Prediction models for major adverse cardiovascular events following ST-segment elevation myocardial infarction and subgroup-specific performance

Abstract

Background: ST-segment elevation myocardial infarction (STEMI) patients are at a high residual risk of major adverse cardiovascular events (MACEs) after revascularization. Risk factors modify prognostic risk in distinct ways in different STEMI subpopulations. We developed a MACEs prediction model in patients with STEMI and examined its performance across subgroups.

Methods: Machine-learning models based on 63 clinical features were trained in patients with STEMI who underwent PCI. The best-performing model (the iPROMPT score) was further validated in an external cohort. Its predictive value and variable contribution were studied in the entire population and subgroups.

Results: Over 2.56 and 2.84 years, 5.0% and 8.33% of patients experienced MACEs in the derivation and external validation cohorts, respectively. The iPROMPT score predictors were ST-segment deviation, brain natriuretic peptide (BNP), low-density lipoprotein cholesterol (LDL-C), estimated glomerular filtration rate (eGFR), age, hemoglobin, and white blood cell (WBC) count. The iPROMPT score improved the predictive value of the existing risk score, with an increase in the area under the curve to 0.837 [95% confidence interval (CI): 0.784-0.889] in the derivation cohort and 0.730 (95% CI: 0.293-1.162) in the external validation cohort. Comparable performance was observed between subgroups. The ST-segment deviation was the most important predictor, followed by LDL-C in hypertensive patients, BNP in males, WBC count in females with diabetes mellitus, and eGFR in patients without diabetes mellitus. Hemoglobin was the top predictor in non-hypertensive patients.

Conclusion: The iPROMPT score predicts long-term MACEs following STEMI and provides insights into the pathophysiological mechanisms for subgroup differences.

Keywords: GRACE score; ST-segment elevation myocardial infarction; diabetes mellitus; hypertension; machine learning; sex.

Figure 1

Analysis overview for identifying best-performing risk prediction model. AUC, area under the curve; NRI, net reclassification index; IDI, integrated discrimination index.

Figure 2

Performance evaluation of iPROMPT score and GRACE score in derivation cohort and external validation cohort. (A,D) Receiver-operating characteristic curve analysis. The accuracy for MACEs between the iPROMPT score and the GRACE risk score in the in derivation cohort (A) and external validation cohort (D). (B,E) The calibration plot shows the relationship between the observed and predicted proportion of events, grouped by quintile of risk in the in derivation cohort (B) and external validation cohort (E). (C,F) DCA curves for validating the clinical utility of the iPROMPT score and the previous model. AUC, area under the curve; GRACE, the Global Registry of Acute Coronary Events risk score; MACEs, major adverse cardiovascular events; DCA, decision curve analysis.

Figure 3

Association between continuous predictors and MACEs and importance of selected variables in iPROMPT. (A) The contribution of each selected variables in the iPROMPT score. (B–G) Regression-adjusted effects of selected continuous covariates in iPROMPT showed their association with MACEs following STEMI. The top of each figure shows the observed values of the continuous risk predictor among STEMI patients in the derivation cohort who experienced MACEs and the bottom shows the observed values of the predictor among those who did not experience MACEs. MACEs risk increased with age (B) and decreased LDL-C (C). MACEs risk declined with eGFR (D), and plateaued at over 100 ml/min/1.73 m². (E) The risk associated with log(BNP) remained flat and increased when log(BNP) >5. The U-shaped association observed between hemoglobin (F), and WBC (G) and MACEs. AUC, area under the curve; BNP, B-type natriuretic peptide; eGFR, estimate glomerular filtration rate; WBC, white blood cell count.

Figure 4

Performance and relative importance of variables in subgroups. (A) Forest plot for AUC and 95% confidence interval depicting overall discriminative efficacy of the iPROMPT score across pre-determined subgroups; (B–D) Alluvial plot of predictors for prognosis prediction identified by variables’ contribution in sex (B), hypertensive (C) and diabetic subgroup (D). AUC, area under the curve; BNP, B-type natriuretic peptide; eGFR, estimate glomerular filtration rate; WBC, white blood cell count.