AUTHOR=de Laat-Kremers Romy , De Jongh Raf , Ninivaggi Marisa , Fiolet Aernoud , Fijnheer Rob , Remijn Jasper , de Laat Bas 

TITLE=Coagulation parameters predict COVID-19-related thrombosis in a neural network with a positive predictive value of 98%

JOURNAL=Frontiers in Immunology

VOLUME=13

YEAR=2022

URL=https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2022.977443

DOI=10.3389/fimmu.2022.977443

ISSN=1664-3224

ABSTRACT=<p>Thrombosis is a major clinical complication of COVID-19 infection. COVID-19 patients show changes in coagulation factors 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. We developed and validated a neural net for the prediction of COVID-19-related thrombosis. The neural net was developed based on the hemostatic and general (laboratory) variables of 149 confirmed COVID-19 patients from two cohorts: at the time of hospital admission (cohort 1 including 133 patients) and at ICU admission (cohort 2 including 16 patients). Twenty-six patients suffered from thrombosis during their hospital stay: 19 patients in cohort 1 and 7 patients in cohort 2. The neural net predicts COVID-19 related thrombosis based on C-reactive protein (relative importance 14%), sex (10%), thrombin generation (TG) time-to-tail (10%), α<sub>2</sub>-Macroglobulin (9%), TG curve width (9%), thrombin-α<sub>2</sub>-Macroglobulin complexes (9%), plasmin generation lag time (8%), serum IgM (8%), TG lag time (7%), TG time-to-peak (7%), thrombin-antithrombin complexes (5%), and age (5%). This neural net can predict COVID-19-thrombosis at the time of hospital admission with a positive predictive value of 98%-100%.</p>