Machine Learned Cellular Phenotypes in Cardiomyopathy Predict Sudden Death

Albert J. Rogers, Anojan Selvalingam, Mahmood I. Alhusseini, David E. Krummen, Cesare Corrado, Firas Abuzaid, Tina Baykaner, Christian Meyer, Paul Clopton, Wayne Giles, Peter Bailis, Steven Niederer, Paul J. Wang, Wouter Jan Rappel, Matei Zaharia, Sanjiv M. Narayan

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)172-184
Number of pages13
JournalCirculation Research
Issue number2
Publication statusPublished - 22 Jan 2021


  • artificial intelligence
  • coronary disease
  • death, sudden, cardiac
  • heart failure
  • ion channels
  • systems biology


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