Machine learning-based biomarker profile derived from 4210 serially measured proteins predicts clinical outcome of patients with heart failure.

ABSTRACT:

Aims

Risk assessment tools are needed for timely identification of patients with heart failure (HF) with reduced ejection fraction (HFrEF) who are at high risk of adverse events. In this study, we aim to derive a small set out of 4210 repeatedly measured proteins, which, along with clinical characteristics and established biomarkers, carry optimal prognostic capacity for adverse events, in patients with HFrEF.

Methods and results

In 382 patients, we performed repeated blood sampling (median follow-up: 2.1 years) and applied an aptamer-based multiplex proteomic approach. We used machine learning to select the optimal set of predictors for the primary endpoint (PEP: composite of cardiovascular death, heart transplantation, left ventricular assist device implantation, and HF ho

SUBMITTER: de Bakker M 

PROVIDER: S-EPMC10689916 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature