Abstract Number: PB0591
Meeting: ISTH 2022 Congress
Background: Thrombosis is a major complication of a SARS-CoV-2 infection. COVID-19 patients show changes in coagulation factor levels and functional coagulation tests that indicate an important role for the coagulation system in the pathogenesis of COVID-19. However, the multifactorial nature of thrombosis complicates the prediction of thrombotic events based on a single hemostatic variable.
Aims: We used a neural network to predict future COVID-19-related thrombosis.
Methods: We developed neural networks for the prediction of thrombosis in COVID-19 patients based on several dedicated coagulation parameters and general laboratory variables measured in plasma samples of 133 COVID-19 patients collected at the time of hospital admission (cohort 1). The neural network was validated in a second cohort of 16 COVID-19 patients admitted to the intensive care unit (cohort 2). In cohort 1 and 2, 19 and 7 patients respectively suffered from thrombosis during their hospital stay.
Results: The neural network predicts COVID-19-related thrombosis based on C-reactive protein (relative importance 14%), sex (10%), thrombin generation (TG) time-to-tail (10%), α2-macroglobulin (9%), TG curve width (9%), thrombin-α2-macroglobulin complexes (9%), plasmin generation lag time (8%), anti-SARS-CoV-2 serum IgM (8%), TG lag time (7%), TG time-to-peak (7%), thrombin-antithrombin complexes (5%), and age (5%). In developmental cohort 1, the neural network identified future thrombosis in COVID-19 patients with a positive predictive value of respectively 98%. The neural network accurately ruled out thrombosis in COVID-19 patients as the negative predictive value of the neural network was 86%. In validation cohort 2, the positive predictive value of the neural network was 100%, and a negative predictive value of 66%.
Conclusion(s): We developed a neural network that can accurately predict the occurrence of COVID-19-related thrombosis and is a promising algorithm to apply to other COVID-19 patient cohorts. The prediction of COVID-19 related thrombosis potentially can give clinicians the opportunity to increase anticoagulant therapy in high risk patients.
To cite this abstract in AMA style:de Laat-Kremers R, de Jongh R, Ninivaggi M, Fiolet A, Fijnheer R, Remijn J, de Laat B. Coagulation parameters predict COVID-19-related thrombosis in a neural network with a positive predictive value of 98% [abstract]. https://abstracts.isth.org/abstract/coagulation-parameters-predict-covid-19-related-thrombosis-in-a-neural-network-with-a-positive-predictive-value-of-98/. Accessed October 1, 2023.
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