Machine learning derived model for the prediction of bleeding in dual antiplatelet therapy patients.

IF 2.8 3区医学 Q2 CARDIAC & CARDIOVASCULAR SYSTEMS Frontiers in Cardiovascular Medicine Pub Date : 2024-10-02 eCollection Date: 2024-01-01 DOI:10.3389/fcvm.2024.1402672

Yang Qian, Lei Wanlin, Wang Maofeng

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Abstract

Objective: This study aimed to develop a predictive model for assessing bleeding risk in dual antiplatelet therapy (DAPT) patients.

Methods: A total of 18,408 DAPT patients were included. Data on patients' demographics, clinical features, underlying diseases, past history, and laboratory examinations were collected from Affiliated Dongyang Hospital of Wenzhou Medical University. The patients were randomly divided into two groups in a proportion of 7:3, with the most used for model development and the remaining for internal validation. LASSO regression, multivariate logistic regression, and six machine learning models, including random forest (RF), k-nearest neighbor imputing (KNN), decision tree (DT), extreme gradient boosting (XGBoost), light gradient boosting machine (LGBM), and Support Vector Machine (SVM), were used to develop prediction models. Model prediction performance was evaluated using area under the curve (AUC), calibration curves, decision curve analysis (DCA), clinical impact curve (CIC), and net reduction curve (NRC).

Results: The XGBoost model demonstrated the highest AUC. The model features were comprised of seven clinical variables, including: HGB, PLT, previous bleeding, cerebral infarction, sex, Surgical history, and hypertension. A nomogram was developed based on seven variables. The AUC of the model was 0.861 (95% CI 0.847-0.875) in the development cohort and 0.877 (95% CI 0.856-0.898) in the validation cohort, indicating that the model had good differential performance. The results of calibration curve analysis showed that the calibration curve of this nomogram model was close to the ideal curve. The clinical decision curve also showed good clinical net benefit of the nomogram model.

Conclusions: This study successfully developed a predictive model for estimating bleeding risk in DAPT patients. It has the potential to optimize treatment planning, improve patient outcomes, and enhance resource utilization.

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用于预测双重抗血小板疗法患者出血情况的机器学习衍生模型。

研究目的本研究旨在开发一种用于评估双重抗血小板疗法（DAPT）患者出血风险的预测模型：共纳入 18,408 名 DAPT 患者。温州医科大学附属东阳医院收集了患者的人口统计学、临床特征、基础疾病、既往史和实验室检查数据。按照 7:3 的比例将患者随机分为两组，其中最多的一组用于模型开发，其余的一组用于内部验证。采用LASSO回归、多元逻辑回归以及随机森林（RF）、k-近邻归因（KNN）、决策树（DT）、极梯度提升（XGBoost）、轻梯度提升机（LGBM）和支持向量机（SVM）等六种机器学习模型开发预测模型。使用曲线下面积（AUC）、校准曲线、决策曲线分析（DCA）、临床影响曲线（CIC）和净减少曲线（NRC）对模型预测性能进行评估：结果：XGBoost 模型的 AUC 值最高。结果：XGBoost 模型的 AUC 最高：HGB、PLT、既往出血、脑梗塞、性别、手术史和高血压。根据七个变量建立了一个提名图。在开发队列中，该模型的 AUC 为 0.861（95% CI 0.847-0.875），在验证队列中为 0.877（95% CI 0.856-0.898），表明该模型具有良好的差异化性能。校准曲线分析结果显示，该提名图模型的校准曲线接近理想曲线。临床决策曲线也显示该提名图模型具有良好的临床净效益：本研究成功开发了一种用于估计 DAPT 患者出血风险的预测模型。结论：本研究成功开发了一种用于估计 DAPT 患者出血风险的预测模型，该模型具有优化治疗计划、改善患者预后和提高资源利用率的潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Frontiers in Cardiovascular Medicine Medicine-Cardiology and Cardiovascular Medicine

CiteScore

3.80

自引率

11.10%

发文量

3529

审稿时长

14 weeks

期刊介绍： Frontiers? Which frontiers? Where exactly are the frontiers of cardiovascular medicine? And who should be defining these frontiers? At Frontiers in Cardiovascular Medicine we believe it is worth being curious to foresee and explore beyond the current frontiers. In other words, we would like, through the articles published by our community journal Frontiers in Cardiovascular Medicine, to anticipate the future of cardiovascular medicine, and thus better prevent cardiovascular disorders and improve therapeutic options and outcomes of our patients.