Performance of Ambulatory Electrocardiographic Data for Prediction of Stroke and Heart Failure Events.

ABSTRACT:

Background

Despite clear associations between arrhythmia burden and cardiovascular risk, clinical risk scores that predict cardiovascular events do not incorporate individual-level arrhythmia characteristics from long-term continuous monitoring (LTCM).

Objectives

This study evaluated the performance of risk models that use data from LTCM and patient claims for prediction of heart failure (HF) and ischemic stroke.

Methods

We retrospectively analyzed features extracted from up to 14 days of LTCM electrocardiogram (ECG) data linked to patient-level claims data for 320,974 Medicare beneficiaries who underwent ZioXT ambulatory monitoring. We created predictive models for HF hospitalization, stroke hospitalization, and new-onset HF within 1 year using LASSO Cox regression f

SUBMITTER: Schwennesen HT 

PROVIDER: S-EPMC11533076 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature