The Derivation and Validation of a Scoring System for Clinical Prognosis in Patients Releiving Cardiac Resynchronization Therapy
10.3969/j.issn.1000-3614.2017.08.008
- VernacularTitle:心脏再同步化治疗患者临床预后风险评分系统的构建与验证
- Author:
Shengwen YANG
;
Zhimin LIU
;
Shangyu LIU
;
Ligang DING
;
Keping CHEN
;
Wei HUA
;
Shu ZHANG
- Keywords:
Cardiac resynchronization therapy;
Risk assessment;
Prognosis
- From:
Chinese Circulation Journal
2017;32(8):761-765
- CountryChina
- Language:Chinese
-
Abstract:
Objective: To create and validate a scoring system for predicting clinical prognosis in patients with cardiac resynchronization therapy (CRT). Methods: A cohort of 367 consecutive patients received CRT in our hospital from 2010-01 to 2015-12 were enrolled. The endpoint follow-up events were all-cause death including heart transplantation and heart failure re-admission. The patients were randomly categorized into 2 groups: Modeling group, to develop HEAL scoring system,n=300 and Veriifcation group, to validate HEAL model,n=67. HEAL system was established by Cox proportional hazards regression model, discrimination between HEAL and EARRN scoring systems was evaluated by AUC of ROC, HEAL calibration was assessed by Hosmer-Lemeshow test and clinical endpoint evaluation by 2 scoring systems were compared by Kaplan-Meier method. Results: Modeling group analysis indicated that hs-CRP (HR=1.137, 95% CI 1.072-1.205,P<0.001), big endothelin-1 (HR=1.934, 95% CI 1.066-3.507,P=0.03), left atrial diameter (HR=1.045, 95% CI 1.007-1.084,P=0.02) and NYHA IV (HR=2.583, 95% CI 1.331-5.013,P=0.005) were the independent risk factors of adverse prognosis in CRT patients. Based on β partial regression coefifcient, HEAL scoring system was established to classify the patient's risk levels: low risk<4, moderate risk 4-10 and high risk>10. AUC for risk classification in Modeling group and Verification group were 0.719(95% CI 0.629-0.809) and 0.708 (95% CI 0.539-0.878), HEAL can well distinguish clinical prognosis in patients at different risk levels (log-rank test showed in Modeling groupP<0.001 and in Veriifcation groupP=0.002); Hosmer-Lemeshow test presented good calibration,P=0.952. All 367 patients were respectively evaluated by HEAL and EARRN scoring systems, HEAL had the better discrimination than EARRN as AUC 0.763 (95% CI 0.692-0.833) vs AUC 0.602 (95% CI 0.517-0.687). Conclusion: HEAL scoring system can effectively predict adverse prognosis in CRT patients, it had the better discrimination than EARRN system and was valuable to distinguish high risk patients in clinical practice.
