Zanaty Mario, Park Brian J, Seaman Scott C, Cliffton William E, Woodiwiss Timothy, Piscopo Anthony, Howard Matthew A, Abode-Iyamah Kingsley
Department of Neurosurgery, University of Iowa, Iowa City, IA, United States.
Department of Neurosurgery, Mayo Clinic, Jacksonville, FL, United States.
Front Neurol. 2020 Jan 15;10:1401. doi: 10.3389/fneur.2019.01401. eCollection 2019.
The aging of the western population and the increased use of oral anticoagulation (OAC) and antiplatelet drugs (APD) will result in a clinical dilemma on how to balance the recurrence risk of chronic subdural hematoma (cSDH) with the risk of withholding blood thinners. To identify features that predicts recurrence, thromboembolism (TEE), hospital stay and mortality. To identify the optimal window for resuming APD or OAC. We performed a retrospective multivariate analysis of a prospectively collected database. We then build machine learning models for outcomes prediction. We identified 596 patients. The rate of recurrence was 22.17%, that of thromboembolism was 0.9% and that of mortality was 14.78%. Smoking, platelet dysfunction, CKD, and alcohol use were independent predictors of higher recurrence, while resolution of the SDH was protective. OAC use had higher odds of developing TEEs. CKD, developing a new neurological deficit or a TEEs were independent predictors of higher mortality. We find the optimal time of resuming OAC to be after 2 days but before 21 days as these patients had the lowest recurrence of bleeding associated with a low risk of stroke. The ML model achieved an accuracy of 93, precision of 0.84 and recall of 0.80 for recurrence prediction. ML models for hospital stay performed poorly ( = 0.33). ML model for stroke was overfitted given the low number of events. ML modeling is feasible. However, large well-designed prospective multicenter studies are needed for accurate ML so that clinicians can balance the risks of recurrence with the risk of TEEs, especially for high-risk anticoagulated patients.
西方人口老龄化以及口服抗凝药（OAC）和抗血小板药物（APD）使用的增加，将导致在如何平衡慢性硬膜下血肿（cSDH）复发风险与停用血液稀释剂风险方面出现临床困境。以确定预测复发、血栓栓塞（TEE）、住院时间和死亡率的特征。确定恢复使用APD或OAC的最佳时机。我们对一个前瞻性收集的数据库进行了回顾性多变量分析。然后我们建立了用于结果预测的机器学习模型。我们纳入了596例患者。复发率为22.17%，血栓栓塞率为0.9%，死亡率为14.78%。吸烟、血小板功能障碍、慢性肾脏病（CKD）和饮酒是复发率较高的独立预测因素，而硬膜下血肿的消退具有保护作用。使用OAC发生TEE的几率更高。CKD、出现新的神经功能缺损或TEE是死亡率较高的独立预测因素。我们发现恢复使用OAC的最佳时间是在2天后但在21天之前，因为这些患者与中风低风险相关的出血复发率最低。该机器学习模型在复发预测方面的准确率为93，精确率为0.84，召回率为0.80。住院时间的机器学习模型表现不佳（F = 0.33）。鉴于事件数量较少，中风的机器学习模型存在过拟合。机器学习建模是可行的。然而，需要进行大型精心设计的前瞻性多中心研究以实现准确的机器学习，以便临床医生能够平衡复发风险与TEE风险，特别是对于高风险抗凝患者。
