Machine Learning Algorithm Predicts Cardiac Resynchronization Therapy Outcomes: Lessons From the COMPANION Trial

Abstract

Background: Cardiac resynchronization therapy (CRT) reduces morbidity and mortality in heart failure patients with reduced left ventricular function and intraventricular conduction delay. However, individual outcomes vary significantly. This study sought to use a machine learning algorithm to develop a model to predict outcomes after CRT.

Methods and results: Models were developed with machine learning algorithms to predict all-cause mortality or heart failure hospitalization at 12 months post-CRT in the COMPANION trial (Comparison of Medical Therapy, Pacing, and Defibrillation in Heart Failure). The best performing model was developed with the random forest algorithm. The ability of this model to predict all-cause mortality or heart failure hospitalization and all-cause mortality alone was compared with discrimination obtained using a combination of bundle branch block morphology and QRS duration. In the 595 patients with CRT-defibrillator in the COMPANION trial, 105 deaths occurred (median follow-up, 15.7 months). The survival difference across subgroups differentiated by bundle branch block morphology and QRS duration did not reach significance (P=0.08). The random forest model produced quartiles of patients with an 8-fold difference in survival between those with the highest and lowest predicted probability for events (hazard ratio, 7.96; P<0.0001). The model also discriminated the risk of the composite end point of all-cause mortality or heart failure hospitalization better than subgroups based on bundle branch block morphology and QRS duration.

Conclusions: In the COMPANION trial, a machine learning algorithm produced a model that predicted clinical outcomes after CRT. Applied before device implant, this model may better differentiate outcomes over current clinical discriminators and improve shared decision-making with patients.

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

Figure 1. Flow diagram of study

Model development (left hand column) performed with 481 patients from the CRT-P cohort of the COMPANION trial for whom complete device implant data was available. Six machine learning algorithms were tested to predict the absence of death or HF (HF) hospitalization at 12 months. The best model was validated in the 595 CRT-D patients from the COMPANION trial (right hand column) by evaluating event driven outcomes.

Figure 2. Receiver operating characteristic curves for best models

Using area under curve (AUC), the Random Forest model with 550 trees (blue, AUC = 0.74, 95% CI: 0.72-0.76) was superior to that produced by multivariate logistic regression (red, AUC = 0.67, 95% CI: 0.65-0.69) or sequential minimal optimization to train a support vector machine (SMO, black, AUC = 0.67, 95% CI: 0.65-0.68). The improvement in AUC for the Random Forest model was statistically significant compared to that of the multiple logistic regression or SMO model, p < 0.001 for both.

Figure 3. Survival free of all-cause mortality or HF hospitalization

Kaplan-Meier curves for all-cause mortality or HF hospitalization partitioned by bundle branch block morphology / QRS duration: BBB / QRS 1 = LBBB and QRS ≥ 150 ms, BBB / QRS 2 = non-LBBB and QRS ≥ 150 ms or LBBB and QRS < 150 ms, and BBB / QRS 3 = non-LBBB and QRS < 150 ms (A) or Random Forest model sub-divided into quartiles with Quartile 1 expected to have the best outcomes and Quartile 4 the worst (B). Hazard ratio for each subgroup compared to reference for that partition (C).

Figure 4. Survival free of all-cause mortality

Kaplan-Meier curves for all-cause mortality partitioned by bundle branch block morphology / QRS duration: BBB / QRS 1 = LBBB and QRS ≥ 150 ms, BBB / QRS 2 = non-LBBB and QRS ≥ 150 ms or LBBB and QRS < 150 ms, and BBB / QRS 3 = non-LBBB and QRS < 150 ms (A) or Random Forest model sub-divided into quartiles with Quartile 1 expected to have the best outcomes and Quartile 4 the worst (B). Hazard ratio for each subgroup compared to reference for that partition (C).

Figure 5. Reclassification from bundle branch block morphology / QRS duration to Random Forest quartile

Fifty-two patients with LBBB and QRS duration ≥ 150 ms (BBB / QRS 1) were in Random Forest Quartile 4 and thirty-two patients without LBBB and QRS duration ≥ 150 ms (BBB / QRS 2 or 3) were in Quartile 1. Both all-cause mortality or HF hospitalizations (A) and all-cause mortality alone (B) were significantly different between these groups, favouring the patients with BBB / QRS 2 or 3 in Quartile 1.