Development and validation of a novel bleeding risk prediction tool for aspirin users with a low body mass index

Aspirin is commonly utilized in the management and prevention of various diseases. However, in specific individuals, particularly those with low body mass index (BMI), aspirin can elevate the risk of bleeding. Achieving a delicate equilibrium between the desirable antiplatelet effects and potential bleeding complications is a notable consideration. The objective of this study was to create a novel bleeding risk prediction tool for aspirin users with a low BMI. A total of 2436 aspirin users with a low BMI were included in this study conducted at the Affiliated Dongyang Hospital of Wenzhou Medical University. Patient data, comprising demographics, clinical characteristics, comorbidities, medical history, and laboratory tests, were collected. The patients were randomly divided into two groups, with a 7:3 ratio, for model development and internal validation purposes. The identification of clinically significant features associated with bleeding was achieved through the utilization of the Least Absolute Shrinkage and Selection Operator (LASSO) regression and boruta analysis. Subsequently, these important features underwent multivariate logistic regression analysis. Based on independent bleeding risk factors, a logistic regression model was constructed and presented as a nomogram. Model performance was evaluated using metrics such as the area under the curve (AUC), calibration curves, decision curve analysis (DCA), clinical impact curve (CIC), and net reduction curve (NRC) in both the training and testing sets. LASSO analysis identified two clinical features, while Boruta analysis identified nine clinical features out of a total of 21 features. Subsequent multivariate logistic regression analysis selected significant independent risk factors. The boruta model, which demonstrated the highest AUC, consisted of six clinical variables: hemoglobin, platelet count, previous bleeding, tumor, smoke, and diabetes mellitus. These variables were integrated into a visually represented nomogram. The model exhibited an AUC of 0.832 (95% CI: 0.788-0.875) in the training dataset and 0.775 (95% CI: 0.698-0.853) in the test dataset, indicating excellent discriminatory performance. Calibration curve analysis revealed close alignment with the ideal curve. Furthermore, DCA, CIC, and NRC demonstrated favorable clinical net benefit for the model. This study has successfully created a novel risk prediction tool specifically designed for aspirin users with a low BMI. This tool enables the stratification of low BMI patients based on their anticipated bleeding risk.