# Predicting cardiovascular outcomes in elderly patients with acute coronary syndrome: a nomogram approach

## TL;DR

This study creates a tool to predict heart risks in elderly patients with a specific heart condition, using clinical data to identify high-risk individuals.

## Contribution

The novel contribution is a validated nomogram for predicting MACE in elderly STEMI patients using clinical and procedural factors.

## Key findings

- A nomogram with 8 selected factors predicted MACE with 71% AUC accuracy.
- Calibration plots confirmed the model's alignment with observed outcomes.
- The model showed discriminative power for MACE prediction via decision curve analysis.

## Abstract

Although ST Elevation Myocardial Infarction (STEMI) diagnosis and therapy have improved, high-risk categories like elderly persons still have a significant chance of MACE despite treatment.

This study attempts to construct a predictive nomogram for MACE incidence using clinical data from a STEMI registry.

Tehran Heart Center’s computerized record recognized all 65-year-old STEMI primary PCI patients consecutively. This retrospective study examined demographic, laboratory, clinical, and intra-procedural factors. Post-PCI univariate and multivariate analyses identified MACE risk variables. Decision curve analysis, ROC, and calibration plots validated predictive nomograms. R Studio and R used “tidyverse” and “rms” packages for all analyses.

The 1946 study included 70% training and 30% testing patients. Basic demographic and clinical variables were identical for both groups. The average follow-up was 17 months. 8 factors were selected for the nomogram after univariate and multivariate analysis: left-ventricular ejection fraction (LVEF), serum creatinine, hemoglobin, and fasting blood glucose levels, presence of valvular heart disease, post-PCI TIMI flow grade, diameter of the culprit lesion stent, and presence or absence of shock after PCI. The post-PCI MACE prediction AUC was 71%. Calibration plots showed that the nomogram model was well-calibrated and close to observed outcomes. Decision curve analysis also revealed that the model predicted MACE discriminatively.

A nomogram successfully predicts MACE risk in older STEMI patients using laboratory, clinical, and procedural parameters. This algorithm may identify vulnerable high-risk patients for more aggressive preventative interventions.

not applicable.