Development and validation of machine learning-derived frailty index in predicting outcomes of patients undergoing percutaneous coronary intervention.

ABSTRACT:

Introduction

Frailty is associated with increased mortality in patients with percutaneous coronary intervention (PCI). Existing operationalized frailty measurement tools are limited and require resource intensive process. We developed and validated a tool to identify and stratify frailty using collected data for patients who underwent PCI and explored its predictive power to predict adverse clinical outcomes post PCI.

Methods

Between 2014 and 2015, 1,732 patients who underwent semi-urgent or elective PCI in a tertiary centre were included. Variables including demographics, co-morbidities, investigations and clinical outcomes to 33 ± 37 months were analysed. Logistic regression model and Extreme Gradient Boosting (XGBoost) machine learning model were constructed to identify p

SUBMITTER: Soong JTY 

PROVIDER: S-EPMC11795679 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature