ECG2HF AI model predicted 10-year incident heart failure with AUCs of 0.84-0.86 and improved net reclassification by 16-23% vs Pooled Cohorts Equations.
Does the ECG2HF artificial intelligence model improve prediction of 10-year incident heart failure compared to the Pooled Cohorts Equations to Prevent HF score in individuals aged 30 to 79 years without prior heart failure?
A publicly available AI model using standard 12-lead ECGs accurately predicts 10-year incident heart failure risk, outperforming traditional clinical risk scores.
Absolute Event Rate: 0% vs 0%
BACKGROUND: ECG-based artificial intelligence may enable efficient prediction of incident heart failure (HF) risk to facilitate preventive efforts. Prior models are proprietary, with modest or inconsistent accuracy. We sought to develop and validate a generalizable and publicly available convolutional neural network to predict incident HF using the 12-lead ECG waveform (ECG-to-HF ECG2HF). METHODS: We developed ECG2HF in 94 636 patients receiving longitudinal ambulatory care at Massachusetts General Hospital (MGH), and validated it in 3 test sets: MGH, Brigham and Women’s Hospital (BWH), and Beth Israel Deaconess Medical Center (BIDMC), among 93 868 individuals aged 30 to 79 years without HF. HF events at 10 years were identified using a validated electronic health record-based natural language processing model. Discrimination was quantified using the area under the receiver operating characteristic curve. We then compared discrimination and net reclassification (at <10%, 10% to 20%, ≥20% 10-year risk categories) using ECG2HF versus the 15-component Pooled Cohorts Equations to Prevent HF score. RESULTS: The test sets comprised MGH (13 954 individuals, 441 events, age 57±13 years, 48% women), BWH (54 396 individuals, 1809 events, age 57±13 years, 55% women), and BIDMC (25 457 individuals, 901 events, age 57±13 years, 53% women). Over 10 years, the cumulative risk of HF was 4.6% (95% CI, 4.1–5.0) in MGH, 5.0% (4.8–5.2) in BWH, and 4.4% (4.1–4.7) in BIDMC. ECG2HF discriminated 10-year incident HF in each test set (area under the receiver operating characteristic curve: MGH 0.86 0.84–0.87; BWH 0.85 0.84–0.86; BIDMC 0.84 0.83–0.86). Compared with the Pooled Cohorts Equations to Prevent HF, ECG2HF provided favorable discrimination (improvement in area under the receiver operating characteristic curve MGH/BWH 0.061 0.025–0.097; BIDMC 0.038 −0.0096 to 0.086) and net reclassification (NRI MGH/BWH 0.16 0.077–0.24; BIDMC 0.23 0.10–0.35) of 10-year HF risk. CONCLUSIONS: ECG2HF is a publicly available 12-lead ECG-based artificial intelligence model that discriminates the risk of future HF with favorable and consistent performance across 3 large health care samples from the northeastern United States. ECG2HF may enable efficient prioritization of high-risk individuals for HF-related preventive measures.
Khurshid et al. (Mon,) reported a other. ECG2HF AI model predicted 10-year incident heart failure with AUCs of 0.84-0.86 and improved net reclassification by 16-23% vs Pooled Cohorts Equations.