Prediction of gastrointestinal hemorrhage in cardiology inpatients using an interpretable XGBoost model
Yahui Li1, Xujie Wang2, Xuhui Liu3,3
1Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Hubei Key Laboratory of Genetics and Molecular Mechanisms of Cardiological Disorders, Huazhong University of Science and Technology, 1095 Jiefang Ave, Wuhan, 430030, Hubei, China.
Insights
A new machine learning model accurately predicts gastrointestinal bleeding (GIB) risk in cardiology patients. Key predictors include hemoglobin, creatinine, and D-dimer, enabling early intervention and personalized prevention strategies.
Area of Science:
- Cardiology
- Medical Informatics
- Machine Learning
Background:
- Gastrointestinal bleeding (GIB) is a significant concern for cardiovascular patients, increasing morbidity and mortality.
- Existing predictive models for GIB in this population often lack accuracy and interpretability.
Purpose of the Study:
- To develop an interpretable and practical machine learning model for predicting GIB risk in cardiology inpatients.
- To identify key predictors of GIB in this patient cohort.
Main Methods:
- Retrospective analysis of electronic health records from 10,706 cardiology inpatients.
- Development and evaluation of seven machine learning classifiers, with a focus on XGBoost.
- Utilized SHapley Additive exPlanations (SHAP) for model interpretability.
Main Results:
- The XGBoost model achieved an AUC of 0.995, accuracy of 0.975, sensitivity of 0.769, and specificity of 0.996 in the validation cohort.
- Top predictors identified: hemoglobin, creatinine, D-dimer, NT-proBNP, glucose, white blood cell count, body weight, serum albumin, urea, and age.
- SHAP analysis confirmed hemoglobin, creatinine, and D-dimer as primary risk contributors.
Conclusions:
- The developed XGBoost model provides a highly accurate and interpretable tool for predicting GIB risk in cardiology inpatients.
- This model can support clinical decision-making through early risk identification and the implementation of personalized prevention strategies.
Abstract:
Gastrointestinal bleeding (GIB) occurs more frequently in cardiovascular patients than in the general population, significantly affecting morbidity and mortality. However, existing predictive models often lack sufficient accuracy and interpretability. We developed an interpretable and practical machine learning model to predict the risk of GIB in cardiology inpatients. This retrospective study analyzed electronic health records of 10,706 patients admitted to the Department of Cardiology at the Second Hospital of Lanzhou University from October 8, 2019, to October 30, 2024. Variables with > 30% missing data were excluded, leaving 35 potential predictors. The dataset was randomly split into a training cohort (80%, n = 9,356) and a test cohort (20%, n = 2,340). GIB occurred in 110 patients (1.03%). Ten variables were identified as the strongest predictors: hemoglobin (importance score: 0.16), creatinine (0.12), D-dimer (0.10), NT-proBNP (0.06), glucose (0.06), white blood cell count (0.06), body weight (0.06), serum albumin (0.04), urea (0.04), and age (0.04). Among seven machine learning classifiers, XGBoost performed best, with an AUC of 0.995 in the validation cohort. In the validation set, the model achieved an accuracy of 0.975, sensitivity of 0.769, and specificity of 0.996. SHapley Additive exPlanations (SHAP) analysis confirmed hemoglobin, creatinine, and D-dimer as the top contributors to GIB risk. The model demonstrated excellent calibration (Brier score = 0.016), and decision curve analysis supported its clinical utility across various risk thresholds. The XGBoost model offers high accuracy and interpretability in predicting GIB risk among cardiology inpatients. It holds promise for clinical decision support by enabling early risk identification and personalized prevention strategies.
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