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Background <p>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.</p> Methods <p>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.</p> Results <p>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 (&lt; 6 points). The scoring system also performed well in the validation set with the curve of 0.740 (95% CI, 0.695–0.784).</p> Conclusion <p>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.</p>

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Clinical factors associated with severe coronary stenosis in patients undergoing cardiac surgery

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月24日

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  • Rui Wang,
  • Wei Gao,
  • Xinyu Che,
  • Ruopei Shen,
  • Chunfeng Dai,
  • Yan Xia,
  • Ao Chen,
  • Danbo Lu,
  • Jiaqi Ma,
  • Hungju Chen,
  • Chenguang Li,
  • Zhangwei Chen,
  • Juying Qian,
  • Junbo Ge

摘要

Background

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.

Methods

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.

Results

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).

Conclusion

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.