Key result
The integrated Tri-HF model outperformed single-component models for incident HF prediction (AUROC 0.811; 95% CI 0.789-0.834), though PREVENT-HF plus ECG-AI performed nearly identically without PRS.
Why the study?
Clinical risk factor scores, polygenic risk scores, and ECG-AI models each demonstrate moderate discrimination of future HF risk, but the comparative utility of an integrated approach remained unknown.
Does an integrated multimodal risk model combining clinical risk factors (PREVENT-HF), polygenic risk scores (PRS), and ECG-based AI (ECG2HF) improve the prediction of incident heart failure compared to single-component models in biobank populations?
Cohort (n=66,037)
Yes
Does an integrated multimodal risk model combining clinical risk factors (PREVENT-HF), polygenic risk scores (PRS), and ECG-based AI (ECG2HF) improve the prediction of incident heart failure compared to single-component models in biobank populations?
Combining clinical risk scores (PREVENT-HF) with an ECG-AI model significantly improves incident heart failure prediction over single modalities, whereas adding a polygenic risk score provides no meaningful incremental discrimination at the population level.
Background Heart failure (HF) is a progressive syndrome associated with high morbidity and mortality. The presence of clinically overt HF is a marker of worsening prognosis, highlighting opportunities for upstream prevention targeting at-risk individuals. Several methods to predict future HF exist, including clinical risk factor scores, polygenic risk scores (PRS), and electrocardiogram-based artificial intelligence (ECG-AI) models. Although individual methods demonstrate moderate discrimination of HF risk, the comparative utility of an integrated approach remains unknown. Methods We estimated the risk of incident HF using three previously validated approaches: the PREVENT-HF clinical risk score, a contemporary HF PRS, and an ECG-AI model for future HF (ECG2HF). HF events were identified using ICD codes and a validated electronic health record-based natural language processing (NLP) model. We assessed discrimination using time-dependent AUROC in two biobank cohorts external to the development of each risk component: the UK Biobank (UKB) and the Mass General Brigham Biobank (MGBB). Cox models incorporating two- and three-component (Tri-HF) combinations were developed in UKB and assessed in both datasets (Figure 1). Results Models were evaluated in UKB (N = 51,237, HF events = 388, age 64.7 ± 7.5 years, 51.6% women) and MGBB (N = 14,800, HF events = 492, age 55.7 ± 12.0 years, 53.8% women). In UKB, Tri-HF outperformed all single-component models with respect to HF discrimination (Tri-HF AUROC 0.811 [95% CI 0.789-0.834]; PREVENT-HF 0.788 [0.767-0.809]; PRS 0.544 [0.514-0.574]; ECG2HF 0.725 [0.696-0.754]). Performance was similar in MGBB (Tri-HF 0.811 [0.789-0.832]; PREVENT-HF 0.746 [0.723-0.770]; PRS 0.545 [0.518-0.571]; ECG2HF 0.786 [0.764-0.808]). Of note, the two-component model comprising PREVENT-HF + ECG2HF performed nearly identically to Tri-HF (UKB: AUROC 0.811 [0.787-0.834]; MGBB: 0.810 [0.790-0.831]). When stratified by Tri-HF, individuals in the highest tertile of risk showed higher cumulative incidence of HF (UKB: high 2.88% [95% CI 2.54-3.22%], intermediate 0.56% [0.41-0.70], low 0.25% [0.15-0.34]; MGBB: high 11.36% [10.17-12.53], intermediate 2.96% [2.37-3.54], low 0.76% [0.47-1.05]). When assessing HF incidence across model components, individuals in the highest tertile of risk for all three components showed increasing cumulative HF incidence (UKB: 4.72% [3.65-5.79]; MGBB: 16.36% [13.11-19.49]) versus those classified as high-risk by only two, one, or zero (UKB: 0.30% [0.20-0.41]; MGBB: 0.99% [0.66-1.32]) models (Figure 2). Conclusions An integrated HF prediction model offers favorable predictive utility compared to clinical factors, PRS, or ECG-AI prediction alone. Yet, population-level HF risk prediction using clinical factors and ECG-AI is not meaningfully improved by the addition of HF PRS. Prospective evaluation of preventive interventions focused on individuals at high predicted risk of HF is warranted.Figure 1Figure 2
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Shahid et al. (2026) conducted a cohort in Incident heart failure (n=66,037). Integrated multimodal heart failure prediction model (Tri-HF: PREVENT-HF clinical score, polygenic risk score, and ECG2HF AI model) vs. Single- and two-component prediction models (PREVENT-HF, PRS, ECG2HF) was evaluated on Incident heart failure discrimination (time-dependent AUROC) and cumulative incidence across risk tertiles (AUROC 0.811, 95% CI 0.789-0.834). The integrated Tri-HF model outperformed single-component models for incident HF prediction (AUROC 0.811; 95% CI 0.789-0.834), though PREVENT-HF plus ECG-AI performed nearly identically without PRS.
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