Heparin-induced thrombocytopenia (HIT) is characterized by platelet destruction as a result of irreversible binding of heparin-triggered antibodies to the platelet surface. While HIT is uncommon, the condition results in increased risk of bleeding and potentially deadly thromboses. A mathematical model was constructed to describe changes in platelet count in response to intravenous heparin administration. The ordinary differential equation model consists of a pharmacokinetic model of intravenous heparin administration, multi-compartment representations for platelet and antibody dynamics, and a biologically-motivated platelet reserve compartment. The model was constructed in Pyomo, a Python-based modeling environment with direct linkage to high-performance optimization packages for use in model parameter estimation and model-based treatment design. Platelet count and heparin administration data from N=6 clinical patients diagnosed with typical onset heparin-induced thrombocytopenia were used to fit model parameters to individual patient profiles. Quality of fit was calculated using ipopt, with a least squares regression objective function between patient data and the model prediction (the model was discretized using orthogonal collocation on finite elements for ipopt implementation). The model was further applied to data sets representing cases outside the designed scope of the model, including (i) bolus heparin dosing; and (ii) rapid-onset HIT presumed to be a result of previous heparin exposure, to determine model versatility and to identify challenges requiring model parameter or structural modification. With its existing ability to fit typical onset HIT patients, this model has the potential to aid clinicians in the early diagnosis of heparin-induced thrombocytopenia.
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