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Abstract The interplay between inflammation and coagulation is a central driver of thrombotic risk across various diseases. While mathematical models of blood coagulation are well established, there remains a critical gap in quantitative frameworks that capture inflammation-induced hypercoagulability. In this study, we develop a mathematical model that explicitly simulates the interaction between pro-inflammatory cytokines and the coagulation cascade. The model incorporates key mechanisms, including: (i) upregulation of tissue factor (TF) by IL-1 β , IL-6, and TNF- α ; (ii) suppression of natural anticoagulants, namely antithrombin III (ATIII) and tissue factor pathway inhibitor (TFPI), by IL-6 and TNF- α ; and (iii) feedback amplification of proinflammatory cytokines by thrombin. By encoding the bidirectional feedback between inflammatory and coagulation pathways, the model captures essential features of inflammation-driven hypercoagulability and enables systematic quantification of how variability in inflammatory extent and duration results in heterogeneous thrombin generation (TG) dynamics. To evaluate its effectiveness, we integrate the model with TG assays and apply it to virtual patient cohorts representing four clinically distinct conditions: COVID-19, sickle cell disease (SCD), type 2 diabetes mellitus (T2DM) and Hemophilia A. Model simulations predict that disease-specific inflammatory environments induce distinct shifts in TG dynamics. In COVID-19 and T2DM, elevated cytokine levels lead to shortened lag times and increased thrombin peak, whereas in SCD, shortened lag times are accompanied by a reduced thrombin peak. These effects are strongly modulated by both cytokine concentration and duration of exposure. These results demonstrate that the proposed computational model augments conventional TG assays by mechanistically linking inflammatory signaling to disease-specific coagulation responses. Collectively, the proposed computational framework extends conventional TG assays by considering the interplay between inflammation and coagulation, thereby providing a potential tool for predicting disease progression and identifying disease-specific therapeutic targets to advance personalized management strategies in thrombo-inflammatory disorders.
Li et al. (Mon,) studied this question.