Background: Heart failure (HF) results in persistent risk and long-term comorbidities. This is particularly true for patients with lifelong HF sequelae of cardiovascular disease such as patients with congenital heart disease (CHD).
Purpose: We developed hART (heart failure Attentive Risk Trajectory), a deep-learning model to predict HF trajectories in CHD patients.
Methods: hART is designed to capture the contextual relationships between medical events within a patient's history. It is trained to predict future HF risk by using the masked self-attention mechanism that forces it to focus only on the most relevant segments of the past medical events.
Results: To demonstrate the utility of hART, we used a large cohort containing healthcare administrative data from the Quebec CHD database (137,493 patients, 35-year follow-up). hART achieves an area under the precision-recall of 28% for HF risk prediction, which is 33% improvement over existing methods. Patients with severe CHD lesion showed a consistently elevated predicted HF risks throughout their lifespan, and patients with genetic syndromes exhibited elevated HF risks until the age of 50. The impact of the birth condition decreases on long-term HF risk. The timing of interventions such as arrhythmia surgery had varying impacts on the lifespan HF risk among the individuals. Arrhythmic surgery performed at a younger age had minimal long-term effects on HF risk, while surgeries during adulthood had a significant lasting impact.
Conclusion: Together, we show that hART can detect meaningful lifelong HF risk in CHD patients by capturing both long and short-range dependencies in their past medical events.
Keywords: CHD morbidity; Heart failure hospitalization prediction; Longitudinal outcome prediction; Machine learning.
Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.