Characteristics that Predict Response After Cardiac Resynchronization Therapy

Device-based Optimization of Cardiac Resynchronization-One Size Does Not Fit All

We report on a 72-year-old female patient who was sent to our clinic for evaluation of a biventricular intracardiac defibrillator (BIV-ICD). The patient was diagnosed with ischemic cardiomyopathy and showed a persistently low ejection fraction in the range of 20%-25% with New York Heart Association class III heart failure symptoms despite being on guideline-directed medical therapy, including a β-blocker and a combination of sacubitril and valsartan, for >3 months. In addition, the patient had underlying right bundle branch block (RBBB) with a QRS duration of 160 ms. The device was programmed with a Sync-AV algorithm on with nominal settings (delta of -50 ms). The thresholds and lead impedances were acceptable. Electrocardiography was performed in the postoperative period, showing persistent RBBB similar to the baseline electrocardiogram without much QRS narrowing. In this report, we discuss the mechanism and troubleshooting of this problem.

Machine Learning Prediction of Cardiac Resynchronisation Therapy Response From Combination of Clinical and Model-Driven Data

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.