Machine Learning Prediction of Response to Cardiac Resynchronization Therapy: Improvement Versus Current Guidelines

Albert K. Feeny, John Rickard, Divyang Patel, Saleem Toro, Kevin M. Trulock, Carolyn J. Park, Michael A. Labarbera, Niraj Varma, Mark J. Niebauer, Sunil Sinha, Eiran Z. Gorodeski, Richard A. Grimm, Xinge Ji, John Barnard, Anant Madabhushi, David D. Spragg, Mina K. Chung

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


Background: Cardiac resynchronization therapy (CRT) has significant nonresponse rates. We assessed whether machine learning (ML) could predict CRT response beyond current guidelines. Methods: We analyzed CRT patients from Cleveland Clinic and Johns Hopkins. A training cohort was created from all Johns Hopkins patients and an equal number of randomly sampled Cleveland Clinic patients. All remaining patients comprised the testing cohort. Response was defined as ≥10% increase in left ventricular ejection fraction. ML models were developed to predict CRT response using different combinations of classification algorithms and clinical variable sets on the training cohort. The model with the highest area under the curve was evaluated on the testing cohort. Probability of response was used to predict survival free from a composite end point of death, heart transplant, or placement of left ventricular assist device. Predictions were compared with current guidelines. Results: Nine hundred twenty-five patients were included. On the training cohort (n=470: 235, Johns Hopkins; 235, Cleveland Clinic), the best ML model was a naive Bayes classifier including 9 variables (QRS morphology, QRS duration, New York Heart Association classification, left ventricular ejection fraction and end-diastolic diameter, sex, ischemic cardiomyopathy, atrial fibrillation, and epicardial left ventricular lead). On the testing cohort (n=455, Cleveland Clinic), ML demonstrated better response prediction than guidelines (area under the curve, 0.70 versus 0.65; P=0.012) and greater discrimination of event-free survival (concordance index, 0.61 versus 0.56; P<0.001). The fourth quartile of the ML model had the greatest risk of reaching the composite end point, whereas the first quartile had the least (hazard ratio, 0.34; P<0.001). Conclusions: ML with 9 variables incrementally improved prediction of echocardiographic CRT response and survival beyond guidelines. Performance was not improved by incorporating more variables. The model offers potential for improved shared decision-making in CRT (online calculator: Significant remaining limitations confirm the need to identify better variables to predict CRT response.

Original languageEnglish (US)
Article numbere007316
JournalCirculation: Arrhythmia and Electrophysiology
Issue number7
StatePublished - Jul 1 2019


  • algorithms
  • cardiac resynchronization therapy
  • heart failure
  • machine learning
  • patient selection

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine
  • Physiology (medical)


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