A Predictive Model for Super-Response to Cardiac Resynchronization Therapy: The QQ-LAE Score. (28th August 2020) - Liu, Xi; Hu, Yiran; Hua, Wei; Yang, Shengwen; Gu, Min; Niu, Hong-Xia; Ding, Li-Gang; Wang, Jing; Zhang, Shu
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.