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<p>Backgroud:&#xa0;No universally accepted model exists for predicting bleeding risk in patients receiving low-molecular-weight heparin or fondaparinux. Objective:&#xa0;This study leveraged seven machine learning algorithms to build a short-term bleeding risk prediction platform for this population. Methods:&#xa0;This retrospective real-world observational study included hospitalized patients who received low-molecular-weight heparin or fondaparinux between January 2022 and December 2023. After applying predefined criteria, the cohort were randomly split into training (70%) and validation (30%) sets. Predictors were identified using LASSO regression. Seven machine learning models, including Logistic Regression (LR), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), Neural Network (NN), extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), and CatBoost, were developed and evaluated. The best-performing model was implemented as an internal web-based bleeding risk prediction tool. Results:&#xa0;Among 1,691 hospitalized patients receiving low-molecular-weight heparin or fondaparinux, 126 (7.5%) experienced bleeding events. The cohort was randomly split into training (<i>n</i> = 1,184) and validation (<i>n</i> = 507) sets. LASSO regression identified 12 predictors, including surgical site, pre-medication INR, hemoglobin, platelet count, renal function, body mass index (BMI), indication, and comorbidities. Seven machine learning models were developed and evaluated. In the validation cohort, CatBoost achieved the best discrimination (AUC = 0.659), followed by XGBoost (AUC = 0.651) and LR (AUC = 0.622). CatBoost also demonstrated the highest accuracy (86.0%) and F1 score (0.297), with strong specificity (89.2%) but limited sensitivity (42.9%). Although all models showed robust negative predictive performance (PR-AUC &gt; 0.93), positive predictive capacity was modest (PR-AUC &lt; 0.20) in validation. Based on its overall performance, CatBoost was deployed as an internal web-based bleeding risk calculator. Conclusions:&#xa0;CatBoost emerged as the optimal model among those tested for predicting bleeding risk in patients receiving low-molecular-weight heparin or fondaparinux, demonstrating modest but superior discrimination, acceptable calibration, and favorable clinical utility. However, the model had limited ability to correctly identify patients who experienced bleeding, as indicated by low positive predictive performance. Given its high negative predictive value, it was better suited for ruling out rather than confirming bleeding risk. A web-based risk calculator based on CatBoost has been developed for internal use. Nevertheless, prospective multicenter validation is required before clinical implementation.</p>

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Machine learning in bleeding risk assessment for low-molecular-weight heparin or fondaparinux: a predictive model study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月6日

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  • Haiyan Chen,
  • Qiwei Ran,
  • Fangli Hu,
  • Xiang Zheng

摘要

Backgroud: No universally accepted model exists for predicting bleeding risk in patients receiving low-molecular-weight heparin or fondaparinux. Objective: This study leveraged seven machine learning algorithms to build a short-term bleeding risk prediction platform for this population. Methods: This retrospective real-world observational study included hospitalized patients who received low-molecular-weight heparin or fondaparinux between January 2022 and December 2023. After applying predefined criteria, the cohort were randomly split into training (70%) and validation (30%) sets. Predictors were identified using LASSO regression. Seven machine learning models, including Logistic Regression (LR), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), Neural Network (NN), extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), and CatBoost, were developed and evaluated. The best-performing model was implemented as an internal web-based bleeding risk prediction tool. Results: Among 1,691 hospitalized patients receiving low-molecular-weight heparin or fondaparinux, 126 (7.5%) experienced bleeding events. The cohort was randomly split into training (n = 1,184) and validation (n = 507) sets. LASSO regression identified 12 predictors, including surgical site, pre-medication INR, hemoglobin, platelet count, renal function, body mass index (BMI), indication, and comorbidities. Seven machine learning models were developed and evaluated. In the validation cohort, CatBoost achieved the best discrimination (AUC = 0.659), followed by XGBoost (AUC = 0.651) and LR (AUC = 0.622). CatBoost also demonstrated the highest accuracy (86.0%) and F1 score (0.297), with strong specificity (89.2%) but limited sensitivity (42.9%). Although all models showed robust negative predictive performance (PR-AUC > 0.93), positive predictive capacity was modest (PR-AUC < 0.20) in validation. Based on its overall performance, CatBoost was deployed as an internal web-based bleeding risk calculator. Conclusions: CatBoost emerged as the optimal model among those tested for predicting bleeding risk in patients receiving low-molecular-weight heparin or fondaparinux, demonstrating modest but superior discrimination, acceptable calibration, and favorable clinical utility. However, the model had limited ability to correctly identify patients who experienced bleeding, as indicated by low positive predictive performance. Given its high negative predictive value, it was better suited for ruling out rather than confirming bleeding risk. A web-based risk calculator based on CatBoost has been developed for internal use. Nevertheless, prospective multicenter validation is required before clinical implementation.