Work

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

Year: 2025

Type: article

Abstract: 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.

Source: BMC Cardiovascular Disorders

DOI: 10.1186/s12872-025-05067-y