Frontiers in Immunology (Sep 2022)

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

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

DOI
https://doi.org/10.3389/fimmu.2022.977443
Journal volume & issue
Vol. 13

Abstract

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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%), α2-Macroglobulin (9%), TG curve width (9%), thrombin-α2-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%.

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