Zheng Zhe, Zhang Lu, Li Xi, Hu Shengshou
Department of Cardiovascular Surgery and State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, China,
Front Med. 2013 Dec;7(4):477-85. doi: 10.1007/s11684-013-0284-0. Epub 2013 Sep 18.
This study aims to construct a logistically derived additive score for predicting in-hospital mortality risk in Chinese patients undergoing coronary artery bypass surgery (CABG). Data from 9839 consecutive CABG patients in 43 Chinese centers were collected between 2007 and 2008 from the Chinese Coronary Artery Bypass Grafting Registry. This database was randomly divided into developmental and validation subsets (9:1). The data in the developmental dataset were used to develop the model using logistic regression. Calibration and discrimination characteristics were assessed using the validation dataset. Thresholds were defined for each model to distinguish different risk groups. After excluding 275 patients with incomplete information, the overall mortality rate of the remaining 9564 patients was 2.5%. The SinoSCORE model was constructed based on 11 variables: age, preoperative NYHA stage III or IV, chronic renal failure, extracardiac arteriopathy, chronic obstructive pulmonary disease, preoperative atrial fibrillation or flutter (within 2 weeks), left ventricular ejection fraction, other elective surgery, combined valve procedures, preoperative critical state, and BMI. In the developmental dataset, calibration using a Hosmer-Lemeshow (HL) test was at P = 0.44 and discrimination based on the area under the receiver operating characteristic curve (ROC) was 0.80. In the validation dataset, the HL test was at P = 0.34 and the area under the ROC (AUC) was 0.78. A logistically derived additive model for predicting in-hospital mortality among Chinese patients undergoing CABG was developed based on the most up-to-date multi-center data from China.
本研究旨在构建一个基于逻辑回归的累加评分系统，用于预测中国接受冠状动脉旁路移植术（CABG）患者的院内死亡风险。2007年至2008年期间，从中国冠状动脉旁路移植术注册中心收集了43个中国中心9839例连续接受CABG患者的数据。该数据库被随机分为开发子集和验证子集（9:1）。开发数据集中的数据用于通过逻辑回归建立模型。使用验证数据集评估校准和区分特征。为每个模型定义阈值以区分不同风险组。在排除275例信息不完整的患者后，其余9564例患者的总死亡率为2.5%。基于11个变量构建了中国心脏手术风险评估（SinoSCORE）模型：年龄、术前纽约心脏协会（NYHA）III或IV级、慢性肾衰竭、心外动脉病变、慢性阻塞性肺疾病、术前房颤或房扑（2周内）、左心室射血分数、其他择期手术、联合瓣膜手术、术前危急状态和体重指数（BMI）。在开发数据集中，使用Hosmer-Lemeshow（HL）检验进行校准，P = 0.44，基于受试者工作特征曲线（ROC）下面积的区分度为0.80。在验证数据集中，HL检验P = 0.34，ROC曲线下面积（AUC）为0.78。基于中国最新的多中心数据，开发了一个用于预测中国接受CABG患者院内死亡的基于逻辑回归的累加模型。
