The STEP-HF risk score to predict new-onset heart failure after ST- segment elevation myocardial infarction (Killip Ⅰ) treated by primary percutaneous coronary intervention

The STEP-HF risk score to predict new-onset heart failure after ST- segment elevation myocardial infarction (Killip Ⅰ) treated by primary percutaneous coronary intervention | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The STEP-HF risk score to predict new-onset heart failure after ST- segment elevation myocardial infarction (Killip Ⅰ) treated by primary percutaneous coronary intervention Shuo Yang, Haoyue Li, Jiaoyang Xu, Xingjie Hao, Jing Zhang, Rihong Huang, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9431561/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 15 You are reading this latest preprint version Abstract Background: Heart failure (HF) is a serious complication after ST-segment elevation myocardial infarction (STEMI). Even among Killip class I patients undergoing primary percutaneous coronary intervention (pPCI), new-onset in-hospital HF remains common. Simple, validated risk tools for this population are limited. Methods: Using data from the Chinese STEMI pPCI Registry (NCT04996901), we included 4,005 Killip I STEMI patients undergoing pPCI (derivation cohort n=2,940; external validation n=1,065). Multivariable logistic regression identified independent predictors of new-onset in-hospital HF (Killip ≥II) to develop a risk score. Results: Eight independent predictors formed the STEP-HF risk score: age >65 years, previous atrial fibrillation, triple vessel lesion, anterior infarction, symptom-onset-to-balloon time >5 hours, pre-pPCI TIMI flow grade 0, neutrophil-to-lymphocyte ratio >8.5, and aspartate aminotransferase >65 U/L. The score stratified patients into low (0–3), intermediate (4–7), and high (≥8) risk categories. Observed HF rates were 3.38%, 9.97%, and 22.13% in derivation cohort, and 4.25%, 8.80%, and 17.89% in validation cohort. The score demonstrated good discrimination (c-statistic 0.705 and 0.656) and calibration (P=0.314 and 0.223). Conclusion: The STEP-HF risk score is a simple, externally validated tool to identify high-risk Killip I STEMI patients for in-hospital HF after pPCI, enabling early risk stratification and targeted management. Health sciences/Biomarkers Health sciences/Cardiology Health sciences/Diseases Health sciences/Medical research Health sciences/Risk factors ST-segment elevation myocardial infarction heart failure Risk score Killip Ⅰ Figures Figure 1 Figure 2 Highlights 1.The STEP-HF score is the first externally validated risk score for predicting in-hospital new-onset heart failure in Killip I STEMI patients undergoing primary PCI. 2.The score incorporates eight readily available predictors, including clinical, laboratory, and angiographic parameters. 3.It effectively stratifies patients into low-, intermediate-, and high-risk categories with significantly different incidence of in-hospital heart failure. 4.The score demonstrated good discrimination and adequate calibration in both derivation and external validation cohorts. 5.This practical tool may facilitate early risk stratification and guide more intensive monitoring and therapy for high-risk patients. INTRODUCTION Heart failure (HF) is correlated with poor prognosis in acute ST-segment elevation myocardial infarction (STEMI) 1 . Despite aggressive therapy with primary percutaneous coronary intervention (pPCI) for STEMI patients, 3.6%-14.2% individuals without overt HF (Killip Ⅰ) might still develop clinical HF during hospitalization 2 . Based on data from China Chest Pain Center (CCPC) Database, the rates of in-hospital HF was approximately increased from 13.2% to 14.0% before and after COVID-19 outbreak even under effective reperfusion 3 . There exists three time points of HF onset including HF at the index MI presentation, new-onset HF during the first admission, and HF after discharge. The major concern in the past decades have focused on medium-long-term HF events after discharge. What is more important, clinical new-onset HF during their first hospitalization was considered as an earlier indicator for poor prognosis in terms of dynamic pathogenesis 4 . It has been reported that baseline BNP level or BNP-change following myocardial infarction improves risk prediction for poorer short- and long-term outcomes in addition to the GRACE score 5 ; 6 . Several other clinical factors, such as age, sex, diabetes, etc., have also been associated with a higher risk of new-onset HF and mortality 7 ; 8 . However, HF is the consequence of coaction of multiple factors, and could not be ascribed to single factor. Perhaps these studies did not consider this feature. Poor specificity and variability have limited the accurate prediction for these clinical indexes above mentioned. Considering the adverse outcome and future cost, the probability of in-hospital HF must first be accurately and simply estimated, allowing clinicians to decide whether HF is likely present or absent or intermediate, in which case more active treatment is required. To fill this gap, we aimed to develop a more practical risk score to predict the in-hospital occurrence of new-onset HF amongst STEMI with Killip class Ⅰ after receiving pPCI, which in turn helped high risk patients to get more benefit from the early therapeutic strategies. METHODS Design of the study This retrospective observational study collected data from the Chinese STEMI PPCI Registry (NCT04996901). Briefly, target population was STEMI (final diagnosis) with indication for pPCI without thrombolytic therapy from January 1, 2015 to August 31, 2021, based on established international guidelines and standards 9 . Considering the medical level was relatively comparable among 3 official economic-geographic regions of Mainland China (East, Central and North), we intended study hospitals to reflect average treatment capacity in these 3 regions in China. The patients from western region were not considered owing to its heterogeneity of medical technology. We involved patients admitted to the 7 largest interventional cardiology centers in China (East, Central and North; Renmin Hospital of Wuhan University; The First College of Clinical Medical Science, China Three Gorges University& Yichang Central People's Hospital; The No1. People's Hospital of Xiang Yang; First Affiliated Hospital of Dalian Medical University; Jiangxi provincial People's Hospital Affiliated to Nanchang University; The Central Hospital of Enshi Tujia and Miao Autonomous Prefecture; Wuhan Third Hospital & Tongren Hospital of Wuhan University). Each center was the largest local hospital with the greatest clinical capacity for treating STEMI. The data contains clinical characteristics, procedural information, medication therapy, blood test on admission, and in-hospital events. Approximately 65 baseline variables were collected for each patient. The trained investigators identified all hospitalizations for STEMI with pPCI from each local hospital databases, and then assigned each case a unique study ID. The data set variables were entered into a web-based data collection program that allowed checks to ensure the accuracy of the collected data. To improve the accuracy of data extraction, we used resident doctors and abstractors with medical background together to identify data elements requiring medical knowledge for recognition in local hospitals. As described in China PEACE-Retrospective Acute Myocardial Infarction Study 10 , approximately 5% of the abstracted records was randomly selected and checked the accuracy. If the accuracy rate is less than 98%, all cases records were re-reviewed again in this hospital, followed by updating in web-based data collection program. The local research ethics committee of each participating hospital has approved this study (Approval No. WDRY2021-K054 from Renmin Hospital of Wuhan University). The investigation conforms with the principles outlined in the Declaration of Helsinki 11 . Due to the retrospective nature of the study, the Ethics Committee of Renmin Hospital of Wuhan University waived the need of obtaining informed consent. All datasets were transferred and analyzed by Renmin Hospital of Wuhan University, and The School of Public Health, Huazhong University of Science and Technology, China. Patient population The study population consisted of 5586 patients with STEMI receiving pPCI. Given potential benefit of pPCI for STEMI patients presenting both <12 h and 12-24 h after symptom onset 13 , patients finally diagnosed with STEMI within 24 h of symptoms’ onset were enrolled. Exclusion criteria included STEMI beyond 24 h of symptom onset, without or failure pPCI, and Killip class ≥Ⅱ on admission. We also excluded patients with missing HF status or other information and cardiac arrest from symptom onset to hospital admission. Finally, a subset of 4005 consecutive STEMI patients with Killip Ⅰ were retained for further analysis (Figure S1 in the Supplement). The patients from 5 large centers at central China were served as the derivation cohort; whereas patients included in other two North and East China regions were treated as the external validation cohort. Outcome measures and study definitions New-onset HF was defined as Killip class ≥Ⅱ after hospitalization, when signs of bibasilar rales or pulmonary oedema appeared for the first time after the initial clinical evaluation 14 . The patients who met any of the following treatment strategies were considered in-hospital HF and possible indicated signs of HF should be retrospectively retrieved from the medical records 15 : i) requiring high-flow oxygen inhalation; ii) requiring inotrope or mechanical support; iii) requiring various types of diuretics. In terms of in-hospital death, either death or treatment withdrawal because of terminal status at discharge was included in this study 16 . Assessments of terminal status were conducted by the designated physicians based on medical records. STEMI was defined according to the fourth universal definition of MI as ST segment elevation (measured at the J-point) or new left bundle branch block on the electrocardiographic, accompanied by cardiac biomarkers elevation 17 . Total ischemic time was defined as the time between symptom onset and first balloon inflation in the case of pPCI. Time of symptom onset was based on patient interview and required documentation by each hospital center. Preoperative ventricular arrhythmia was defined as either ventricular tachycardia or ventricular fibrillation that causes hemodynamic disturbance before pPCI 18 . High-degree atrioventricular block on admission was defined as the presence of presumed new-onset third-degree or second-degree type 2 atrioventricular blocks on any ECG before pPCI 19 . For every patient, demographic and clinical characteristics were recorded and individual risk factors were assessed during hospitalization. Biological parameters were available by reviewing clinical records on admission. Multivessel coronary artery disease was defined according to angiography results 20 : Infarct related artery (IRA) plus at least one non-infarct related epicardial artery (N-IRA) with at least one lesion deemed angiographically significant (>70% diameter stenosis in one plane or > 50% in 2 planes). The N-IRA should be a major (>2 mm) epicardial coronary artery or branch (>2 mm) and be suitable for stent implantation. Complete revascularization includes the treatment of IRA and all N-IRAs. Investigator –reported TIMI flow grade was used in this study instead of core lab evaluation in the real clinical world. Patients in the derivation cohort were included in the 1-year follow-up.The primary outcomes of this study included new-onset heart failure and all-cause death within 1 year, and these data were mainly obtained through outpatient visits or telephone contact. Loss to follow-up was defined as failure to contact the patient after three attempts.Among the 2,940 patients in the derivation cohort, 199 patients (6.8%) did not complete the 1-year follow-up.Survival data were ultimately obtained for 2741 patients (93.2 %). Statistical analysis Data were reported as number (percentages) for the categorical variables and median (interquartile range, IQR) for the continuous variables. For the categorical variables, the or Fisher exact test was used as appropriate, and for the continuous variables, the Wilcoxon rank-sum test was applied. The derivation cohort was used to identify the predictors of new-onset in-hospital HF and develop a risk-scoring system 21 . Predictors were first analyzed with univariable logistic regression to identify the candidate variables that were significantly associated with new-onset HF. For ease of clinical use, continuous variables were dichotomized according to receiver-operating characteristic curves and Youden’s index to identify the optimal and clinically relevant cut-off points for discrimination. To develop the final model, the missing data were imputed with random forest imputation (miss Forest R package, version 1.5) 22 . The significant variables ( P <0.05) in the univariable analysis were entered into multivariable logistic regression model. The significant variables ( P < 0.05) in multivariable analysis were further fitted in the final multivariable logistic regression model. Then, these coefficients of significant multivariable predictors were divided by the smallest coefficient value in the final model and rounded to the nearest integer to assign a risk score weight for each predictor in the model as previous described 21 . The risk score of each patient was calculated by summing up these weights on a continuous scale. We evaluated diagnostic performance of the risk-scoring system by the area under the receiver-operating characteristic curve (AUC), or c-statistic in the derivation and validation cohorts. We also assessed the calibration by performing the Hosmer-Lemeshow goodness-of-fit test and by plotting the observed vs. predicted incidence rate across the risk score in the derivation and validation cohorts. In addition, we evaluated the clinical usefulness of the risk-scoring system using decision curve analysis (rmda R package, version 1.6) 23 by estimating the net benefit of using the model to risk-stratify patients according to different decision thresholds of in-hospital HF risk, compared with the two alternatives of assuming that none or all will be at high risk, as well as a model based only on one clinical variable. Discriminative performance of the STEP‑HF model and GRACE score was compared using time‑dependent AUC with the DeLong test, and incremental prognostic value was assessed by the integrated discrimination improvement and net reclassification improvement via bootstrap resamples. Differences in 30‑day and 1‑year mortality across STEP‑HF risk strata were tested using ANOVA, with post hoc pairwise comparisons by Wilcoxon rank‑sum tests. All tests were two‑sided with P < 0.05 considered significant; analyses were performed using R software (versions 4.0.5 and 4.4.1). RESULTS Baseline characteristics and outcomes A total of 2940 patients [median age 59 (51–68) years, 18.1% female] and 1065 patients [median age 62 (53–70) years, 17.7% female] were included in the derivation and validation cohorts, respectively. Baseline characteristics of patients within the derivation and validation cohorts were shown in Table 1. In clinical characteristics, patients in the validation cohort were slight older, and had lower blood pressure and heart rate. In addition, patients in the validation cohort presented relatively lower rates of smoking, hyperlipidaemia, previous myocardial infarction and stroke, and higher rates of diabetes, previous atrial fibrillation and PCI. Meanwhile, the population in validation cohort was more likely to harbour multivessel coronary disease and complete revascularization. TIMI flow grade 0 before pPCI and rate of IRA stent implantation were also more common in validation cohort. Although patients from validation cohorts were more likely to present bradyarrhythmia, other malignant arrhythmia was equally achieved in both cohorts before pPCI. In addition, several serum biochemical indexes ( i.e. neutrophil count, AST, AST, GLU, TC and TG) on admission showed different baseline levels between two cohorts, as well as the standard medical therapies and procedural characteristics during hospitalization. New-onset in-hospital HF occurred in 10.3% of patients in the validation cohort compared with 10.7% of patients in the derivation cohort ( P > 0.05). The median time from admission to the development of new-onset HF was 39 h (95% CI 24-72 h) and 48 h (95% CI 29-78 h) in derivation and derivation cohort, respectively ( P = 0.005). In-hospital mortality and treatment withdraw in the validation cohort displayed slight lower rates as compared with that in the derivation cohort (0.4% vs. 1.1%, P = 0.046). Predictors of new-onset HF in hospital Clinical, biological and procedural characteristics were evaluated using univariable logistic regression in the derivation cohort (Table S1 in the Supplement). Certain variables identified through univariable analysis were entered into a multivariable model. Finally, eight predictors, reported in Figure 1, were independently associated with new-onset HF in hospital by multivariable analysis: age >65 years, previous atrial fibrillation, triple vessel lesion, anterior myocardial infarction, symptom onset-to-balloon time > 5 h, TIMI flow grade 0 before pPCI, neutrophil-to-lymphocyte ratio (NLR) > 8.5 and aspartate aminotransferase (AST) > 65 U/L on admission. Derivation and validation of the STEP-HF risk score The score weights were assigned to these eight variables based on strength of association in the multivariable logistic regression with new-onset HF as outcome. The score weights of each variable ranged from 1 for NLR >8.5, TIMI flow grade 0 before pPCI, symptom onset-to-balloon time > 5 h and anterior myocardial infarction, to 4 for age >65 years and previous atrial fibrillation. The individual risk score was calculated by adding each component and theoretically ranged from 0 to 16. The scoring system was names as STEP-HF risk score and an online calculator is available at https://hust-sph.shinyapps.io/STEP-HF/. The actual scores were ranged from 0 to 16 in the derivation cohort and from 0 to 13 in the validation cohort. The relationship between the score value and the observed incidence of new-onset HF is shown in the Figure 2A. The OR associated with one-point increase of the score were 1.31 (95% CI 1.26–1.37; P < 0.001) and 1.24 (95% CI 1.15–1.34; P < 0.001) in the derivation and validation cohort. The STEP-HF risk score demonstrated reliable discrimination ability with AUC of 0.705 and 0.656 in the derivation and validation cohorts, respectively (Figure S2 in the Supplement). The calibration plots of predicted vs. observed incidence of new-onset HF in the derivation and validation cohort across risk score showed good calibration with the P -values of the Hosmer–Lemeshow test being 0.314 and 0.223 (Figure 2B). When the individuals were stratified into three groups according to the predicted incidence of new-onset HF and approximate quartiles in the derivation cohort (Figure 2C), the low-risk group for a score ≤3 corresponding to the first quartile had the predicted incidence <4.86% (828 patients, 28.16% of the derivation cohort), the intermediate risk group for a score of 4-7 corresponding to the interquartile range had the predicted incidence ≥6.37% and ≤13.86% (1484 patients, 50.48%), and the high risk for a score ≥8 corresponding to the forth quartile had the predicted incidence ≥17.7% (628 patients, 21.36%). The observed incidences of new-onset HF according to these cut-offs were 3.38%, 9.97%, and 22.13%, respectively, in the derivation cohort (Figure 2C). Moreover, the distribution of patients of the validation cohort according to their predicted risk was as follows: 19.91% (n = 212) low-risk, 53.33% (n = 568) intermediate risk, and 26.76% (n = 285) high-risk. Observed incidence of new-onset HF according to these increasing levels of risk was 4.25%, 8.80%, and 17.89%, respectively, in the validation cohort (Figure 2C). In addition, among all the individuals in the derivation and validation cohorts, all-cause mortality and treatment withdraw rate in hospital was higher in the high-risk group (15/913, 1.64%) compared to that in the non-high-risk group (22/3092, 0.71%) with OR = 2.33 (95% CI 1.20–4.51; P = 0.012). Sensitivity Analyses The prediction model was further compared with the simple models that only including age or previous atrial fibrillation information. The AUC of the model only including age were 0.629 and 0.620 in the derivation and validation cohort. The AUC of the model only including previous atrial fibrillation were 0.514 and 0.505 in the derivation and validation cohorts. In addition, the STEP-HF risk score model had more net clinical benefit compared with the simple models including only age or previous atrial fibrillation predictor for some for relevant decision thresholds (Figure S3 in the Supplement). Predictive performance and risk stratification of the STEP-HF score In the derivation cohort, the STEP-HF score achieved a significantly higher AUC for in-hospital AHF prediction than the GRACE score (AUC: 0.701 vs. 0.669; P = 0.026), accompanied by significant improvements in IDI (0.021) and NRI (0.238; both P < 0.001)(Table 2). Furthermore, the STEP-HF score was further compared with the GRACE score, and both scores showed comparable predictive performance for 30-day all-cause mortality (AUC: 0.756 vs. 0.752; P = 0.923) and 1-year all-cause mortality (AUC: 0.718 vs. 0.669; P = 0.202)(Table 2) . Patients stratified by STEP-HF score into 3 risk groups showed stepwise increases in all-cause mortality(Figure S4 in the Supplement). These findings indicate that the STEP-HF score has superior predictive performance and reclassification ability for in-hospital AHF, while showing comparable predictive efficiency for 30-day and 1-year all-cause mortality relative to the GRACE score. DISCUSSION In this analysis, we developed and externally validated a novel clinical risk score for predicting new-onset HF during hospitalization in STEMI patients treated with pPCI. The data for all the variables used in the risk models, including clinical, laboratory, and angiographic parameters, are available during the acute phase of AMI management and within the first few hours of hospital admission. The results showed that our model has a high prediction accuracy in new-onset HF prediction. In this retrospective observational study, we reported that the incidences of new-onset HF during hospitalization were 10.3% and 10.7% of patients in the validation cohort and in the derivation cohort, respectively, which were higher than earlier studies in the United States and Europe 2 . The most likely explanation for the high incidence of new-onset HF was that STEMI patients involved in our study had a longer symptom onset-to-balloon time (median 5 hours in both cohorts) compared with previous studies (about 3 hours) 24 ; 25 . The data from Cardiovascular Disease in China (CCC)-Acute Coronary Syndrome (ACS) Project, showed that patients took a much longer average time from symptom onset to reach the hospital (median 5.9 hours) as well 26 . A lack of awareness of STEMI symptoms, preference public or private transport instead of ambulance to hospital, bad traffic in urban areas, and scarcity of efficient transportation in rural areas may be responsible for the pre-hospital delay in China 26 . In the present study, we developed and externally validated a novel clinical risk score, which included 8 variables for predicting new-onset HF during hospitalization in STEMI patients treated with pPCI. The prognosis value of traditional predictors, including age, anterior MI and triple vessel lesion, was again confirmed in our study. Parameters predicting the extent of myocardial injury, such as TIMI flow grade 0 before pPCI and prolonged symptom-onset-to-balloon time 27 , appeared to be powerful determinants of the new-onset HF during hospitalization in our study. In addition, previous atrial fibrillation also emerged as an independent risk factor for new-onset HF development after STEMI, although it was not evaluated in the previous models. Atrial fibrillation promotes systemic inflammation and endothelial dysfunction, eventually favoring development of coronary heart disease and AMI 28 . Coronary thromboembolism is another possible mechanism for STEMI in AF patients 29 . Atrial fibrillation results in an increased myocardial oxygen demand and hemodynamic alterations, further aggravating myocardial ischemia and cardiac output reduction 30 , which contribute to increased risk of new-onset HF during hospitalization in STEMI patients. The values of laboratory parameters included in this study were the first test results after admission, which can reflect the initial situation of the STEMI patients and avoid the influence of subsequent treatment on laboratory results as much as possible. For the above reasons, peak cardiac troponin level incorporated widely in previous studies was not included in our study. The common biomarkers of myocardial injury, such as myoglobin, creatine kinase isoenzyme, and the sensitive troponin Ⅰ, were measured by qualitative and semi-quantitative detection in some hospitals in China, therefore, these parameters were also not included. Instead, increasing levels of AST (>65 U/L), a forgotten biomarker of myocardial injury, were independently associated with new-onset HF endpoint. It has been shown that peak activities of AST correlates well with total creatine kinase and creatine kinase isoenzyme peak activities, indicating that AST also can reflect the infarct size 31 . In a prospective observational cohort study, Gao et al. showed that increased AST correlates significantly with short- and long-term all-cause mortality in patients with STEMI undergoing pPCI 32 . A large amount of evidence has accumulated showing that NLR, as a potential inflammatory marker, contributed to the pathogenesis of ventricular remodeling and cardiac dysfunction after STEMI 33 . In our patient population in whom NLR was more than 6.3, a greater probability was observed for development of new-onset HF. A single biological parameter was unlikely to include all relevant factors for a thorough clinical evaluation. However, the STEP-HF risk score combined several important clinical implications with easily available laboratory indexes, which provided a simple tool to help decision making. LIMITATIONS First, certain dynamic parameters of prognostic values for HF in the STEMI setting, such as electrocardiogram, B-type natriuretic peptides and soluble ST2, were not available in a significant proportion of patients, and were therefore not included in our models. Secondly, TIMI flow grade <3 after pPCI occurred in less than 1.5% of enrolled patients and was not an independent predictor for new-onset HF in hospital in our study. Post-procedural TIMI flow reflects epicardial coronary perfusion but not myocardial perfusion. Future studies are needed to identify the predictive value of myocardial perfusion indicators on new-onset HF in STEMI patients after pPCI. Thirdly, whether the present risk score can predict long-term HF after discharge or can be used in higher Killip class (≥Ⅱ) patients remains to be elucidated by further studies. CONCLUSIONS In summary, to our knowledge, this is the first study to construct the risk score to provide a good estimation of an individual patient’s risk of new-onset HF during hospitalization in STEMI patients treated with pPCI. The results of the present study need to be confirmed and validated in prospective large series of patients with STEMI treated with pPCI. DECLARATIONS Funding This work was supported by the Chinese Society of Cardiology's Foundation (CSCF2021A02). Acknowledgements We want to acknowledge the participants and investigators of the Chinese STEMI PPCI Registry. Author Contributions Dr Jing Chen have had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: Jing Chen, Hong Jiang and Shanshan Guo. Acquisition, analysis, or interpretation of data: All authors. Drafting of the manuscript: Shuo Yang, Jiaoyang Xu, Haoyue Li, Jing Chen. Obtained funding: Jing Chen. Conflict of Interest Disclosures None declared. Role of the Funder/Sponsor The funder had no role in whole design and conduct of this study; data collection, management, analysis, and interpretation; as well as the preparation, review, and approval of the manuscript; and the decision to the manuscript for publication. 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TABLES Table 1 Baseline characteristics in the derivation and validation cohorts Variable Derivation cohort (n=2940) Validation cohort (n=1065) P -value Clinical characteristic Age(years) 59 (51-68) 62 (53-70) <0.0001 Female sex 533 (18.1%) 188 (17.7%) 0.764 Smoking 1595 (54.3%) 536 (50.3%) 0.031 Hypertension 1494 (50.8%) 565 (53.1%) 0.225 Diabetes 479 (16.3%) 234 (22%) <0.0001 Hyperlipidaemia 272 (9.3%) 8 (0.8%) <0.0001 Previous atrial fibrillation 43 (1.5%) 31 (2.9%) 0.004 Previous heart failure 7 (0.2%) 3 (0.3%) 0.732 Previous myocardial infarction 108 (3.7%) 24 (2.3%) 0.034 Previous PCI 117 (4%) 60 (5.6%) 0.031 Previous stroke 198 (6.7%) 32 (3%) <0.0001 Presentation Anterior myocardial infarction 1417 (48.2%) 472 (44.3%) 0.033 Symptom onset to balloon time (h) 5 (3-7.62) 5 (3-7) 0.261 Ventricular arrhythmia before pPCI 72 (2.4%) 25 (2.3%) 0.946 Bradyarrhythmia before pPCI 77 (2.6%) 85 (8%) <0.0001 Syncope before pPCI 52 (1.8%) 13 (1.2%) 0.284 Haemodynamic on admission Heart rate (b.p.m.) 77 (69-86) 74 (66-85) 0.0004 Systolic blood pressure (mmHg) 125 (111-140) 120 (106-134) <0.0001 Diastolic blood pressure (mmHg) 79 (70-88) 73 (65-82) <0.0001 Procedural characteristics Infarcted-related coronary artery 0.027 Left main 6 (0.2%) 0 (0%) 0.352 Left anterior descending 1431 (48.7%) 480 (45.1%) 0.048 Left circumflex 347 (11.8%) 116 (10.9%) 0.459 Right 1156 (39.3%) 469 (44%) 0.008 Stent implantation 2703 (91.9%) 1027 (96.4%) <0.0001 TIMI flow grade 0 before pPCI 2082 (70.8%) 793 (74.5%) 0.026 TIMI flow grade after pPCI 0.008 1 6 (0.2%) 2 (0.2%) 1 2 38 (1.3%) 3 (0.3%) 0.004 3 2896 (98.5%) 1060 (99.5%) 0.008 Multivessel disease <0.0001 Single vessel lesion 1470 (50%) 514 (48.3%) 0.349 Double vessel lesion 820 (27.9%) 367 (34.5%) <0.0001 Triple vessel lesion 650 (22.1%) 184 (17.3%) 0.001 Complete revascularization 1683 (57.2%) 727 (68.3%) <0.0001 Therapy characteristics Dual antiplatelet therapy 2916 (99.2%) 1059 (99.4%) 0.540 Statin 2912 (99%) 1063 (99.8%) 0.023 β-blocker 2549 (86.7%) 809 (76%) <0.0001 ACEI/ARB/ARNI 2044 (69.5%) 644 (60.5%) <0.0001 Cardiotonic drugs 31 (1.1%) 73 (6.9%) <0.0001 Diuretic 373 (12.7%) 143 (13.4%) 0.573 Sprironolactone 413 (14%) 195 (18.3%) 0.001 Calcium channel blocker 427 (14.5%) 53 (5%) <0.0001 Intra–aortic balloon pump 6 (0.2%) 3 (0.3%) 0.707 Respirator 20 (0.7%) 2 (0.2%) 0.105 Continuous renal replacement therapy 4 (0.1%) 2 (0.2%) 0.660 Blood tests on admission White blood cell count (10 9 /L) 10.28 (8.34-12.5) 10.27 (8.41-12.43) 0.678 Neutrophil count (10 9 /L) 8.23 (6.3-10.47) 7.9 (6.03-10.22) 0.011 Lymphocyte count (10 9 /L) 1.25 (0.89-1.78) 1.23 (0.86-1.8) 0.729 NLR 6.68 (4.07-10.38) 6.34 (3.95-10.09) 0.236 Platelet count (10 9 /L) 205 (169-246) 206 (174-245) 0.771 Haemoglobin count (g/L) 141 (129-152) 141 (129-150) 0.262 ALT (U/L) 37 (24-58) 45 (29-68) <0.0001 AST (U/L) 108 (43.55-251) 149 (69-269.5) <0.0001 Uric acid (μmol/L) 355 (293-421) 347.5 (286.25-422.75) 0.294 Glucose (mmol/L) 6.98 (5.84-8.77) 6.16 (5.22-7.81) <0.0001 Cholesterol (mmol/L) 4.57 (3.94-5.26) 4.73 (4.11-5.44) <0.0001 Triglyceride (mmol/L) 1.3 (0.87-1.97) 1.49 (1.06-2.1) <0.0001 HDL-C (mmol/L) 1.09 (0.91-1.31) 1.08 (0.94-1.26) 0.311 LDL-C (mmol/L) 2.8 (2.24-3.36) 2.76 (2.3-3.26) 0.339 Serum creatinine (μmol/L) 71.2 (61-84) 71 (61-84) 0.857 Quantitative data are expressed as median (interquartile range). Categorical variables are expressed as number (percentage). PCI, percutaneous coronary intervention; pPCI, primary percutaneous coronary intervention; TIMI, thrombolysis in myocardial infarction; ACEI, angiotensin converting enzyme inhibitor; ARB, angiotensin receptor blocker; ARNI, angiotensin receptor-neprilysin inhibitor; NLR, neutrophil-to-lymphocyte ratio; ALT, alanine transaminase; AST, aspartate aminotransferase; HDL-C, High-density lipoprotein cholesterol; LDL-C, Low-density lipoprotein cholesterol; Table 2. Comparison of predictive performance between the STEP-HF score and GRACE score for in-hospital acute heart failure (AHF) and mortality outcomes. Outcome Model AUC P1 IDI P2 NRI P3 In-hospital AHF STEP-HF Score 0.701 0.026 0.021 <0.001 0.238 <0.001 GRACE Score 0.669 NA NA NA NA 30-day Mortality STEP-HF Score 0.756 0.923 0 0.917 0.009 0.824 GRACE Score 0.752 NA NA NA NA 1-year Mortality STEP-HF Score 0.718 0.202 -0.005 0.585 -0.105 0.465 GRACE Score 0.669 NA NA NA NA AHF, acute heart failure; AUC, area under the receiver operating characteristic curve; IDI, integrated discrimination improvement; NRI, net reclassification improvement; GRACE, Global Registry of Acute Coronary Events. P1 values denote the statistical significance of AUC differences between the STEP-HF score and GRACE score; P2 and P3 represent the significance of IDI and NRI, respectively. NA, these metrics assess incremental value of the STEP-HF score over the reference GRACE model. Additional Declarations No competing interests reported. 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A.\u003cstrong\u003e \u003c/strong\u003eRelationship between the risk score value and the observed incidence of new-onset HF in hospitalization in the derivation and validation cohorts. B. Calibration plots showing the predicted probability vs. observed incidence of new-onset HF in hospitalization in the derivation and validation cohorts. The diagonal grey dotted line represents the perfect calibration (y = x). The Hosmer-Lemeshow goodness-of-fit test results using the predicted probabilities were \u003cem\u003eP\u003c/em\u003e = 0.314, and 0.233 for the derivation and validation cohorts, respectively, indicating support for a properly calibrated model. C. Observed incidence of new-onset HF in hospitalization according to categories of the STEP-HF risk score in the derivation and validation cohorts.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9431561/v1/1056667122a4f3b185304927.png"},{"id":108809997,"identity":"8510db85-fa3f-4262-a6c2-b1e8f31382bf","added_by":"auto","created_at":"2026-05-08 15:56:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":786119,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9431561/v1/f7bfa6bb-ed3c-4af1-81a2-eae448582a4c.pdf"},{"id":108805683,"identity":"c4dafc28-06a7-40b1-aced-b1a8104ad49a","added_by":"auto","created_at":"2026-05-08 15:26:37","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1587528,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-9431561/v1/c5e948d8e5b0ad469f058e82.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The STEP-HF risk score to predict new-onset heart failure after ST- segment elevation myocardial infarction (Killip Ⅰ) treated by primary percutaneous coronary intervention","fulltext":[{"header":"Highlights ","content":"\u003cp\u003e1.The STEP-HF score is the first externally validated risk score for predicting in-hospital new-onset heart failure in Killip I STEMI patients undergoing primary PCI.\u003c/p\u003e\n\u003cp\u003e2.The score incorporates eight readily available predictors, including clinical, laboratory, and angiographic parameters.\u003c/p\u003e\n\u003cp\u003e3.It effectively stratifies patients into low-, intermediate-, and high-risk categories with significantly different incidence of in-hospital heart failure.\u003c/p\u003e\n\u003cp\u003e4.The score demonstrated good discrimination and adequate calibration in both derivation and external validation cohorts.\u003c/p\u003e\n\u003cp\u003e5.This practical tool may facilitate early risk stratification and guide more intensive monitoring and therapy for high-risk patients.\u003c/p\u003e"},{"header":"INTRODUCTION","content":"\u003cp\u003eHeart failure (HF) is correlated with poor prognosis in acute ST-segment elevation myocardial infarction (STEMI)\u003csup\u003e1\u003c/sup\u003e.\u0026nbsp;Despite aggressive therapy with\u0026nbsp;primary percutaneous coronary intervention\u0026nbsp;(pPCI) for STEMI patients, 3.6%-14.2% individuals without overt HF (Killip Ⅰ) might still develop clinical HF during hospitalization\u003csup\u003e2\u003c/sup\u003e. Based on data from China Chest Pain Center (CCPC) Database, the rates of in-hospital HF was approximately increased from 13.2% to 14.0% before and after COVID-19 outbreak even under effective reperfusion\u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThere exists three time points of HF onset including HF at the index MI presentation, new-onset HF during the first admission, and HF after discharge. The major concern in the past decades have focused on medium-long-term HF events after discharge. What is more important, clinical new-onset HF during their first hospitalization was considered as an earlier indicator for poor prognosis in terms of dynamic pathogenesis\u003csup\u003e4\u003c/sup\u003e.\u0026nbsp;It has been reported that baseline BNP level or\u0026nbsp;BNP-change following myocardial infarction improves risk prediction for poorer short- and long-term outcomes in addition to the GRACE score\u003csup\u003e5\u003c/sup\u003e; \u003csup\u003e6\u003c/sup\u003e.\u0026nbsp;Several other clinical factors, such as age, sex, diabetes, etc., have also been associated with a higher risk of new-onset HF and mortality\u003csup\u003e7\u003c/sup\u003e; \u003csup\u003e8\u003c/sup\u003e. However, HF is the consequence of coaction of multiple factors, and could not be ascribed to single factor. Perhaps these studies did not consider this feature. Poor specificity and variability have limited the accurate prediction for these clinical indexes above mentioned. Considering the adverse outcome and future cost, the probability of in-hospital HF must first be accurately and simply estimated, allowing clinicians to decide whether HF is likely present or absent or intermediate, in which case more active treatment is required.\u003c/p\u003e\n\u003cp\u003eTo fill this gap, we aimed to develop a more practical risk score to predict the in-hospital occurrence of new-onset HF amongst STEMI with Killip class Ⅰ after receiving pPCI, which in turn helped high risk patients to get more benefit from the early therapeutic strategies.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cstrong\u003eDesign of the study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective observational study collected data from the Chinese STEMI PPCI Registry (NCT04996901). Briefly, target population was STEMI (final diagnosis) with indication for pPCI without thrombolytic therapy\u0026nbsp;from January 1, 2015 to August 31, 2021,\u0026nbsp;based on established international guidelines and standards\u003csup\u003e9\u003c/sup\u003e. Considering the medical level was relatively comparable among 3 official economic-geographic regions of Mainland China (East, Central and North), we intended study hospitals to reflect average treatment capacity in these 3 regions in China. The patients from western region were not considered owing to its heterogeneity of medical technology. We involved patients admitted to the 7 largest interventional cardiology centers in China (East, Central and North; Renmin Hospital of Wuhan University; The First College of Clinical Medical Science, China Three Gorges University\u0026amp; Yichang Central People\u0026apos;s Hospital; The No1. People\u0026apos;s Hospital of Xiang Yang; First Affiliated Hospital of Dalian Medical University; Jiangxi provincial People\u0026apos;s Hospital Affiliated to Nanchang University; The Central Hospital of Enshi Tujia and Miao Autonomous Prefecture; Wuhan Third Hospital \u0026amp; Tongren Hospital of Wuhan University). Each center was the largest local hospital with the greatest clinical capacity for treating STEMI.\u003c/p\u003e\n\u003cp\u003eThe data contains clinical characteristics, procedural information, medication therapy, blood test on admission, and in-hospital events. Approximately 65 baseline variables were collected for each patient. The trained investigators identified all hospitalizations for STEMI with pPCI from each local hospital databases, and then assigned each case a unique study ID. The data set variables were entered into a web-based data collection program that allowed checks to ensure the accuracy of the collected data. To improve the accuracy of data extraction, we used resident\u0026nbsp;doctors and abstractors with medical background together to identify data elements requiring medical knowledge for recognition in local hospitals. As described in China PEACE-Retrospective Acute Myocardial Infarction Study\u003csup\u003e10\u003c/sup\u003e, approximately 5% of the abstracted records was randomly selected and checked the accuracy. If the accuracy rate is less than 98%, all cases records were re-reviewed again in this hospital, followed by updating in web-based data collection program.\u003c/p\u003e\n\u003cp\u003eThe local research ethics committee of each participating hospital has approved this study (Approval No. WDRY2021-K054 from Renmin Hospital of Wuhan University).\u0026nbsp;The investigation conforms with the principles outlined in the \u003cem\u003eDeclaration of Helsinki\u003c/em\u003e \u003csup\u003e11\u003c/sup\u003e. Due to the retrospective nature of the study, the Ethics Committee of Renmin Hospital of Wuhan University waived the need of obtaining informed consent.\u0026nbsp;All datasets were transferred and analyzed by Renmin Hospital of Wuhan University, and The School of Public Health, Huazhong University of Science and Technology, China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study population consisted of 5586 patients with STEMI receiving pPCI. Given potential benefit of pPCI for STEMI patients presenting both \u0026lt;12 h and 12-24 h after symptom onset\u003csup\u003e13\u003c/sup\u003e, patients finally diagnosed with STEMI within 24 h of symptoms\u0026rsquo; onset were enrolled. Exclusion criteria included STEMI beyond 24 h of symptom onset, without or failure pPCI, and Killip class \u0026ge;Ⅱ on admission. We also excluded patients with missing HF status or other information and cardiac arrest from symptom onset to hospital admission. Finally, a subset of 4005 consecutive STEMI patients with Killip Ⅰ were retained for further analysis (Figure S1 in the Supplement). The patients from 5 large centers at central China were served as the derivation cohort; whereas patients included in other two North and East China regions were treated as the external validation cohort.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcome measures and study definitions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNew-onset HF was defined as Killip class \u0026ge;Ⅱ after hospitalization, when signs of bibasilar rales or pulmonary oedema appeared for the first time after the initial clinical evaluation\u003csup\u003e14\u003c/sup\u003e. The patients who met any of the following treatment strategies were considered in-hospital HF and possible indicated signs of HF should be retrospectively retrieved from the medical records\u003csup\u003e15\u003c/sup\u003e: i) requiring high-flow oxygen inhalation; ii) requiring inotrope or mechanical support; iii) requiring various types of diuretics.\u0026nbsp;In terms of in-hospital death, either death or treatment withdrawal because of terminal status at discharge was included in this study\u003csup\u003e16\u003c/sup\u003e. Assessments of terminal status were conducted by the designated physicians based on medical records.\u003c/p\u003e\n\u003cp\u003eSTEMI was defined according to the fourth universal definition of MI as ST segment elevation (measured at the J-point) or new left bundle branch block on the electrocardiographic, accompanied by cardiac biomarkers elevation\u003csup\u003e17\u003c/sup\u003e. Total ischemic time was defined as the time between symptom onset and first balloon inflation in the case of pPCI. Time of symptom onset was based on patient interview and required documentation by each hospital center. Preoperative ventricular arrhythmia was defined as either ventricular tachycardia or ventricular fibrillation that causes hemodynamic disturbance before pPCI\u003csup\u003e18\u003c/sup\u003e. High-degree atrioventricular block on admission was defined as the presence of presumed new-onset third-degree or second-degree type 2 atrioventricular blocks on any ECG before pPCI\u003csup\u003e19\u003c/sup\u003e. For every patient, demographic and clinical characteristics were recorded and individual risk factors were assessed during hospitalization. Biological parameters were available by reviewing clinical records on admission.\u003c/p\u003e\n\u003cp\u003eMultivessel coronary artery disease was defined according to angiography results\u003csup\u003e20\u003c/sup\u003e: Infarct related artery (IRA) plus at least one non-infarct related epicardial artery (N-IRA) with at least one lesion deemed angiographically significant (\u0026gt;70% diameter stenosis in one plane or \u0026gt; 50% in 2 planes). The N-IRA should be a major (\u0026gt;2 mm) epicardial coronary artery or branch (\u0026gt;2 mm) and be suitable for stent implantation. Complete revascularization includes the treatment of IRA and all N-IRAs. Investigator \u0026ndash;reported TIMI flow grade was used in this study instead of core lab evaluation in the real clinical world.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePatients in the derivation cohort were included in the 1-year follow-up.The primary outcomes of this study included new-onset heart failure and all-cause death within 1 year, and these data were mainly obtained through outpatient visits or telephone contact. Loss to follow-up was defined as failure to contact the patient after three attempts.Among the 2,940 patients in the derivation cohort, 199 patients (6.8%) did not complete the 1-year follow-up.Survival data were ultimately obtained for 2741 patients (93.2 %).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were reported as number (percentages) for the categorical variables and median (interquartile range, IQR) for the continuous variables. For the categorical variables, the \u003cimg width=\"15\" height=\"19\" src=\"https://myfiles.space/user_files/58893_b39df98f09c4a4bb/58893_custom_files/img1778073324.gif\" v:shapes=\"_x0000_i1025\" alt=\"image\"\u003e or Fisher exact test was used as appropriate, and for the continuous variables, the Wilcoxon rank-sum test was applied. The derivation cohort was used to identify the predictors of new-onset in-hospital HF and develop a risk-scoring system\u003csup\u003e21\u003c/sup\u003e. Predictors were first analyzed with univariable logistic regression to identify the candidate variables that were significantly associated with new-onset HF. For ease of clinical use, continuous variables were dichotomized according to receiver-operating characteristic curves and Youden\u0026rsquo;s index to identify the optimal and clinically relevant cut-off points for discrimination. To develop the final model, the missing data were imputed with random forest imputation (miss Forest R package, version 1.5)\u003csup\u003e22\u003c/sup\u003e. The significant variables (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05) in the univariable analysis were entered into multivariable logistic regression model. The significant variables (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in multivariable analysis were further fitted in the final multivariable logistic regression model. Then, these coefficients of significant multivariable predictors were divided by the smallest coefficient value in the final model and rounded to the nearest integer to assign a risk score weight for each predictor in the model as previous described\u003csup\u003e21\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe risk score of each patient was calculated by summing up these weights on a continuous scale. We evaluated diagnostic performance of the risk-scoring system by the area under the receiver-operating characteristic curve (AUC), or c-statistic in the derivation and validation cohorts. We also assessed the calibration by performing the Hosmer-Lemeshow goodness-of-fit test and by plotting the observed vs. predicted incidence rate across the risk score in the derivation and validation cohorts. In addition, we evaluated the clinical usefulness of the risk-scoring system using decision curve analysis (rmda R package, version 1.6)\u003csup\u003e23\u003c/sup\u003e by estimating the net benefit of using the model to risk-stratify patients according to different decision thresholds of in-hospital HF risk, compared with the two alternatives of assuming that none or all will be at high risk, as well as a model based only on one clinical variable. Discriminative performance of the STEP‑HF model and GRACE score was compared using time‑dependent AUC with the DeLong test, and incremental prognostic value was assessed by the integrated discrimination improvement and net reclassification improvement via bootstrap resamples. Differences in 30‑day and 1‑year mortality across STEP‑HF risk strata were tested using ANOVA, with post hoc pairwise comparisons by Wilcoxon rank‑sum tests.\u003c/p\u003e\n\u003cp\u003eAll tests were two‑sided with P \u0026lt; 0.05 considered significant; analyses were performed using R software (versions 4.0.5 and 4.4.1).\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003eBaseline characteristics and outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 2940 patients [median age 59 (51\u0026ndash;68) years, 18.1% female] and 1065 patients [median age 62 (53\u0026ndash;70) years, 17.7% female] were included in the derivation and validation cohorts, respectively. Baseline characteristics of patients within the derivation and validation cohorts were shown in Table 1. In clinical characteristics, patients in the validation cohort were slight older, and had lower blood pressure and heart rate. In addition, patients in the validation cohort presented relatively lower rates of smoking, hyperlipidaemia, previous myocardial infarction and stroke, and higher rates of diabetes, previous atrial fibrillation and PCI.\u003c/p\u003e\n\u003cp\u003eMeanwhile, the population in validation cohort was more likely to harbour multivessel coronary disease and complete revascularization. TIMI flow grade 0 before pPCI and rate of IRA stent implantation were also more common in validation cohort. Although patients from validation cohorts were more likely to present bradyarrhythmia, other malignant arrhythmia was equally achieved in both cohorts before pPCI. In addition, several serum biochemical indexes (\u003cem\u003ei.e.\u003c/em\u003e neutrophil count, AST, AST, GLU, TC and TG) on admission showed different baseline levels between two cohorts, as well as the standard medical therapies and procedural characteristics during hospitalization.\u003c/p\u003e\n\u003cp\u003eNew-onset in-hospital HF occurred in 10.3% of patients in the validation cohort compared with 10.7% of patients in the derivation cohort (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05). The median time from admission to the development of new-onset HF was 39 h (95% \u003cem\u003eCI\u003c/em\u003e 24-72 h) and 48 h (95% \u003cem\u003eCI\u003c/em\u003e 29-78 h) in derivation and derivation cohort, respectively (\u003cem\u003eP\u003c/em\u003e = 0.005). In-hospital mortality and treatment withdraw in the validation cohort displayed slight lower rates as compared with that in the derivation cohort (0.4% vs. 1.1%, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.046).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePredictors of new-onset HF in hospital\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical, biological and procedural characteristics were evaluated using univariable logistic regression in the derivation cohort (Table S1 in the Supplement). Certain variables identified through univariable analysis were entered into a multivariable model. Finally, eight predictors, reported in Figure 1, were independently associated with new-onset HF in hospital by multivariable analysis: age \u0026gt;65 years, previous atrial fibrillation, triple vessel lesion, anterior myocardial infarction,\u0026nbsp;symptom onset-to-balloon time\u0026nbsp;\u0026gt; 5 h, TIMI flow grade 0 before pPCI, neutrophil-to-lymphocyte ratio (NLR) \u0026gt; 8.5 and aspartate aminotransferase (AST) \u0026gt; 65 U/L on admission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDerivation and validation of the STEP-HF risk score\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe score weights were assigned to these eight variables based on strength of association in the multivariable logistic regression with new-onset HF as outcome. The score weights of each variable ranged from 1 for NLR \u0026gt;8.5, TIMI flow grade 0 before pPCI, symptom onset-to-balloon time\u0026nbsp;\u0026gt; 5 h and anterior myocardial infarction, to 4 for age \u0026gt;65 years and previous atrial fibrillation. The individual risk score was calculated by adding each component and theoretically ranged from 0 to 16. The scoring system was names as STEP-HF risk score and an online calculator is available at https://hust-sph.shinyapps.io/STEP-HF/. The actual scores were ranged from 0 to 16 in the derivation cohort and from 0 to 13 in the validation cohort. The relationship between the score value and the observed incidence of new-onset HF is shown in the Figure 2A. The OR associated with one-point increase of the score were 1.31 (95% \u003cem\u003eCI\u003c/em\u003e 1.26\u0026ndash;1.37; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 1.24 (95% \u003cem\u003eCI\u003c/em\u003e 1.15\u0026ndash;1.34; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in the derivation and validation cohort.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe STEP-HF risk score demonstrated reliable discrimination ability with AUC of 0.705 and 0.656 in the derivation and validation cohorts, respectively (Figure S2 in the Supplement). The calibration plots of predicted vs. observed incidence of new-onset HF in the derivation and validation cohort across risk score showed good calibration with the \u003cem\u003eP\u003c/em\u003e-values of the Hosmer\u0026ndash;Lemeshow test being 0.314 and 0.223 (Figure 2B).\u003c/p\u003e\n\u003cp\u003eWhen the individuals were stratified into three groups according to the predicted incidence of new-onset HF and approximate quartiles in the derivation cohort (Figure 2C), the low-risk group for a score \u0026le;3 corresponding to the first quartile had the predicted incidence \u0026lt;4.86% (828 patients, 28.16% of the derivation cohort), the intermediate risk group for a score of 4-7 corresponding to the interquartile range had the predicted incidence \u0026ge;6.37% and \u0026le;13.86% (1484 patients, 50.48%), and the high risk for a score \u0026ge;8 corresponding to the forth quartile had the predicted incidence\u0026thinsp;\u0026ge;17.7% (628 patients, 21.36%). The observed incidences of new-onset HF according to these cut-offs were 3.38%, 9.97%, and 22.13%, respectively, in the derivation cohort (Figure 2C). Moreover, the distribution of patients of the validation cohort according to their predicted risk was as follows: 19.91% (n\u0026thinsp;=\u0026thinsp;212) low-risk, 53.33% (n\u0026thinsp;=\u0026thinsp;568) intermediate risk, and 26.76% (n\u0026thinsp;=\u0026thinsp;285) high-risk. Observed incidence of new-onset HF according to these increasing levels of risk was 4.25%, 8.80%, and 17.89%, respectively, in the validation cohort (Figure 2C). In addition, among all the individuals in the derivation and validation cohorts, all-cause mortality and treatment withdraw rate in hospital was higher in the high-risk group (15/913, 1.64%) compared to that in the non-high-risk group (22/3092, 0.71%) with OR = 2.33 (95% \u003cem\u003eCI\u003c/em\u003e 1.20\u0026ndash;4.51; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;= 0.012).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSensitivity Analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe prediction model was further compared with the simple models that only including age or previous atrial fibrillation information. The AUC of the model only including age were 0.629 and 0.620 in the derivation and validation cohort. The AUC of the model only including previous atrial fibrillation were 0.514 and 0.505 in the derivation and validation cohorts. In addition, the STEP-HF risk score model had more net clinical benefit compared with the simple models including only age or previous atrial fibrillation predictor for some for relevant decision thresholds (Figure S3 in the Supplement).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePredictive performance and risk stratification of the STEP-HF score\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the derivation cohort, the STEP-HF score achieved a significantly higher AUC for in-hospital AHF prediction than the GRACE score (AUC: 0.701 vs. 0.669; P = 0.026), accompanied by significant improvements in IDI (0.021) and NRI (0.238; both P \u0026lt; 0.001)(Table 2). Furthermore, the STEP-HF score was further compared with the GRACE score, and both scores showed comparable predictive performance for 30-day all-cause mortality (AUC: 0.756 vs. 0.752; P = 0.923) and 1-year all-cause mortality (AUC: 0.718 vs. 0.669; P = 0.202)(Table 2) . Patients stratified by STEP-HF score into 3 risk groups showed stepwise increases in all-cause mortality(Figure S4 in the Supplement). These findings indicate that the STEP-HF score has superior predictive performance and reclassification ability for in-hospital AHF, while showing comparable predictive efficiency for 30-day and 1-year all-cause mortality relative to the GRACE score.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this analysis, we developed and externally validated a novel clinical risk score for predicting new-onset HF during hospitalization in STEMI patients treated with pPCI. The data for all the variables used in the risk models, including clinical, laboratory, and angiographic parameters, are available during the acute phase of AMI management and within the first few hours of hospital admission. The results showed that our model has a high prediction accuracy in new-onset HF prediction.\u003c/p\u003e\n\u003cp\u003eIn this retrospective observational study, we reported that the incidences of new-onset HF during hospitalization were 10.3% and 10.7% of patients in the validation cohort and in the derivation cohort, respectively, which were higher than earlier studies in the United States and Europe \u003csup\u003e2\u003c/sup\u003e. The most likely explanation for the high incidence of new-onset HF was that STEMI patients involved in our study had a longer symptom onset-to-balloon time (median 5 hours in both cohorts) compared with previous studies (about 3 hours)\u003csup\u003e24\u003c/sup\u003e; \u003csup\u003e25\u003c/sup\u003e. The data from Cardiovascular Disease in China (CCC)-Acute Coronary Syndrome (ACS) Project, showed that patients took a much longer average time from symptom onset to reach the hospital (median 5.9 hours) as well \u003csup\u003e26\u003c/sup\u003e. A lack of awareness of STEMI symptoms, preference public or private transport instead of ambulance to hospital, bad traffic in urban areas, and scarcity of efficient transportation in rural areas may be responsible for the pre-hospital delay in China\u003csup\u003e26\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn the present study, we developed and externally validated a novel clinical risk score, which included 8 variables for predicting new-onset HF during hospitalization in STEMI patients treated with pPCI. The prognosis value of traditional predictors, including age, anterior MI and triple vessel lesion, was again confirmed in our study. Parameters predicting the extent of myocardial injury, such as TIMI flow grade 0 before pPCI and prolonged symptom-onset-to-balloon time\u003csup\u003e27\u003c/sup\u003e, appeared to be powerful determinants of the new-onset HF during hospitalization in our study. In addition, previous atrial fibrillation also emerged as an independent risk factor for new-onset HF development after STEMI, although it was not evaluated in the previous models. Atrial fibrillation promotes systemic inflammation and endothelial dysfunction, eventually favoring development of coronary heart disease and AMI\u003csup\u003e28\u003c/sup\u003e. Coronary thromboembolism is another possible mechanism for STEMI in AF patients\u003csup\u003e29\u003c/sup\u003e. Atrial fibrillation results in an increased myocardial oxygen demand and hemodynamic alterations, further aggravating myocardial ischemia and cardiac output reduction\u003csup\u003e30\u003c/sup\u003e, which contribute to increased risk of new-onset HF during hospitalization in STEMI patients.\u003c/p\u003e\n\u003cp\u003eThe values of laboratory parameters included in this study were the first test results after admission, which can reflect the initial situation of the STEMI patients and avoid the influence of subsequent treatment on laboratory results as much as possible. For the above reasons, peak cardiac troponin level incorporated widely in previous studies was not included in our study. The common biomarkers of myocardial injury, such as myoglobin, creatine kinase isoenzyme, and the sensitive troponin Ⅰ, were measured by qualitative and semi-quantitative detection in some hospitals in China, therefore, these parameters were also not included. Instead, increasing levels of AST (\u0026gt;65 U/L), a forgotten biomarker of myocardial injury, were independently associated with new-onset HF endpoint. It has been shown that peak activities of AST correlates well with total creatine kinase and creatine kinase isoenzyme peak activities, indicating that AST also can reflect the infarct size\u003csup\u003e31\u003c/sup\u003e. In a prospective observational cohort study, Gao et al. showed that increased AST correlates significantly with short- and long-term all-cause mortality in patients with STEMI undergoing pPCI\u003csup\u003e32\u003c/sup\u003e. A large amount of evidence has accumulated showing that NLR, as a potential inflammatory marker, contributed to the pathogenesis of ventricular remodeling and cardiac dysfunction after STEMI\u003csup\u003e33\u003c/sup\u003e. In our patient population in whom NLR was more than 6.3, a greater probability was observed for development of new-onset HF. A single biological parameter was unlikely to include all relevant factors for a thorough clinical evaluation. However, the STEP-HF risk score combined several important clinical implications with easily available laboratory indexes, which provided a simple tool to help decision making.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLIMITATIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirst, certain dynamic parameters of prognostic values for HF in the STEMI setting, such as electrocardiogram, B-type natriuretic peptides and soluble ST2, were not available in a significant proportion of patients, and were therefore not included in our models. Secondly, TIMI flow grade \u0026lt;3 after pPCI occurred in less than 1.5% of enrolled patients and was not an independent predictor for new-onset HF in hospital in our study. Post-procedural TIMI flow reflects epicardial coronary perfusion but not myocardial perfusion. Future studies are needed to identify the predictive value of myocardial perfusion indicators on new-onset HF in STEMI patients after pPCI. Thirdly, whether the present risk score can predict long-term HF after discharge or can be used in higher Killip class (\u0026ge;Ⅱ) patients remains to be elucidated by further studies.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eIn summary, to our knowledge, this is the first study to construct the risk score to provide a good estimation of an individual patient\u0026rsquo;s risk of new-onset HF during hospitalization in STEMI patients treated with pPCI. The results of the present study need to be confirmed and validated in prospective large series of patients with STEMI treated with pPCI.\u003c/p\u003e"},{"header":"DECLARATIONS","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Chinese Society of Cardiology\u0026apos;s Foundation (CSCF2021A02).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe want to acknowledge the participants and investigators of the Chinese STEMI PPCI Registry.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDr Jing Chen have had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.\u003c/p\u003e\n\u003cp\u003eConcept and design:\u0026nbsp;Jing Chen, Hong Jiang and Shanshan Guo.\u003c/p\u003e\n\u003cp\u003eAcquisition, analysis, or interpretation of data:\u0026nbsp;All authors.\u003c/p\u003e\n\u003cp\u003eDrafting of the manuscript:\u0026nbsp;Shuo Yang, Jiaoyang Xu, Haoyue Li, Jing Chen.\u003c/p\u003e\n\u003cp\u003eObtained funding: Jing Chen.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Disclosures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone declared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRole of the Funder/Sponsor\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe funder had no role in whole design and conduct of this study; data collection, management, analysis, and interpretation; as well as the preparation, review, and approval of the manuscript; and the decision to the manuscript for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclaimer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the funding agents.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"REFERENCES","content":"\u003col\u003e\n\u003cli\u003eVogel, B. et al. St-Segment Elevation Myocardial Infarction. \u003cem\u003eNat. Rev. Dis. Primers\u003c/em\u003e. \u003cstrong\u003e5\u003c/strong\u003e, 39 (2019).\u003c/li\u003e\n\u003cli\u003eShah, R. V. et al. Risk of Heart Failure Complication During Hospitalization for Acute Myocardial Infarction in a Contemporary Population: Insights From the National Cardiovascular Data Action Registry. \u003cem\u003eCirc. Heart Fail.\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, 693-702 (2012).\u003c/li\u003e\n\u003cli\u003eXiang, D. et al. Management and Outcomes of Patients with Stemi During the Covid-19 Pandemic in China. \u003cem\u003eJ. Am. Coll. Cardiol.\u003c/em\u003e \u003cstrong\u003e76\u003c/strong\u003e, 1318-1324 (2020).\u003c/li\u003e\n\u003cli\u003eCahill, T. J. \u0026amp; Kharbanda, R. K. Heart Failure After Myocardial Infarction in the Era of Primary Percutaneous Coronary Intervention: Mechanisms, Incidence and Identification of Patients at Risk. \u003cem\u003eWorld J. Cardiol.\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 407-415 (2017).\u003c/li\u003e\n\u003cli\u003eCarvalho, L. et al. Change of Bnp Between Admission and Discharge After St-Elevation Myocardial Infarction (Killip I) Improves Risk Prediction of Heart Failure, Death, and Recurrent Myocardial Infarction Compared to Single Isolated Measurement in Addition to the Grace Score. \u003cem\u003eEur. Heart J. Acute Cardiovasc. Care\u003c/em\u003e. \u003cstrong\u003e8\u003c/strong\u003e, 643-651 (2019).\u003c/li\u003e\n\u003cli\u003eSinnaeve, P. R. et al. Reperfusion Before Percutaneous Coronary Intervention in St-Elevation Myocardial Infarction Patients is Associated with Lower N-Terminal Pro-Brain Natriuretic Peptide Levels During Follow-Up, Irrespective of Pre-Treatment with Full-Dose Fibrinolysis. \u003cem\u003eEur. Heart J.\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e, 2213-2219 (2009).\u003c/li\u003e\n\u003cli\u003eEzekowitz, J. A. et al. Is there a Sex Gap in Surviving an Acute Coronary Syndrome Or Subsequent Development of Heart Failure? \u003cem\u003eCirculation\u003c/em\u003e. \u003cstrong\u003e142\u003c/strong\u003e, 2231-2239 (2020).\u003c/li\u003e\n\u003cli\u003eTaniguchi, T. et al. Incidence and Prognostic Impact of Heart Failure Hospitalization During Follow-Up After Primary Percutaneous Coronary Intervention in St-Segment Elevation Myocardial Infarction. \u003cem\u003eAm. J. Cardiol.\u003c/em\u003e \u003cstrong\u003e119\u003c/strong\u003e, 1729-1739 (2017).\u003c/li\u003e\n\u003cli\u003eIbanez, B. et al. 2017 Esc Guidelines for the Management of Acute Myocardial Infarction in Patients Presenting with St-Segment Elevation: The Task Force for the Management of Acute Myocardial Infarction in Patients Presenting with St-Segment Elevation of the European Society of Cardiology (Esc). \u003cem\u003eEur. Heart J.\u003c/em\u003e \u003cstrong\u003e39\u003c/strong\u003e, 119-177 (2018).\u003c/li\u003e\n\u003cli\u003eDharmarajan, K. et al. The China Patient-Centered Evaluative Assessment of Cardiac Events (China Peace) Retrospective Study of Acute Myocardial Infarction: Study Design. \u003cem\u003eCirc Cardiovasc Qual Outcomes\u003c/em\u003e. \u003cstrong\u003e6\u003c/strong\u003e, 732-740 (2013).\u003c/li\u003e\n\u003cli\u003eRICKHAM, P. P. Human Experimentation. Code of Ethics of the World Medical Association. Declaration of Helsinki. \u003cem\u003eBr Med J\u003c/em\u003e. \u003cstrong\u003e2\u003c/strong\u003e, 177 (1964).\u003c/li\u003e\n\u003cli\u003eLawton, J. S. et al. 2021 Acc/Aha/Scai Guideline for Coronary Artery Revascularization: A Report of the American College of Cardiology/American Heart Association Joint Committee On Clinical Practice Guidelines. \u003cem\u003eCirculation\u003c/em\u003e. \u003cstrong\u003e145\u003c/strong\u003e, e18-e114 (2022).\u003c/li\u003e\n\u003cli\u003eCenko, E. et al. Sex-Related Differences in Heart Failure After St-Segment Elevation Myocardial Infarction. \u003cem\u003eJ. Am. Coll. Cardiol.\u003c/em\u003e \u003cstrong\u003e74\u003c/strong\u003e, 2379-2389 (2019).\u003c/li\u003e\n\u003cli\u003ePonikowski, P. et al. 2016 Esc Guidelines for the Diagnosis and Treatment of Acute and Chronic Heart Failure: The Task Force for the Diagnosis and Treatment of Acute and Chronic Heart Failure of the European Society of Cardiology (Esc). Developed with the Special Contribution of the Heart Failure Association (Hfa) of the Esc. \u003cem\u003eEur. J. Heart Fail.\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 891-975 (2016).\u003c/li\u003e\n\u003cli\u003eLi, J. et al. St-Segment Elevation Myocardial Infarction in China From 2001 to 2011 (the China Peace-Retrospective Acute Myocardial Infarction Study): A Retrospective Analysis of Hospital Data. \u003cem\u003eLancet\u003c/em\u003e. \u003cstrong\u003e385\u003c/strong\u003e, 441-451 (2015).\u003c/li\u003e\n\u003cli\u003eThygesen, K. et al. Fourth Universal Definition of Myocardial Infarction (2018). \u003cem\u003eJ. Am. Coll. Cardiol.\u003c/em\u003e \u003cstrong\u003e72\u003c/strong\u003e, 2231-2264 (2018).\u003c/li\u003e\n\u003cli\u003eArmstrong, P. W. A Comparison of Pharmacologic Therapy with/without Timely Coronary Intervention Vs. Primary Percutaneous Intervention Early After St-Elevation Myocardial Infarction: The West (Which Early St-Elevation Myocardial Infarction Therapy) Study. \u003cem\u003eEur. Heart J.\u003c/em\u003e \u003cstrong\u003e27\u003c/strong\u003e, 1530-1538 (2006).\u003c/li\u003e\n\u003cli\u003ePokorney, S. D. et al. High-Degree Atrioventricular Block, Asystole, and Electro-Mechanical Dissociation Complicating Non-St-Segment Elevation Myocardial Infarction. \u003cem\u003eAm. Heart J.\u003c/em\u003e \u003cstrong\u003e171\u003c/strong\u003e, 25-32 (2016).\u003c/li\u003e\n\u003cli\u003eThiele, H. et al. Pci Strategies in Patients with Acute Myocardial Infarction and Cardiogenic Shock. \u003cem\u003eN. Engl. J. Med.\u003c/em\u003e \u003cstrong\u003e377\u003c/strong\u003e, 2419-2432 (2017).\u003c/li\u003e\n\u003cli\u003eSullivan, L. M., Massaro, J. M. \u0026amp; D\u0026apos;Agostino, R. S. Presentation of Multivariate Data for Clinical Use: The Framingham Study Risk Score Functions. \u003cem\u003eStat. Med.\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, 1631-1660 (2004).\u003c/li\u003e\n\u003cli\u003eStekhoven, D. J. \u0026amp; Buhlmann, P. Missforest--Non-Parametric Missing Value Imputation for Mixed-Type Data. \u003cem\u003eBioinformatics\u003c/em\u003e. \u003cstrong\u003e28\u003c/strong\u003e, 112-118 (2012).\u003c/li\u003e\n\u003cli\u003eKerr, K. F., Brown, M. D., Zhu, K. \u0026amp; Janes, H. Assessing the Clinical Impact of Risk Prediction Models with Decision Curves: Guidance for Correct Interpretation and Appropriate Use. \u003cem\u003eJ. Clin. Oncol.\u003c/em\u003e \u003cstrong\u003e34\u003c/strong\u003e, 2534-2540 (2016).\u003c/li\u003e\n\u003cli\u003eAntoni, M. L. et al. Cardiovascular Mortality and Heart Failure Risk Score for Patients After St-Segment Elevation Acute Myocardial Infarction Treated with Primary Percutaneous Coronary Intervention (Data From the Leiden Mission! Infarct Registry). \u003cem\u003eAm. J. Cardiol.\u003c/em\u003e \u003cstrong\u003e109\u003c/strong\u003e, 187-194 (2012).\u003c/li\u003e\n\u003cli\u003eStone, G. W. et al. Bivalirudin During Primary Pci in Acute Myocardial Infarction. \u003cem\u003eN. Engl. J. Med.\u003c/em\u003e \u003cstrong\u003e358\u003c/strong\u003e, 2218-2230 (2008).\u003c/li\u003e\n\u003cli\u003eHao, Y. et al. Performance of Management Strategies with Class I Recommendations Among Patients Hospitalized with St-Segment Elevation Myocardial Infarction in China. \u003cem\u003eJama Cardiol.\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 484-491 (2022).\u003c/li\u003e\n\u003cli\u003eHahn, J. Y. et al. Relation of Left Ventricular Infarct Transmurality and Infarct Size After Primary Percutaneous Coronary Angioplasty to Time From Symptom Onset to Balloon Inflation. \u003cem\u003eAm. J. Cardiol.\u003c/em\u003e \u003cstrong\u003e102\u003c/strong\u003e, 1163-1169 (2008).\u003c/li\u003e\n\u003cli\u003eBorschel, C. S. \u0026amp; Schnabel, R. B. The Imminent Epidemic of Atrial Fibrillation and its Concomitant Diseases - Myocardial Infarction and Heart Failure - A Cause for Concern. \u003cem\u003eInt. J. Cardiol.\u003c/em\u003e \u003cstrong\u003e287\u003c/strong\u003e, 162-173 (2019).\u003c/li\u003e\n\u003cli\u003eShibata, T. et al. Prevalence, Clinical Features, and Prognosis of Acute Myocardial Infarction Attributable to Coronary Artery Embolism. \u003cem\u003eCirculation\u003c/em\u003e. \u003cstrong\u003e132\u003c/strong\u003e, 241-250 (2015).\u003c/li\u003e\n\u003cli\u003eClark, D. M., Plumb, V. J., Epstein, A. E. \u0026amp; Kay, G. N. Hemodynamic Effects of an Irregular Sequence of Ventricular Cycle Lengths During Atrial Fibrillation. \u003cem\u003eJ. Am. Coll. Cardiol.\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e, 1039-1045 (1997).\u003c/li\u003e\n\u003cli\u003ePanteghini, M., Pagani, F. \u0026amp; Cuccia, C. Activity of Serum Aspartate Aminotransferase Isoenzymes in Patients with Acute Myocardial Infarction. \u003cem\u003eClin. Chem.\u003c/em\u003e \u003cstrong\u003e33\u003c/strong\u003e, 67-71 (1987).\u003c/li\u003e\n\u003cli\u003eGao, M. et al. Association of Serum Transaminases with Short- And Long-Term Outcomes in Patients with St-Elevation Myocardial Infarction Undergoing Primary Percutaneous Coronary Intervention. \u003cem\u003eBmc Cardiovasc. Disord.\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, 43 (2017).\u003c/li\u003e\n\u003cli\u003eSwirski, F. K. \u0026amp; Nahrendorf, M. Leukocyte Behavior in Atherosclerosis, Myocardial Infarction, and Heart Failure. \u003cem\u003eScience\u003c/em\u003e. \u003cstrong\u003e339\u003c/strong\u003e, 161-166 (2013).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"TABLES","content":"\u003cp\u003e\u003cstrong\u003eTable 1 Baseline characteristics in the derivation and validation cohorts\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDerivation cohort\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=2940)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eValidation cohort\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=1065)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical characteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eAge(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e59 (51-68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e62 (53-70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eFemale sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e533 (18.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e188 (17.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.764\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e1595 (54.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e536 (50.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e1494 (50.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e565 (53.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e479 (16.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e234 (22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eHyperlipidaemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e272 (9.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e8 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003ePrevious atrial fibrillation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e43 (1.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e31 (2.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003ePrevious heart failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e7 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e3 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.732\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003ePrevious myocardial infarction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e108 (3.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e24 (2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003ePrevious PCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e117 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e60 (5.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003ePrevious stroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e198 (6.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e32 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePresentation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eAnterior myocardial infarction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e1417 (48.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e472 (44.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eSymptom onset to balloon time (h)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e5 (3-7.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e5 (3-7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.261\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eVentricular arrhythmia before pPCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e72 (2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e25 (2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.946\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eBradyarrhythmia before pPCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e77 (2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e85 (8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eSyncope before pPCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e52 (1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e13 (1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.284\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHaemodynamic on admission\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eHeart rate (b.p.m.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e77 (69-86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e74 (66-85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.0004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eSystolic blood pressure (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e125 (111-140)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e120 (106-134)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eDiastolic blood pressure (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e79 (70-88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e73 (65-82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProcedural characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eInfarcted-related coronary artery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eLeft main\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e6 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.352\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eLeft anterior descending\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e1431 (48.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e480 (45.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eLeft circumflex\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e347 (11.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e116 (10.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.459\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eRight\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e1156 (39.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e469 (44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eStent implantation\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e2703 (91.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e1027 (96.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eTIMI flow grade 0 before pPCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e2082 (70.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e793 (74.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eTIMI flow grade after pPCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e6 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e2 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e38 (1.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e3 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e2896 (98.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e1060 (99.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eMultivessel disease\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eSingle vessel\u0026nbsp;lesion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e1470 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e514 (48.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.349\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eDouble vessel lesion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e820 (27.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e367 (34.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eTriple vessel\u0026nbsp;lesion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e650 (22.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e184 (17.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eComplete revascularization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e1683 (57.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e727 (68.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTherapy characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eDual antiplatelet therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e2916 (99.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e1059 (99.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.540\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eStatin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e2912 (99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e1063 (99.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e\u0026beta;-blocker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e2549 (86.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e809 (76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eACEI/ARB/ARNI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e2044 (69.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e644 (60.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eCardiotonic drugs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e31 (1.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e73 (6.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eDiuretic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e373 (12.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e143 (13.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.573\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eSprironolactone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e413 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e195 (18.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eCalcium channel blocker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e427 (14.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e53 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eIntra\u0026ndash;aortic balloon pump\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e6 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e3 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.707\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eRespirator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e20 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e2 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eContinuous renal replacement therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e4 (0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e2 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.660\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlood tests on admission\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eWhite blood cell count (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e10.28 (8.34-12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e10.27 (8.41-12.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.678\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eNeutrophil count (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e8.23 (6.3-10.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e7.9 (6.03-10.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eLymphocyte count (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e1.25 (0.89-1.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e1.23 (0.86-1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.729\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e6.68 (4.07-10.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e6.34 (3.95-10.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003ePlatelet count (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e205 (169-246)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e206 (174-245)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.771\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eHaemoglobin count (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e141 (129-152)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e141 (129-150)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.262\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eALT (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e37 (24-58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e45 (29-68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eAST \u0026nbsp;(U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e108 (43.55-251)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e149 (69-269.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eUric acid (\u0026mu;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e355 (293-421)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e347.5 (286.25-422.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.294\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eGlucose (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e6.98 (5.84-8.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e6.16 (5.22-7.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eCholesterol (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e4.57 (3.94-5.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e4.73 (4.11-5.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eTriglyceride (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e1.3 (0.87-1.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e1.49 (1.06-2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e1.09 (0.91-1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e1.08 (0.94-1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.311\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e2.8 (2.24-3.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e2.76 (2.3-3.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.339\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003eSerum creatinine (\u0026mu;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e71.2 (61-84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e71 (61-84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.857\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eQuantitative data are expressed as median (interquartile range). Categorical variables are expressed as number (percentage). PCI, percutaneous coronary intervention; pPCI, primary percutaneous coronary intervention; TIMI, thrombolysis in myocardial infarction; ACEI, angiotensin converting enzyme inhibitor; ARB, angiotensin receptor blocker; ARNI, angiotensin receptor-neprilysin inhibitor; NLR, neutrophil-to-lymphocyte ratio; ALT, alanine transaminase; AST, aspartate aminotransferase; HDL-C, High-density lipoprotein cholesterol; LDL-C, Low-density lipoprotein cholesterol;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Comparison of predictive performance between the STEP-HF score and GRACE score for in-hospital acute heart failure (AHF) and mortality outcomes.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"593\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eIDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eNRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eP3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 122px;\"\u003e\n \u003cp\u003eIn-hospital AHF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eSTEP-HF Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eGRACE Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.669\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 122px;\"\u003e\n \u003cp\u003e30-day Mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eSTEP-HF Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.923\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.824\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eGRACE Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 122px;\"\u003e\n \u003cp\u003e1-year Mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eSTEP-HF Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.465\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eGRACE Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.669\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAHF, acute heart failure; AUC, area under the receiver operating characteristic curve; IDI, integrated discrimination improvement; NRI, net reclassification improvement; GRACE, Global Registry of Acute Coronary Events. P1 values denote the statistical significance of AUC differences between the STEP-HF score and GRACE score; P2 and P3 represent the significance of IDI and NRI, respectively. NA, these metrics assess incremental value of the STEP-HF score over the reference GRACE model.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"ST-segment elevation myocardial infarction, heart failure, Risk score, Killip Ⅰ","lastPublishedDoi":"10.21203/rs.3.rs-9431561/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9431561/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eHeart failure (HF) is a serious complication after ST-segment elevation myocardial infarction (STEMI). Even among Killip class I patients undergoing primary percutaneous coronary intervention (pPCI), new-onset in-hospital HF remains common. Simple, validated risk tools for this population are limited.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003eUsing data from the Chinese STEMI pPCI Registry (NCT04996901), we included 4,005 Killip I STEMI patients undergoing pPCI (derivation cohort n=2,940; external validation n=1,065). Multivariable logistic regression identified independent predictors of new-onset in-hospital HF (Killip ≥II) to develop a risk score.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003eEight independent predictors formed the STEP-HF risk score: age \u0026gt;65 years, previous atrial fibrillation, triple vessel lesion, anterior infarction, symptom-onset-to-balloon time \u0026gt;5 hours, pre-pPCI TIMI flow grade 0, neutrophil-to-lymphocyte ratio \u0026gt;8.5, and aspartate aminotransferase \u0026gt;65 U/L. The score stratified patients into low (0–3), intermediate (4–7), and high (≥8) risk categories. Observed HF rates were 3.38%, 9.97%, and 22.13% in derivation cohort, and 4.25%, 8.80%, and 17.89% in validation cohort. The score demonstrated good discrimination (c-statistic 0.705 and 0.656) and calibration (P=0.314 and 0.223).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThe STEP-HF risk score is a simple, externally validated tool to identify high-risk Killip I STEMI patients for in-hospital HF after pPCI, enabling early risk stratification and targeted management.\u003c/p\u003e","manuscriptTitle":"The STEP-HF risk score to predict new-onset heart failure after ST- segment elevation myocardial infarction (Killip Ⅰ) treated by primary percutaneous coronary intervention","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-06 16:33:17","doi":"10.21203/rs.3.rs-9431561/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-13T10:26:29+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-07T21:29:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-06T13:08:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-29T14:27:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-27T19:01:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"110991252387737236801981977819760721891","date":"2026-04-27T16:02:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"6911012373308919181333678076054874889","date":"2026-04-27T14:43:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"310302368167230198530027658684622464709","date":"2026-04-27T14:35:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"95285566107912773621099859038869690036","date":"2026-04-27T13:41:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"67441756792166221702763019098282679175","date":"2026-04-27T13:41:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-27T13:22:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-27T13:21:26+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-24T05:58:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-22T03:33:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-04-22T03:18:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0d6451c1-0980-40b6-b2ba-c316760e6c94","owner":[],"postedDate":"May 6th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-13T10:26:29+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-07T21:29:04+00:00","index":82,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-06T13:08:00+00:00","index":81,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-29T14:27:13+00:00","index":79,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":67419705,"name":"Health sciences/Biomarkers"},{"id":67419706,"name":"Health sciences/Cardiology"},{"id":67419707,"name":"Health sciences/Diseases"},{"id":67419708,"name":"Health sciences/Medical research"},{"id":67419709,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-05-06T16:33:18+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-06 16:33:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9431561","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9431561","identity":"rs-9431561","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}