Zhang Ren-Jian-Zhi, Yu Xin-Yi, Wang Jing, Lv Jian, Zheng Yan, Yu Ming-Huan, Zang Yi-Rui, Shi Jian-Wei, Wang Jia-Hui, Wang Li, Liu Zhi-Gang
Department of Cardiovascular Surgery, TEDA International Cardiovascular Hospital, Chinese Academy of Medical Sciences & Graduate School of Peking Union Medical College, Tianjin, 300457, China.
Department of Cardiovascular Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 6913114, China.
Heliyon. 2023 Mar 21;9(3):e14656. doi: 10.1016/j.heliyon.2023.e14656. eCollection 2023 Mar.
Developing and assessing a risk prediction model of postoperative atrial fibrillation (POAF) after coronary artery bypass grafting (CABG), and aims to provide a reference for the prediction and prevention.
A retrospective case-control study.
Three major urban teaching and university hospitals and tertiary referral centers.
consecutive patients undergoing CABG.
The study was retrospective and no interventions were administered to patients.
In the study, the overall new-onset POAF prevalence was approximately 28%. A prediction model for POAF with nine significant indicators was developed, and identified new predictors of POAF: left ventricular end diastolic diameter (LVEDD), intraoperative defibrillation, and intraoperative temporary pacing lead implantation. The model had good discrimination in both the derivation and validation cohorts, with the area under the receiver operating characteristic curves (AUCs) of 0.621 (95% CI = 0.602-0.640) and 0.616 (95% CI = 0.579-0.651), respectively, and showed good calibration. Compared with CHADS-VASc, HATCH score, and the prediction model of POAF after CABG developed based on a small sample of clinical data from a single center in China, the model in this study had better discrimination.
We have developed and validated a new prediction model of POAF after CABG using multicenter data that can be used in the clinic for early identification of high-risk patients of POAF, and to help effectively prevent POAF in postoperative patients.
建立并评估冠状动脉旁路移植术(CABG)后术后房颤(POAF)的风险预测模型,旨在为预测和预防提供参考。
一项回顾性病例对照研究。
三个主要城市的教学和大学医院以及三级转诊中心。
连续接受CABG的患者。
该研究为回顾性研究,未对患者进行干预。
在该研究中,总体新发POAF患病率约为28%。开发了一个包含九个重要指标的POAF预测模型,并确定了POAF的新预测因素:左心室舒张末期内径(LVEDD)、术中除颤和术中临时起搏导线植入。该模型在推导队列和验证队列中均具有良好的辨别力,受试者操作特征曲线(AUC)下面积分别为0.621(95%CI = 0.602 - 0.640)和0.616(95%CI = 0.579 - 0.651),并显示出良好的校准。与CHADS-VASc、HATCH评分以及基于中国单个中心的少量临床数据开发的CABG后POAF预测模型相比,本研究中的模型具有更好的辨别力。
我们使用多中心数据建立并验证了一种新的CABG后POAF预测模型,该模型可用于临床早期识别POAF高危患者,并有助于有效预防术后患者发生POAF。