Wang Rui, Gao Wei, Che Xinyu, Shen Ruopei, Dai Chunfeng, Xia Yan, Chen Ao, Lu Danbo, Ma Jiaqi, Chen Hungju, Li Chenguang, Chen Zhangwei, Qian Juying, Ge Junbo
School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China.
Department of Cardiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Cardiovascular Diseases, Shanghai, China.
BMC Cardiovasc Disord. 2025 May 24;25(1):395. doi: 10.1186/s12872-025-04835-0.
Many cardiovascular patients undergoing valve surgeries require coronary angiography (CAG). Positive results may lead to bypass surgery, while negative results require no treatment. Although informative, CAG is costly and exposes patients to significant radiation. This study aimed to develop a model to reduce unnecessary procedures.
A retrospective cohort study was conducted on 5,086 patients who underwent valve repair/replacement or other cardiac surgeries at Zhongshan Hospital between 2016 and 2021 and received CAG. Patients treated between 2016 and 2020 formed the training set, while those treated in 2021 constituted the validation set. Severe coronary stenosis was defined as a ≥ 50% reduction in luminal diameter. Logistic regression analysis identified independent predictors in the training set, and a scoring system (Coronary Angiography Positivity Prediction Score) was constructed based on the β-coefficients of each variable. The model was evaluated for discrimination and calibration.
Among 4,049 patients, 536 (13.2%) had severe coronary stenosis. Independent predictors included age ≥ 60 years, male sex, hypertension, diabetes, hyperlipidemia, and left ventricular ejection fraction ≤ 58%. The scoring system ranged from 0 to 11 points and demonstrated good discrimination, with an area under the receiver operating characteristic curve of 0.715 (95% confidence interval: 0.694-0.740) in the training set. In the high-risk group (≥ 6 points), the probability of severe coronary stenosis was 23.1%, compared to 8% in the low-risk group (< 6 points). The scoring system also performed well in the validation set with the curve of 0.740 (95% CI, 0.695-0.784).
We developed and validated a scoring system based on six clinical variables to predict severe coronary stenosis in patients undergoing valve surgeries. This tool may help optimize individual treatment strategies and reduce unnecessary CAG procedures.
许多接受瓣膜手术的心血管疾病患者需要进行冠状动脉造影(CAG)。阳性结果可能会导致搭桥手术,而阴性结果则无需治疗。尽管CAG提供了有用信息,但成本高昂且会使患者暴露于大量辐射之下。本研究旨在开发一种模型以减少不必要的检查程序。
对2016年至2021年期间在中山医院接受瓣膜修复/置换或其他心脏手术并接受CAG检查的5086例患者进行了一项回顾性队列研究。2016年至2020年期间接受治疗的患者组成训练集,2021年接受治疗的患者构成验证集。严重冠状动脉狭窄定义为管腔直径减少≥50%。逻辑回归分析在训练集中确定了独立预测因素,并基于每个变量的β系数构建了一个评分系统(冠状动脉造影阳性预测评分)。对该模型的区分度和校准度进行了评估。
在4049例患者中,536例(13.2%)患有严重冠状动脉狭窄。独立预测因素包括年龄≥60岁、男性、高血压、糖尿病、高脂血症以及左心室射血分数≤58%。评分系统的范围为0至11分,显示出良好的区分度,训练集中受试者工作特征曲线下面积为0.715(95%置信区间:0.694 - 0.740)。在高危组(≥6分)中,严重冠状动脉狭窄的概率为23.1%,而低危组(<6分)为8%。该评分系统在验证集中也表现良好,曲线下面积为0.740(95%CI,0.695 - 0.784)。
我们开发并验证了一种基于六个临床变量的评分系统,用于预测接受瓣膜手术患者的严重冠状动脉狭窄。该工具可能有助于优化个体化治疗策略并减少不必要的CAG检查程序。