Dataset Information

Association of apical rocking with super-response to cardiac resynchronisation therapy.

ABSTRACT:

Background

Super-responders to cardiac resynchronisation therapy (CRT) show an exceptional improvement in left ventricular ejection fraction (LVEF). Previous studies showed that apical rocking was independently associated with echocardiographic response to CRT. However, little is known about the association between apical rocking and super-response to CRT.

Objectives

To determine the independent association of LV apical rocking with super-response to CRT in a large cohort.

Methods

A cohort of 297 consecutive heart failure patients treated with primary indication for CRT-D were included in an observational registry. Apical rocking was defined as motion of the left ventricular (LV) apical myocardium perpendicular to the LV long axis. 'Super-response' was defined by the top quartile of LVEF response based on change from baseline to follow-up echocardiogram. Best-subset regression analysis identified predictors of LVEF super-response to CRT.

Results

Apical rocking was present in 45 % of patients. Super-responders had an absolute mean LVEF increase of 27 % (LVEF 22.0 % ± 5.7 at baseline and 49.0 % ± 7.5 at follow-up). Apical rocking was significantly more common in super-responders compared with non-super-responders (76 and 34 %, P < 0.001). In univariate analysis, female gender (OR 2.39, 95 % CI 1.38-4.11), lower LVEF at baseline (OR 0.91 95 % CI 0.87-0.95), non-ischaemic aetiology (OR 4.15, 95 % CI 2.33-7.39) and apical rocking (OR 6.19, 95 % CI 3.40-11.25) were associated with super-response. In multivariate analysis, apical rocking was still strongly associated with super-response (OR 5.82, 95 % CI 2.68-12.61). Super-responders showed an excellent clinical prognosis with a very low incidence of heart failure admission, cardiac mortality and appropriate ICD therapy.

Conclusion

Apical rocking is independently associated with super-response to CRT.

SUBMITTER: Ghani A

PROVIDER: S-EPMC4692828 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description: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.