A 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 (TG) test 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 patients who underwent vitamin K antagonist (VKA) treatment, based on TG test results. Five NNs were trained to diagnose APS in 48 VKA-treated patients with APS and 64 VKA-treated controls, using TG and thrombin dynamics parameters as inputs. The 2 best-performing NNs were selected (accuracy, 96%; sensitivity, 96%-98%; and specificity, 95%-97%) and further validated in an independent cohort of VKA-anticoagulated patients with APS (n = 33) and controls (n = 62). Independent clinical validation favored 1 of the 2 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 for us to develop an NN that diagnoses APS with an accuracy of 92% in individuals with VKA anticoagulation (n = 95). After further clinical validation, the NN could serve as a screening and diagnostic tool for patients with thrombosis, especially because there is no need to interrupt anticoagulant therapy.