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Analysis of risk factors and development of a predictive model for IABP application in post-cardiac valve replacement
Rukeya Hashan1,2, Wang Zhengkai1,2
1Department of Critical Care Medicine, First Afliated Hospital of Xinjiang Medical University, Urumqi, China.
Insights
A predictive model identifies key risk factors for requiring intra-aortic balloon pump (IABP) support after heart valve replacement surgery (HVRS). This tool aids in preoperative risk stratification for better patient outcomes.
Area of Science:
- Cardiovascular Surgery
- Medical Informatics
- Clinical Risk Prediction
Background:
- Heart valve replacement surgery (HVRS) can necessitate intra-aortic balloon pump (IABP) support in some patients.
- Identifying patients at high risk for IABP requirement is crucial for optimizing perioperative management and outcomes.
Purpose of the Study:
- To identify independent risk factors associated with the need for IABP following HVRS.
- To develop and validate a predictive model for IABP requirement post-HVRS.
Main Methods:
- Retrospective cohort study of 161 HVRS patients.
- Risk factors identified using univariate, LASSO, and multivariate logistic regression.
- Model developed and internally validated using training/validation sets (7:3 ratio) with ROC, Hosmer-Lemeshow, and DCA.
Main Results:
- Five independent risk factors identified: age, stroke volume, cardiac output, cardiac index, and left ventricular end-systolic diameter.
- The predictive model demonstrated excellent discrimination (AUCtrain=0.946, AUCval=0.933) and good calibration.
- Decision curve analysis confirmed the model's clinical utility for risk stratification.
Conclusions:
- A predictive model using five routinely available preoperative variables effectively stratifies IABP risk after HVRS.
- The model shows strong discriminatory performance and potential clinical applicability for preoperative risk assessment.
- This tool can aid clinicians in identifying patients who may benefit from closer monitoring or alternative strategies post-HVRS.
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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