Although the electrocardiogram (ECG) is not a primary diagnostic tool for heart failure (HF), it can provide early markers that are useful for HF risk stratification. Most ECG-based HF prediction studies rely on a single-time-point recording; however, we hypothesize that longitudinal changes across multiple ECGs contain additional predictive information.
In this study, we used data from the MIMIC-IV database and selected patients with at least two ECG recordings. Incident HF was defined based on ICD-9/10 codes with up to 10 years of follow-up. The final cohort included 65,427 patients (HF-prevalence 10%). Patients were split into training and test sets. Using 5-fold-cross-validation on the training set, we developed two random forest models for 3-year HF prediction: (1) a model based on interpretable ECG biomarkers from the most recent ECG (RR interval, P-wave duration, QRS duration, QT interval, QTcB, P-, QRS-, T-wave-axes, ventricular electrical dyssynchrony (VED), and ventricular duration (VD)); and (2) a model that augmented these features with longitudinal changes, defined as the range between minimum and maximum feature values across all available ECGs.
In 5-fold-cross-validation, the combined model (single-time-point + longitudinal ECG features) outperformed the single-time-point ECG model, achieving an AUROC of 0.84 vs. 0.79 and an AUPRC of 0.70 vs. 0.63 for the 3-year prediction task.
On an independent test set with up to 10-year follow-up (13,086 subjects, 10% HF-prevalence), the longitudinal model again improved risk stratification compared to the single-time-point ECG model. In Cox regression analysis, the longitudinal model demonstrated a higher hazard ratio (2.16 vs. 1.39) and improved Harrell's concordance index (0.77 vs. 0.69) after adjusting for age and sex.
SHAP analysis identified the ranges of T-wave axis, VD, P-wave duration, and VED as the most important predictors. Overall, these results demonstrate that incorporating longitudinal ECG information enhances HF risk stratification.