Performance of risk models to predict mortality risk for patients with heart failure: evaluation in an integrated health system.

ABSTRACT:

Background

Referral of patients with heart failure (HF) who are at high mortality risk for specialist evaluation is recommended. Yet, most tools for identifying such patients are difficult to implement in electronic health record (EHR) systems.

Objective

To assess the performance and ease of implementation of Machine learning Assessment of RisK and EaRly mortality in Heart Failure (MARKER-HF), a machine-learning model that uses structured data that is readily available in the EHR, and compare it with two commonly used risk scores: the Seattle Heart Failure Model (SHFM) and Meta-Analysis Global Group in Chronic (MAGGIC) Heart Failure Risk Score.

Design

Retrospective, cohort study.

Participants

Data from 6764 adults with HF were abstracted from EHRs at a large in

SUBMITTER: Ahmad FS 

PROVIDER: S-EPMC11371523 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature