Cardiac mortality prediction in patients undergoing PCI based on real and synthetic data

TL;DR

This study develops a machine learning model to predict 3-year cardiac mortality after PCI using real and synthetic patient data, highlighting the importance of data augmentation and feature analysis for improved clinical risk assessment.

Contribution

The paper introduces a novel approach of using synthetic data augmentation to enhance minority class prediction and identifies key clinical features influencing mortality risk.

Findings

Synthetic data improves minority class recall with minimal AUROC loss.

Key features include Age, Ejection Fraction, Peripheral Artery Disease, and Cerebrovascular Disease.

Augmentation exposes model brittleness and improves clinical risk estimates.

Abstract

Patient status, angiographic and procedural characteristics encode crucial signals for predicting long-term outcomes after percutaneous coronary intervention (PCI). The aim of the study was to develop a predictive model for assessing the risk of cardiac death based on the real and synthetic data of patients undergoing PCI and to identify the factors that have the greatest impact on mortality. We analyzed 2,044 patients, who underwent a PCI for bifurcation lesions. The primary outcome was cardiac death at 3-year follow-up. Several machine learning models were applied to predict three-year mortality after PCI. To address class imbalance and improve the representation of the minority class, an additional 500 synthetic samples were generated and added to the training set. To evaluate the contribution of individual features to model performance, we applied permutation feature importance. An additional experiment was conducted to evaluate how the model's predictions would change after removing non-informative features from the training and test datasets. Without oversampling, all models achieve high overall accuracy (0.92-0.93), yet they almost completely ignore the minority class. Across models, augmentation consistently increases minority-class recall with minimal loss of AUROC, improves probability quality, and yields more clinically reasonable risk estimates on the constructed severe profiles. According to feature importance analysis, four features emerged as the most influential: Age, Ejection Fraction, Peripheral Artery Disease, and Cerebrovascular Disease. These results show that straightforward augmentation with realistic and extreme cases can expose, quantify, and reduce brittleness in imbalanced clinical prediction using only tabular records, and motivate routine reporting of probability quality and stress tests alongside headline metrics.