Abstract
Objectives It is important to identify super-responders who can derive most benefits from cardiac resynchronization therapy (CRT). We aimed to establish a scoring model that can be used for predicting super-response to CRT. Methods We retrospectively reviewed 387 CRT patients. Multivariate logistic regression analysis was performed to identify predictors for super-response (defined as an absolute increase in left ventricular ejection fraction of ≥15% at 6-month follow-up) and to create a score model. Multivariate Cox proportional-hazard regression analysis was conducted to assess associations with the long-term endpoint (defined as cardiac death/heart transplant, heart failure (HF) hospitalization, or all-cause death) across the score categories at follow-up. Results Among 387 patients, 109 (28.2%) met super-response. In multivariable analysis, 5 independent predictors (QQ-LAE) were identified: prior no fragmented QRS (odds ratio (OR) = 3.10 (1.39, 6.94)), QRS duration ≥170 ms (OR = 2.37 (1.35, 4.12)), left bundle branch block (OR = 2.57 (1.04, 6.37)), left atrial diameter <45 mm (OR = 3.27 (1.81, 5.89)), and left ventricular end-diastolic dimension <75 mm (OR = 4.11 (1.99, 8.48)). One point was attributed to each predictor, and three score categories were identified. The proportion of super-response after 6-month CRT implantation in patients with scores 0–3, 4, and 5 was 14.6%, 40.3%, and 64.1%, respectively (P < 0.001). Patients with score 5 had an 88% reduction in the risk of cardiac death/heart transplant (P=0.042), a 71% reduction in the risk of HF hospitalization (P=0.048), and an 89% reduction in the risk of all-cause mortality (P=0.028) compared to patients with scores 0–3. Conclusions The QQ-LAE score can be used for prediction of super-response to CRT and selection of most suitable patients in clinical practices.
Highlights
Randomized trials have demonstrated that cardiac resynchronization therapy (CRT) improves cardiac performance in patients with heart failure with reduced ejection fraction (HFrEF), and reduces mortality and heart failure (HF) hospitalization [1, 2]
Several studies have indicated that CRT dramatically improves left ventricular ejection fraction (LVEF) with excellent long-term outcome in patients with HFrEF or the “super-responders” [3,4,5]
Inclusion criteria of this retrospective study included the following: LVEF ≤35%, QRS width ≥130 ms, and New York Heart Association (NYHA) class II-IV, despite optimized pharmacological treatment. e study excluded patients who received CRT for pacemaker/defibrillator upgrade, died of noncardiac death causes during the 6-month follow-up period, missed the 6-month follow-up, or were lost to the follow-up in our hospital
Summary
Randomized trials have demonstrated that cardiac resynchronization therapy (CRT) improves cardiac performance in patients with heart failure with reduced ejection fraction (HFrEF), and reduces mortality and heart failure (HF) hospitalization [1, 2]. Several studies have indicated that CRT dramatically improves left ventricular ejection fraction (LVEF) with excellent long-term outcome in patients with HFrEF or the “super-responders” [3,4,5]. Previous studies have considered varying preimplant factors for predicting a super-response to CRT, the factors they focused on are isolated and have limited benefits for clinical practices [3,4,5,6]. There remain limited studies of building a predictive model that can distinguish super-responders from eligible patients [7, 8]. Erefore, our study is aimed at identifying the predictors of super-response in patients with HFrEF who received a CRT device, and designing a simple and practical score model for super-response, considering the add-on effects of the predictors There remain limited studies of building a predictive model that can distinguish super-responders from eligible patients [7, 8]. erefore, our study is aimed at identifying the predictors of super-response in patients with HFrEF who received a CRT device, and designing a simple and practical score model for super-response, considering the add-on effects of the predictors
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