Cheng Zhenzhen, Wang Yishun, Liu Jing, Ming Yue, Yao Yuanyuan, Wu Zhong, Guo Yingqiang, Du Lei, Yan Min
Department of Anesthesiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.
Department of Anesthesiology, West China Hospital of Sichuan University, Chengdu, China.
Front Cardiovasc Med. 2023 May 17;10:1132428. doi: 10.3389/fcvm.2023.1132428. eCollection 2023.
On-pump valve surgeries are associated with high morbidity and mortality. The present study aimed to reliably predict a composite outcome of postoperative complications using a minimum of easily accessible clinical parameters.
A total of 7,441 patients who underwent valve surgery were retrospectively analyzed. Data for 6,220 patients at West China Hospital of Sichuan University were used to develop a predictive model, which was validated using data from 1,221 patients at the Second Affiliated Hospital of Zhejiang University School of Medicine. The primary outcome was a composite of major complications: all-cause death in hospital, stroke, myocardial infarction, and severe acute kidney injury. The predictive model was constructed using the least absolute shrinkage and selection operator as well as multivariable logistic regression. The model was assessed in terms of the areas under receiver operating characteristic curves, calibration, and decision curve analysis.
The primary outcome occurred in 129 patients (2.1%) in the development cohort and 71 (5.8%) in the validation cohort. Six variables were retained in the predictive model: New York Heart Association class, diabetes, glucose, blood urea nitrogen, operation time, and red blood cell transfusion during surgery. The C-statistics were 0.735 (95% CI, 0.686-0.784) in the development cohort and 0.761 (95% CI, 0.694-0.828) in the validation cohort. For both cohorts, calibration plots showed good agreement between predicted and actual observations, and ecision curve analysis showed clinical usefulness. In contrast, the well-established SinoSCORE did not accurately predict the primary outcome in either cohort.
This predictive nomogram based on six easily accessible variables may serve as an "early warning" system to identify patients at high risk of major complications after valve surgery.
[www.ClinicalTrials.gov], identifier [NCT04476134].
体外循环下瓣膜手术与高发病率和死亡率相关。本研究旨在使用最少的易于获取的临床参数可靠地预测术后并发症的综合结局。
对总共7441例行瓣膜手术的患者进行回顾性分析。使用四川大学华西医院6220例患者的数据建立预测模型，并使用浙江大学医学院附属第二医院1221例患者的数据进行验证。主要结局是主要并发症的综合：住院全因死亡、中风、心肌梗死和严重急性肾损伤。使用最小绝对收缩和选择算子以及多变量逻辑回归构建预测模型。通过受试者操作特征曲线下面积、校准和决策曲线分析对模型进行评估。
在开发队列中，129例患者（2.1%）出现主要结局，在验证队列中，71例患者（5.8%）出现主要结局。预测模型中保留了六个变量：纽约心脏协会分级、糖尿病、血糖、血尿素氮、手术时间和手术期间红细胞输注。开发队列中的C统计量为0.735（95%CI，0.686 - 0.784），验证队列中的C统计量为0.761（95%CI，0.694 - 0.828）。对于两个队列，校准图显示预测值与实际观察值之间具有良好的一致性，决策曲线分析显示具有临床实用性。相比之下，成熟的中国心脏手术风险评估系统（SinoSCORE）在两个队列中均未准确预测主要结局。
这种基于六个易于获取变量的预测列线图可作为一种“早期预警”系统，用于识别瓣膜手术后发生主要并发症的高危患者。
