The predicted composition based capacity for thrombin generation in response to a TF challenge represents an integrative method to identify an individual’s propensity for developing DVT. The most influential variable was the MaxR, which, corrected for by all propagation phase variables (except CT), resulted in an OR of 2.6(95% CI: 1.8-3.8) for the highest 10 percent of MaxR values. The use of the numerical simulation model allows for the combination of the results of the individual reactions into a complete ensemble describing not only the observed progress in thrombin formation but also the activation progress for each of the proteins as intermediates in the path towards thrombin formation. These results were verified by recapitulating thrombin generation based upon the protein profiles of the specified LETS population in a synthetic plasma model. Therefore, the individual’s hemostatic profile is translated into a defined pattern, which can be used as an evaluation tool for thrombosis risk, potentially with the ability to influence clinical decision making. Our results show that there may exist subthreshold venous thrombosis states in individuals (ie. OC use) that depend only on a trigger, which will create a heightened prothrombotic state that presents itself upon challenge. Thus, illustrating that evaluating comprehensive thrombin generation via an individual’s protein profile can be a measure of an individual’s prothrombotic state.
Understanding the relationship of TF initiated thrombin generation to clinical outcome has been hindered by the absence of comprehensive assays and in the ability to collect blood from patients during a hemostatic challenge. The use of the numerical simulation model enables the estimation of TF initiated dynamic thrombin generation using available tools (ie. patients’ factor levels). The numerical simulation method thus allows the evaluation of well-studied clinical databases, such as the LETS population and compare dynamic thrombin generation retrospectively to determined clinical phenotypes. Although these studies do not include the contribution of the anticoagulant protein C pathway, the contribution of platelets, the contact pathway or the vasculature, it does measure all the plasma pro- and anti-coagulant proteins of the TF pathway to thrombin generation that are evaluated in current laboratories.
In this study, we translated the active thrombin profiles into thrombin parameters that incorporate the initiation, propagation and termination phases of thrombin generation. The principal regulator of the initiation phase of thrombin generation is TFPI, the stoichiometric inhibitor of the factor VIIa-TF-factor Xa enzyme-product complex. Thrombin generation simulations performed using either active, total or free TFPI values yielded similar risk estimates. Previously it has been reported that for the LETS population, low levels of TFPI, especially low TFPI-free and total antigen in plasma, constituted a risk factor for DVT (45
). In the numerical system, the difference between TFPI subsets is small. In previous numerical simulations for healthy individuals (the LETS control group), the effect from any individual protein on thrombin generation outcome was <9% (27
); thus it is the interplay between the procoagulants and the anticoagulants that determines the extent of thrombin generation.
MaxR was the most useful predictor of DVT. This parameter incorporates the velocity at which thrombin is formed, and is obtained as the slope of the thrombin generation curve (). The range of mean MaxR in the subpopulations was from 2.8–4.3 nM/s; with the cases between 3.3–4.3 nM/s and the controls between 2.8–3.8 nM/s. Previous studies of genetic bleeding disorders have shown that in hemophilia the MaxR ranges from 0.2–0.5nM/s(52
). From these studies we can begin to evaluate a thrombotic point or threshold using MaxR in healthy individuals.
During investigation of the influence of MaxR relative to risk factors, sex, age, BMI, OC use, alcohol consumption and smoking, we found that MaxR was associated with risk in all strata of these factors. MaxR was highest in patients using OCs. OC use is an established risk factor for both venous and arterial thrombosis (16
). We found that OC use strongly increases a woman’s thrombin generation profile by affecting all three phases of TF initiated thrombin generation in both case and control women. All of these women were premenopausal, age 15–49, who had no recent miscarriage, were not pregnant, nor within 30 days postpartum and were only selected when OC use at the index date was the same as at the time of the blood draw. Previously, Bloemenkamp et al.(55
), showed that OC use had a more pronounced hemostatic effect in women who had suffered DVT with regard to the levels of FVII, FXII, protein C, AT, protein S and APC-sr than in healthy women, suggesting the existence of ‘hyperresponders’. Heightened thrombin generation in healthy women on OC use has also been shown experimentally using special fluorogenic thrombin substrates and monitoring clotting in PPP and PRP(56
). We see comparable results, in that women with a DVT that are on OC use have the most pronounced acceleration of thrombin generation. Our results in this study also indicate that OC use had a larger impact on thrombin generation profiles in control women than women with a diagnosed DVT. Since all of the simulated thrombin generation curves are initiated with the same amount of TF stimulus, shifts in the thrombin generation curve are caused by other factors that are present in these individuals. OC potentially causes a subclinical prothrombotic phenotype in these healthy control women that may become apparent when presented with a challenge. Since exogenous hormones are used by more than a hundred million women worldwide as OCs or for postmenopausal hormone replacement, methods for stratification of thrombotic risk are essential.
Since venous thrombosis is defined by a vasoocclusive event, the differentiation of cases and controls is of temporal quality. The accidental occurrence of precipitating environmental factors, e.g. trauma, may lead to an individual becoming a case, while another, with the same thrombin generation profile, in the absence of those factors, will remain a control. This amplification to generate thrombin in the controls may be only differentiated from the cases by time. Hemophilia patients with severe FVIII deficiency do not all have similar bleeding pathology. Potentially, the more thrombin they can generate the lower the bleeding risk. Conversely, the more thrombin a “healthy” individual can produce the higher the thrombotic risk when a risk situation occurs. Our model uses the combined influence of all of the plasma pro- and anti- coagulants of the TF pathway on dynamic thrombin generation. Overall, our results suggest that evaluating hypothetical thrombin generation based upon the individual’s blood composition may be useful as a predictive marker for evaluating thrombosis.