Key result
Novel clinical model accurately predicts postoperative IABP need with ~0.95 AUC after valve surgery.
Why the study?
Postoperative complications such as low cardiac output syndrome requiring IABP after heart valve replacement surgery remain important challenges, and risk factors for IABP use need to be identified to improve preoperative risk assessment.
Cohort (n=161)
No
Objective To identify risk factors for intra-aortic balloon pump (IABP) requirement following heart valve replacement surgery (HVRS) and to develop a predictive model. Methods This retrospective cohort study analyzed 161 HVRS patients (October 2023 to January 2025) from the First Affiliated Hospital of Xinjiang Medical University. Patients were stratified into IABP ( n = 58) and non-IABP ( n = 103) groups. Independent risk factors were identified through univariate analysis, LASSO regression, and multivariate logistic regression. The cohort was randomly split into training and validation sets (7:3 ratio) for model development and internal validation. Model performance was assessed using receiver operating characteristic (ROC) curves, Hosmer-Lemeshow calibration, and decision curve analysis (DCA). Results Significant differences were observed between groups across multiple parameters (all P < 0.05), including demographics, inflammatory markers, cardiac biomarkers, and echocardiographic indices. Multivariate analysis identified five independent risk factors for postoperative IABP use: age (OR = 1.138, 95% CI: 1.067–1.226), stroke volume (SV) (OR = 1.155, 95% CI: 1.060–1.296), cardiac output (CO) (OR = 5.700, 95% CI: 2.700–12.040), cardiac index (CI) (OR = 4.982, 95% CI: 2.879–10.119), and left ventricular end-systolic diameter (LVESD) (OR = 1.463, 95% CI: 1.157–1.849). The prediction model showed excellent discrimination in both the training set (AUC = 0.946, 95% CI: 0.910–0.982) and the validation set (AUC = 0.933, 95% CI: 0.876–0.990). Good calibration was indicated by Hosmer-Lemeshow test ( P > 0.05 for both sets), and decision curve analysis confirmed the model's clinical utility. Conclusion A model incorporating five routinely available preoperative variables effectively stratifies the risk of requiring IABP after HVRS, demonstrating strong discriminatory performance and potential clinical applicability for preoperative risk assessment.
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Rukeya Hashan (2026) conducted a cohort in Heart valve disease requiring replacement surgery (n=161). Preoperative clinical and echocardiographic predictive model (Age, SV, CO, CI, LVESD) vs. Standard risk assessment was evaluated on Prediction of postoperative intra-aortic balloon pump (IABP) requirement (Training set discrimination) (AUC 0.946, 95% CI 0.910-0.982). A predictive model incorporating preoperative age, stroke volume, cardiac output, cardiac index, and left ventricular end-systolic diameter showed excellent discrimination (AUC 0.946) for predicting the need for postoperative intra-aortic balloon pump support.
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