The Hypercoagulable State: A Study of Clot Waveform Analysis, Thrombin Generation, and Clot Scanning Electron Microscopy.

Abstract

Context.—: Clot waveform analysis (CWA) is a method that provides a detailed view of the clotting process for simple clotting tests such as prothrombin time (PT) or activated partial thromboplastin time (aPTT). Coagulometers with optical clot detection systems capture detailed information during each analysis, which can be used for CWA at no additional reagent expense.

Objective.—: To investigate (1) whether CWA can detect a hypercoagulable state in different clinical conditions similar to the thrombin generation (TG) assay, and (2) whether there are differences in the texture of in vitro clots by scanning electron microscopy (SEM).

Design.—: PT INR (international normalized ratio), aPTT ratio, CWA, D-dimer, fibrinogen, von Willebrand factor antigen (vWF:Ag), von Willebrand factor ristocetin cofactor (vWF:RiCo) activity, TG assays, and clot scans by SEM were obtained for 191 patients (65 with COVID-19, 51 with systemic sclerosis, 51 with liver cirrhosis, 13 with high Padua Prediction Score [PPS] without antithrombotic prophylaxis, and 11 with low PPS). A texture analysis for images acquired by SEM was performed with MATLAB software. Data are described as median and range.

Results.—: Compared to healthy controls, patients with COVID-19, systemic sclerosis, high PPS, and low PPS had higher levels of CWA, fibrinogen, D-dimer, and TG, as well as thicker clots by SEM. The highest values of both vWF:Ag and vWF:RiCo were found in patients with COVID-19.

Conclusions.—: We have shown that similar to the TG assay, CWA can detect a hypercoagulable state in patients at increased risk of clotting. Furthermore, we identified differences of in vitro clot texture by SEM that may provide further insight into the underlying pathology. Even though CWA is currently considered a research tool, it might one day become a clinically accepted test and provide value-added information to PT or aPTT testing at minimal computational costs.