Using Machine-Learning for Prediction of the Response to Cardiac Resynchronization Therapy: The SMART-AV Study

Abstract

Objectives: This study aimed to apply machine learning (ML) to develop a prediction model for short-term cardiac resynchronization therapy (CRT) response to identifying CRT candidates for early multidisciplinary CRT heart failure (HF) care.

Background: Multidisciplinary optimization of cardiac resynchronization therapy (CRT) delivery can improve long-term CRT outcomes but requires substantial staff resources.

Methods: Participants from the SMART-AV (SmartDelay-Determined AV Optimization: Comparison of AV Optimization Methods Used in Cardiac Resynchronization Therapy [CRT]) trial (n = 741; age: 66 ± 11 years; 33% female; 100% New York Heart Association HF functional class III-IV; 100% ejection fraction ≤35%) were randomly split into training/testing (80%; n = 593) and validation (20%; n = 148) samples. Baseline clinical, electrocardiographic, echocardiographic, and biomarker characteristics, and left ventricular (LV) lead position (43 variables) were included in 8 ML models (random forests, convolutional neural network, lasso, adaptive lasso, plugin lasso, elastic net, ridge, and logistic regression). A composite of freedom from death and HF hospitalization and a >15% reduction in LV end-systolic volume index at 6 months after CRT was the end point.

Results: The primary end point was met by 337 patients (45.5%). The adaptive lasso model was the most more accurate (area under the receiver operating characteristic curve: 0.759; 95% CI: 0.678-0.840), well calibrated, and parsimonious (19 predictors; nearly half potentially modifiable). Participants in the 5th quintile compared with those in the 1st quintile of the prediction model had 14-fold higher odds of composite CRT response (odds ratio: 14.0; 95% CI: 8.0-14.4). The model predicted CRT response with 70% accuracy, 70% sensitivity, and 70% specificity, and should be further validated in prospective studies.

Conclusions: ML predicts short-term CRT response and thus may help with CRT procedure and early post-CRT care planning. (SmartDelay-Determined AV Optimization: A Comparison of AV Optimization Methods Used in Cardiac Resynchronization Therapy [CRT] [SMART-AV]; NCT00677014).

Keywords: cardiac resynchronization therapy; machine learning.

Conflict of interest statement

Funding Support and Author Disclosures This work was supported in part by the National Institutes of Health (HL118277), the Medical Research Foundation of Oregon, and Oregon Health and Science University President Bridge funding (to Dr Tereshchenko). The SMART-AV trial was sponsored by Boston Scientific. Drs Stivland and Stein are employees of Boston Scientific. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.

Copyright © 2021 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Figures

Figure 1.. Patient management scenarios.

Possible patient management scenarios based on a pre-procedural prediction of short-term (6-months) CRT response.

Figure 2.. Calibration.

The calibration belt with 80% and 95% CI on the external sample shows the observed and predicted CRT response proportions in (A) logistic regression, (B) lasso, (C) adaptive lasso, (D) plugin lasso, (E) elastic net, and (F) ridge models for all participants. The appropriately calibrated model fits the red diagonal line, and neither 80% nor 95% CI is entirely under or above the red line (never crosses the red line).

Figure 3.. The final adaptive lasso model performance in subgroups.

ROC AUC in the validation sample, in subgroups.

Figure 4.. Odds of composite CRT response.

Probabilities of composite CRT response by quintiles of the adaptive lasso model. The 1st quintile is a reference. A. Unadjusted (model 1, red ovals) and adjusted by the ITT AV-delay optimization assignment (model 2, blue diamonds). B. Relative odds of composite CRT response in SMART-AV participants randomized to echo-optimized AV delay (orange triangles) and Smart-AV-delay algorithm (green rectangles). Fixed AV delay group was a reference. Black lines correspond to 95% CI bounds.

Figure 5.. Sex-specific predictors of CRT response.

Venn diagram shows sex-specific predictors of CRT response in women (red), men (blue), and all participants (gray), and those predictors that overlap.

Central illustration.. Prediction of short-term CRT response.

A. Importance of the selected predictors in the adaptive lasso model. The most important predictors were added to the model early. B. Probabilities of composite CRT response by quintiles of the adaptive lasso model. The odds of the endpoint in participants in the 2nd, 3rd, 4th, and 5th quintiles of CRT-response-predicted probability are compared to those in the 1st quintile.