Evaluation of a nomogram model for predicting in-hospital mortality risk in patients with acute ST-elevation myocardial infarction and acute heart failure post-PCI.

Objectives: This study aims to identify the risk factors contributing to in-hospital mortality in patients with acute ST-elevation myocardial infarction (STEMI) who develop acute heart failure (AHF) post-percutaneous coronary intervention (PCI). Based on these factors, we constructed a nomogram to effectively identify high-risk patients.

Methods: In the study, a collective of 280 individuals experiencing an acute STEMI who then developed AHF following PCI were evaluated. These subjects were split into groups for training and validation purposes. Utilizing lasso regression in conjunction with logistic regression analysis, researchers sought to pinpoint factors predictive of mortality and to create a corresponding nomogram for forecasting purposes. To evaluate the model's accuracy and usefulness in clinical settings, metrics such as the concordance index (C-index), calibration curves, and decision curve analysis (DCA) were employed.

Results: Key risk factors identified included blood lactate, D-dimer levels, gender, left ventricular ejection fraction (LVEF), and Killip class IV. The nomogram demonstrated high accuracy (C-index: training set 0.838, validation set 0.853) and good fit (Hosmer-Lemeshow test: χ² = 0.545, p = 0.762), confirming its clinical utility.

Conclusion: The developed clinical prediction model is effective in accurately forecasting mortality among patients with acute STEMI who develop AHF after PCI.