Developing an ML-Based Pretest Probability Model of Obstructive CAD in Patients With Stable Chest Pain.

ABSTRACT:

Background

Updated pretest probability models (ESC2019, the PTP model supported by the European Society of Cardiology after a pooled analysis; and RF-CL, the risk factor-weighted model) are recommended for initial evaluation of patients with stable chest pain before coronary computed tomography angiography to reduce unnecessary examination by recent guidelines. However, the reliability of those pretest probability models has not been fully investigated, especially in Chinese population.

Objectives

This study aims to build a machine learning-based pretest probability model in patients with stable chest pain and compare it with ESC2019 and RF-CL model in a Chinese population.

Methods

This is an analysis of the Chinese registry in China, with a large scale, foresight, an

SUBMITTER: Dou G 

PROVIDER: S-EPMC12277197 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature