# Analysis of risk factors and development of a predictive model for IABP application in post-cardiac valve replacement patients

## TL;DR

This study identifies risk factors and creates a predictive model to determine if patients will need an intra-aortic balloon pump after heart valve replacement surgery.

## Contribution

The study introduces a predictive model using five preoperative variables to assess the need for IABP after heart valve replacement.

## Key findings

- Five independent risk factors for IABP use were identified: age, stroke volume, cardiac output, cardiac index, and left ventricular end-systolic diameter.
- The predictive model demonstrated excellent discrimination with an AUC of 0.946 in the training set and 0.933 in the validation set.
- The model showed good calibration and clinical utility according to the Hosmer-Lemeshow test and decision curve analysis.

## Abstract

To identify risk factors for intra-aortic balloon pump (IABP) requirement following heart valve replacement surgery (HVRS) and to develop a predictive model.

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).

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.

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.