Abstract
Background: Up to 30–50% of chronic heart failure patients who underwent cardiac resynchronization therapy (CRT) do not respond to the treatment. Therefore, patient stratification for CRT and optimization of CRT device settings remain a challenge.Objective: The main goal of our study is to develop a predictive model of CRT outcome using a combination of clinical data recorded in patients before CRT and simulations of the response to biventricular (BiV) pacing in personalized computational models of the cardiac electrophysiology.Materials and Methods: Retrospective data from 57 patients who underwent CRT device implantation was utilized. Positive response to CRT was defined by a 10% increase in the left ventricular ejection fraction in a year after implantation. For each patient, an anatomical model of the heart and torso was reconstructed from MRI and CT images and tailored to ECG recorded in the participant. The models were used to compute ventricular activation time, ECG duration and electrical dyssynchrony indices during intrinsic rhythm and BiV pacing from the sites of implanted leads. For building a predictive model of CRT response, we used clinical data recorded before CRT device implantation together with model-derived biomarkers of ventricular excitation in the left bundle branch block mode of activation and under BiV stimulation. Several Machine Learning (ML) classifiers and feature selection algorithms were tested on the hybrid dataset, and the quality of predictors was assessed using the area under receiver operating curve (ROC AUC). The classifiers on the hybrid data were compared with ML models built on clinical data only.Results: The best ML classifier utilizing a hybrid set of clinical and model-driven data demonstrated ROC AUC of 0.82, an accuracy of 0.82, sensitivity of 0.85, and specificity of 0.78, improving quality over that of ML predictors built on clinical data from much larger datasets by more than 0.1. Distance from the LV pacing site to the post-infarction zone and ventricular activation characteristics under BiV pacing were shown as the most relevant model-driven features for CRT response classification.Conclusion: Our results suggest that combination of clinical and model-driven data increases the accuracy of classification models for CRT outcomes.
Highlights
Cardiac resynchronization therapy (CRT) is one of the most effective non-pharmacological therapies for patients with chronic heart failure (CHF)
We found an average positive response to BiV pacing in all clinical indicators and corresponding simulated indexes of the CRT outcome in the entire patient cohort
This criterion was used in clinical studies, and allowed us to compare qualitatively the results of our predictive models for CRT response with the findings reported recently by Feeny et al (2019)
Summary
Cardiac resynchronization therapy (CRT) is one of the most effective non-pharmacological therapies for patients with chronic heart failure (CHF). It enhances the pumping function increasing the left-ventricular (LV) ejection fraction (EF), promotes reversed cardiac remodeling, and improves patients’ quality of life (Abraham et al, 2002; Bristow et al, 2004). In a recent study (Feeny et al, 2019), Feeny and co-authors using supervised ML approaches selected 9 clinical features (QRS morphology, QRS duration, New York Heart Association CHF classification, LV EF and end-diastolic diameter (EDD), sex, ischemic cardiomyopathy, atrial fibrillation, and epicardial LV lead) that were sufficient to predict patient improvement with fairly high accuracy. Patient stratification for CRT and optimization of CRT device settings remain a challenge
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