BACKGROUND: The generation of thrombin in time is the combined effect of the processes of prothrombin conversion and thrombin inactivation. Measurement of prothrombin consumption used to provide valuable information on hemostatic disorders, but is no longer used, due to its elaborate nature. OBJECTIVES: Because thrombin generation (TG) curves are easily obtained with modern techniques, we developed a method to extract the prothrombin conversion curve from the TG curve, using a computational model for thrombin inactivation. METHODS: Thrombin inactivation was modelled computationally by a reaction scheme with antithrombin, α(2) Macroglobulin and fibrinogen, taking into account the presence of the thrombin substrate ZGGR-AMC used to obtain the experimental data. The model was validated by comparison with data obtained from plasma as well as from a reaction mixture containing the same reactants as plasma. RESULTS: The computational model fitted experimental data within the limits of experimental error. Thrombin inactivation curves were predicted within 2 SD in 96% of healthy subjects. Prothrombin conversion was calculated in 24 healthy subjects and validated by comparison with the experimental consumption of prothrombin during TG. The endogenous thrombin potential (ETP) mainly depends on the total amount of prothrombin converted and the thrombin decay capacity, and the peak height is determined by the maximum prothrombin conversion rate and the thrombin decay capacity. CONCLUSIONS: Thrombin inactivation can be accurately predicted by the proposed computational model and prothrombin conversion can be extracted from a TG curve using this computational prediction. This additional computational analysis of TG facilitates the analysis of the process of disturbed TG.
BACKGROUND: The generation of thrombin in time is the combined effect of the processes of prothrombin conversion and thrombin inactivation. Measurement of prothrombin consumption used to provide valuable information on hemostatic disorders, but is no longer used, due to its elaborate nature. OBJECTIVES: Because thrombin generation (TG) curves are easily obtained with modern techniques, we developed a method to extract the prothrombin conversion curve from the TG curve, using a computational model for thrombin inactivation. METHODS:Thrombin inactivation was modelled computationally by a reaction scheme with antithrombin, α(2) Macroglobulin and fibrinogen, taking into account the presence of the thrombin substrate ZGGR-AMC used to obtain the experimental data. The model was validated by comparison with data obtained from plasma as well as from a reaction mixture containing the same reactants as plasma. RESULTS: The computational model fitted experimental data within the limits of experimental error. Thrombin inactivation curves were predicted within 2 SD in 96% of healthy subjects. Prothrombin conversion was calculated in 24 healthy subjects and validated by comparison with the experimental consumption of prothrombin during TG. The endogenous thrombin potential (ETP) mainly depends on the total amount of prothrombin converted and the thrombin decay capacity, and the peak height is determined by the maximum prothrombin conversion rate and the thrombin decay capacity. CONCLUSIONS:Thrombin inactivation can be accurately predicted by the proposed computational model and prothrombin conversion can be extracted from a TG curve using this computational prediction. This additional computational analysis of TG facilitates the analysis of the process of disturbed TG.
Authors: Shengshi Huang; Wouter van der Heijden; Isaie J Reuling; Jun Wan; Qiuting Yan; Romy M W de Laat-Kremers; Andre J Van der Ven; Philip G de Groot; Matthew McCall; Robert W Sauerwein; Teun Bousema; Mark Roest; Marisa Ninivaggi; Quirijn de Mast; Bas de Laat Journal: PLoS One Date: 2022-07-15 Impact factor: 3.752
Authors: Romy M W Kremers; Stéphane Zuily; Hilde Kelchtermans; Tessa C Peters; Saartje Bloemen; Véronique Regnault; H Coenraad Hemker; Philip G de Groot; Denis Wahl; Bas de Laat Journal: Blood Adv Date: 2018-06-12
Authors: Romy M W Kremers; Marie-Claire Kleinegris; Marisa Ninivaggi; Bas de Laat; Hugo Ten Cate; Ger H Koek; Rob J Wagenvoord; H Coenraad Hemker Journal: PLoS One Date: 2017-05-04 Impact factor: 3.240
Authors: Grzegorz Gajos; Aleksander Siniarski; Joanna Natorska; Michał Ząbczyk; Jakub Siudut; Krzysztof Piotr Malinowski; Renata Gołębiowska-Wiatrak; Paweł Rostoff; Anetta Undas Journal: Cardiovasc Diabetol Date: 2018-11-22 Impact factor: 9.951
Authors: Romy M W Kremers; Abdulrahman B O Mohamed; Leonie Pelkmans; Salwa Hindawi; H Coenraad Hemker; H Bas de Laat; Dana Huskens; Raed Al Dieri Journal: PLoS One Date: 2015-10-28 Impact factor: 3.240