Early Detection of Cardiac Rupture Risk in Acute Myocardial Infarction: A Comprehensive Predictive Model

Early Detection of Cardiac Rupture Risk in Acute Myocardial Infarction: A Comprehensive Predictive Model | 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 Detection of Cardiac Rupture Risk in Acute Myocardial Infarction: A Comprehensive Predictive Model Yaxin Wang, Ruifeng Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7260965/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background Cardiac rupture is a critical and often fatal complication following acute myocardial infarction (AMI). Early identification of patients at high risk of this event is crucial for timely intervention and improved outcomes. Objectives This study aimed to identify clinical predictors of cardiac rupture in AMI patients and develop a predictive nomogram for clinical use. Methods We conducted a retrospective case-control study at Beijing Friendship Hospital, involving AMI patients treated from January 2018 to the December 2023. Patients were divided into two groups: those who experienced cardiac rupture and those who did not, matched at a 1:4 ratio, then this study included 30 with cardiac rupture and 120 controls. Using least absolute shrinkage and selection operator (LASSO) regression, univariate and multivariate logistic regression analyses, we identified key predictors of cardiac rupture. A nomogram was constructed based on these predictors and validated using receiver operating characteristic (ROC) curves and calibration plots. Results Significant predictors identified by LASSO-logistics regression were N-terminal pro-B type natriuretic peptide (NT-proBNP) on admission, decreased Osmolality, increased right ventricle size, elevated Gensini score, presence of anemia, and elevated glucose levels. The nomogram demonstrated good predictive accuracy with an area under the ROC curve of 0.942 (0.892–0.991) and the Hosmer-Lemeshow statistic, which measures the goodness of fit for the model, was calculated to be 3.315 with a p-value of 0.950. Conclusions The developed nomogram effectively identifies AMI patients at high risk of cardiac rupture, integrating multiple clinical parameters. This tool can aid clinicians in early risk stratification and decision-making, potentially reducing the incidence of this lethal complication. Acute myocardial infarction cardiac rupture predictive nomogram Gensini score LASSO regression logistic regression Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Acute myocardial infarction (AMI) remains a leading cause of morbidity and mortality worldwide, despite significant advances in cardiovascular medicine. In-hospital mortality from mechanical complications is still high and can't be neglected even with prompt surgery or interventional repair. And 10%-15% of deaths from AMI can be attributed to cardiac rupture.[ 1 , 2 ] Among the severe mechanical complications, cardiac rupture (CR) is particularly catastrophic, often resulting in rapid clinical deterioration and high mortality rates. [ 3 ]The incidence of CR post-AMI is relatively low, reported between 1–4% in recent studies, yet it accounts for up to 24% of pre-hospital deaths related to AMI, and it usually occurs within the first 5 days after AMI, and more than 90% occurs within 2 weeks after AMI, proved by Smith et al. and José et al[ 4 – 6 ]. Early identification of patients at high risk for CR following AMI is crucial for timely intervention and potentially life-saving management strategies. Several risk factors have been associated with increased susceptibility to CR, including advanced age, female sex, hypertension, and delayed hospital presentation [ 7 ]. Furthermore, specific echocardiographic and biochemical markers have shown promise in early risk stratification.[ 8 ] Despite these advancements, there remains a substantial gap in the early prediction and prevention of CR. Current models for predicting CR are often based on data from the pre-thrombolytic and thrombolytic eras, with limited applicability in the contemporary setting of routine revascularization and advanced pharmacotherapy.[ 9 ] This underscores the pressing need for updated research that integrates modern clinical practices and emerging data analytics techniques. This study aims to identify novel predictive factors for CR in patients with AMI by analyzing a comprehensive dataset encompassing clinical histories, vital signs, laboratory results, imaging findings, coronary angiography, and medication profiles. By leveraging advanced statistical models, we seek to develop a predictive model that can be clinically applied to identify high-risk patients early in the course of their treatment. The ultimate goal is to enhance preventive strategies and improve outcomes for this vulnerable patient population. Material and methods Ethics statement This study was approved by the Ethics Committee of Beijing Friendship Hospital in accordance with the ethical standards of the 1964 Declaration of Helsinki and its later amendments (Approval no. 2018–P2-030-01). As the study was retrospective and data were collected anonymously, informed consent was waived and innominate data were used. At the same time, there is the least risk of harm to every participant. The personal information of patients stored in the database remained confidential and was strictly inaccessible to anyone beyond the research team. All of these were deemed unnecessary according to our national regulations. Clinical trial number is not applicable. Study Design This study is a retrospective case-control analysis conducted at Beijing Friendship Hospital, involving patients admitted from 2018 to the December 2023. The primary objective is to identify factors that predict CR following AMI. Patient Selection and Data Collection Patients included in the study were diagnosed with AMI based on clinical findings, electrocardiograms, and cardiac biomarker levels. From this study as showed in figure 1, 30 patients who experienced CR post-AMI were identified and constituted the case group. For each case, four controls (patients with AMI who did not experience CR) were selected, matched by admission date, to form a control group (n=120). This 1:4 case-control setup helps in maintaining statistical power while controlling for temporal variations in clinical practice and hospital protocols. As the study was retrospective, informed consent was waived. Inclusion Criteria 1. Diagnosis of AMI: Patients must be diagnosed with AMI based on the following: - Clinical symptoms consistent with acute myocardial ischemia. - Electrocardiographic changes indicative of new ischemia (new ST-T changes or new left bundle branch block). - Development of pathological Q waves in the electrocardiograph. - Imaging evidence of new loss of viable myocardium or new regional wall motion abnormality. - Identification of an intracoronary thrombus by angiography or autopsy. - Elevated levels of cardiac biomarkers (troponins or CK-MB) above the 99th percentile of the upper reference limit. 2. Age: Patients aged 18 years or older. 3. Admission Date: Patients admitted to Beijing Friendship Hospital between January 2018 and the present. Exclusion Criteria 1. Previous History of CR: Patients with a history of cardiac rupture prior to the study period. 2. Significant Valvular Heart Disease: Patients with severe valvular heart disease that could confound the assessment of myocardial infarction and its complications. 3. Co-existing Terminal Illnesses: Patients with terminal illnesses with a life expectancy of less than one year not related to AMI. 4. Recent Surgery or Trauma: Patients who have undergone major surgery or experienced significant trauma within 30 days prior to the AMI event, as these conditions could independently affect cardiac integrity. 5. Lack of Comprehensive Data: Patients for whom essential clinical data (e.g., complete medical history, laboratory results, imaging data) are not available. 6. Withdrawal of Consent: Patients who withdraw consent for their data to be used in the study at any point. Analytical Approach The study utilizes a comprehensive analysis of baseline data, including demographic characteristics, clinical history, vital signs, laboratory results, imaging findings, and therapeutic interventions. The analytical strategy is structured as follows: 1. LASSO Regression: To address multicollinearity and enhance the selection of relevant predictors, least absolute shrinkage and selection operator (LASSO) regression will initially be employed. This technique is particularly useful for reducing the complexity of the model by penalizing the absolute size of the regression coefficients and thus, helps in variable selection. 2. Univariate Logistic Regression Analysis: Variables identified by LASSO as potential predictors will subsequently be analyzed using univariate logistic regression to assess their individual impact on the likelihood of CR following AMI. 3. Multivariate Logistic Regression Analysis: Significant predictors from the univariate analysis will be included in a multivariate logistic regression model to adjust for confounding factors and determine the independent effect of each variable. 4. Development of a Nomogram Based on the results from the multivariate analysis, a nomogram will be constructed. This practical tool will be designed to predict the individual probability of CR in AMI patients, facilitating clinical decision-making. 5. Model Validation The predictive accuracy and generalizability of the nomogram will be evaluated using the receiver operating characteristic (ROC) curve to assess its discriminative ability, and calibration curves to compare the predicted versus observed outcomes. These validation techniques are essential to ensure the reliability and clinical utility of the predictive model. 6. Data Management Data will be collected and managed using secure, HIPAA-compliant electronic data capture tools. All patient identifiers will be removed to ensure confidentiality and data will be analyzed in a de-identified format. 7. Descriptive Statistics Descriptive statistics will be used to summarize the demographic and clinical characteristics of the study population. Continuous variables will be expressed as means ± standard deviations if normally distributed, or medians with interquartile ranges if distributions are skewed. Categorical variables will be summarized as counts and percentages. All analyses will be conducted using R statistical software (R Foundation for Statistical Computing, Vienna, Austria). A p-value of less than 0.05 will be considered statistically significant for all tests. Results Table 1A analyzing risk factors for cardiac rupture following AMI, baseline data were compared between patients who experienced cardiac rupture (CR group, n=30) and those who did not (non-CR group, n=120). The findings revealed that the CR group had significantly older patients, higher incidence of ST-elevation myocardial infarction (STEMI), more severe initial heart function classification (higher Killip class), lower blood pressures, shorter hospital stays, increased mortality rates, higher glucose levels, and more pronounced liver function abnormalities (elevated transaminases and direct bilirubin). Additionally, there was a higher usage of diuretics and nitrates in the CR group. These factors may be associated with an increased risk of cardiac rupture, suggesting the need for early identification and targeted treatment strategies in high-risk patients. Table 1A. Baseine characters of enrolled subjects Variable non-CR group n=120 CR group ， n=30 P-value Cardiac rupture,n(%) 0 (0.00%) 30 (100.00%) 0.00 Sex, male, n(%) 86 (71.67%) 17 (56.67%) 0.173 Age, years (IQR) 67.00 (59.00, 77.00) 78.50 (70.50, 84.75) 0.000 STEMI, n(%) 66(55.00%) 25(83.33%) 0.003 Killip ≥ grade II, n(%) 0 (0.00%) 21 (70.00%) 0.000 SBP, mmHg,mean±SD 134.28 ± 21.96 120.53 ± 19.44 0.002 DBP, mmHg, mean±SD 74.95 ± 11.20 69.70 ± 13.32 0.029 MAP, mmHg, mean±SD 94.72 ± 12.81 86.64 ± 13.91 0.003 Pulse, bpm (IQR) 75.00 (66.00, 84.25) 75.00 (65.50, 96.75) 0.574 Death, n(%) 3 (2.50%) 28 (93.33%) 0.000 Hospitalization day, day(IQR) 8.00 (6.00, 11.00) 5.00 (3.25, 8.00) 0.000 Hypertension, n(%) 83 (69.17%) 19 (63.33%) 0.694 Diabetes, n(%) 44 (36.67%) 9 (30.00%) 0.639 Glucose, mmol/L(IQR) 5.75 (4.84, 7.16) 7.62 (6.17, 11.00) 0.000 Hyperlipidemia, n(%) 86 (71.67%) 11 (36.67%) 0.001 Smoking, n(%) 49 (40.83%) 9 (30.00%) 0.379 Alcohol, n(%) 37 (30.83%) 6 (20.00%) 0.343 CABG history, n(%) 1 (0.83%) 3 (10.00%) 0.031 Chronic heart failure, n(%) 1 (0.83%) 0 (0.00%) 1.000 Aspirin, n(%) 110 (91.67%) 29 (96.67%) 0.584 Clopidogrel/Ticagrelor, n(%) 102 (85.00%) 29 (96.67%) 0.158 Statin, n(%) 101 (84.17%) 29 (96.67%) 0.133 β blocker, n(%) 94 (78.33%) 22 (73.33%) 0.733 CCB, n(%) 22 (18.33%) 2 (6.67%) 0.200 Diuretic, n(%) 18 (15.00%) 14 (46.67%) 0.000 ACEI/ARB/ARNI, n(%) 81 (67.50%) 14 (46.67%) 0.057 Nitrate, n(%) 34 (28.33%) 24 (80.00%) 0.000 CR=cardiac rupture, STEMI=ST-elevated myocardial infarction, SBP=systolic blood pressure, DBP=diastolic blood pressure, MAP=mean artery pressure, CABG=coronary artery bypass grafting, CCB=calcium channel blocker, ACEI= angiotension converting enzyme inhibitor, ARB=angiotensin II receptor blocker, ARNI=angiotensin receptor & neprilysin inhibitor. Table 1B presents a comparative analysis of various biochemical and hematological parameters between non-CR group and CR group. Significant differences were noted in several parameters, indicating a more severe inflammatory and coagulative response in the CR group. Elevated levels of hypersensitive C-reactive protein and white blood cell count in the CR group (p=0.017 and p=0.000, respectively) suggest a heightened inflammatory state. The CR group also showed higher monocyte counts, lower eosinophil counts, and elevated neutrophil counts (all p=0.000), further supporting the presence of acute inflammation. Hemoglobin levels were lower in the CR group (p=0.045), and significant alterations in coagulation parameters such as international normalized ratio, prothrombin time, and fibrinogen degradation products (all p=0.000) were observed, indicating a disturbed coagulation pathway. Additionally, changes in thyroid function and electrolyte levels, such as higher free T4 and calcium (p=0.003 and p=0.001, respectively), and lower sodium levels (p=0.000) were noted in the CR group. These findings suggest that patients with cardiac rupture post-myocardial infarction exhibit a distinct profile of inflammation, coagulation disturbances, and metabolic imbalances, which could be potential markers or contributors to the severity of their condition. Table 1B. Peripheral blood test parameters between CR and non-CR groups following myocardial infarction Variable non-CR group ， n=120 CR group ， n=30 P-value hs-CRP, mg/L(IQR) 7.06 (2.32, 19.61) 13.95 (4.95, 31.07) 0.017 WBC, 10 9 /L, mean±SD 7.91 ± 2.43 10.56 ± 3.53 0.000 RBC,10 12 /L, mean±SD 4.31 ± 0.64 4.26 ± 0.65 0.689 Platelet, 10 9 /L(IQR) 212.50 (173.00, 252.50) 214.00 (155.00, 253.50) 0.728 Monocyte, 10 9 /L(IQR) 0.33 (0.22, 0.45) 0.57 (0.34, 0.84) 0.000 Lymphocyte, 109/L(IQR) 1.44 (1.12, 2.00) 1.19 (0.98, 1.70) 0.149 Basophil, 10 9 /L(IQR) 0.03 (0.02, 0.04) 0.02 (0.01, 0.04) 0.012 Eosinophil, 10 9 /L(IQR) 0.12 (0.09, 0.19) 0.03 (0.01, 0.10) 0.000 Hemoglobin,g/L(IQR) 138.00 (123.00, 149.25) 130.00 (110.50, 140.75) 0.045 Neutrophil, 10 9 /L(IQR) 5.48 (4.07, 7.08) 7.91 (6.63, 10.09) 0.000 INR(IQR) 1.03 (0.99, 1.08) 1.11 (1.05, 1.21) 0.000 APTT, s(IQR) 27.60 (25.15, 30.30) 28.40 (27.30, 32.40) 0.112 AT-III, mg/L, mean±SD 87.11 ± 10.69 82.14 ± 11.77 0.027 PT, s(IQR) 11.90 (11.40, 12.50) 12.75 (12.12, 13.38) 0.000 PT(A), %(IQR) 95.85 (86.97, 105.33) 82.55 (74.30, 92.77) 0.000 FDP, g/L(IQR) 1.90 (1.00, 3.00) 3.12 (2.50, 4.00) 0.000 Fibrinogen, g/L(IQR) 2.93 (2.31, 3.61) 3.44 (3.22, 4.72) 0.002 D dimer, mg/L(IQR) 0.70 (0.50, 0.90) 0.90 (0.62, 1.25) 0.036 TSH,uIU/mL(IQR) 1.20 (0.72, 2.03) 1.17 (0.86, 2.18) 0.733 Thyroid uptake, %(IQR) 43.00 (42.00, 44.48) 43.51 (43.43, 45.65) 0.005 Thyroxine, ug/dL(IQR) 82.21 (72.46, 95.16) 87.75 (80.13, 105.49) 0.046 Triiodothyronine, ng/ml(IQR) 77.32 (65.34, 86.98) 76.77 (56.55, 80.45) 0.293 FT3, pmol/L(IQR) 2.62 (2.37, 2.85) 2.73 (2.55, 3.13) 0.036 FT4, pmol/L(IQR) 0.88 (0.79, 1.08) 1.04 (0.90, 1.26) 0.003 Calcium, mmol/L(IQR) 2.13 (2.07, 2.19) 2.19 (2.14, 2.25) 0.001 Potassium, mmol/L(IQR) 4.12 (3.87, 4.36) 4.12 (3.81, 4.33) 0.622 Chloride, mmol/L,mean±SD 102.40 ± 4.35 103.23 ± 3.33 0.328 Sodium, mmol/L(IQR) 141.20 (138.28, 142.70) 137.20 (135.18, 139.80) 0.000 CO2, mmol/L, mean±SD 23.65 ± 3.59 22.46 ± 2.72 0.092 Lactate, mmol/L(IQR) 2.02 (1.73, 2.37) 3.33 (2.36, 4.36) 0.000 Osmolality, mosm/L(IQR) 293.70 (289.10, 298.55) 289.65 (282.80, 294.18) 0.004 AG, mmol/L(IQR) 16.50 (14.30, 18.23) 14.90 (12.88, 17.52) 0.065 Uric acid, umol/L 343.45 (287.45, 401.50) 350.77 (286.82, 380.75) 0.674 ALT, IU/L(IQR) 20.50 (12.00, 36.69) 32.50 (19.25, 46.25) 0.020 AST, IU/L(IQR) 33.00 (19.65, 96.50) 113.20 (44.05, 188.57) 0.001 DB,umol/L(IQR) 2.46 (1.76, 3.59) 5.41 (3.17, 7.93) 0.000 IB, umol/L(IQR) 9.72 (7.84, 13.73) 13.52 (9.08, 15.88) 0.080 TC, mmol/L(IQR) 4.50 (3.58, 5.05) 4.21 (3.75, 4.77) 0.484 Triglyceride, mmol/L(IQR) 1.30 (0.99, 1.97) 1.40 (0.80, 1.58) 0.657 HDL, mmol/L(IQR) 1.03 (0.91, 1.21) 1.05 (0.92, 1.22) 0.746 LDL, mmol/L(IQR) 2.56 (1.90, 3.03) 2.42 (1.99, 2.90) 0.559 Creatinine, umol/L(IQR) 84.60 (74.15, 100.48) 86.65 (73.10, 106.18) 0.893 Urea nitrogen, mmol/L(IQR) 5.46 (4.50, 7.08) 6.26 (5.58, 7.77) 0.097 CR=cardiac rupture, hs-CRP=hypersensitive C reactive protein, WBC=white blood cell, RBC=red blood cell, INR=international normalized ratio, AT-III=antithrombin III, PT=prothrombin time, APTT=activated partial thromboplastin time, PT(A)=prothrombin time activity, FDP=fibrinogen degradation products, TSH=thyroid stimulating hormone, FT3=free triiodothyronine, FT4=free thyroxine, CO2=carbon dioxide, AG=anion gap, ALT=alanine aminotransferase, AST=aspartate aminotransferase, DB=direct bilirubin, IB=indirect bilirubin, TC=total cholesterol, HDL=high density lipoprotein, LDL=low density lipoprotein Table 1C provides a comparison of clinical and cardiac parameters between non-CR group and CR group. Notably, the CR group had a significantly higher percentage of anterior wall AMI (53.33% vs. 25.83%, p=0.007) and lateral wall AMI (20.00% vs. 5.83%, p=0.035). The Gensin score, which is indicative of coronary artery disease severity, was also significantly higher in the CR group (p=0.001). Furthermore, the CR group exhibited a markedly higher incidence of severe complications such as cardiogenic shock (73.33% vs. 1.67%, p=0.000), use of intra-aortic balloon pump (IABP) (36.67% vs. 0.83%, p=0.000), respiratory failure (26.67% vs. 4.17%, p=0.000), acute renal insufficiency (16.67% vs. 0.00%, p=0.000), metabolic acidosis (33.33% vs. 2.50%, p=0.000), and anemia (30.00% vs. 7.50%, p=0.002). These findings suggest that patients in the CR group were more likely to suffer from severe cardiovascular instability and systemic complications. Additionally, there were significant differences in ventricular tachycardia (13.33% vs. 1.67%, p=0.017) and atrial fibrillation (30.00% vs. 11.67%, p=0.027), indicating a higher burden of cardiac arrhythmias in the CR group. The left ventricular ejection fraction was also significantly lower in the CR group (p=0.000), suggesting impaired cardiac function. These data highlight the critical nature and complexity of care required for patients in the CR group following myocardial infarction. Table 1C. Clinical features and inhosptal prognosis between CR and non-CR groups following myocardial infarction Variable non-CR group ， n=120 CR group ， n=30 P-value Anterior wall, n(%) 31 (25.83%) 16 (53.33%) 0.007 Inferior wall, n(%) 31 (25.83%) 12 (40.00%) 0.191 Posterior wall, n(%) 15 (12.50%) 5 (16.67%) 0.764 Lateral wall, n(%) 7 (5.83%) 6 (20.00%) 0.035 Gensin score, (IQR) 101.05 (71.88, 105.50) 101.09 (101.09, 158.00) 0.001 Cardiogenic shock, n(%) 2 (1.67%) 22 (73.33%) 0.000 Stent implanted, n(%) 80 (66.67%) 17 (56.67%) 0.417 Thrombectomy, n(%) 7 (5.83%) 4 (13.33%) 0.309 IABP, n(%) 1 (0.83%) 11 (36.67%) 0.000 RF, n(%) 5 (4.17%) 8 (26.67%) 0.000 AKI, n(%) 0 (0.00%) 5 (16.67%) 0.000 Metabolic acidosis, n(%) 3 (2.50%) 10 (33.33%) 0.000 Anemia, n(%) 9 (7.50%) 9 (30.00%) 0.002 PH, n(%) 4 (3.33%) 7 (23.33%) 0.001 Hypokalemia, n(%) 14 (11.67%) 11 (36.67%) 0.003 VPB, n(%) 5 (4.17%) 5 (16.67%) 0.041 VT, n(%) 2 (1.67%) 4 (13.33%) 0.017 AF, n(%) 14 (11.67%) 9 (30.00%) 0.027 APB, n(%) 1 (0.83%) 2 (6.67%) 0.189 AVB, n(%) 3 (2.50%) 6 (20.00%) 0.001 CK, IU/L(IQR) 24.05 (4.89, 80.45) 36.10 (8.70, 111.20) 0.645 cTnI on admission, ng/ml(IQR) 2.37 (0.77, 9.66) 7.46 (1.03, 21.77) 0.312 NT pro-BNP on admission, pg/ml(IQR) 1529.50 (636.75, 6570.25) 30000.00 (10706.00, 30000.00) 0.000 LAD, mm(IQR) 3.85 (3.40, 4.10) 3.81 (3.60, 4.10) 0.942 EDD, mm(IQR) 5.10 (4.70, 5.50) 5.16 (5.00, 5.30) 0.629 ESD, mm(IQR) 3.40 (3.10, 3.90) 3.75 (3.53, 4.00) 0.009 EDV, ml(IQR) 123.81(102.36, 147.42) 123.81 (123.81, 133.46) 0.468 ESV, ml(IQR) 47.44 (35.00, 75.31) 58.18 (37.28, 65.82) 0.493 Stroke volume, ml(IQR) 72.49 (62.89, 82.85) 76.60 (66.03, 78.13) 0.879 LVEF, %(IQR) 61.00 (0.53.00, 66.00) 51.00 (47.00, 56.00) 0.000 Fractional shortening, (IQR) 0.33 (0.27, 0.36) 0.32 (0.27, 0.36) 0.471 RVD, mm(IQR) 1.70 (1.50, 1.80) 1.80 (1.50, 2.00) 0.306 Early diastolic filling velocity, m/s(IQR) 77.00 (63.00, 90.50) 79.46 (63.00, 93.62) 0.916 Late diastolic filling velocity, m/s, mean±SD 90.82 ± 21.68 94.39 ± 23.68 0.429 E/A ratio, (IQR) 0.89 (0.72, 1.30) 0.81 (0.65, 1.17) 0.245 MR velocity, m/s (IQR) 332.12 (266.00, 367.25) 350.00 (277.50, 390.25) 0.153 AV forward velocity, m/s(IQR) 127.50 (115.00, 145.25) 126.50 (112.25, 142.75) 0.730 LVOT, mm(IQR) 93.00 (81.00, 104.00) 90.00 (79.25, 98.48) 0.314 TR vel, m/s(IQR) 233.50 (215.00, 269.25) 266.00 (221.25, 307.50) 0.025 SPAP, mmHg(IQR) 37.53 (33.88, 37.53) 37.53 (37.53, 43.56) 0.041 CR=cardiac rupture, IABP=intra-aortic balloon pump, RF=respiratory failure, AKI=acute renal insufficiency, PH=pulmonary hypertension, VPB=ventricular premature beat, VT=ventricular tachycardia, AF=atrial fibrillation, APB=atrial premature beat, AVB=atrioventricular block, CK=creatine kinase, cTnI=cardiac troponin I, NT pro-BNP=N-terminal pro-B type natriuretic peptide, LAD=left atrium diameter, EDD=end diastolic diameter, ESD=end systolic diameter, EDV=end diastolic volume, ESV=end systolic volume, LVEF=left ventriclular ejection fraction, RVD=right ventricle diameter, MR vol=mitral regurgitant jet velocity, E/A=late diastolic transmitral flow velocity, AV=aortic valve, LVOT=left ventricular outflow tract, TR=tricuspid regurgitant, SPAP=systolic pulmonary artery pressure. In our study as figure 2A and 2B showed, the optimal lambda value of 2.3907e-2 was meticulously determined and employed within the LASSO regression framework to select a subset of 20 variables from an initial pool of 70 potential predictors. This refined set of variables exhibited significant contributions to the model, as evidenced by their respective regression coefficients. Specifically, the variables along with their coefficients are as follows: Mean artery pressure (-1.1099e-2), indicating a minor negative impact; CABG history (1.9502e+0), Lateral wall AMI (1.0710e+0), and Right ventricle (1.1092e+0) all exhibited strong positive associations. Conversely, Left atrium (-1.0119e-1) and E/A ratio (-3.8654e-1) were negatively correlated. Metabolic acidosis (5.0606e-1), Anemia (3.1070e-1), and Fibrinogen degradation products (4.9113e-2) also showed positive, albeit varying degrees of influence. Notably, Chloride (1.7735e-2) and Free T4 (4.5417e-2) had modest positive coefficients, while Sodium (-7.6212e-2), Urea nitrogen (-3.8524e-2), and Osmolality (-1.5133e-3) demonstrated slight negative contributions. Glucose (8.3132e-2) and Gensin score (1.1626e-2) were positively associated, albeit with smaller magnitudes. Additionally, biomarkers related to patient admission status, such as cTnI on admission (8.4934e-3) and NT pro-BNP on admission (1.2862e-4), displayed minimal but discernible positive effects. Table 2 presents both univariable and multivariable odds ratios (ORs) for various clinical factors affecting patients, with their respective 95% confidence intervals (CIs) and p-values. Key findings include significant associations of lower mean artery pressure, history of coronary artery bypass grafting (CABG), metabolic acidosis, anemia, lower sodium levels, higher glucose levels, lower osmolality, higher cardiac troponin I (cTnI) on admission, higher NT pro-BNP on admission, and higher Gensin scores with increased odds of adverse outcomes. Notably, metabolic acidosis and anemia are strongly associated with adverse outcomes in both univariable and multivariable analyses (ORs 19.50 and 5.29, respectively, in univariable; 15.887 for anemia in multivariable). Additionally, right ventricular size is significantly associated with outcomes in both analyses (ORs 4.58 in univariable and 34.429 in multivariable). Other factors such as glucose and osmolality also show significant associations in multivariable analysis, suggesting their potential role in predicting clinical outcomes. Table 2 Logistic regression for selected characters predicting CR OR univariable OR multivariable OR and 95%CI P OR and 95%CI P MAP 0.95 (0.92-0.99) 0.004 CABG history 13.22 (1.32-132.07) 0.028 Lateral wall 3.23 (0.95-11.01) 0.061 Metabolic acidosis 19.50 (4.93-77.09) 0.001 Anemia 5.29 (1.88-14.88) 0.002 15.887(1.667-151.392) 0.016 Chloride 1.05 (0.95-1.16) 0.326 Sodium 0.78 (0.69-0.87) 0.001 Urea nitrogen 1.02 (0.94-1.12) 0.606 Glucose 1.21 (1.07-1.36) 0.002 1.609(1.150-2.250) 0.005 Osmolality 0.93 (0.88-0.98) 0.007 0.881(0.796-0.976) 0.015 cTnI on admission 1.04 (1.02-1.07) 0.001 NT-proBNP on admission 1.00 (1.00-1.00) 0.001 1.000(1.000-1.000) 0.000 FDP 1.17 (1.04-1.31) 0.009 FT4 1.68 (0.97-2.91) 0.062 Left atrium 0.85 (0.38-1.88) 0.683 Right ventricle 4.58 (1.83-11.47) 0.001 34.429(2.738-432.922) 0.006 E/A ratio 0.77 (0.40-1.45) 0.411 Gensin score 1.02 (1.01-1.03) 0.001 1.040(1.018-1.063) 0 CR=cardiac rupture, MAP=mean artery pressure, CABG=coronary artery bypass grafting, cTnI=cardiac troponin I, NT pro-BNP=N-terminal pro-B type natriuretic peptide, FDP=fibrinogen degradation products, FT4=free thyroxine, E/A=late diastolic transmitral flow velocity Figure 3 illustrates the relationship between several key variables and the overall model score. These variables include right ventricle (diameter), genesis score, NT pro-BNP on admission, osmolality, glucose, and anemia, each depicted with a curve demonstrating their respective impacts on the model's score. The x-axis represents the range of values for each variable, while the y-axis indicates their effect on the score. Notably, variables such as right ventricle (diameter) and genesis score show a significant contribution to the model, as indicated by stars next to their names, suggesting their pivotal roles in determining the outcome. The distribution of total points is also shown, providing a visual representation of score probabilities across the model. This visualization aids in understanding how these variables influence the overall effectiveness of the model in predicting outcomes. The ROC curve as showed in figure 4 analysis provided valuable insights into the diagnostic performance of the model. The best threshold value identified was 169.799, which achieved a sensitivity of 86.67% and a specificity of 5.83%. The positive predictive value (PPV) stood at 78.79%, while the negative predictive value (NPV) was notably high at 96.58%. Despite the low specificity, the model's overall ability to distinguish between the conditions was robust, as indicated by an AUC (Area Under the Curve) of 0.942, with a 95% confidence interval ranging from 0.892 to 0.991. However, the Youden index was -0.075, suggesting a potential imbalance between sensitivity and specificity at the chosen threshold. This analysis underscores the model's strong predictive power, particularly in correctly identifying negative cases (high NPV), though improvements might be necessary to enhance specificity. This research conducted a calibration test using the Hosmer-Lemeshow statistic to validate the nomogram model I developed (Figure 5). The Hosmer-Lemeshow statistic, which measures the goodness of fit for the model, was calculated to be 3.315 with a p-value of 0.950. This high p-value indicates that there is no significant difference between the observed outcomes and the predictions made by the nomogram model, suggesting that the model fits the data well. Thus, the calibration test supports the reliability of the model in accurately predicting the outcomes based on the input variables. Discussion CR is one of the most serious mechanical complications of AMI and also one of the most important causes of death in patients with acute myocardial infarction.[ 10 , 11 ] With the popularization and deepening of coronary intervention, the incidence of cardiac rupture has significantly decreased to 1% -3%, but the mortality rate is still high, about 60%-86%.Qun Lu’s research[ 12 ] found that CR continues to be a leading contributor to in-hospital mortality among STEMI patients, particularly in elderly individuals with extensive infarction areas and pronounced inflammatory responses. Although they analyzed those risk factors, up to now however, there is no effective means to prevent heart rupture clinically after myocardial infarction [ 13 – 15 ]. Therefore, in order to improve AMI patient’s prognosis, accurate evaluation and timely intervention of this complication is crucial for clinical practice. By comparing data between CR and non-CR patients in the last decade, this study identified several key predictors of cardiac rupture following acute myocardial infarction using a comprehensive set of clinical variables. Notably, elevated levels of NT-proBNP, right ventricular enlargement, and high Gensini scores were strongly associated with an increased risk of cardiac rupture. These findings are consistent with previous research indicating that increased NT-proBNP levels and right ventricular dysfunction are markers of severe cardiac stress and structural compromise, which can predispose patients to rupture[ 4 , 16 , 17 ]. Further, this may be due to heart failure (HF) that may increase the risk of heart rupture. When severe HF occurs, the myocardial contractility decreases, the pumping function of the heart is impaired, which may cause a rapid rise in ventricular pressure, so that increase the risk of heart rupture. Some patients may have severe heart failure symptoms after the occurrence of AMI, so that it delays the implementation of the standard treatment of myocardial infarction, some even lose the chance to undergo PCI therapy, finally increasing the probability of a heart rupture.[ 18 , 19 ] Moreover, the Gensini score, which quantifies the degree of coronary artery stenosis and assesses coronary artery disease severity, underscores the role of extensive coronary blockage in the pathophysiology of cardiac rupture post-AMI.[ 20 ] It is not directly related to heart rupture, but can indirectly reflect the risk of heart rupture after myocardial infarction. Generally, higher Gensini scores indicate more severe coronary artery stenosis. [ 21 ] When coronary arteries are severely narrowed or blocked, the blood supply to the myocardium will be reduced or even interrupted, leading to AMI. On the other hands, patients with high Gensini scores may have delayed treatment due to the severity or complexity of their condition. Patients who fail to restore coronary artery blood flow in a timely manner have an increased risk of heart rupture after myocardial infarction.[ 22 ] Anemia and hyperglycemia are also identified as significant predictors. These conditions may exacerbate myocardial oxygen demand-supply mismatch and contribute to the weakening of the myocardial wall. Directly speaking, anemia leads to reduced blood oxygen-carrying capacity, affecting the oxygen supply of the myocardium.[ 23 ] The cardiac muscle itself is in a state of hypoxia, and anemia may further aggravate the situation.[ 24 ] Moreover, during myocardial reperfusion treatment, the myocardium receiving reperfusion after ischemia is more likely to be damaged, which may increase the risk of cardiac rupture after myocardial infarction. Long-term anemia causes compensatory changes in cardiac function, such as increased heart rate and increased cardiac load, factors that may also increase the incidence of cardiac rupture after MI. [ 25 – 27 ] For patients with AMI, either STEMI or NSTEMI, reduced admission hemoglobin levels were significantly associated with adverse outcomes such as congestive heart failure, recurrent ischemia, and cardiovascular death. Among patients with STEMI, when hemoglobin concentration falls below 14g/dL, and with NSTEMI below 11g/dL, cardiovascular mortality increases for every 1 g/dL decrease[ 28 ]. In addition, nutritional anemia, such as the megaloblastic anemia caused by vitamin B12 deficiency, has been shown by many studies to be one of the cardiovascular risk factors. [ 29 ] The effects of changes in blood glucose values on the cardiovascular system are very complex. In the state of hyperglycemia, the level of saccharification end products and oxidative stress increases, and the inflammatory response of the body is also aggravated. Metabolic disorders may affect the energy metabolism and free radical production of myocardial tissue, and further aggravate the damage of myocardial infarction. At the same time, the process of damaged myocardial fibrosis will also be affected, and eventually lead to the aggravation of the structural and functional damage of the myocardial tissue, so that the myocardial infarction focus is more likely to rupture.[ 30 – 35 ] Osmolality is another indicator of our screening. The role in cardiac rupture is also a complex and important issue that is closely related to inflammatory response, edema formation, and tissue stability. Around the infarcted myocardium, cellular necrosis and inflammatory responses will release multiple inflammatory mediators and cytokines, leading osmotic pressure changing as follow and water moving between tissues, that is the formation of local edema. Edema increases the volume and pressure of the tissue, especially in the cardiac muscle layer, which may increase the risk of MI rupture.[ 36 – 38 ] The inclusion of osmolality as a predictor highlights the potential impact of fluid and electrolyte imbalances on cardiac stability post-infarction, a relatively under-explored area in cardiac rupture literature. [ 39 – 41 ] The role of some parameters in predicting cardiac rupture has been supported by various studies, but our research further quantifies their impact and integrates these with other less traditional markers like osmolality. The predictive nomogram developed from these findings provides a practical tool for clinicians to assess the risk of cardiac rupture in AMI patients. By integrating multiple clinical variables, this nomogram allows for a more nuanced risk stratification compared to traditional methods. Early identification of high-risk patients could lead to more aggressive monitoring, timely interventions, and potentially the use of supportive therapies that stabilize myocardial structure and function. The comprehensive nature of our nomogram, which includes these diverse variables, sets it apart from existing models that often focus on a narrower set of predictors. However, there are still some limitations to this study. The retrospective design limits our ability to infer causality. Additionally, the study population from a single center may not be generalizable to all AMI patients, particularly those in different geographic or healthcare settings, so that may discern potential differences between groups. Future studies should aim to validate our findings in a prospective multicenter trial to enhance the generalizability and robustness of the predictive model. Further research should explore the interplay between these risk factors in a prospective setting, potentially examining the biochemical and molecular mechanisms underlying their association with cardiac rupture. Additionally, investigating the impact of immediate therapeutic interventions based on high-risk profiles generated by the nomogram could provide insights into effective strategies to prevent cardiac rupture in AMI patients. Conclusion In conclusion, our study highlights several crucial predictors of cardiac rupture post-AMI and integrates them into a clinically applicable nomogram. This tool has the potential to significantly improve the management of AMI patients by enabling early identification and intervention for those at high risk of cardiac rupture. Continued research and validation of this model are essential to refine its accuracy and clinical utility. Abbreviations CR=cardiac rupture STEMI=ST-elevated myocardial infarction SBP=systolic blood pressure DBP=diastolic blood pressure MAP=mean artery pressure CABG=coronary artery bypass grafting CCB=calcium channel blocker ACEI=angiotensin converting enzyme inhibitor ARB=angiotensin II receptor blocker ARNI=angiotensin receptor & neprilysin inhibitor hs-CRP=hypersensitive C reactive protein WBC=white blood cell RBC=red blood cell INR=international normalized ratio AT-III=antithrombin III PT=prothrombin time APTT=activated partial thromboplastin time PT(A)=prothrombin time activity FDP=fibrinogen degradation products TSH=thyroid stimulating hormone FT3=free triiodothyronine FT4=free thyroxine CO2=carbon dioxide AG=anion gap ALT=alanine aminotransferase AST=aspartate aminotransferase DB=direct bilirubin IB=indirect bilirubin TC=total cholesterol HDL=high density lipoprotein LDL=low density lipoprotein IABP=intra-aortic balloon pump RF=respiratory failure AKI=acute renal insufficiency PH=pulmonary hypertension VPB=ventricular premature beat VT=ventricular tachycardia AF=atrial fibrillation APB=atrial premature beat AVB=atrioventricular block CK=creatine kinase cTnI=cardiac troponin I NT-proBNP=N-terminal pro-B type natriuretic peptide LAD=left atrium diameter EDD=end diastolic diameter ESD=end systolic diameter EDV=end diastolic volume ESV=end systolic volume LVEF=left ventricular ejection fraction RVD=right ventricle diameter MR vol=mitral regurgitant jet velocity E/A=late diastolic transmitral flow velocity AV=aortic valve LVOT=left ventricular outflow tract TR=tricuspid regurgitant SPAP=systolic pulmonary artery pressure. Declarations Ethics approval This study was approved by the Ethics Committee of Beijing Friendship Hospital in accordance with the ethical standards of the 1964 Declaration of Helsinki and its later amendments (Approval no. 2018–P2-030-01). Clinical trial number is not applicable. Consent for publication As the study was retrospective and data were collected anonymously, informed consent was waived and innominate data were used. Competing Interests The authors declare that there is no competing financial interests or personal relationships, which may affect the work of this paper. Availability of data and materials The data were collected anonymously and screened according to the inclusion and exclusion Criteria of this research. We have uploaded supplementary information file online. Datasets supporting the conclusions of this study are available from the corresponding author on reasonable request. Funding This study was supported by the National Natural Science Foundation of China (Grant No. 81600276). Authors' contributions Conceptualization and Methodology by RF.L, YX.W.; Data Collection by YX.W.; Original Draft Preparation by YX.W.; Visualization and supervision by RF.L; Investigation and Resources by RF.L, YX.W.; Data Analysis by RF.L, YX.W. All authors reviewed the manuscript. Authors information Department of Emergency, Beijing Friendship Hospital Affiliated to Capital Medical University, Beijing, China Yaxin Wang (First author) Department of Cardiology, Beijing Friendship Hospital Affiliated to Capital Medical University, Beijing, China Ruifeng Liu (Corresponding author) Acknowledgements We thank all study participants, research and department staffs, who participated in this work in Beijing Friendship hospital. References Hochman JS, Buller CE, Sleeper LA, Boland J, Dzavik V, Sanborn TA, Godfrey E, White HD, Lim J, LeJemtel T. Cardiogenic shock complicating acute myocardial infarction–etiologies, management and outcome: a report from the SHOCK Trial Registry. SHould we emergently revascularize Occluded Coronaries for cardiogenic shocK? J Am Coll Cardiol. 2000;36(3 Suppl A):1063–70. Qian G, Jin RJ, Fu ZH, Yang YQ, Su HL, Dong W, Guo J, Jing J, Guo YL, Chen YD. Development and validation of clinical risk score to predict the cardiac rupture in patients with STEMI. Am J Emerg Med 2017, 35(4):589–93. Virani SS, Alonso A, Benjamin EJ, Bittencourt MS, Callaway CW, Carson AP, Chamberlain AM, Chang AR, Cheng S, Delling FN, et al. Heart Disease and Stroke Statistics-2020 Update: A Report From the American Heart Association. Circulation. 2020;141(9):e139–596. Valeur N, Clemmensen P, Saunamäki K, Grande P. The prognostic value of pre-discharge exercise testing after myocardial infarction treated with either primary PCI or fibrinolysis: a DANAMI-2 sub-study. Eur Heart J. 2005;26(2):119–27. López-Sendón J, González A, López de Sá E, Coma-Canella I, Roldán I, Domínguez F, Maqueda I, Martín Jadraque L. Diagnosis of subacute ventricular wall rupture after acute myocardial infarction: sensitivity and specificity of clinical, hemodynamic and echocardiographic criteria. J Am Coll Cardiol. 1992;19(6):1145–53. Kageyama S, Nakanishi Y, Murata K, Nawada R, Onodera T, Sakamoto A, Yamazaki F, Miura Y, Maekawa Y. Mortality and predictors of survival in patients with recent ventricular septal rupture. Heart Vessels. 2020;35(12):1672–80. Gong FF, Vaitenas I, Malaisrie SC, Maganti K. Mechanical Complications of Acute Myocardial Infarction: A Review. JAMA Cardiol. 2021;6(3):341–9. Kumar R, Kumar P, Srivastava PK, Kumar P. Echocardiographic and Angiographic Assessment of Right Ventricular Function and Right Coronary Artery Stenosis in Acute Inferior Wall Myocardial Infarction. Cureus. 2023;15(10):e46403. Sulzgruber P, El-Hamid F, Koller L, Forster S, Goliasch G, Wojta J, Niessner A. Long-term outcome and risk prediction in patients suffering acute myocardial infarction complicated by post-infarction cardiac rupture. Int J Cardiol. 2017;227:399–403. Schlotter F, Huber K, Hassager C, Halvorsen S, Vranckx P, Pöss J, Krychtiuk K, Lorusso R, Bonaros N, Calvert PA et al. Ventricular septal defect complicating acute myocardial infarction: diagnosis and management. A Clinical Consensus Statement of the Association for Acute CardioVascular Care (ACVC) of the ESC, the European Association of Percutaneous Cardiovascular Interventions (EAPCI) of the ESC and the ESC Working Group on Cardiovascular Surgery. European heart journal 2024. Damluji AA, van Diepen S, Katz JN, Menon V, Tamis-Holland JE, Bakitas M, Cohen MG, Balsam LB, Chikwe J. Mechanical Complications of Acute Myocardial Infarction: A Scientific Statement From the American Heart Association. Circulation. 2021;144(2):e16–35. Lu Q, Liu P, Huo JH, Wang YN, Ma AQ, Yuan ZY, Du XJ, Bai L. Cardiac rupture complicating acute myocardial infarction: the clinical features from an observational study and animal experiment. BMC Cardiovasc Disord. 2020;20(1):409. Fang X, Wang H, Han D, Xie E, Yang X, Wei J, Gu S, Gao F, Zhu N, Yin X, et al. Ferroptosis as a target for protection against cardiomyopathy. Proc Natl Acad Sci USA. 2019;116(7):2672–80. Chen X, Xu S, Zhao C, Liu B. Role of TLR4/NADPH oxidase 4 pathway in promoting cell death through autophagy and ferroptosis during heart failure. Biochem Biophys Res Commun. 2019;516(1):37–43. Fang X, Cai Z, Wang H, Han D, Cheng Q, Zhang P, Gao F, Yu Y, Song Z, Wu Q, et al. Loss of Cardiac Ferritin H Facilitates Cardiomyopathy via Slc7a11-Mediated Ferroptosis. Circul Res. 2020;127(4):486–501. Torabi A, Cleland JG, Khan NK, Loh PH, Clark AL, Alamgir F, Caplin JL, Rigby AS, Goode K. The timing of development and subsequent clinical course of heart failure after a myocardial infarction. Eur Heart J. 2008;29(7):859–70. Stone GW, Selker HP, Thiele H, Patel MR, Udelson JE, Ohman EM, Maehara A, Eitel I, Granger CB, Jenkins PL, et al. Relationship Between Infarct Size and Outcomes Following Primary PCI: Patient-Level Analysis From 10 Randomized Trials. J Am Coll Cardiol. 2016;67(14):1674–83. Yau TM, Pagani FD, Mancini DM, Chang HL, Lala A, Woo YJ, Acker MA, Selzman CH, Soltesz EG, Kern JA, et al. Intramyocardial Injection of Mesenchymal Precursor Cells and Successful Temporary Weaning From Left Ventricular Assist Device Support in Patients With Advanced Heart Failure: A Randomized Clinical Trial. JAMA. 2019;321(12):1176–86. Chan MY, Efthymios M, Tan SH, Pickering JW, Troughton R, Pemberton C, Ho HH, Prabath JF, Drum CL, Ling LH, et al. Prioritizing Candidates of Post-Myocardial Infarction Heart Failure Using Plasma Proteomics and Single-Cell Transcriptomics. Circulation. 2020;142(15):1408–21. Adams MR, Nakagomi A, Keech A, Robinson J, McCredie R, Bailey BP, Freedman SB, Celermajer DS. Carotid intima-media thickness is only weakly correlated with the extent and severity of coronary artery disease. Circulation. 1995;92(8):2127–34. Zeller T, Seiffert M, Müller C, Scholz M, Schäffer A, Ojeda F, Drexel H, Mündlein A, Kleber ME, März W, et al. Genome-Wide Association Analysis for Severity of Coronary Artery Disease Using the Gensini Scoring System. Front Cardiovasc Med. 2017;4:57. Rampidis GP, Benetos G, Benz DC, Giannopoulos AA, Buechel RR. A guide for Gensini Score calculation. Atherosclerosis. 2019;287:181–3. Kaiafa G, Kanellos I, Savopoulos C, Kakaletsis N, Giannakoulas G, Hatzitolios AI. Is anemia a new cardiovascular risk factor? Int J Cardiol. 2015;186:117–24. Kanic V, Kompara G, Vollrath M, Suran D, Kanic Z. Age-Specific Sex-Based Differences in Anemia in Patients with Myocardial Infarction. J Womens Health (Larchmt). 2019;28(7):1004–10. Ibáñez B, Heusch G, Ovize M, Van de Werf F. Evolving therapies for myocardial ischemia/reperfusion injury. J Am Coll Cardiol. 2015;65(14):1454–71. Eapen ZJ, Tang WH, Felker GM, Hernandez AF, Mahaffey KW, Lincoff AM, Roe MT. Defining heart failure end points in ST-segment elevation myocardial infarction trials: integrating past experiences to chart a path forward. Circulation Cardiovasc Qual outcomes. 2012;5(4):594–600. Heusch G. Cardioprotection: chances and challenges of its translation to the clinic. Lancet (London England). 2013;381(9861):166–75. Sabatine MS, Morrow DA, Giugliano RP, Burton PB, Murphy SA, McCabe CH, Gibson CM, Braunwald E. Association of hemoglobin levels with clinical outcomes in acute coronary syndromes. Circulation. 2005;111(16):2042–9. Polat E, Demir MC, Kucukdemirci O. Investigation of Vitamin B12 Deficiency in Patients with Acute Coronary Syndrome and its Relationship with Gensini Score. Clin Lab 2022, 68(2). Liu J, Zhou Y, Huang H, Liu R, Kang Y, Zhu T, Wu J, Gao Y, Li Y, Wang C, et al. Impact of stress hyperglycemia ratio on mortality in patients with critical acute myocardial infarction: insight from american MIMIC-IV and the chinese CIN-II study. Cardiovasc Diabetol. 2023;22(1):281. Gao S, Huang S, Lin X, Xu L, Yu M. Prognostic implications of stress hyperglycemia ratio in patients with myocardial infarction with nonobstructive coronary arteries. Ann Med. 2023;55(1):990–9. Karakasis P, Stalikas N, Patoulias D, Pamporis K, Karagiannidis E, Sagris M, Stachteas P, Bougioukas KI, Anastasiou V, Daios S et al. Prognostic value of stress hyperglycemia ratio in patients with acute myocardial infarction: A systematic review with Bayesian and frequentist meta-analysis. Trends Cardiovasc Med 2023. Abdu FA, Galip J, Qi P, Zhang W, Mohammed AQ, Liu L, Yin G, Mohammed AA, Mareai RM, Jiang R, et al. Association of stress hyperglycemia ratio and poor long-term prognosis in patients with myocardial infarction with non-obstructive coronary arteries. Cardiovasc Diabetol. 2023;22(1):11. Paolisso P, Foà A, Bergamaschi L, Angeli F, Fabrizio M, Donati F, Toniolo S, Chiti C, Rinaldi A, Stefanizzi A, et al. Impact of admission hyperglycemia on short and long-term prognosis in acute myocardial infarction: MINOCA versus MIOCA. Cardiovasc Diabetol. 2021;20(1):192. Ishihara M. Acute hyperglycemia in patients with acute myocardial infarction. Circulation journal: official J Japanese Circulation Soc. 2012;76(3):563–71. Butler TL, Egan JR, Graf FG, Au CG, McMahon AC, North KN, Winlaw DS. Dysfunction induced by ischemia versus edema: does edema matter? J Thorac Cardiovasc Surg. 2009;138(1):141–7. 147.e141. Garcia-Dorado D, Andres-Villarreal M, Ruiz-Meana M, Inserte J, Barba I. Myocardial edema: a translational view. J Mol Cell Cardiol. 2012;52(5):931–9. Andrés-Villarreal M, Barba I, Poncelas M, Inserte J, Rodriguez-Palomares J, Pineda V, Garcia-Dorado D. Measuring Water Distribution in the Heart: Preventing Edema Reduces Ischemia-Reperfusion Injury. J Am Heart Association 2016, 5(12). Szczepanska-Sadowska E, Wsol A, Cudnoch-Jedrzejewska A, Żera T. Complementary Role of Oxytocin and Vasopressin in Cardiovascular Regulation. International journal of molecular sciences 2021, 22(21). Sears CG, Poulsen AH, Eliot M, Howe CJ, James KA, Harrington JM, Roswall N, Overvad K, Tjønneland A, Raaschou-Nielsen O, et al. Urine cadmium and acute myocardial infarction among never smokers in the Danish Diet, Cancer and Health cohort. Environ Int. 2021;150:106428. Stiermaier T, Thiele H, Eitel I. Early myocardial edema after acute myocardial infarction is stable and not bimodal in humans - Evidence from a large CMR multicenter study. Int J Cardiol. 2017;246:87–9. Additional Declarations No competing interests reported. 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LASSO regression analysis: the coefficient path plot.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Coefficient Path Plot displays the trajectories of the coefficients of each predictor variable as the penalty parameter λ varies. The plot typically has a vertical axis representing the coefficient values, a lower horizontal axis representing the logarithm of the penalty parameter λ (log(λ)), and an upper horizontal axis indicating the number of non-zero coefficients in the model at a given λ.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB. LASSO regression analysis: the cross-validation curve plots\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Cross-Validation Curve plots the model's performance, often measured by the mean squared error (MSE) or deviance, against the logarithm of the penalty parameter λ. 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The points system (ranging from 10 to 100) serves as a reference for assessing the overall score or risk. By aligning specific values for each parameter on their respective scales, a user can intersect these lines to find a total score on the \"Total points\" scale, ranging from 60 to 280. Furthermore, the nomogram provides probabilities (denoted as Pr() values) ranging from 0.002 to 0.998, representing the likelihood of a particular outcome or event based on the total points calculated. This feature enables users to make informed decisions or predictions based on the combined assessment of multiple factors.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7260965/v1/81ac046f7849360c384d92c2.png"},{"id":92477492,"identity":"b8b1e431-8a1b-491e-9911-fd79c8dba66f","added_by":"auto","created_at":"2025-09-30 07:24:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1640988,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC Curve was employed to validate the nomogram.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7260965/v1/14051e0de459bf0ea912ff5e.png"},{"id":92480174,"identity":"cda13e8f-423c-4ead-988b-af9c57b393cf","added_by":"auto","created_at":"2025-09-30 07:40:12","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1843110,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCalibration Curve was employed to validate the nomogram.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7260965/v1/0760c82f1ffce65c81f8b56e.png"},{"id":92481244,"identity":"663696a5-517a-4e5f-891a-5e029496661b","added_by":"auto","created_at":"2025-09-30 07:48:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11567974,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7260965/v1/a48a8a76-338e-4670-8baa-b87b0e1a0eb0.pdf"},{"id":92475257,"identity":"21645807-7739-42bc-b4e4-020af40ee910","added_by":"auto","created_at":"2025-09-30 07:16:11","extension":"xls","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":123904,"visible":true,"origin":"","legend":"","description":"","filename":"supplementalmaterial20250822.xls","url":"https://assets-eu.researchsquare.com/files/rs-7260965/v1/0e647c4fa5d8f8488469794e.xls"}],"financialInterests":"No competing interests reported.","formattedTitle":"Early Detection of Cardiac Rupture Risk in Acute Myocardial Infarction: A Comprehensive Predictive Model","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute myocardial infarction (AMI) remains a leading cause of morbidity and mortality worldwide, despite significant advances in cardiovascular medicine. In-hospital mortality from mechanical complications is still high and can't be neglected even with prompt surgery or interventional repair. And 10%-15% of deaths from AMI can be attributed to cardiac rupture.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] Among the severe mechanical complications, cardiac rupture (CR) is particularly catastrophic, often resulting in rapid clinical deterioration and high mortality rates. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]The incidence of CR post-AMI is relatively low, reported between 1\u0026ndash;4% in recent studies, yet it accounts for up to 24% of pre-hospital deaths related to AMI, and it usually occurs within the first 5 days after AMI, and more than 90% occurs within 2 weeks after AMI, proved by Smith et al. and Jos\u0026eacute; et al[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Early identification of patients at high risk for CR following AMI is crucial for timely intervention and potentially life-saving management strategies. Several risk factors have been associated with increased susceptibility to CR, including advanced age, female sex, hypertension, and delayed hospital presentation [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Furthermore, specific echocardiographic and biochemical markers have shown promise in early risk stratification.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] Despite these advancements, there remains a substantial gap in the early prediction and prevention of CR. Current models for predicting CR are often based on data from the pre-thrombolytic and thrombolytic eras, with limited applicability in the contemporary setting of routine revascularization and advanced pharmacotherapy.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] This underscores the pressing need for updated research that integrates modern clinical practices and emerging data analytics techniques. This study aims to identify novel predictive factors for CR in patients with AMI by analyzing a comprehensive dataset encompassing clinical histories, vital signs, laboratory results, imaging findings, coronary angiography, and medication profiles. By leveraging advanced statistical models, we seek to develop a predictive model that can be clinically applied to identify high-risk patients early in the course of their treatment. The ultimate goal is to enhance preventive strategies and improve outcomes for this vulnerable patient population.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Beijing Friendship Hospital in accordance with the ethical standards of the 1964 Declaration of Helsinki and its later amendments (Approval no. 2018\u0026ndash;P2-030-01). As the study was retrospective and data were collected anonymously, informed consent was waived and innominate data were used. At the same time, there is the least risk of harm to every participant. The personal information of patients stored in the database remained confidential and was strictly inaccessible to anyone beyond the research team. All of these were deemed unnecessary according to our national regulations. Clinical trial number is not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is a retrospective case-control analysis conducted at Beijing Friendship Hospital, involving patients admitted from 2018 to the December 2023. The primary objective is to identify factors that predict CR following AMI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient Selection and Data Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients included in the study were diagnosed with AMI based on clinical findings, electrocardiograms, and cardiac biomarker levels. From this study as showed in figure 1, 30 patients who experienced CR post-AMI were identified and constituted the case group. For each case, four controls (patients with AMI who did not experience CR) were selected, matched by admission date, to form a control group (n=120). This 1:4 case-control setup helps in maintaining statistical power while controlling for temporal variations in clinical practice and hospital protocols. As the study was retrospective, informed consent was waived.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInclusion Criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1. Diagnosis of AMI: Patients must be diagnosed with AMI based on the following:\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp;- Clinical symptoms consistent with acute myocardial ischemia.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp;- Electrocardiographic changes indicative of new ischemia (new ST-T changes or new left bundle branch block).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp;- Development of pathological Q waves in the electrocardiograph.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp;- Imaging evidence of new loss of viable myocardium or new regional wall motion abnormality.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp;- Identification of an intracoronary thrombus by angiography or autopsy.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp;- Elevated levels of cardiac biomarkers (troponins or CK-MB) above the 99th percentile of the upper reference limit.\u003c/p\u003e\n\u003cp\u003e2. Age: Patients aged 18 years or older.\u003c/p\u003e\n\u003cp\u003e3. Admission Date: Patients admitted to Beijing Friendship Hospital between January 2018 and the present.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExclusion Criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1. Previous History of CR: Patients with a history of cardiac rupture prior to the study period.\u003c/p\u003e\n\u003cp\u003e2. Significant Valvular Heart Disease: Patients with severe valvular heart disease that could confound the assessment of myocardial infarction and its complications.\u003c/p\u003e\n\u003cp\u003e3. Co-existing Terminal Illnesses: Patients with terminal illnesses with a life expectancy of less than one year not related to AMI.\u003c/p\u003e\n\u003cp\u003e4. Recent Surgery or Trauma: Patients who have undergone major surgery or experienced significant trauma within 30 days prior to the AMI event, as these conditions could independently affect cardiac integrity.\u003c/p\u003e\n\u003cp\u003e5. Lack of Comprehensive Data: Patients for whom essential clinical data (e.g., complete medical history, laboratory results, imaging data) are not available.\u003c/p\u003e\n\u003cp\u003e6. Withdrawal of Consent: Patients who withdraw consent for their data to be used in the study at any point.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalytical Approach\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study utilizes a comprehensive analysis of baseline data, including demographic characteristics, clinical history, vital signs, laboratory results, imaging findings, and therapeutic interventions. The analytical strategy is structured as follows:\u003c/p\u003e\n\u003cp\u003e1. LASSO Regression: To address multicollinearity and enhance the selection of relevant predictors, least absolute shrinkage and selection operator (LASSO) regression will initially be employed. This technique is particularly useful for reducing the complexity of the model by penalizing the absolute size of the regression coefficients and thus, helps in variable selection.\u003c/p\u003e\n\u003cp\u003e2. Univariate Logistic Regression Analysis: Variables identified by LASSO as potential predictors will subsequently be analyzed using univariate logistic regression to assess their individual impact on the likelihood of CR following AMI.\u003c/p\u003e\n\u003cp\u003e3. Multivariate Logistic Regression Analysis: Significant predictors from the univariate analysis will be included in a multivariate logistic regression model to adjust for confounding factors and determine the independent effect of each variable.\u003c/p\u003e\n\u003cp\u003e4. Development of a Nomogram\u003c/p\u003e\n\u003cp\u003eBased on the results from the multivariate analysis, a nomogram will be constructed. This practical tool will be designed to predict the individual probability of CR in AMI patients, facilitating clinical decision-making.\u003c/p\u003e\n\u003cp\u003e5. Model Validation\u003c/p\u003e\n\u003cp\u003eThe predictive accuracy and generalizability of the nomogram will be evaluated using the receiver operating characteristic (ROC) curve to assess its discriminative ability, and calibration curves to compare the predicted versus observed outcomes. These validation techniques are essential to ensure the reliability and clinical utility of the predictive model.\u003c/p\u003e\n\u003cp\u003e6. Data Management\u003c/p\u003e\n\u003cp\u003eData will be collected and managed using secure, HIPAA-compliant electronic data capture tools. All patient identifiers will be removed to ensure confidentiality and data will be analyzed in a de-identified format.\u003c/p\u003e\n\u003cp\u003e7. Descriptive Statistics\u003c/p\u003e\n\u003cp\u003eDescriptive statistics will be used to summarize the demographic and clinical characteristics of the study population. Continuous variables will be expressed as means \u0026plusmn; standard deviations if normally distributed, or medians with interquartile ranges if distributions are skewed. Categorical variables will be summarized as counts and percentages. All analyses will be conducted using R statistical software (R Foundation for Statistical Computing, Vienna, Austria). A p-value of less than 0.05 will be considered statistically significant for all tests.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTable 1A analyzing risk factors for cardiac rupture following AMI, baseline data were compared between patients who experienced cardiac rupture (CR group, n=30) and those who did not (non-CR group, n=120). The findings revealed that the CR group had significantly older patients, higher incidence of ST-elevation myocardial infarction (STEMI), more severe initial heart function classification (higher Killip class), lower blood pressures, shorter hospital stays, increased mortality rates, higher glucose levels, and more pronounced liver function abnormalities (elevated transaminases and direct bilirubin). Additionally, there was a higher usage of diuretics and nitrates in the CR group. These factors may be associated with an increased risk of cardiac rupture, suggesting the need for early identification and targeted treatment strategies in high-risk patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1A. Baseine characters of enrolled subjects\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003enon-CR group\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=120\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCR group\u003c/strong\u003e\u003cstrong\u003e，\u003c/strong\u003e\u003cstrong\u003en=30\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCardiac rupture,n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (0.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30 (100.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSex, male, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e86 (71.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17 (56.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAge, years (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e67.00 (59.00, 77.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e78.50 (70.50, 84.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSTEMI, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e66(55.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25(83.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eKillip\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026ge;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;grade II, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (0.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21 (70.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSBP, mmHg,mean\u0026plusmn;SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e134.28 \u0026plusmn; 21.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e120.53 \u0026plusmn; 19.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDBP, mmHg, mean\u0026plusmn;SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e74.95 \u0026plusmn; 11.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e69.70 \u0026plusmn; 13.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMAP, mmHg, mean\u0026plusmn;SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e94.72 \u0026plusmn; 12.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e86.64 \u0026plusmn; 13.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePulse, bpm (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e75.00 (66.00, 84.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e75.00 (65.50, 96.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.574\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDeath, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3 (2.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28 (93.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHospitalization day, day(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.00 (6.00, 11.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.00 (3.25, 8.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e83 (69.17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19 (63.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.694\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e44 (36.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9 (30.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.639\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGlucose, mmol/L(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.75 (4.84, 7.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.62 (6.17, 11.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHyperlipidemia, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e86 (71.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11 (36.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e49 (40.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9 (30.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.379\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e37 (30.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6 (20.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.343\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCABG history, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 (0.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3 (10.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eChronic heart failure, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 (0.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (0.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAspirin, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e110 (91.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29 (96.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.584\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eClopidogrel/Ticagrelor, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e102 (85.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29 (96.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eStatin, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e101 (84.17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29 (96.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta; blocker, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e94 (78.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22 (73.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.733\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCCB, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22 (18.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2 (6.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDiuretic, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18 (15.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14 (46.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eACEI/ARB/ARNI, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e81 (67.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14 (46.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eNitrate, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e34 (28.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24 (80.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003eCR=cardiac rupture, STEMI=ST-elevated myocardial infarction, SBP=systolic blood pressure, DBP=diastolic blood pressure, MAP=mean artery pressure, CABG=coronary artery bypass grafting, CCB=calcium channel blocker, ACEI= angiotension converting enzyme inhibitor, ARB=angiotensin II receptor blocker, ARNI=angiotensin receptor \u0026amp; neprilysin inhibitor.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 1B presents a comparative analysis of various biochemical and hematological parameters between non-CR group and CR group. Significant differences were noted in several parameters, indicating a more severe inflammatory and coagulative response in the CR group. Elevated levels of hypersensitive C-reactive protein and white blood cell count in the CR group (p=0.017 and p=0.000, respectively) suggest a heightened inflammatory state. The CR group also showed higher monocyte counts, lower eosinophil counts, and elevated neutrophil counts (all p=0.000), further supporting the presence of acute inflammation. Hemoglobin levels were lower in the CR group (p=0.045), and significant alterations in coagulation parameters such as international normalized ratio, prothrombin time, and fibrinogen degradation products (all p=0.000) were observed, indicating a disturbed coagulation pathway. Additionally, changes in thyroid function and electrolyte levels, such as higher free T4 and calcium (p=0.003 and p=0.001, respectively), and lower sodium levels (p=0.000) were noted in the CR group. These findings suggest that patients with cardiac rupture post-myocardial infarction exhibit a distinct profile of inflammation, coagulation disturbances, and metabolic imbalances, which could be potential markers or contributors to the severity of their condition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1B. Peripheral blood test parameters between CR and non-CR groups following myocardial infarction\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003enon-CR group\u003c/strong\u003e\u003cstrong\u003e，\u003c/strong\u003e\u003cstrong\u003en=120\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCR group\u003c/strong\u003e\u003cstrong\u003e，\u003c/strong\u003e\u003cstrong\u003en=30\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ehs-CRP, mg/L(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.06 (2.32, 19.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.95 (4.95, 31.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eWBC, 10\u003csup\u003e9\u003c/sup\u003e/L, mean\u0026plusmn;SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.91 \u0026plusmn; 2.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.56 \u0026plusmn; 3.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eRBC,10\u003csup\u003e12\u003c/sup\u003e/L, mean\u0026plusmn;SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.31 \u0026plusmn; 0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.26 \u0026plusmn; 0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.689\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePlatelet, 10\u003csup\u003e9\u003c/sup\u003e/L(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e212.50 (173.00, 252.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e214.00 (155.00, 253.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.728\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMonocyte, 10\u003csup\u003e9\u003c/sup\u003e/L(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.33 (0.22, 0.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.57 (0.34, 0.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLymphocyte, 109/L(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.44 (1.12, 2.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.19 (0.98, 1.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eBasophil, 10\u003csup\u003e9\u003c/sup\u003e/L(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.03 (0.02, 0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.02 (0.01, 0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eEosinophil, 10\u003csup\u003e9\u003c/sup\u003e/L(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.12 (0.09, 0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.03 (0.01, 0.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHemoglobin,g/L(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e138.00 (123.00, 149.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e130.00 (110.50, 140.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eNeutrophil, 10\u003csup\u003e9\u003c/sup\u003e/L(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.48 (4.07, 7.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.91 (6.63, 10.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eINR(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.03 (0.99, 1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.11 (1.05, 1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAPTT, s(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e27.60 (25.15, 30.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28.40 (27.30, 32.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAT-III, mg/L, mean\u0026plusmn;SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e87.11 \u0026plusmn; 10.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e82.14 \u0026plusmn; 11.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePT, s(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.90 (11.40, 12.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.75 (12.12, 13.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePT(A), %(IQR)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e95.85 (86.97, 105.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e82.55 (74.30, 92.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFDP, g/L(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.90 (1.00, 3.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.12 (2.50, 4.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFibrinogen, g/L(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.93 (2.31, 3.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.44 (3.22, 4.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eD dimer, mg/L(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.70 (0.50, 0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.90 (0.62, 1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTSH,uIU/mL(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.20 (0.72, 2.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.17 (0.86, 2.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.733\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eThyroid uptake, %(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e43.00 (42.00, 44.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e43.51 (43.43, 45.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eThyroxine, ug/dL(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e82.21 (72.46, 95.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e87.75 (80.13, 105.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTriiodothyronine, ng/ml(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e77.32 (65.34, 86.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e76.77 (56.55, 80.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.293\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFT3, pmol/L(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.62 (2.37, 2.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.73 (2.55, 3.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFT4, pmol/L(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.88 (0.79, 1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.04 (0.90, 1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCalcium, mmol/L(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.13 (2.07, 2.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.19 (2.14, 2.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePotassium, mmol/L(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.12 (3.87, 4.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.12 (3.81, 4.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.622\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eChloride, mmol/L,mean\u0026plusmn;SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e102.40 \u0026plusmn; 4.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e103.23 \u0026plusmn; 3.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.328\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSodium, mmol/L(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e141.20 (138.28, 142.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e137.20 (135.18, 139.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCO2, mmol/L, mean\u0026plusmn;SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23.65 \u0026plusmn; 3.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22.46 \u0026plusmn; 2.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLactate, mmol/L(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.02 (1.73, 2.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.33 (2.36, 4.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eOsmolality, mosm/L(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e293.70 (289.10, 298.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e289.65 (282.80, 294.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAG, mmol/L(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16.50 (14.30, 18.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14.90 (12.88, 17.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eUric acid, umol/L\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e343.45 (287.45, 401.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e350.77 (286.82, 380.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.674\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eALT, IU/L(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20.50 (12.00, 36.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32.50 (19.25, 46.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAST, IU/L(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33.00 (19.65, 96.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e113.20 (44.05, 188.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDB,umol/L(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.46 (1.76, 3.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.41 (3.17, 7.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eIB, umol/L(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.72 (7.84, 13.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.52 (9.08, 15.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTC, mmol/L(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.50 (3.58, 5.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.21 (3.75, 4.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.484\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTriglyceride, mmol/L(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.30 (0.99, 1.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.40 (0.80, 1.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.657\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHDL, mmol/L(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.03 (0.91, 1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.05 (0.92, 1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.746\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLDL, mmol/L(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.56 (1.90, 3.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.42 (1.99, 2.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.559\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCreatinine, umol/L(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e84.60 (74.15, 100.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e86.65 (73.10, 106.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.893\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eUrea nitrogen, mmol/L(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.46 (4.50, 7.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.26 (5.58, 7.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003eCR=cardiac rupture, hs-CRP=hypersensitive C reactive protein, WBC=white blood cell, RBC=red blood cell, INR=international normalized ratio, AT-III=antithrombin III, PT=prothrombin time, APTT=activated partial thromboplastin time, PT(A)=prothrombin time activity, FDP=fibrinogen degradation products, TSH=thyroid stimulating hormone, FT3=free triiodothyronine, FT4=free thyroxine, CO2=carbon dioxide, AG=anion gap, ALT=alanine aminotransferase, AST=aspartate aminotransferase, DB=direct bilirubin, IB=indirect bilirubin, TC=total cholesterol, HDL=high density lipoprotein, LDL=low density lipoprotein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 1C provides a comparison of clinical and cardiac parameters between non-CR group and CR group. Notably, the CR group had a significantly higher percentage of anterior wall AMI (53.33% vs. 25.83%, p=0.007) and lateral wall AMI (20.00% vs. 5.83%, p=0.035). The Gensin score, which is indicative of coronary artery disease severity, was also significantly higher in the CR group (p=0.001). Furthermore, the CR group exhibited a markedly higher incidence of severe complications such as cardiogenic shock (73.33% vs. 1.67%, p=0.000), use of intra-aortic balloon pump (IABP) (36.67% vs. 0.83%, p=0.000), respiratory failure (26.67% vs. 4.17%, p=0.000), acute renal insufficiency (16.67% vs. 0.00%, p=0.000), metabolic acidosis (33.33% vs. 2.50%, p=0.000), and anemia (30.00% vs. 7.50%, p=0.002). These findings suggest that patients in the CR group were more likely to suffer from severe cardiovascular instability and systemic complications. Additionally, there were significant differences in ventricular tachycardia (13.33% vs. 1.67%, p=0.017) and atrial fibrillation (30.00% vs. 11.67%, p=0.027), indicating a higher burden of cardiac arrhythmias in the CR group. The left ventricular ejection fraction was also significantly lower in the CR group (p=0.000), suggesting impaired cardiac function. These data highlight the critical nature and complexity of care required for patients in the CR group following myocardial infarction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1C. Clinical features and inhosptal prognosis between CR and non-CR groups following myocardial infarction\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003enon-CR group\u003c/strong\u003e\u003cstrong\u003e，\u003c/strong\u003e\u003cstrong\u003en=120\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCR group\u003c/strong\u003e\u003cstrong\u003e，\u003c/strong\u003e\u003cstrong\u003en=30\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAnterior wall, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e31 (25.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16 (53.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eInferior wall, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e31 (25.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12 (40.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePosterior wall, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15 (12.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5 (16.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.764\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLateral wall, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7 (5.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6 (20.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGensin score, (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e101.05 (71.88, 105.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e101.09 (101.09, 158.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCardiogenic shock, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2 (1.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22 (73.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eStent implanted, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e80 (66.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17 (56.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.417\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eThrombectomy, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7 (5.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4 (13.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.309\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eIABP, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 (0.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11 (36.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eRF, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5 (4.17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8 (26.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAKI, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (0.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5 (16.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMetabolic acidosis, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3 (2.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10 (33.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAnemia, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9 (7.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9 (30.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePH, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4 (3.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7 (23.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHypokalemia, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14 (11.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11 (36.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVPB, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5 (4.17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5 (16.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVT, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2 (1.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4 (13.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAF, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14 (11.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9 (30.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAPB, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 (0.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2 (6.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.189\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAVB, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3 (2.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6 (20.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCK, IU/L(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24.05 (4.89, 80.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36.10 (8.70, 111.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.645\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ecTnI on admission, ng/ml(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.37 (0.77, 9.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.46 (1.03, 21.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.312\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eNT pro-BNP on admission, pg/ml(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1529.50 (636.75, 6570.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30000.00 (10706.00, 30000.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLAD, mm(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.85 (3.40, 4.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.81 (3.60, 4.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.942\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eEDD, mm(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.10 (4.70, 5.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.16 (5.00, 5.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.629\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eESD, mm(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.40 (3.10, 3.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.75 (3.53, 4.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eEDV, ml(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e123.81(102.36, 147.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e123.81 (123.81, 133.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.468\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eESV, ml(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e47.44 (35.00, 75.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e58.18 (37.28, 65.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.493\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eStroke volume, ml(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e72.49 (62.89, 82.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e76.60 (66.03, 78.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.879\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLVEF, %(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e61.00 (0.53.00, 66.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51.00 (47.00, 56.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFractional shortening, (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.33 (0.27, 0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.32 (0.27, 0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.471\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eRVD, mm(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.70 (1.50, 1.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.80 (1.50, 2.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.306\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eEarly diastolic filling velocity, m/s(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e77.00 (63.00, 90.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e79.46 (63.00, 93.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.916\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLate diastolic filling velocity, m/s, mean\u0026plusmn;SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e90.82 \u0026plusmn; 21.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e94.39 \u0026plusmn; 23.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.429\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eE/A ratio, (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.89 (0.72, 1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.81 (0.65, 1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.245\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMR velocity, m/s (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e332.12 (266.00, 367.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e350.00 (277.50, 390.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAV forward velocity, m/s(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e127.50 (115.00, 145.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e126.50 (112.25, 142.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.730\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLVOT, mm(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e93.00 (81.00, 104.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e90.00 (79.25, 98.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTR vel, m/s(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e233.50 (215.00, 269.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e266.00 (221.25, 307.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSPAP, mmHg(IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e37.53 (33.88, 37.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e37.53 (37.53, 43.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003eCR=cardiac rupture, IABP=intra-aortic balloon pump, RF=respiratory failure, AKI=acute renal insufficiency, PH=pulmonary hypertension, VPB=ventricular premature beat, VT=ventricular tachycardia, AF=atrial fibrillation, APB=atrial premature beat, AVB=atrioventricular block, CK=creatine kinase, cTnI=cardiac troponin I, NT pro-BNP=N-terminal pro-B type natriuretic peptide, LAD=left atrium diameter, EDD=end diastolic diameter, ESD=end systolic diameter, EDV=end diastolic volume, ESV=end systolic volume, LVEF=left ventriclular ejection fraction, RVD=right ventricle diameter, MR vol=mitral regurgitant jet velocity, E/A=late diastolic transmitral flow velocity, AV=aortic valve, LVOT=left ventricular outflow tract, TR=tricuspid regurgitant, SPAP=systolic pulmonary artery pressure.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eIn our study as figure 2A and 2B showed, the optimal lambda value of 2.3907e-2 was meticulously determined and employed within the LASSO regression framework to select a subset of 20 variables from an initial pool of 70 potential predictors. This refined set of variables exhibited significant contributions to the model, as evidenced by their respective regression coefficients. Specifically, the variables along with their coefficients are as follows: Mean artery pressure (-1.1099e-2), indicating a minor negative impact; CABG history (1.9502e+0), Lateral wall AMI (1.0710e+0), and Right ventricle (1.1092e+0) all exhibited strong positive associations. Conversely, Left atrium (-1.0119e-1) and E/A ratio (-3.8654e-1) were negatively correlated. Metabolic acidosis (5.0606e-1), Anemia (3.1070e-1), and Fibrinogen degradation products (4.9113e-2) also showed positive, albeit varying degrees of influence. Notably, Chloride (1.7735e-2) and Free T4 (4.5417e-2) had modest positive coefficients, while Sodium (-7.6212e-2), Urea nitrogen (-3.8524e-2), and Osmolality (-1.5133e-3) demonstrated slight negative contributions. Glucose (8.3132e-2) and Gensin score (1.1626e-2) were positively associated, albeit with smaller magnitudes. Additionally, biomarkers related to patient admission status, such as cTnI on admission (8.4934e-3) and NT pro-BNP on admission (1.2862e-4), displayed minimal but discernible positive effects.\u003c/p\u003e\n\u003cp\u003eTable 2 presents both univariable and multivariable odds ratios (ORs) for various clinical factors affecting patients, with their respective 95% confidence intervals (CIs) and p-values. Key findings include significant associations of lower mean artery pressure, history of coronary artery bypass grafting (CABG), metabolic acidosis, anemia, lower sodium levels, higher glucose levels, lower osmolality, higher cardiac troponin I (cTnI) on admission, higher NT pro-BNP on admission, and higher Gensin scores with increased odds of adverse outcomes. Notably, metabolic acidosis and anemia are strongly associated with adverse outcomes in both univariable and multivariable analyses (ORs 19.50 and 5.29, respectively, in univariable; 15.887 for anemia in multivariable). Additionally, right ventricular size is significantly associated with outcomes in both analyses (ORs 4.58 in univariable and 34.429 in multivariable). Other factors such as glucose and osmolality also show significant associations in multivariable analysis, suggesting their potential role in predicting clinical outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 Logistic regression for selected characters predicting CR\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR univariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR multivariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR and 95%CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR and 95%CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMAP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.95 (0.92-0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCABG history\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e13.22 (1.32-132.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLateral wall\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e3.23 (0.95-11.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetabolic acidosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e19.50 (4.93-77.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnemia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e5.29 (1.88-14.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e15.887(1.667-151.392)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChloride\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.05 (0.95-1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSodium\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.78 (0.69-0.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrea nitrogen\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.02 (0.94-1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGlucose\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.21 (1.07-1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.609(1.150-2.250)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOsmolality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.93 (0.88-0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.881(0.796-0.976)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ecTnI on admission\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.04 (1.02-1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNT-proBNP on admission\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.00 (1.00-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.000(1.000-1.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFDP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.17 (1.04-1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFT4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.68 (0.97-2.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLeft atrium\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.85 (0.38-1.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRight ventricle\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e4.58 (1.83-11.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e34.429(2.738-432.922)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eE/A ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.77 (0.40-1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGensin score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.02 (1.01-1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.040(1.018-1.063)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 100px;\"\u003e\n \u003cp\u003eCR=cardiac rupture, MAP=mean artery pressure, CABG=coronary artery bypass grafting, cTnI=cardiac troponin I, NT pro-BNP=N-terminal pro-B type natriuretic peptide, FDP=fibrinogen degradation products, FT4=free thyroxine, E/A=late diastolic transmitral flow velocity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eFigure 3 illustrates the relationship between several key variables and the overall model score. These variables include right ventricle (diameter), genesis score, NT pro-BNP on admission, osmolality, glucose, and anemia, each depicted with a curve demonstrating their respective impacts on the model\u0026apos;s score. The x-axis represents the range of values for each variable, while the y-axis indicates their effect on the score. Notably, variables such as right ventricle (diameter) and genesis score show a significant contribution to the model, as indicated by stars next to their names, suggesting their pivotal roles in determining the outcome. The distribution of total points is also shown, providing a visual representation of score probabilities across the model. This visualization aids in understanding how these variables influence the overall effectiveness of the model in predicting outcomes.\u003c/p\u003e\n\u003cp\u003eThe ROC curve as showed in figure 4 analysis provided valuable insights into the diagnostic performance of the model. The best threshold value identified was 169.799, which achieved a sensitivity of 86.67% and a specificity of 5.83%. The positive predictive value (PPV) stood at 78.79%, while the negative predictive value (NPV) was notably high at 96.58%. Despite the low specificity, the model\u0026apos;s overall ability to distinguish between the conditions was robust, as indicated by an AUC (Area Under the Curve) of 0.942, with a 95% confidence interval ranging from 0.892 to 0.991. However, the Youden index was -0.075, suggesting a potential imbalance between sensitivity and specificity at the chosen threshold. This analysis underscores the model\u0026apos;s strong predictive power, particularly in correctly identifying negative cases (high NPV), though improvements might be necessary to enhance specificity.\u003c/p\u003e\n\u003cp\u003eThis research conducted a calibration test using the Hosmer-Lemeshow statistic to validate the nomogram model I developed (Figure 5). The Hosmer-Lemeshow statistic, which measures the goodness of fit for the model, was calculated to be 3.315 with a p-value of 0.950. This high p-value indicates that there is no significant difference between the observed outcomes and the predictions made by the nomogram model, suggesting that the model fits the data well. Thus, the calibration test supports the reliability of the model in accurately predicting the outcomes based on the input variables.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eCR is one of the most serious mechanical complications of AMI and also one of the most important causes of death in patients with acute myocardial infarction.[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] With the popularization and deepening of coronary intervention, the incidence of cardiac rupture has significantly decreased to 1% -3%, but the mortality rate is still high, about 60%-86%.Qun Lu\u0026rsquo;s research[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] found that CR continues to be a leading contributor to in-hospital mortality among STEMI patients, particularly in elderly individuals with extensive infarction areas and pronounced inflammatory responses. Although they analyzed those risk factors, up to now however, there is no effective means to prevent heart rupture clinically after myocardial infarction [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Therefore, in order to improve AMI patient\u0026rsquo;s prognosis, accurate evaluation and timely intervention of this complication is crucial for clinical practice.\u003c/p\u003e\u003cp\u003eBy comparing data between CR and non-CR patients in the last decade, this study identified several key predictors of cardiac rupture following acute myocardial infarction using a comprehensive set of clinical variables. Notably, elevated levels of NT-proBNP, right ventricular enlargement, and high Gensini scores were strongly associated with an increased risk of cardiac rupture. These findings are consistent with previous research indicating that increased NT-proBNP levels and right ventricular dysfunction are markers of severe cardiac stress and structural compromise, which can predispose patients to rupture[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Further, this may be due to heart failure (HF) that may increase the risk of heart rupture. When severe HF occurs, the myocardial contractility decreases, the pumping function of the heart is impaired, which may cause a rapid rise in ventricular pressure, so that increase the risk of heart rupture. Some patients may have severe heart failure symptoms after the occurrence of AMI, so that it delays the implementation of the standard treatment of myocardial infarction, some even lose the chance to undergo PCI therapy, finally increasing the probability of a heart rupture.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eMoreover, the Gensini score, which quantifies the degree of coronary artery stenosis and assesses coronary artery disease severity, underscores the role of extensive coronary blockage in the pathophysiology of cardiac rupture post-AMI.[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] It is not directly related to heart rupture, but can indirectly reflect the risk of heart rupture after myocardial infarction. Generally, higher Gensini scores indicate more severe coronary artery stenosis. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] When coronary arteries are severely narrowed or blocked, the blood supply to the myocardium will be reduced or even interrupted, leading to AMI. On the other hands, patients with high Gensini scores may have delayed treatment due to the severity or complexity of their condition. Patients who fail to restore coronary artery blood flow in a timely manner have an increased risk of heart rupture after myocardial infarction.[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eAnemia and hyperglycemia are also identified as significant predictors. These conditions may exacerbate myocardial oxygen demand-supply mismatch and contribute to the weakening of the myocardial wall. Directly speaking, anemia leads to reduced blood oxygen-carrying capacity, affecting the oxygen supply of the myocardium.[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] The cardiac muscle itself is in a state of hypoxia, and anemia may further aggravate the situation.[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] Moreover, during myocardial reperfusion treatment, the myocardium receiving reperfusion after ischemia is more likely to be damaged, which may increase the risk of cardiac rupture after myocardial infarction. Long-term anemia causes compensatory changes in cardiac function, such as increased heart rate and increased cardiac load, factors that may also increase the incidence of cardiac rupture after MI. [\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] For patients with AMI, either STEMI or NSTEMI, reduced admission hemoglobin levels were significantly associated with adverse outcomes such as congestive heart failure, recurrent ischemia, and cardiovascular death. Among patients with STEMI, when hemoglobin concentration falls below 14g/dL, and with NSTEMI below 11g/dL, cardiovascular mortality increases for every 1 g/dL decrease[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In addition, nutritional anemia, such as the megaloblastic anemia caused by vitamin B12 deficiency, has been shown by many studies to be one of the cardiovascular risk factors. [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] The effects of changes in blood glucose values on the cardiovascular system are very complex. In the state of hyperglycemia, the level of saccharification end products and oxidative stress increases, and the inflammatory response of the body is also aggravated. Metabolic disorders may affect the energy metabolism and free radical production of myocardial tissue, and further aggravate the damage of myocardial infarction. At the same time, the process of damaged myocardial fibrosis will also be affected, and eventually lead to the aggravation of the structural and functional damage of the myocardial tissue, so that the myocardial infarction focus is more likely to rupture.[\u003cspan additionalcitationids=\"CR31 CR32 CR33 CR34\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eOsmolality is another indicator of our screening. The role in cardiac rupture is also a complex and important issue that is closely related to inflammatory response, edema formation, and tissue stability. Around the infarcted myocardium, cellular necrosis and inflammatory responses will release multiple inflammatory mediators and cytokines, leading osmotic pressure changing as follow and water moving between tissues, that is the formation of local edema. Edema increases the volume and pressure of the tissue, especially in the cardiac muscle layer, which may increase the risk of MI rupture.[\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] The inclusion of osmolality as a predictor highlights the potential impact of fluid and electrolyte imbalances on cardiac stability post-infarction, a relatively under-explored area in cardiac rupture literature. [\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] The role of some parameters in predicting cardiac rupture has been supported by various studies, but our research further quantifies their impact and integrates these with other less traditional markers like osmolality.\u003c/p\u003e\u003cp\u003eThe predictive nomogram developed from these findings provides a practical tool for clinicians to assess the risk of cardiac rupture in AMI patients. By integrating multiple clinical variables, this nomogram allows for a more nuanced risk stratification compared to traditional methods. Early identification of high-risk patients could lead to more aggressive monitoring, timely interventions, and potentially the use of supportive therapies that stabilize myocardial structure and function. The comprehensive nature of our nomogram, which includes these diverse variables, sets it apart from existing models that often focus on a narrower set of predictors.\u003c/p\u003e\u003cp\u003eHowever, there are still some limitations to this study. The retrospective design limits our ability to infer causality. Additionally, the study population from a single center may not be generalizable to all AMI patients, particularly those in different geographic or healthcare settings, so that may discern potential differences between groups. Future studies should aim to validate our findings in a prospective multicenter trial to enhance the generalizability and robustness of the predictive model. Further research should explore the interplay between these risk factors in a prospective setting, potentially examining the biochemical and molecular mechanisms underlying their association with cardiac rupture. Additionally, investigating the impact of immediate therapeutic interventions based on high-risk profiles generated by the nomogram could provide insights into effective strategies to prevent cardiac rupture in AMI patients.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our study highlights several crucial predictors of cardiac rupture post-AMI and integrates them into a clinically applicable nomogram. This tool has the potential to significantly improve the management of AMI patients by enabling early identification and intervention for those at high risk of cardiac rupture. Continued research and validation of this model are essential to refine its accuracy and clinical utility.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCR=cardiac rupture\u003c/p\u003e\n\u003cp\u003eSTEMI=ST-elevated myocardial infarction\u003c/p\u003e\n\u003cp\u003eSBP=systolic blood pressure\u003c/p\u003e\n\u003cp\u003eDBP=diastolic blood pressure\u003c/p\u003e\n\u003cp\u003eMAP=mean artery pressure\u003c/p\u003e\n\u003cp\u003eCABG=coronary artery bypass grafting\u003c/p\u003e\n\u003cp\u003eCCB=calcium channel blocker\u003c/p\u003e\n\u003cp\u003eACEI=angiotensin converting enzyme inhibitor\u003c/p\u003e\n\u003cp\u003eARB=angiotensin II receptor blocker\u003c/p\u003e\n\u003cp\u003eARNI=angiotensin receptor \u0026amp; neprilysin inhibitor\u003c/p\u003e\n\u003cp\u003ehs-CRP=hypersensitive C reactive protein\u003c/p\u003e\n\u003cp\u003eWBC=white blood cell\u003c/p\u003e\n\u003cp\u003eRBC=red blood cell\u003c/p\u003e\n\u003cp\u003eINR=international normalized ratio\u003c/p\u003e\n\u003cp\u003eAT-III=antithrombin III\u003c/p\u003e\n\u003cp\u003ePT=prothrombin time\u003c/p\u003e\n\u003cp\u003eAPTT=activated partial thromboplastin time\u003c/p\u003e\n\u003cp\u003ePT(A)=prothrombin time activity\u003c/p\u003e\n\u003cp\u003eFDP=fibrinogen degradation products\u003c/p\u003e\n\u003cp\u003eTSH=thyroid stimulating hormone\u003c/p\u003e\n\u003cp\u003eFT3=free triiodothyronine\u003c/p\u003e\n\u003cp\u003eFT4=free thyroxine\u003c/p\u003e\n\u003cp\u003eCO2=carbon dioxide\u003c/p\u003e\n\u003cp\u003eAG=anion gap\u003c/p\u003e\n\u003cp\u003eALT=alanine aminotransferase\u003c/p\u003e\n\u003cp\u003eAST=aspartate aminotransferase\u003c/p\u003e\n\u003cp\u003eDB=direct bilirubin\u003c/p\u003e\n\u003cp\u003eIB=indirect bilirubin\u003c/p\u003e\n\u003cp\u003eTC=total cholesterol\u003c/p\u003e\n\u003cp\u003eHDL=high density lipoprotein\u003c/p\u003e\n\u003cp\u003eLDL=low density lipoprotein\u003c/p\u003e\n\u003cp\u003eIABP=intra-aortic balloon pump\u003c/p\u003e\n\u003cp\u003eRF=respiratory failure\u003c/p\u003e\n\u003cp\u003eAKI=acute renal insufficiency\u003c/p\u003e\n\u003cp\u003ePH=pulmonary hypertension\u003c/p\u003e\n\u003cp\u003eVPB=ventricular premature beat\u003c/p\u003e\n\u003cp\u003eVT=ventricular tachycardia\u003c/p\u003e\n\u003cp\u003eAF=atrial fibrillation\u003c/p\u003e\n\u003cp\u003eAPB=atrial premature beat\u003c/p\u003e\n\u003cp\u003eAVB=atrioventricular block\u003c/p\u003e\n\u003cp\u003eCK=creatine kinase\u003c/p\u003e\n\u003cp\u003ecTnI=cardiac troponin I\u003c/p\u003e\n\u003cp\u003eNT-proBNP=N-terminal pro-B type natriuretic peptide\u003c/p\u003e\n\u003cp\u003eLAD=left atrium diameter\u003c/p\u003e\n\u003cp\u003eEDD=end diastolic diameter\u003c/p\u003e\n\u003cp\u003eESD=end systolic diameter\u003c/p\u003e\n\u003cp\u003eEDV=end diastolic volume\u003c/p\u003e\n\u003cp\u003eESV=end systolic volume\u003c/p\u003e\n\u003cp\u003eLVEF=left ventricular ejection fraction\u003c/p\u003e\n\u003cp\u003eRVD=right ventricle diameter\u003c/p\u003e\n\u003cp\u003eMR vol=mitral regurgitant jet velocity\u003c/p\u003e\n\u003cp\u003eE/A=late diastolic transmitral flow velocity\u003c/p\u003e\n\u003cp\u003eAV=aortic valve\u003c/p\u003e\n\u003cp\u003eLVOT=left ventricular outflow tract\u003c/p\u003e\n\u003cp\u003eTR=tricuspid regurgitant\u003c/p\u003e\n\u003cp\u003eSPAP=systolic pulmonary artery pressure.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Beijing Friendship Hospital in accordance with the ethical standards of the 1964 Declaration of Helsinki and its later amendments (Approval no. 2018\u0026ndash;P2-030-01). Clinical trial number is not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs the study was retrospective and data were collected anonymously, informed consent was waived and innominate data were used.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no competing financial interests or personal relationships, which may affect the work of this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data were collected anonymously and screened according to the inclusion and exclusion Criteria of this research. We have uploaded supplementary information file online. Datasets supporting the conclusions of this study are available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (Grant No. 81600276).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization and Methodology by RF.L, YX.W.; Data Collection by YX.W.; Original Draft Preparation by YX.W.; Visualization and supervision by RF.L; Investigation and Resources by RF.L, YX.W.; Data Analysis by RF.L, YX.W. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDepartment of Emergency, Beijing Friendship Hospital Affiliated to Capital Medical University, Beijing, China\u003c/p\u003e\n\u003cp\u003eYaxin Wang (First author)\u003c/p\u003e\n\u003cp\u003eDepartment of Cardiology, Beijing Friendship Hospital Affiliated to Capital Medical University, Beijing, China\u003c/p\u003e\n\u003cp\u003eRuifeng Liu (Corresponding author)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all study participants, research and department staffs, who participated in this work in Beijing Friendship hospital.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHochman JS, Buller CE, Sleeper LA, Boland J, Dzavik V, Sanborn TA, Godfrey E, White HD, Lim J, LeJemtel T. Cardiogenic shock complicating acute myocardial infarction\u0026ndash;etiologies, management and outcome: a report from the SHOCK Trial Registry. SHould we emergently revascularize Occluded Coronaries for cardiogenic shocK? J Am Coll Cardiol. 2000;36(3 Suppl A):1063\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQian G, Jin RJ, Fu ZH, Yang YQ, Su HL, Dong W, Guo J, Jing J, Guo YL, Chen YD. Development and validation of clinical risk score to predict the cardiac rupture in patients with STEMI. Am J Emerg Med 2017, 35(4):589\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVirani SS, Alonso A, Benjamin EJ, Bittencourt MS, Callaway CW, Carson AP, Chamberlain AM, Chang AR, Cheng S, Delling FN, et al. Heart Disease and Stroke Statistics-2020 Update: A Report From the American Heart Association. Circulation. 2020;141(9):e139\u0026ndash;596.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eValeur N, Clemmensen P, Saunam\u0026auml;ki K, Grande P. The prognostic value of pre-discharge exercise testing after myocardial infarction treated with either primary PCI or fibrinolysis: a DANAMI-2 sub-study. Eur Heart J. 2005;26(2):119\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eL\u0026oacute;pez-Send\u0026oacute;n J, Gonz\u0026aacute;lez A, L\u0026oacute;pez de S\u0026aacute; E, Coma-Canella I, Rold\u0026aacute;n I, Dom\u0026iacute;nguez F, Maqueda I, Mart\u0026iacute;n Jadraque L. Diagnosis of subacute ventricular wall rupture after acute myocardial infarction: sensitivity and specificity of clinical, hemodynamic and echocardiographic criteria. J Am Coll Cardiol. 1992;19(6):1145\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKageyama S, Nakanishi Y, Murata K, Nawada R, Onodera T, Sakamoto A, Yamazaki F, Miura Y, Maekawa Y. Mortality and predictors of survival in patients with recent ventricular septal rupture. Heart Vessels. 2020;35(12):1672\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGong FF, Vaitenas I, Malaisrie SC, Maganti K. Mechanical Complications of Acute Myocardial Infarction: A Review. JAMA Cardiol. 2021;6(3):341\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKumar R, Kumar P, Srivastava PK, Kumar P. Echocardiographic and Angiographic Assessment of Right Ventricular Function and Right Coronary Artery Stenosis in Acute Inferior Wall Myocardial Infarction. Cureus. 2023;15(10):e46403.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSulzgruber P, El-Hamid F, Koller L, Forster S, Goliasch G, Wojta J, Niessner A. Long-term outcome and risk prediction in patients suffering acute myocardial infarction complicated by post-infarction cardiac rupture. Int J Cardiol. 2017;227:399\u0026ndash;403.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchlotter F, Huber K, Hassager C, Halvorsen S, Vranckx P, P\u0026ouml;ss J, Krychtiuk K, Lorusso R, Bonaros N, Calvert PA et al. Ventricular septal defect complicating acute myocardial infarction: diagnosis and management. A Clinical Consensus Statement of the Association for Acute CardioVascular Care (ACVC) of the ESC, the European Association of Percutaneous Cardiovascular Interventions (EAPCI) of the ESC and the ESC Working Group on Cardiovascular Surgery. \u003cem\u003eEuropean heart journal\u003c/em\u003e 2024.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDamluji AA, van Diepen S, Katz JN, Menon V, Tamis-Holland JE, Bakitas M, Cohen MG, Balsam LB, Chikwe J. Mechanical Complications of Acute Myocardial Infarction: A Scientific Statement From the American Heart Association. Circulation. 2021;144(2):e16\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLu Q, Liu P, Huo JH, Wang YN, Ma AQ, Yuan ZY, Du XJ, Bai L. Cardiac rupture complicating acute myocardial infarction: the clinical features from an observational study and animal experiment. BMC Cardiovasc Disord. 2020;20(1):409.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFang X, Wang H, Han D, Xie E, Yang X, Wei J, Gu S, Gao F, Zhu N, Yin X, et al. Ferroptosis as a target for protection against cardiomyopathy. Proc Natl Acad Sci USA. 2019;116(7):2672\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen X, Xu S, Zhao C, Liu B. Role of TLR4/NADPH oxidase 4 pathway in promoting cell death through autophagy and ferroptosis during heart failure. Biochem Biophys Res Commun. 2019;516(1):37\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFang X, Cai Z, Wang H, Han D, Cheng Q, Zhang P, Gao F, Yu Y, Song Z, Wu Q, et al. Loss of Cardiac Ferritin H Facilitates Cardiomyopathy via Slc7a11-Mediated Ferroptosis. Circul Res. 2020;127(4):486\u0026ndash;501.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTorabi A, Cleland JG, Khan NK, Loh PH, Clark AL, Alamgir F, Caplin JL, Rigby AS, Goode K. The timing of development and subsequent clinical course of heart failure after a myocardial infarction. Eur Heart J. 2008;29(7):859\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStone GW, Selker HP, Thiele H, Patel MR, Udelson JE, Ohman EM, Maehara A, Eitel I, Granger CB, Jenkins PL, et al. Relationship Between Infarct Size and Outcomes Following Primary PCI: Patient-Level Analysis From 10 Randomized Trials. J Am Coll Cardiol. 2016;67(14):1674\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYau TM, Pagani FD, Mancini DM, Chang HL, Lala A, Woo YJ, Acker MA, Selzman CH, Soltesz EG, Kern JA, et al. Intramyocardial Injection of Mesenchymal Precursor Cells and Successful Temporary Weaning From Left Ventricular Assist Device Support in Patients With Advanced Heart Failure: A Randomized Clinical Trial. JAMA. 2019;321(12):1176\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChan MY, Efthymios M, Tan SH, Pickering JW, Troughton R, Pemberton C, Ho HH, Prabath JF, Drum CL, Ling LH, et al. Prioritizing Candidates of Post-Myocardial Infarction Heart Failure Using Plasma Proteomics and Single-Cell Transcriptomics. Circulation. 2020;142(15):1408\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAdams MR, Nakagomi A, Keech A, Robinson J, McCredie R, Bailey BP, Freedman SB, Celermajer DS. Carotid intima-media thickness is only weakly correlated with the extent and severity of coronary artery disease. Circulation. 1995;92(8):2127\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZeller T, Seiffert M, M\u0026uuml;ller C, Scholz M, Sch\u0026auml;ffer A, Ojeda F, Drexel H, M\u0026uuml;ndlein A, Kleber ME, M\u0026auml;rz W, et al. Genome-Wide Association Analysis for Severity of Coronary Artery Disease Using the Gensini Scoring System. Front Cardiovasc Med. 2017;4:57.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRampidis GP, Benetos G, Benz DC, Giannopoulos AA, Buechel RR. A guide for Gensini Score calculation. Atherosclerosis. 2019;287:181\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKaiafa G, Kanellos I, Savopoulos C, Kakaletsis N, Giannakoulas G, Hatzitolios AI. Is anemia a new cardiovascular risk factor? Int J Cardiol. 2015;186:117\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKanic V, Kompara G, Vollrath M, Suran D, Kanic Z. Age-Specific Sex-Based Differences in Anemia in Patients with Myocardial Infarction. J Womens Health (Larchmt). 2019;28(7):1004\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIb\u0026aacute;\u0026ntilde;ez B, Heusch G, Ovize M, Van de Werf F. Evolving therapies for myocardial ischemia/reperfusion injury. J Am Coll Cardiol. 2015;65(14):1454\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEapen ZJ, Tang WH, Felker GM, Hernandez AF, Mahaffey KW, Lincoff AM, Roe MT. Defining heart failure end points in ST-segment elevation myocardial infarction trials: integrating past experiences to chart a path forward. Circulation Cardiovasc Qual outcomes. 2012;5(4):594\u0026ndash;600.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHeusch G. Cardioprotection: chances and challenges of its translation to the clinic. Lancet (London England). 2013;381(9861):166\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSabatine MS, Morrow DA, Giugliano RP, Burton PB, Murphy SA, McCabe CH, Gibson CM, Braunwald E. Association of hemoglobin levels with clinical outcomes in acute coronary syndromes. Circulation. 2005;111(16):2042\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePolat E, Demir MC, Kucukdemirci O. Investigation of Vitamin B12 Deficiency in Patients with Acute Coronary Syndrome and its Relationship with Gensini Score. Clin Lab 2022, 68(2).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu J, Zhou Y, Huang H, Liu R, Kang Y, Zhu T, Wu J, Gao Y, Li Y, Wang C, et al. Impact of stress hyperglycemia ratio on mortality in patients with critical acute myocardial infarction: insight from american MIMIC-IV and the chinese CIN-II study. Cardiovasc Diabetol. 2023;22(1):281.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGao S, Huang S, Lin X, Xu L, Yu M. Prognostic implications of stress hyperglycemia ratio in patients with myocardial infarction with nonobstructive coronary arteries. Ann Med. 2023;55(1):990\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKarakasis P, Stalikas N, Patoulias D, Pamporis K, Karagiannidis E, Sagris M, Stachteas P, Bougioukas KI, Anastasiou V, Daios S et al. Prognostic value of stress hyperglycemia ratio in patients with acute myocardial infarction: A systematic review with Bayesian and frequentist meta-analysis. Trends Cardiovasc Med 2023.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAbdu FA, Galip J, Qi P, Zhang W, Mohammed AQ, Liu L, Yin G, Mohammed AA, Mareai RM, Jiang R, et al. Association of stress hyperglycemia ratio and poor long-term prognosis in patients with myocardial infarction with non-obstructive coronary arteries. Cardiovasc Diabetol. 2023;22(1):11.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePaolisso P, Fo\u0026agrave; A, Bergamaschi L, Angeli F, Fabrizio M, Donati F, Toniolo S, Chiti C, Rinaldi A, Stefanizzi A, et al. Impact of admission hyperglycemia on short and long-term prognosis in acute myocardial infarction: MINOCA versus MIOCA. Cardiovasc Diabetol. 2021;20(1):192.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIshihara M. Acute hyperglycemia in patients with acute myocardial infarction. Circulation journal: official J Japanese Circulation Soc. 2012;76(3):563\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eButler TL, Egan JR, Graf FG, Au CG, McMahon AC, North KN, Winlaw DS. Dysfunction induced by ischemia versus edema: does edema matter? J Thorac Cardiovasc Surg. 2009;138(1):141\u0026ndash;7. 147.e141.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGarcia-Dorado D, Andres-Villarreal M, Ruiz-Meana M, Inserte J, Barba I. Myocardial edema: a translational view. J Mol Cell Cardiol. 2012;52(5):931\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAndr\u0026eacute;s-Villarreal M, Barba I, Poncelas M, Inserte J, Rodriguez-Palomares J, Pineda V, Garcia-Dorado D. Measuring Water Distribution in the Heart: Preventing Edema Reduces Ischemia-Reperfusion Injury. J Am Heart Association 2016, 5(12).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSzczepanska-Sadowska E, Wsol A, Cudnoch-Jedrzejewska A, Żera T. Complementary Role of Oxytocin and Vasopressin in Cardiovascular Regulation. \u003cem\u003eInternational journal of molecular sciences\u003c/em\u003e 2021, 22(21).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSears CG, Poulsen AH, Eliot M, Howe CJ, James KA, Harrington JM, Roswall N, Overvad K, Tj\u0026oslash;nneland A, Raaschou-Nielsen O, et al. Urine cadmium and acute myocardial infarction among never smokers in the Danish Diet, Cancer and Health cohort. Environ Int. 2021;150:106428.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStiermaier T, Thiele H, Eitel I. Early myocardial edema after acute myocardial infarction is stable and not bimodal in humans - Evidence from a large CMR multicenter study. Int J Cardiol. 2017;246:87\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Acute myocardial infarction, cardiac rupture, predictive nomogram, Gensini score, LASSO regression, logistic regression","lastPublishedDoi":"10.21203/rs.3.rs-7260965/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7260965/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCardiac rupture is a critical and often fatal complication following acute myocardial infarction (AMI). Early identification of patients at high risk of this event is crucial for timely intervention and improved outcomes.\u003c/p\u003e\u003cp\u003e\u003cb\u003eObjectives\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study aimed to identify clinical predictors of cardiac rupture in AMI patients and develop a predictive nomogram for clinical use.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe conducted a retrospective case-control study at Beijing Friendship Hospital, involving AMI patients treated from January 2018 to the December 2023. Patients were divided into two groups: those who experienced cardiac rupture and those who did not, matched at a 1:4 ratio, then this study included 30 with cardiac rupture and 120 controls. Using least absolute shrinkage and selection operator (LASSO) regression, univariate and multivariate logistic regression analyses, we identified key predictors of cardiac rupture. A nomogram was constructed based on these predictors and validated using receiver operating characteristic (ROC) curves and calibration plots.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSignificant predictors identified by LASSO-logistics regression were N-terminal pro-B type natriuretic peptide (NT-proBNP) on admission, decreased Osmolality, increased right ventricle size, elevated Gensini score, presence of anemia, and elevated glucose levels. The nomogram demonstrated good predictive accuracy with an area under the ROC curve of 0.942 (0.892\u0026ndash;0.991) and the Hosmer-Lemeshow statistic, which measures the goodness of fit for the model, was calculated to be 3.315 with a p-value of 0.950.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe developed nomogram effectively identifies AMI patients at high risk of cardiac rupture, integrating multiple clinical parameters. This tool can aid clinicians in early risk stratification and decision-making, potentially reducing the incidence of this lethal complication.\u003c/p\u003e","manuscriptTitle":"Early Detection of Cardiac Rupture Risk in Acute Myocardial Infarction: A Comprehensive Predictive Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-30 07:16:06","doi":"10.21203/rs.3.rs-7260965/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-09-27T12:45:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"27935043821960737790073772185733082672","date":"2025-09-26T12:08:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"112161276161825390028739737918564670889","date":"2025-09-23T07:54:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-18T08:05:20+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-25T02:52:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-22T10:19:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-22T07:31:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2025-08-22T07:26:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"49cbfc9e-d2d1-44fb-8d03-8f38eed553f7","owner":[],"postedDate":"September 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-09-30T07:16:07+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-30 07:16:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7260965","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7260965","identity":"rs-7260965","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}