Abstract
Bivalirudin, used in patients with heparin-induced thrombocytopenia, is a direct thrombin inhibitor. Since it is a rarely used drug, clinical experience with its dosing is sparse. We develop two approaches to predict the Partial Thromboplastin Time (PTT) based on bivalirudin infusion rates. The first approach is model free and utilizes regularized regression. It is flexible enough to be used as predictors bivalirudin infusion rates measured over several time instances before the time at which a PTT prediction is sought. The second approach is model based and proposes a specific model for obtaining PTT which uses a shorter history of the past measurements. We learn population-wide model parameters by solving a nonlinear optimization problem. We also devise an adaptive algorithm based on the extended Kalman filter that can adapt model parameters to individual patients. The latter adaptive model emerges as the most promising as it yields reduced mean error compared to the model-free approach. The model accuracy we demonstrate on actual patient measurements is sufficient to be useful in guiding the optimal therapy.
Highlights
Bivalirudin antagonizes the effect of thrombin in the blood clotting cascade, thereby preventing complications from blood clotting
We have developed two main approaches to predict the effect of bivalirudin in cardiac surgical patients
We find that a linear kernel performs best and that the corresponding set of predictors uses a collection of physiological variables characterizing bivalirudin infusion rate, several coagulation indicators, and indicators of renal and liver function sampled over a set of four time instances before the time at which a Partial Thromboplastin Time (PTT) prediction is sought
Summary
Bivalirudin antagonizes the effect of thrombin in the blood clotting cascade, thereby preventing complications from blood clotting. Only empirical titration of bivalirudin based on clinical experience or a simple nomogram is used to achieve desired anticoagulation [4] For this reason, a mathematical model that predicts the PTT based on the past infusion rates of bivalirudin following dose adjustment would be extremely useful in guiding optimal therapy. The dynamic system model we obtain performs only somewhat worse than the model-free approach, even though it uses a shorter history of past measurements Building on this model, we develop an adaptive on-line algorithm based on the extended Kalman filter than can adapt the model parameters to individual patients. The algorithm starts from population-wide optimal parameters and, as it observes inputs and outputs, modifies model parameter values to better fit an individual patient This adaptive model outperforms the model-free method in terms of average prediction error, despite using a shorter history of past measurements. Prime denotes transpose, ||·|| denotes the Euclidean norm, 0 denotes a vector or matrix with all components set to zero, and I is the identity matrix
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