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

Superorganisms or loose collections of species? A unifying theory of community patterns along environmental gradients

The question whether communities should be viewed as superorganisms or loose collections of individual species has been the subject of a long-standing debate in ecology. Each view implies different spatiotemporal community patterns. Along spatial environmental gradients, the organismic view predicts that species turnover is discontinuous, with sharp boundaries between communities, while the individualistic view predicts gradual changes in species composition. Using a spatially explicit multispecies competition model, we show that organismic and individualistic forms of community organisation are two limiting cases along a continuum of outcomes. A high variance of competition strength leads to the emergence of organism-like communities due to the presence of alternative stable states, while weak and uniform interactions induce gradual changes in species composition. Dispersal can play a confounding role in these patterns. Our work highlights the critical importance of considering species interactions to understand and predict the responses of species and communities to environmental changes.</p