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Updated: Oct 21, 2025
Leveraging Turbidity and Thromboelastography for Complementary Clot Characterization
Published on: June 4, 2020
Point-of-care detection and differentiation of anticoagulant therapy - development of thromboelastometry-guided
Simon T Schäfer1, Anne-Christine Otto1, Alice-Christin Acevedo1
1Department of Anaesthesiology, University Hospital Munich, LMU Munich, Munich, Germany.
Insights
A new algorithm using thromboelastometry accurately detects direct oral anticoagulants (DOACs), including direct Xa-inhibitors (DXaIs) and direct thrombin inhibitors (DTIs), differentiating them from other coagulopathies.
Area of Science:
- Hematology
- Clinical Chemistry
- Medical Diagnostics
Background:
- Detecting direct oral anticoagulants (DOACs) in emergencies is difficult.
- Previous methods could detect some DOACs but not all, and differentiation from other coagulopathies was unclear.
- This study investigated a novel approach for comprehensive DOAC detection.
Purpose of the Study:
- To test if a decision tree-based thromboelastometry algorithm can accurately detect and differentiate all direct Xa-inhibitors (DXaIs), direct thrombin inhibitors (DTIs), vitamin K antagonists (VKAs), and dilutional coagulopathy (DIL).
- To assess the diagnostic performance of this algorithm in a clinical setting.
Main Methods:
- A prospective observational trial included 50 anticoagulated patients and 20 healthy volunteers.
- Standard and modified thromboelastometric tests were performed on blood samples.
- Statistical analysis involved decision tree analysis and ROC curve analysis to determine accuracy, sensitivity, and specificity.
Main Results:
- Standard tests showed 78% accuracy in differentiating anticoagulants and coagulopathies.
- Modified tests combined with decision trees improved accuracy to 98% for detecting and differentiating DTIs, DXaIs, VKAs, and DIL.
- ROC analysis confirmed high sensitivity and specificity for the algorithm's diagnostic capabilities.
Conclusions:
- Decision tree-based machine learning algorithms utilizing thromboelastometry can reliably detect direct thrombin inhibitors (DTIs) and direct Xa-inhibitors (DXaIs).
- This approach enables accurate differentiation of DOACs from vitamin K antagonists (VKAs), dilutional coagulopathy (DIL), and healthy controls.
- The findings support the clinical utility of this algorithm in managing anticoagulated patients, especially in emergency situations.
Background:
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.
Methods:
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).
Results:
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.
Conclusions:
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.
Trial Registration:
Clinical trial number: German clinical trials database ID: DRKS00015704 .
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