The antiphospholipid syndrome (APS) is diagnosed by the presence of lupus anticoagulant and/or antibodies against cardiolipin or β2-glycoprotein-1 and the occurrence of thrombosis or pregnancy morbidity. The assessment of overall coagulation is known to differ in APS patients compared to normal subjects. The accelerated production of key factor thrombin causes a prothrombotic state in APS patients, and the reduced efficacy of the activated protein C pathway promotes this effect. Even though significant differences exist in the coagulation profile between normal controls and APS patients, it is not possible to rely on a single test result to diagnose APS. A neural network is a computing system inspired by the human brain that can be trained to distinguish between healthy subjects and patients based on subject specific data. In a first cohort of patients, we developed a neural networking that diagnoses APS. We clinically validated this neural network in a separate cohort consisting of APS patients, normal controls, controls visiting the hospital for other indications and two diseased control groups (thrombosis patients and auto-immune disease patients). The positive predictive value ranged from 62% in the hospital controls to 91% in normal controls and the negative predictive value of the neural network ranged from 86% in the thrombosis control group to 95% in the hospital controls. The sensitivity of the neural network was higher than 90% in all control groups. In conclusion, we developed a neural network that accurately diagnoses APS in the validation cohort. After further clinical validation in newly diagnosed patients, this neural network could possibly be clinically implemented to diagnose APS based on thrombin generation data.
Keywords: Antiphospholipid syndrome; Artificial intelligence; Diagnosis; Neural network; Thrombin generation.
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