Mitrophanov2016 - Extended Mitrophanov2011 Blood Coagulation Model (additional thrombin reactions)
Mathematical model of blood coagulation. Extended model of Mitrophanov2011 (which is an extension of Hockin2002). Additional reactions added involving thrombin. Modelling the effects of dilution and addition of recombinant factor VIIa, II, VII, IX, X, AT.
- A Step Toward Balance: Thrombin Generation Improvement via Procoagulant Factor and Antithrombin Supplementation.
- Mitrophanov AY, Szlam F, Sniecinski RM, Levy JH, Reifman J
- Anesthesia and analgesia , 9/ 2016 , Volume 123 , Issue 3 , pages: 535-546
- From the *DoD Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, US Army Medical Research and Materiel Command, Fort Detrick, Maryland; †Department of Anesthesiology, Emory University School of Medicine, Atlanta, Georgia; and Departments of ‡ Anesthesiology and §Surgery, Duke University School of Medicine, Durham, North Carolina.
- The use of prothrombin complex concentrates in trauma- and surgery-induced coagulopathy is complicated by the possibility of thromboembolic events. To explore the effects of these agents on thrombin generation (TG), we investigated combinations of coagulation factors equivalent to 3- and 4-factor prothrombin complex concentrates with and without added antithrombin (AT), as well as recombinant factor VIIa (rFVIIa), in a dilutional model. These data were then used to develop a computational model to test whether such a model could predict the TG profiles of these agents used to treat dilutional coagulopathy.We measured TG in plasma collected from 10 healthy volunteers using Calibrated Automated Thrombogram. TG measurements were performed in undiluted plasma, 3-fold saline-diluted plasma, and diluted plasma supplemented with the following factors: rFVIIa (group rFVIIa); factors (F)II, FIX, FX, and AT (group "combination of coagulation factors" [CCF]-AT); or FII, FVII, FIX, and FX (group CCF-FVII). We extended an existing computational model of TG to include additional reactions that impact the Calibrated Automated Thrombogram readout. We developed and applied a computational strategy to train the model using only a subset of the obtained TG data and used the remaining data for model validation.rFVIIa decreased lag time and the time to thrombin peak generation beyond their predilution levels (P < 0.001) but did not restore normal thrombin peak height (P < 0.001). CCF-FVII supplementation decreased lag time (P = 0.034) and thrombin peak time (P < 0.001) and increased both peak height (P < 0.001) and endogenous thrombin potential (P = 0.055) beyond their predilution levels. CCF-AT supplementation in diluted plasma resulted in an improvement in TG without causing the exaggerated effects of rFVIIa and CCF-FVII supplementation. The differences between the effects of CCF-AT and supplementation with rFVIIa and CCF-FVII were significant for lag time (P < 0.001 and P = 0.005, respectively), time to thrombin peak (P < 0.001 and P = 0.004, respectively), velocity index (P < 0.001 and P = 0.019, respectively), thrombin peak height (P < 0.001 for both comparisons), and endogenous thrombin potential (P = 0.034 and P = 0.019, respectively). The computational model generated subject-specific predictions and identified typical patterns of TG improvement.In this study of the effects of hemodilution, CCF-AT supplementation improved the dilution-impaired plasma TG potential in a more balanced way than either rFVIIa alone or CCF-FVII supplementation. Predictive computational modeling can guide plasma dilution/supplementation experiments.