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LASSO-derived model for the prediction of bleeding in aspirin users
Chen Liang1, Lei Wanling2, Wang Maofeng3
1Department of General Surgery, Affiliated Dongyang Hospital, Wenzhou Medical University, Dongyang, 322100, Zhejiang, China.
Insights
This study developed a LASSO regression model to predict bleeding risk in aspirin users. The model accurately identifies patients at higher risk, aiding clinical decision-making for aspirin therapy.
Area of Science:
- Cardiovascular Medicine
- Pharmacology
- Biostatistics
Background:
- Aspirin is crucial for preventing panvascular diseases like stroke and coronary heart disease (CHD).
- Balancing aspirin's benefits against increased bleeding risk is a clinical challenge.
- Predictive tools for aspirin-associated bleeding risk are needed.
Purpose of the Study:
- To develop and validate a predictive model for assessing bleeding risk in individuals using aspirin.
- To identify key factors contributing to bleeding events in aspirin users.
Main Methods:
- A cohort of 58,415 aspirin users was analyzed.
- Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariate logistic regression were used for model development.
- Model performance was evaluated using ROC curves (AUC), calibration, and decision curve analysis (DCA).
Main Results:
- A LASSO-derived model incorporating sex, operation, previous bleeding, hemoglobin, platelet count, and cerebral infarction was developed.
- The model demonstrated high predictive performance with AUCs of 0.866 (training) and 0.861 (testing).
- The model showed good calibration and favorable clinical net benefit via DCA.
Conclusions:
- The developed LASSO-derived predictive model effectively assesses bleeding risk in aspirin users.
- This model offers a potential tool for optimizing aspirin therapy and managing bleeding complications in clinical practice.
Abstract:
Aspirin is widely used for both primary and secondary prevention of panvascular diseases, such as stroke and coronary heart disease (CHD). The optimal balance between reducing panvascular disease events and the potential increase in bleeding risk remains unclear. This study aimed to develop a predictive model specifically designed to assess bleeding risk in individuals using aspirin. A total of 58,415 individuals treated with aspirin were included in this study. Detailed data regarding patient demographics, clinical characteristics, comorbidities, medical history, and laboratory test results were collected from the Affiliated Dongyang Hospital of Wenzhou Medical University. The patients were randomly divided into two groups at a ratio of 7:3. The larger group was used for model development, while the smaller group was used for internal validation. To develop the prediction model, we employed least absolute shrinkage and selection operator (LASSO) regression followed by multivariate logistic regression. The performance of the model was assessed through metrics such as the area under the receiver operating characteristic (ROC) curve (AUC), calibration curves, and decision curve analysis (DCA). The LASSO-derived model employed in this study incorporated six variables, namely, sex, operation, previous bleeding, hemoglobin, platelet count, and cerebral infarction. It demonstrated excellent performance at predicting bleeding risk among aspirin users, with a high AUC of 0.866 (95% CI 0.857-0.874) in the training dataset and 0.861 (95% CI 0.848-0.875) in the test dataset. At a cutoff value of 0.047, the model achieved moderate sensitivity (83.0%) and specificity (73.9%). The calibration curve analysis revealed that the nomogram closely approximated the ideal curve, indicating good calibration. The DCA curve demonstrated a favorable clinical net benefit associated with the nomogram model. Our developed LASSO-derived predictive model has potential as an alternative tool for predicting bleeding in clinical settings.
