Multimodal prediction of incident heart failure using clinical data, polygenic risk scores, and electrocardiogram-based artificial intelligence

Abstract

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. 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). 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). 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