Predicting and evaluating the effect of bivalirudin in cardiac surgical patients.
Predicting and evaluating the effect of bivalirudin in cardiac surgical patients.
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预测和评估比伐卢定在心脏手术患者中的效果。
DOI：
10.1109/tbme.2013.2280636
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Paschalidis,IoannisCh
中科院分区：
文献类型：
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作者：
Zhao,Qi;Edrich,Thomas;Paschalidis,IoannisCh
比伐卢定是一种直接凝血酶抑制剂，用于肝素诱导的血小板减少症患者。由于它是一种很少使用的药物，其给药的临床经验很少。我们开发了两种基于比伐卢定输注速率预测部分凝血活酶时间（PTT）的方法。第一种方法是无模型的，利用正则化回归。它足够灵活，可以用作在寻求PTT预测之前的几个时间点上测量的比伐卢定输注速率的预测因子。第二种方法是基于模型的，并提出了一个特定的模型，用于获得PTT使用较短的历史，过去的测量。我们通过解决非线性优化问题来学习整个种群的模型参数。我们还设计了一个自适应算法的基础上扩展卡尔曼滤波器，可以适应模型参数的个体患者。后一种自适应模型是最有前途的，因为它产生减少的平均误差相比，无模型的方法。我们在实际患者测量上证明的模型准确性足以用于指导最佳治疗。
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.