Sergio Sanchez-Martinez1, Nicolas Duchateau2, Tamas Erdei2, Gabor Kunszt2, Svend Aakhus2, Anna Degiovanni2, Paolo Marino2, Erberto Carluccio2, Gemma Piella2, Alan G Fraser2, Bart H Bijnens2. 1. Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain (S.S.-M., G.P., B.H.B.); Asclepios Research Group, Université Côte d'Azur, Inria, Sophia Antipolis, France (N.D.); Wales Heart Research Institute, Cardiff University, United Kingdom (T.E., A.G.F.); Department of Cardiology, Oslo University Hospital, Norway (G.K., S.A.); Department of Circulation and Imaging, Faculty of Medicine, NTNU, Norwegian University of Science and Technology, Trondheim, Norway (S.A.); Clinic of Cardiology, St. Olav Hospital, Trondheim, Norway (S.A.); Department of Cardiology, University of Eastern Piedmont, Novara, Italy (A.D., P.M.); Division of Cardiology, University Hospital "S.Maria della Misericordia", Perugia, Italy (E.C.); and Catalan Institution for Research and Advanced Studies, Barcelona, Spain (B.H.B.). sergio.sanchezm@upf.edu. 2. Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain (S.S.-M., G.P., B.H.B.); Asclepios Research Group, Université Côte d'Azur, Inria, Sophia Antipolis, France (N.D.); Wales Heart Research Institute, Cardiff University, United Kingdom (T.E., A.G.F.); Department of Cardiology, Oslo University Hospital, Norway (G.K., S.A.); Department of Circulation and Imaging, Faculty of Medicine, NTNU, Norwegian University of Science and Technology, Trondheim, Norway (S.A.); Clinic of Cardiology, St. Olav Hospital, Trondheim, Norway (S.A.); Department of Cardiology, University of Eastern Piedmont, Novara, Italy (A.D., P.M.); Division of Cardiology, University Hospital "S.Maria della Misericordia", Perugia, Italy (E.C.); and Catalan Institution for Research and Advanced Studies, Barcelona, Spain (B.H.B.).
Abstract
BACKGROUND: Current diagnosis of heart failure with preserved ejection fraction (HFpEF) is suboptimal. We tested the hypothesis that comprehensive machine learning (ML) of left ventricular function at rest and exercise objectively captures differences between HFpEF and healthy subjects. METHODS AND RESULTS: One hundred fifty-six subjects aged >60 years (72 HFpEF+33 healthy for the initial analyses; 24 hypertensive+27 breathless for independent evaluation) underwent stress echocardiography, in the MEDIA study (Metabolic Road to Diastolic Heart Failure). Left ventricular long-axis myocardial velocity patterns were analyzed using an unsupervised ML algorithm that orders subjects according to their similarity, allowing exploration of the main trends in velocity patterns. ML identified a continuum from health to disease, including a transition zone associated to an uncertain diagnosis. Clinical validation was performed (1) to characterize the main trends in the patterns for each zone, which corresponded to known characteristics and new features of HFpEF; the ML-diagnostic zones differed for age, body mass index, 6-minute walk distance, B-type natriuretic peptide, and left ventricular mass index (P<0.05) and (2) to evaluate the consistency of the proposed groupings against diagnosis by current clinical criteria; correlation with diagnosis was good (κ, 72.6%; 95% confidence interval, 58.1-87.0); ML identified 6% of healthy controls as HFpEF. Blinded reinterpretation of imaging from subjects with discordant clinical and ML diagnoses revealed abnormalities not included in diagnostic criteria. The algorithm was applied independently to another 51 subjects, classifying 33% of hypertensive and 67% of breathless controls as mild-HFpEF. CONCLUSIONS: The analysis of left ventricular long-axis function on exercise by interpretable ML may improve the diagnosis and understanding of HFpEF.
BACKGROUND: Current diagnosis of heart failure with preserved ejection fraction (HFpEF) is suboptimal. We tested the hypothesis that comprehensive machine learning (ML) of left ventricular function at rest and exercise objectively captures differences between HFpEF and healthy subjects. METHODS AND RESULTS: One hundred fifty-six subjects aged >60 years (72 HFpEF+33 healthy for the initial analyses; 24 hypertensive+27 breathless for independent evaluation) underwent stress echocardiography, in the MEDIA study (Metabolic Road to Diastolic Heart Failure). Left ventricular long-axis myocardial velocity patterns were analyzed using an unsupervised ML algorithm that orders subjects according to their similarity, allowing exploration of the main trends in velocity patterns. ML identified a continuum from health to disease, including a transition zone associated to an uncertain diagnosis. Clinical validation was performed (1) to characterize the main trends in the patterns for each zone, which corresponded to known characteristics and new features of HFpEF; the ML-diagnostic zones differed for age, body mass index, 6-minute walk distance, B-type natriuretic peptide, and left ventricular mass index (P<0.05) and (2) to evaluate the consistency of the proposed groupings against diagnosis by current clinical criteria; correlation with diagnosis was good (κ, 72.6%; 95% confidence interval, 58.1-87.0); ML identified 6% of healthy controls as HFpEF. Blinded reinterpretation of imaging from subjects with discordant clinical and ML diagnoses revealed abnormalities not included in diagnostic criteria. The algorithm was applied independently to another 51 subjects, classifying 33% of hypertensive and 67% of breathless controls as mild-HFpEF. CONCLUSIONS: The analysis of left ventricular long-axis function on exercise by interpretable ML may improve the diagnosis and understanding of HFpEF.
Authors: Damini Dey; Piotr J Slomka; Paul Leeson; Dorin Comaniciu; Sirish Shrestha; Partho P Sengupta; Thomas H Marwick Journal: J Am Coll Cardiol Date: 2019-03-26 Impact factor: 24.094
Authors: Rakesh K Mishra; Geoffrey H Tison; Qizhi Fang; Rebecca Scherzer; Mary A Whooley; Nelson B Schiller Journal: J Am Soc Echocardiogr Date: 2020-01-14 Impact factor: 5.251
Authors: Akhil Narang; Richard Bae; Ha Hong; Yngvil Thomas; Samuel Surette; Charles Cadieu; Ali Chaudhry; Randolph P Martin; Patrick M McCarthy; David S Rubenson; Steven Goldstein; Stephen H Little; Roberto M Lang; Neil J Weissman; James D Thomas Journal: JAMA Cardiol Date: 2021-06-01 Impact factor: 14.676