Background: Resting left ventricular outflow tract obstruction (LVOTO) occurs in 25% of patients with hypertrophic cardiomyopathy (HCM) and is an important cause of symptoms and disease progression. The prevalence and clinical significance of exercise induced LVOTO in patients with symptomatic non-obstructive HCM is uncertain. Methods and results: 87 symptomatic patients (43.3 (13.7) years, 67.8% males) with HCM and no previously documented LVOTO (defined as a gradient >30 mm Hg) underwent echocardiography during upright cardiopulmonary exercise testing: 54 patients (62.1%; 95% CI 51.5 to 71.6) developed LVOTO during exercise (latent LVOTO); 33 (37.9%; 95% CI 28.4 to 48.5) had neither resting nor exercise LVOTO (non-obstructive). Patients with latent LVOTO were more likely to have systolic anterior motion of the mitral valve (SAM) at rest (relative risk 2.1, 95% CI 1.2 to 3.8; p = 0.01), and higher peak oxygen consumption (mean difference: 10.3%, 95% CI 2.1 to 18.5; p = 0.02) than patients with non-obstructive HCM. The only independent predictors of D gradient during exercise were a history of presyncope/syncope, incomplete/complete SAM at rest and Wigle score (all p,0.05). Subsequent invasive reduction of LVOTO in 10 patients with latent obstruction and drug refractory symptoms resulted in improved functional class and less syncope/presyncope (all p,0.05).Conclusions: Approximately two-thirds of patients with symptomatic non-obstructive HCM have latent LVOTO. This study suggests that all patients with symptomatic non-obstructive HCM should have exercise stress echocardiography.Hypertrophic cardiomyopathy (HCM) is an inherited heart muscle disorder characterised by unexplained left ventricular hypertrophy.

In patients with HCM, reduced GLS is an independent factor associated with poor cardiac outcomes, and particularly HF outcomes.

Background: Artificial intelligence (AI) for echocardiography requires training and validation to standards expected of humans. We developed an online platform and established the Unity Collaborative to build a dataset of expertise from 17 hospitals for training, validation, and standardization of such techniques. Methods: The training dataset consisted of 2056 individual frames drawn at random from 1265 parasternal long-axis video-loops of patients undergoing clinical echocardiography in 2015 to 2016. Nine experts labeled these images using our online platform. From this, we trained a convolutional neural network to identify keypoints. Subsequently, 13 experts labeled a validation dataset of the end-systolic and end-diastolic frame from 100 new video-loops, twice each. The 26-opinion consensus was used as the reference standard. The primary outcome was precision SD, the SD of the differences between AI measurement and expert consensus. Results: In the validation dataset, the AI’s precision SD for left ventricular internal dimension was 3.5 mm. For context, precision SD of individual expert measurements against the expert consensus was 4.4 mm. Intraclass correlation coefficient between AI and expert consensus was 0.926 (95% CI, 0.904–0.944), compared with 0.817 (0.778–0.954) between individual experts and expert consensus. For interventricular septum thickness, precision SD was 1.8 mm for AI (intraclass correlation coefficient, 0.809; 0.729–0.967), versus 2.0 mm for individuals (intraclass correlation coefficient, 0.641; 0.568–0.716). For posterior wall thickness, precision SD was 1.4 mm for AI (intraclass correlation coefficient, 0.535 [95% CI, 0.379–0.661]), versus 2.2 mm for individuals (0.366 [0.288–0.462]). We present all images and annotations. This highlights challenging cases, including poor image quality and tapered ventricles. Conclusions: Experts at multiple institutions successfully cooperated to build a collaborative AI. This performed as well as individual experts. Future echocardiographic AI research should use a consensus of experts as a reference. Our collaborative welcomes new partners who share our commitment to publish all methods, code, annotations, and results openly.