Nomogram for predicting the 28-day mortality risk of patients with subendocardial infarction

Subendocardial myocardial infarction (SEMI) represents a more severe form of myocardial infarction. Currently, there lacks a comprehensive clinical index for predicting mortality in cases of subendocardial myocardial infarction. The objective of our study was to develop and evaluate a nomogram for predicting the 28-day risk of mortality among patients with SEMI.

Patients diagnosed with subendocardial infarction were identified from the MIMIC-III database based on ICD-9 codes. Independent risk factors were screened utilizing the least absolute shrinkage and selection operator (LASSO) method alongside multivariate logistic regression. These identified risk factors were then employed to construct a nomogram aimed at predicting the 28-day mortality risk in patients with subendocardial infarction. The performance of the nomogram was evaluated by the Area Under the Curve (AUC), calibration curves, Hosmer-Lemeshow test, Integrated Discrimination Improvement (IDI), Net Reclassification Improvement (NRI), Decision Curve Analysis (DCA).

A total of 3046 patients with subendocardial infarction were included in the study. Logistic regression analysis revealed that age, GCS score, creatinine level, hematocrit, hemoglobin, international normalized ratio, blood urea nitrogen level, urine output, heart rate, respiratory rate, peripheral oxygen saturation, peripheral vascular disease, diabetes complications, and solid tumors were independent risk factors for 28-day mortality. The AUC values of the nomogram surpassed those of the Acute Physiology Score III (APSIII), Simplified Acute Physiology Score II (SAPSII), and Sequential Organ Failure Assessment (SOFA) scoring systems in both the training and validation cohorts. Calculation of the IDI and NRI, along with DCA analysis, indicated a greater net benefit of the nomogram model.

This study successfully identified independent risk factors for 28-day mortality in patients with SEMI. A nomogram model was developed to predict mortality, offering potential assistance in improving the prognosis of SEMI patients.