Thrombin-driven Neural Net Diagnoses the Antiphospholipid Syndrome without the Need for Interruption of Anticoagulation.

Thrombosis is an important manifestation of the antiphospholipid syndrome (APS). The thrombin generation test (TG) is a global hemostasis assay, and increased TG is associated with thrombosis. APS is currently diagnosed based on clinical and laboratory criteria, the latter defined as anti-cardiolipin, anti-β2-glycoprotein I antibodies, or lupus anticoagulant (LA). APS testing is often performed after a thrombotic episode and subsequent administration of anticoagulation, which might hamper the interpretation of clotting assays used for LA testing. We set out to develop an artificial neural network (NN) that can diagnose APS in vitamin K antagonist (VKA) treated patients, based on TG test results. Five NN were trained to diagnose APS in 48 VKA-treated APS patients and 64 VKA-treated controls, using TG and thrombin dynamics parameters as input. The two best-performing NNs were selected (accuracy of 96%; sensitivity 96-98%; specificity 95%-97%) and further validated in an independent cohort of VKA anticoagulated APS patients (n=33) and controls (n=62). Independent clinical validation favored one of the two selected NNs, with a sensitivity of 88% and a specificity of 94% for the diagnosis of APS. In conclusion, the combined use of TG and NN methodology allowed us to develop a NN that diagnoses APS with an accuracy of 92% in VKA anticoagulated individuals (n=95). After further clinical validation, the NN could serve as a screening and diagnostic tool for thrombosis patients, especially because there is no need to interrupt anticoagulant therapy.