A predictive model of super response to cardiac resynchronization therapy in short-term period.

Autor: Atabekov, Tariel A., Mishkina, Anna I., Khlynin, Mikhail S., Sazonova, Svetlana I., Krivolapov, Sergey N., Batalov, Roman E., Popov, Sergey V.
Zdroj: Journal of Interventional Cardiac Electrophysiology; Nov2024, Vol. 67 Issue 8, p1851-1863, 13p
Abstrakt: Background: The left bundle branch block, nonischemic heart failure (HF) and female gender are the most powerful predictors of a super response to cardiac resynchronization therapy (CRT). It is important to identify super responders who can derive most benefits from CRT. We aimed to establish a predicting model that could be used for prognosis of a super response to CRT in short-term period. Methods: Patients with QRS ≥ 130 ms, New York Heart Association (NYHA) II-III class of HF, left ventricle ejection fraction (LVEF) ≤ 35% and indications for CRT were included in the study. Before and 6 month after CRT the electrocardiography, echocardiography and cardiac scintigraphy were performed. The study's primary endpoint was the NYHA class improvement ≥ 1 and left ventricle end systolic volume decrease > 30% or LVEF improvement > 15% after 6 month CRT. Based on collected data, we developed a predictive model regarding a super response to CRT. Results: Of 49 (100.0%) patients, 32 (65.3%) had a super response to CRT. Patients with a super response were likelier to have a lower cardiac index (p = 0.007), higher rates of interventricular delay (IVD) (p = 0.003), phase standard deviation of left ventricle anterior wall (PSD LVAW) (p = 0.009) and ∆QRS (p = 0.02). Only IVD and PSD LVAW were independently associated with a super response to CRT in univariate and multivariate logistic regression. We created a logistic equation and calculated a cut-off value. The resulting ROC curve revealed a discriminative ability with AUC of 0.812 (sensitivity 90.62%; specificity 70.59%). Conclusion: Our predictive model is able to distinguish patients with a super response to CRT. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index
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