Performance of an automated echocardiographic artificial intelligence model. to detect subclinical heart failure with preserved ejection fraction (HFpEF) in community-dwelling older adults
Authors: Subramanian V , Chandra A , Segar M , Yaros K , Upton R , Akerman A , Shah A , Pandey A 
Published: October 2024

Abstract

Background
Heart failure (HF) with preserved Ejection Fraction (HFpEF) is common among older adults and associated with a high burden of morbidity and mortality. Early identification of subclinical HFpEF, defined by abnormalities in cardiac structure and function without symptoms of HF, can prompt early initiation of evidence-based therapies to prevent HFpEF. However, scalable strategies for the identification of subclinical HFpEF are lacking.

Purpose
To evaluate the performance of a validated, automated echocardiographic artificial Intelligence algorithm (Ultromics, UK) to identify subclinical HFpEF among community-dwelling individuals without HF.

Methods
The study included participants of the Dallas Hearts and Minds Study without HF who underwent cardiovascular phenotyping with resting and exercise echocardiography and maximal cardiopulmonary exercise testing. The resting apical 4C echocardiographic images were analyzed by the AI echo algorithm to identify the HFpEF phenotype (AI-HFpEF). Subclinical HFpEF was defined by E/e’ &gt;14 and Peak exercise oxygen uptake (VO2peak) &lt;25th percentile for the cohort. The performance of the AI algorithm to detect subclinical HFpEF was determined using the area under the receiver operating curve and decision curve analysis and was compared with the previously validated H2FpEF score. The association between the AI-HFpEF phenotype and the presence of subclinical HFpEF, VO2peak, exercise E/e’, left ventricular strain, and left atrial strain was assessed using multivariable-adjusted logistic and linear regression models.

Results
The study included 511 participants (mean age: 61 y, 57% women, mean VO2peak = 16.9 ml/kg/min). Using the AI echo algorithm, subclinical HFpEF was detected in 10% of participants (n=76), and 10% were non-diagnostic. The participants with (vs. without) AI-HFpEF phenotype were older and had a higher burden of CVD risk factors, left ventricular hypertrophy, and left atrial dilation. Subclinical HFpEF, as defined by the gold standard criteria, was present in 5.3% of participants. The AUROC for diagnosis of subclinical HFpEF using the AI-HFpEF algorithm was 0.85 vs. 0.78 by the H2FpEF score (see Figure). In the adjusted analysis, the AI-HFpEF phenotype was significantly associated with greater odds of subclinical HFpEF by exercise and echocardiographic phenotyping criteria, higher E/e’ with exercise, lower VO2 peak, and worse LV and LA strain (see Table). In decision curve analysis, the AI-HFpEF algorithm identified 23 additional cases of subclinical HFpEF per 1000 screened participants compared with the H2FpEF score.

Conclusion
The AI-HFpEF algorithm can reliably identify community-dwelling individuals with subclinical HFpEF characterized by diastolic dysfunction at rest or exercise and impaired exercise capacity. Future studies are needed to assess the utility of the AI-HFpEF algorithm in screening for subclinical HFpEF in health systems.