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

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).

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.

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 for variable selection among ambulatory ECG variables and components of the CHA₂DS₂-VASc score.

A model that included components of the CHA₂DS₂-VASc and all ambulatory ECG variables had greater discrimination for HF hospitalization (C-statistic 0.85, 95% CI: 0.84-0.86) than the CHA₂DS₂-VASc (C-statistic 0.73, 95% CI: 0.72-0.74), but performed similarly to the CHA₂DS₂-VASc for prediction of stroke hospitalization (C-statistic 0.75 [95% CI: 0.74-0.77] vs 0.71 [95% CI: 0.70-0.72], respectively). Atrial fibrillation was associated with greater risk in the most predictive models (HF hospitalization, HR: 1.53 [95% CI: 1.35-1.72]; stroke hospitalization, HR: 1.58 [95% CI: 1.30-1.93]), and premature ventricular couplets were associated with greater risk of HF hospitalization (HR: 1.54, 95% CI: 1.43-1.65).

The CHA₂DS₂-VASc performed modestly for prediction of stroke and HF events; predictive ability improved significantly with addition of LTCM ECG covariates. The presence of atrial fibrillation and ventricular ectopy on 14-day LTCM were strongly associated with HF events.