Devereux, Richard B.

Abstract Background and Aims Classification and risk stratification in aortic (AR), mitral (MR), and tricuspid regurgitation (TR) remains a significant clinical challenge. This study aimed to develop an artificial intelligence (AI) system to assess valvular regurgitation and stratify MR-progression risk. Methods Using transthoracic echocardiograms (TTEs) at two sites (internal development/test, external test), the DELINEATE-Regurgitation system was developed to classify AR, MR, and TR severity using colour Doppler videos. Methods of summating video-level classifications into study-level predictions were tested, comparing single-view with multiview approaches integrating predictions across multiple videos. Model agreement with cardiologists was assessed by weighted kappa. A separate AI system (DELINEATE-MR-Progression) analysing colour Doppler videos was developed to predict which patients with mild, mild–moderate, and moderate MR were most likely to progress to moderate–severe or severe MR with analysis by Kaplan–Meier and Cox proportional hazards models. Results A total of 71 660 TTEs with 1 203 980 colour Doppler videos were included. The weighted kappa in internal/external test sets for regurgitation classification was 0.81/0.76 for AR, 0.76/0.72 for MR, and 0.73/0.64 for TR using a multiview approach taking all colour Doppler videos in a study, demonstrating substantial agreement with cardiologist interpretation with superiority of multiview over single view approaches. In the progression analysis, the AI score stratified MR-progression risk even when controlled for clinical factors known to be associated with MR progression [internal test set hazard ratio 4.1 (95% confidence interval 2.5–6.6)]. Conclusions An AI system can accurately classify AR, MR, and TR and predict MR progression beyond currently known risk factors.

Background and Purpose— American Indians have high rates of stroke. Improved risk stratification could enhance prevention, but the ability of biochemical and echocardiographic markers of preclinical disease to improve stroke prediction is not well-defined. Methods— We evaluated such markers as predictors of ischemic stroke in a community-based cohort of American Indians without prevalent cardiovascular or renal disease. Laboratory markers included C-reactive protein, fibrinogen, urine albumin-to-creatinine ratio, and glycohemoglobin (HbA1c), whereas echocardiographic parameters comprised left atrial diameter, left ventricular mass, mitral annular calcification, and the ratio of early to late mitral diastolic velocities. Predictive performance was judged by indices of discrimination, reclassification, and calibration. Results— After adjustment for standard risk factors, only HbA1c, albuminuria, and left atrial diameter were significantly associated with first ischemic stroke. Addition of HbA1c, although not urine albumin-to-creatinine ratio, to a basic clinical model significantly improved the C-statistic (0.714 versus 0.695; P =0.044), whereas left atrial diameter modestly enhanced integrated discrimination improvement (0.90%; P =0.004), but not the C-statistic (0.701; P =0.528). When combined with HbA1c, left atrial diameter further increased integrated discrimination improvement (1.81%; P <0.001) but not the C-statistic (0.716). No marker achieved significant net reclassification improvement. Conclusions— In this cohort at high cardiometabolic risk, HbA1c emerged as the foremost predictor of ischemic stroke when added to traditional risk factors, affording substantially improved discrimination, with a more modest contribution for left atrial diameter. These findings bolster the role of HbA1c in cardiovascular risk assessment among persons with glycometabolic disorders and provide impetus for further study of the incremental value of echocardiography in high-risk populations.