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

Circ Arrhythm Electrophysiol. 2019 Jul;12(7):e007316. doi: 10.1161/CIRCEP.119.007316. Epub 2019 Jun 20.


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.

Keywords: algorithms; cardiac resynchronization therapy; heart failure; machine learning; patient selection.

Publication types

  • Comparative Study
  • Multicenter Study
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Aged
  • Baltimore
  • Cardiac Resynchronization Therapy / adverse effects
  • Cardiac Resynchronization Therapy / mortality
  • Cardiac Resynchronization Therapy / standards*
  • Clinical Decision-Making
  • Decision Support Techniques*
  • Disease Progression
  • Echocardiography / standards
  • Female
  • Heart Failure / diagnosis
  • Heart Failure / mortality
  • Heart Failure / physiopathology
  • Heart Failure / therapy*
  • Heart Transplantation
  • Heart-Assist Devices
  • Humans
  • Machine Learning*
  • Male
  • Middle Aged
  • Ohio
  • Patient Selection
  • Practice Guidelines as Topic / standards*
  • Predictive Value of Tests
  • Progression-Free Survival
  • Recovery of Function
  • Retrospective Studies
  • Risk Assessment
  • Risk Factors
  • Stroke Volume*
  • Time Factors
  • Ventricular Function, Left*