Department of Pharmacy, School of Applied Sciences, University of Huddersfield, Queensgate, HD1 3DH, Huddersfield, UK.
Anticoagulant Services, Calderdale and Huddersfield NHS Foundation Trust Hospital, Lindley, HD3 3EA, Huddersfield, UK.
Am J Cardiovasc Drugs. 2023 May;23(3):287-299. doi: 10.1007/s40256-023-00569-6. Epub 2023 Mar 6.
The clinical outcomes of direct oral anticoagulant (DOAC) dosage regimens in morbid obesity are uncertain due to limited clinical evidence. This study seeks to bridge this evidence gap by identifying the factors associated with clinical outcomes following the dosing of DOACs in morbidly obese patients.
A data-driven observational study was carried out using supervised machine learning (ML) models with a dataset extracted from electronic health records and preprocessed. Following 70%:30% partitioning of the overall dataset via stratified sampling, the selected ML classifiers (e.g., random forest, decision trees, bootstrap aggregation) were applied to the training dataset (70%). The outcomes of the models were evaluated against the test dataset (30%). Multivariate regression analysis explored the association between DOAC regimens and clinical outcomes.
A sample of 4,275 morbidly obese patients was extracted and analysed. The decision trees, random forest, and bootstrap aggregation classifiers achieved acceptable (excellent) values of precision, recall, and F1 scores in terms of their contribution to clinical outcomes. The length of stay, treatment days, and age were ranked highest for relevance to mortality and stroke. Among DOAC regimens, apixaban 2.5 mg twice daily ranked highest for its association with mortality, increasing the mortality risk by 43% (odds ratio [OR] 1.430, 95% confidence interval [CI] 1.181-1.732, p = 0.001). On the other hand, apixaban 5 mg twice daily reduced the odds of mortality by 25% (OR 0.751, 95% CI 0.632-0.905, p = 0.003) but increased the odds of stroke events. No clinically relevant non-major bleeding events occurred in this group.
Data-driven approaches can identify key factors associated with clinical outcomes following the dosing of DOACs in morbidly obese patients. This will help design further studies to explore well tolerated and effective DOAC doses for morbidly obese patients.
由于临床证据有限,直接口服抗凝剂(DOAC)剂量方案在病态肥胖患者中的临床结局尚不确定。本研究旨在通过确定 DOAC 在病态肥胖患者中的剂量后与临床结局相关的因素来填补这一证据空白。
使用有监督机器学习(ML)模型进行了一项数据驱动的观察性研究,该模型使用从电子健康记录中提取并预处理的数据。通过分层抽样将整个数据集分为 70%:30%后,选择 ML 分类器(例如随机森林、决策树、引导聚合)应用于训练数据集(70%)。模型的结果与测试数据集(30%)进行了比较。多元回归分析探讨了 DOAC 方案与临床结局之间的关系。
从 4275 例病态肥胖患者中提取并分析了一个样本。决策树、随机森林和引导聚合分类器在对临床结局的贡献方面达到了可接受的(优秀的)精度、召回率和 F1 分数。住院时间、治疗天数和年龄与死亡率和中风的相关性最高。在 DOAC 方案中,阿哌沙班 2.5mg 每日两次与死亡率的相关性最高,使死亡率风险增加 43%(比值比[OR] 1.430,95%置信区间[CI] 1.181-1.732,p=0.001)。另一方面,阿哌沙班 5mg 每日两次降低了 25%的死亡率风险(OR 0.751,95%CI 0.632-0.905,p=0.003),但增加了中风事件的几率。在这组患者中没有发生临床相关的非主要出血事件。
数据驱动的方法可以确定 DOAC 在病态肥胖患者中的剂量后与临床结局相关的关键因素。这将有助于设计进一步的研究,以探索耐受良好且有效的 DOAC 剂量用于病态肥胖患者。