Small whole heart volume predicts cardiovascular events in patients with stable chest pain: insights from the PROMISE trial

Objectives The size of the heart may predict major cardiovascular events (MACE) in patients with stable chest pain. We aimed to evaluate the prognostic value of 3D whole heart volume (WHV) derived from non-contrast cardiac computed tomography (CT). Methods Among participants randomized to the CT arm of the Prospective Multicenter Imaging Study for Evaluation of Chest Pain (PROMISE), we used deep learning to extract WHV, defined as the volume of the pericardial sac. We compared the WHV across categories of cardiovascular risk factors and coronary artery disease (CAD) characteristics and determined the association of WHV with MACE (all-cause death, myocardial infarction, unstable angina; median follow-up: 26 months). Results In the 3798 included patients (60.5 +/- 8.2 years; 51.5% women), the WHV was 351.9 +/- 57.6 cm(3)/m(2). We found smaller WHV in no- or non-obstructive CAD, women, people with diabetes, sedentary lifestyle, and metabolic syndrome. Larger WHV was found in obstructive CAD, men, and increased atherosclerosis cardiovascular disease (ASCVD) risk score (p < 0.05). In a time-to-event analysis, small WHV was associated with over 4.4-fold risk of MACE (HR (per one standard deviation) = 0.221; 95% CI: 0.068-0.721; p = 0.012) independent of ASCVD risk score and CT-derived CAD characteristics. In patients with non-obstructive CAD, but not in those with no- or obstructive CAD, WHV increased the discriminatory capacity of ASCVD and CT-derived CAD characteristics significantly. Conclusions Small WHV may represent a novel imaging marker of MACE in stable chest pain. In particular, WHV may improve risk stratification in patients with non-obstructive CAD, a cohort with an unmet need for better risk stratification

Automated echocardiographic detection of heart failure with preserved ejection fraction using artificial intelligence

Background: Detection of heart failure with preserved ejection fraction (HFpEF) involves integration of multiple imaging and clinical features which are often discordant or indeterminate. Objectives: We applied artificial intelligence (AI) to analyze a single apical four-chamber (A4C) transthoracic echocardiogram videoclip to detect HFpEF. Methods: A three-dimensional convolutional neural network was developed and trained on A4C videoclips to classify patients with HFpEF (diagnosis of HF, EF≥50%, and echocardiographic evidence of increased filling pressure; cases) versus without HFpEF (EF≥50%, no diagnosis of HF, normal filling pressure; controls). Model outputs were classified as HFpEF, no HFpEF, or non-diagnostic (high uncertainty). Performance was assessed in an independent multi-site dataset and compared to previously validated clinical scores. Results: Training and validation included 2971 cases and 3785 controls (validation holdout, 16.8% patients), and demonstrated excellent discrimination (AUROC:0.97 [95%CI:0.96-0.97] and 0.95 [0.93-0.96] in training and validation, respectively). In independent testing (646 cases, 638 controls), 94 (7.3%) were non-diagnostic; sensitivity (87.8%; 84.5-90.9%) and specificity (81.9%; 78.2-85.6%) were maintained in clinically relevant subgroups, with high repeatability and reproducibility. Of 701 and 776 indeterminate outputs from the HFA-PEFF and H2FPEF scores, the AI HFpEF model correctly reclassified 73.5 and 73.6%, respectively. During follow-up (median [IQR]:2.3 [0.5-5.6] years), 444 (34.6%) patients died; mortality was higher in patients classified as HFpEF by AI (hazard ratio [95%CI]:1.9 [1.5-2.4]). Conclusion: An AI HFpEF model based on a single, routinely acquired echocardiographic video demonstrated excellent discrimination of patients with versus without HFpEF, more often than clinical scores, and identified patients with higher mortality

Impact of cardiac rehabilitation exercise program on left ventricular diastolic function in coronary artery disease: a pilot study.

Abstract Diastolic dysfunction is common in coronary artery disease (CAD). Exercise-based cardiac rehabilitation (CR) improves survival and quality of life but its effect on diastolic function is unclear. We sought to determine the impact of CR on diastolic function. We conducted a prospective study of CAD patients referred for 3-month outpatient CR, with pre-CR and post-CR echocardiograms. Twenty-five outpatients (age [mean ± SD], 66 ± 11 years; 7 [28 %] women; 22 [88 %] with recent acute coronary syndrome) were recruited upon beginning CR; one patient lacking follow-up was excluded from analysis. Before CR, patients' mean ejection fraction was 61 ± 7 %; regional wall motion score index was 1.18 ± 0.28; and left ventricular diastolic dysfunction existed in 21 (88 %). Of the 24 (96 %) patients with post-CR follow-up, 12 (50 %) had improved diastolic function, 2 of the 24 (8 %) had normal diastolic function throughout, nine (38 %) remained at the same grade, and one (4 %) had worsened diastolic function. The E/e' ratio improved significantly after CR (11.9 ± 4.5 vs. 10.7 ± 4.5; P = .048). Fourteen patients with normal or improved diastolic function had a greater decrease in left atrial volume index (-4.2 ± 6.3 vs. 1.6 ± 6.3 mL/m(2); P = .04) and a greater increase in peak untwisting rate (20 ± 36 vs. -42 ± 45 °/s; P = .003) than did patients with no diastolic improvement. Three-month, exercise-based CR was associated with improved left ventricular diastolic function in half of our patients. Further large studies are needed to clarify the effect of CR on diastolic dysfunction in patients with CAD