Predicting CRT outcome by combining markers from personalized ventricular models of electrophysiology and clinical data

Abstract

Abstract Introduction Up to 30% of chronic heart failure (CHF) patients undergoing cardiac resynchronization therapy (CRT) do not respond to the treatment. Low CRT response can be associated with incorrect pacing lead positioning in ventricles and not accounting for individual structural changes in myocardium. Therefore, patient stratification for CRT, operation planning and optimization of CRT device settings remain a challenge. Purpose In this study, we develop a predictive model of CRT outcomes based on a combination of clinical data recorded in patients before CRT and on simulations of the response to biventricular pacing in personalized computational models of the ventricles. Methods We used retrospective data from 27 CHF patients who underwent CRT device implantation. CRT responders were defined using clinical data in a year after therapy as showing a decrease in the end-systolic volume of the left ventricle (LV) and an increase in the ejection fraction. For each patient, an anatomical model of the heart and torso was reconstructed from CT images and used to compute ventricular activation and ECGs without pacing and under bi-ventricular pacing with various locations of stimulating leads. For building a predictive model of patient response to CRT, we used clinical data before CRT (12 features in total per patient) together with model-derived biomarkers under biventricular pacing with clinical lead position (16 features in total per a model). Using nested stratified cross-validation, logistic regression models were fitted to various combinations of datasets. Results The best classifier using a complete set of clinical and simulation data showed an average accuracy = 0.78, ROC AUC = 0.78, sensitivity = 0.75, specificity = 0.8, that were much higher than in the model built on the clinical data only. When the best model was fitted to all data on biventricular pacing with clinical lead position, all ten clinical non-responders were truly classified by the model (Fig. 1, left). Using simulations with optimal lead location as input data for this model, seven out of the ten patients were predicted as responders (Fig. 1, right). Blue and orange colours indicate clinical responders and non-responders. Red circles and numbers mark some clinical non-responding patients that are predicted as responders using optimal TAT values. Conclusion Our pilot results show that combination of clinical and simulation data significantly increases the accuracy of classification models for CRT outcomes. Our results suggest that model predictions on the optimal ventricular lead location may essentially increase the probability of CRT success. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Russian Science Foundation Figure 1