{"doi":"10.1161/circresaha.120.317345","title":"Machine Learned Cellular Phenotypes in Cardiomyopathy Predict Sudden Death","abstract":"RATIONALE: Susceptibility to VT/VF (ventricular tachycardia/fibrillation) is difficult to predict in patients with ischemic cardiomyopathy either by clinical tools or by attempting to translate cellular mechanisms to the bedside. OBJECTIVE: To develop computational phenotypes of patients with ischemic cardiomyopathy, by training then interpreting machine learning of ventricular monophasic action potentials (MAPs) to reveal phenotypes that predict long-term outcomes. METHODS AND RESULTS: We recorded 5706 ventricular MAPs in 42 patients with coronary artery disease and left ventricular ejection fraction ≤40% during steady-state pacing. Patients were randomly allocated to independent training and testing cohorts in a 70:30 ratio, repeated K=10-fold. Support vector machines and convolutional neural networks were trained to 2 end points: (1) sustained VT/VF or (2) mortality at 3 years. Support vector machines provided superior classification. For patient-level predictions, we computed personalized MAP scores as the proportion of MAP beats predicting each end point. Patient-level predictions in independent test cohorts yielded c-statistics of 0.90 for sustained VT/VF (95% CI, 0.76-1.00) and 0.91 for mortality (95% CI, 0.83-1.00) and were the most significant multivariate predictors. Interpreting trained support vector machine revealed MAP morphologies that, using in silico modeling, revealed higher L-type calcium current or sodium-calcium exchanger as predominant phenotypes for VT/VF. CONCLUSIONS: Machine learning of action potential recordings in patients revealed novel phenotypes for long-term outcomes in ischemic cardiomyopathy. Such computational phenotypes provide an approach which may reveal cellular mechanisms for clinical outcomes and could be applied to other conditions.","journal":"Circulation Research","year":2020,"id":61996,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":54,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9552,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":328092,"name":"Anojan Selvalingam","orcid":"0000-0002-7338-1356","position":1,"is_corresponding":false},{"id":330035,"name":"Mahmood Alhusseini","orcid":null,"position":2,"is_corresponding":false},{"id":328093,"name":"David E. Krummen","orcid":"0000-0003-3123-8970","position":3,"is_corresponding":false},{"id":328094,"name":"Cesare Corrado","orcid":"0000-0002-8914-8735","position":4,"is_corresponding":false},{"id":328095,"name":"Firas Abuzaid","orcid":"0000-0002-1424-4554","position":5,"is_corresponding":false},{"id":328096,"name":"Tina Baykaner","orcid":"0000-0002-0476-2972","position":6,"is_corresponding":false},{"id":328097,"name":"Christian Meyer","orcid":"0000-0003-0217-3960","position":7,"is_corresponding":false},{"id":328098,"name":"Paul Clopton","orcid":"0000-0002-0642-0861","position":8,"is_corresponding":false},{"id":328099,"name":"Wayne R. Giles","orcid":"0000-0002-8077-5935","position":9,"is_corresponding":false},{"id":328100,"name":"Peter Bailis","orcid":"0000-0003-1166-7823","position":10,"is_corresponding":false},{"id":328101,"name":"Steven Niederer","orcid":"0000-0002-4612-6982","position":11,"is_corresponding":false},{"id":328102,"name":"Paul J. Wang","orcid":"0000-0002-5467-5877","position":12,"is_corresponding":false},{"id":292741,"name":"Wouter‐Jan Rappel","orcid":"0000-0003-3833-7197","position":13,"is_corresponding":false},{"id":328103,"name":"Matei Zaharia","orcid":"0000-0002-7547-7204","position":14,"is_corresponding":false},{"id":241959,"name":"Sanjiv M. Narayan","orcid":"0000-0001-7552-5053","position":15,"is_corresponding":false},{"id":241956,"name":"Albert J. Rogers","orcid":"0000-0001-6585-534X","position":0,"is_corresponding":true}],"reference_count":51,"raw_metadata":null,"created_at":"2026-07-18T21:10:11.612909Z","pmid":"33167779","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}