Nomogram for major adverse cardiovascular events risk during hospitalization after percutaneous coronary intervention in patients with NSTEMI: Training and validation

Abstract

Objective

This study aimed to develop a nomogram model to predict the risk of in-hospital major adverse cardiovascular events (MACE) following percutaneous coronary intervention (PCI) in Non-ST-segment elevation myocardial infarction (NSTEMI) patients and assess its performance.

Methods

Patient data was collected and individuals were randomly assigned to a training cohort (n = 527) or a validation cohort (n = 227). In the training cohort, LASSO-logistic regression analyses were conducted to identify risk factors associated with MACE in NSTEMI patients. The model's predictive performance, discrimination, and consistency were evaluated using metrics such as the receiver operating characteristic curve, calibration curve, and Decision Curve Analysis. The LASSO-logistic analysis for the training cohort identified BMI (OR:1.49, 95% confidence interval (CI): 1.25–1.78, P = 0.000), adjusted GRACE score (per 10 units GRACE score, adjusted OR [aOR]: 1.20, 95% CI: 1.04–1.37, P = 0.010), and adjusted Gensini score (per 10 units Gensini score, aOR: 1.15, 95% CI: 1.03–1.28, P = 0.013) as predictors of in-hospital MACE for patients with NSTEMI who underwent PCI.

Results

In the development cohort, AUC in the prediction model was 0.871 (95% CI: 0.762–0.980), while in the validation cohort, it was 0.961 (95% CI: 0.927–0.995). The calibration curve and Hosmer-Lemeshow test results indicate that the nomogram was well-calibrated. The DCA curve demonstrates that the DCA map of the nomogram has good clinical application ability. Patients with NSTEMI undergoing PCI are known to have an increased risk of MACE.

Conclusion

The developed nomogram model we established reliably predicts the occurrence of in-hospital MACE in NSTEMI patients undergoing PCI, improving healthcare decision-making accuracy.