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Background <p>Timely assessment of Low cardiac output syndrome (LCOS) risk after off-pump coronary artery bypass grafting (OPCAB) is crucial, yet hindered by the lack of standardized diagnostic criteria beyond symptoms, therapy response, and ultrasound.</p> Purpose <p>This study aims to develop, construct, and internally validate a predictive model to predict low cardiac output syndrome (LCOS) in patients undergoing off-pump coronary artery bypass grafting (OPCAB).</p> Patients and methods <p>Using a clinical dataset of 765 OPCAB patients treated between May 2018 and July 2020, encompassing admission, surgical, and postoperative data, a predictive model was developed. Feature selection was performed using Least Absolute Shrinkage and Selection Operator (LASSO) regression. The selected features were then incorporated into a multivariate logistic regression model to establish the final predictor. Model performance was evaluated using the C-index (discrimination), calibration plots (calibration), and decision curve analysis (clinical validity). Internal validation via bootstrap resampling assessed model robustness.</p> Results <p>The final prediction model incorporated the following predictors: age, smoking history, ejection fraction, left ventricular end-diastolic diameter, lactic acid levels, room-air pre-operative PaO₂, room-air pre-operative oxygen saturation, carotid artery stenosis, myocardial enzyme levels, internal mammary artery condition, intra-operative blood transfusion volume, intra-operative blood loss, ICU stay duration, ventilator time, and IABP implantation. The model demonstrated excellent discrimination, with a C-index of 0.943 (95% CI: 0.893–0.993) in the derivation cohort and 0.9429 in the bootstrap-validated cohort. Decision curve analysis indicated significant net clinical benefit for model-guided intervention when the predicted LCOS risk threshold exceeded 1%.</p> Conclusion <p>This study developed a predictive model that integrates significant risk factors for post-OPCAB LCOS, encompassing demographics, cardiac metrics, laboratory values, procedural variables, and post-operative course. By enabling accurate risk stratification (C-index 0.94), this model may serve as a practical clinical tool to guide timely interventions for high-risk patients.</p>

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Risk factors analysis and prediction model construction of low cardiac output syndrome after off-pump coronary artery bypass grafting

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月12日

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  • Pengfei Guo,
  • Lingjie Wang,
  • Zheng Gao,
  • Bin Wang,
  • Wenlong Yan,
  • Xingchi Qu,
  • Sumin Yang

摘要

Background

Timely assessment of Low cardiac output syndrome (LCOS) risk after off-pump coronary artery bypass grafting (OPCAB) is crucial, yet hindered by the lack of standardized diagnostic criteria beyond symptoms, therapy response, and ultrasound.

Purpose

This study aims to develop, construct, and internally validate a predictive model to predict low cardiac output syndrome (LCOS) in patients undergoing off-pump coronary artery bypass grafting (OPCAB).

Patients and methods

Using a clinical dataset of 765 OPCAB patients treated between May 2018 and July 2020, encompassing admission, surgical, and postoperative data, a predictive model was developed. Feature selection was performed using Least Absolute Shrinkage and Selection Operator (LASSO) regression. The selected features were then incorporated into a multivariate logistic regression model to establish the final predictor. Model performance was evaluated using the C-index (discrimination), calibration plots (calibration), and decision curve analysis (clinical validity). Internal validation via bootstrap resampling assessed model robustness.

Results

The final prediction model incorporated the following predictors: age, smoking history, ejection fraction, left ventricular end-diastolic diameter, lactic acid levels, room-air pre-operative PaO₂, room-air pre-operative oxygen saturation, carotid artery stenosis, myocardial enzyme levels, internal mammary artery condition, intra-operative blood transfusion volume, intra-operative blood loss, ICU stay duration, ventilator time, and IABP implantation. The model demonstrated excellent discrimination, with a C-index of 0.943 (95% CI: 0.893–0.993) in the derivation cohort and 0.9429 in the bootstrap-validated cohort. Decision curve analysis indicated significant net clinical benefit for model-guided intervention when the predicted LCOS risk threshold exceeded 1%.

Conclusion

This study developed a predictive model that integrates significant risk factors for post-OPCAB LCOS, encompassing demographics, cardiac metrics, laboratory values, procedural variables, and post-operative course. By enabling accurate risk stratification (C-index 0.94), this model may serve as a practical clinical tool to guide timely interventions for high-risk patients.