Diagnostic and prognostic evaluation of an echocardiography-based artificial intelligence algorithm for detecting HFpEF: a case-control analysis

Abstract

Abstract Background Heart failure (HF) with preserved Ejection Fraction (HFpEF) is common among older adults and is associated with a high burden of morbidity and mortality. Despite its increasing prevalence, the diagnosis of HFpEF remains challenging. Recently, the FDA cleared an echocardiography-based AI HFpEF model that utilizes a 3-dimensional convolutional neural network to detect HFpEF using a single 4-chamber clip from a resting echocardiogram. However, the external validation of this algorithm against clinically adjudicated and confirmed HFpEF is limited. Purpose To evaluate the diagnostic and prognostic performance of the echocardiography-based AI HFpEF model in a cohort of HFpEF patients and matched controls. Methods The study included patients with clinically adjudicated HFpEF based on clinical history, normal ejection fraction (>45%), and evidence of elevated filling pressure by resting (PCWP > 15 mm Hg) or exercise invasive hemodynamics (PCWP > 25 mm Hg) or echocardiogram (E/e’ >14). The controls were age, sex, and BMI-matched patients without HF and a normal echocardiogram. The performance of the AI HFpEF model was evaluated using receiver operator curves. In patients with HFpEF, the association of the AI-HFpEF phenotype with elevated resting/exercise PCWP and peak exercise oxygen uptake (VO2peak) was assessed using multivariable logistic and linear regression models adjusting for age, sex, race, BMI, and comorbidities (diabetes, hypertension, kidney disease, atrial fibrillation). Results Of the 166 patients referred for evaluation of HFpEF, 82% had clinically adjudicated HFpEF, and 69.8% had elevated LV filling pressure at rest or exercise. In the matched cohorts of patients with clinically adjudicated HFpEF and matched control individuals (N = 122 each), the AI algorithm-based probability of HFpEF demonstrated good performance in identifying clinically adjudicated and hemodynamically confirmed HFpEF (AUROC: 0.75 for each) that was greater than the widely used H2FpEF score (0.69 and 0.70, Figure). In the HFpEF referral cohort, a higher probability of HFpEF based on the AI-algorithm phenotype was significantly associated with lower VO2peak (b [95% CI] per 5% higher probability: -0.11 [-0.21 to -0.01, P-value: 0.03] and greater odds of elevated PCWP (Odds ratio [95% CI] per 5% higher probability: 1.07 [1.01 – 1.15, P-value: 0.04] at rest or exercise after accounting for other confounders. Based on Youden’s index, the AI algorithm-based probability threshold of >0.75 was identified as the optimal cutoff for detecting HFpEF by the AI algorithm, with high sensitivity (0.85) and accuracy (0.74) and, adequate specificity (0.66). Conclusion The echocardiography-based AI HFpEF model demonstrated excellent sensitivity and discrimination in identifying patients with vs. without clinical HFpEF. Furthermore, the AI HFpEF model also had prognostic utility such that individuals with a higher probability of AI-HFpEF phenotype had more severe HFpEF.