Abstract
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 2 -Macroglobulin (9%), TG curve width (9%), thrombin-α 2 -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%.
Keywords: COVID-19; neural network; prediction; thrombin generation; thrombosis.
【저자키워드】 COVID-19, prediction, Neural network, thrombosis., Thrombin generation, 【초록키워드】 IgM, thrombosis, hospital, Sex, C-reactive protein, Laboratory, serum, Cohort, Positive predictive value, COVID-19 infection, Patient, ICU admission, age, Hospital admission, Coagulation system, patients, predict, COVID-19 patient, thrombin, coagulation factor, pathogenesis of COVID-19, variable, multifactorial, thrombotic event, clinical complication, changes in, suffered, complexes, complicate, neural net, 【제목키워드】 thrombosis, Positive predictive value, predict, parameter,