Personalized diagnosis in suspected myocardial infarction.

ABSTRACT:

Background

In suspected myocardial infarction (MI), guidelines recommend using high-sensitivity cardiac troponin (hs-cTn)-based approaches. These require fixed assay-specific thresholds and timepoints, without directly integrating clinical information. Using machine-learning techniques including hs-cTn and clinical routine variables, we aimed to build a digital tool to directly estimate the individual probability of MI, allowing for numerous hs-cTn assays.

Methods

In 2,575 patients presenting to the emergency department with suspected MI, two ensembles of machine-learning models using single or serial concentrations of six different hs-cTn assays were derived to estimate the individual MI probability (ARTEMIS model). Discriminative performance of the models was assessed usin

SUBMITTER: Neumann JT 

PROVIDER: S-EPMC10449973 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature