Schäfer Simon T, Otto Anne-Christine, Acevedo Alice-Christin, Görlinger Klaus, Massberg Steffen, Kammerer Tobias, Groene Philipp
Department of Anaesthesiology, University Hospital Munich, LMU Munich, Munich, Germany.
TEM Innovations, Munich, Germany.
Thromb J. 2021 Sep 7;19(1):63. doi: 10.1186/s12959-021-00313-7.
DOAC detection is challenging in emergency situations. Here, we demonstrated recently, that modified thromboelastometric tests can reliably detect and differentiate dabigatran and rivaroxaban. However, whether all DOACs can be detected and differentiated to other coagulopathies is unclear. Therefore, we now tested the hypothesis that a decision tree-based thromboelastometry algorithm enables detection and differentiation of all direct Xa-inhibitors (DXaIs), the direct thrombin inhibitor (DTI) dabigatran, as well as vitamin K antagonists (VKA) and dilutional coagulopathy (DIL) with high accuracy.
Following ethics committee approval (No 17-525-4), and registration by the German clinical trials database we conducted a prospective observational trial including 50 anticoagulated patients (n = 10 of either DOAC/VKA) and 20 healthy volunteers. Blood was drawn independent of last intake of coagulation inhibitor. Healthy volunteers served as controls and their blood was diluted to simulate a 50% dilution in vitro. Standard (extrinsic coagulation assay, fibrinogen assay, etc.) and modified thromboelastometric tests (ecarin assay and extrinsic coagulation assay with low tissue factor) were performed. Statistical analyzes included a decision tree analyzes, with depiction of accuracy, sensitivity and specificity, as well as receiver-operating-characteristics (ROC) curve analysis including optimal cut-off values (Youden-Index).
First, standard thromboelastometric tests allow a good differentiation between DOACs and VKA, DIL and controls, however they fail to differentiate DXaIs, DTIs and VKAs reliably resulting in an overall accuracy of 78%. Second, adding modified thromboelastometric tests, 9/10 DTI and 28/30 DXaI patients were detected, resulting in an overall accuracy of 94%. Complex decision trees even increased overall accuracy to 98%. ROC curve analyses confirm the decision-tree-based results showing high sensitivity and specificity for detection and differentiation of DTI, DXaIs, VKA, DIL, and controls.
Decision tree-based machine-learning algorithms using standard and modified thromboelastometric tests allow reliable detection of DTI and DXaIs, and differentiation to VKA, DIL and controls.
Clinical trial number: German clinical trials database ID: DRKS00015704 .
在紧急情况下检测直接口服抗凝剂（DOAC）具有挑战性。我们最近证明，改良的血栓弹力测定试验能够可靠地检测和区分达比加群和利伐沙班。然而，是否所有的DOAC都能被检测出来并与其他凝血障碍区分尚不清楚。因此，我们现在检验了这样一个假设，即基于决策树的血栓弹力测定算法能够高精度地检测和区分所有直接Xa因子抑制剂（DXaIs）、直接凝血酶抑制剂（DTI）达比加群，以及维生素K拮抗剂（VKA）和稀释性凝血障碍（DIL）。
经伦理委员会批准（第17 - 525 - 4号），并在德国临床试验数据库注册后，我们进行了一项前瞻性观察性试验，纳入50例接受抗凝治疗的患者（DOAC/VKA各10例）和20名健康志愿者。采血与最后一次服用凝血抑制剂无关。健康志愿者作为对照，其血液在体外进行稀释以模拟50%的稀释度。进行了标准检测（外源性凝血试验、纤维蛋白原检测等）和改良的血栓弹力测定试验（蛇毒试验和低组织因子外源性凝血试验）。统计分析包括决策树分析，描述准确性、敏感性和特异性，以及受试者工作特征（ROC）曲线分析，包括最佳临界值（约登指数）。
首先，标准血栓弹力测定试验能够很好地区分DOAC与VKA、DIL和对照，但它们无法可靠地区分DXaIs、DTIs和VKAs，总体准确率为78%。其次，增加改良血栓弹力测定试验后，检测出9/10的DTI患者和28/30的DXaI患者，总体准确率为94%。复杂的决策树甚至将总体准确率提高到了98%。ROC曲线分析证实了基于决策树的结果，显示出对DTI、DXaIs、VKA、DIL和对照进行检测和区分时具有高敏感性和特异性。
使用标准和改良血栓弹力测定试验的基于决策树的机器学习算法能够可靠地检测DTI和DXaIs，并与VKA、DIL和对照进行区分。
临床试验编号：德国临床试验数据库ID：DRKS00015704 。
