","identity":"rs-7398337","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}