A Predictive Model for the Anticoagulant Bivalirudin Administered to Cardiac Surgical Patients.
A Predictive Model for the Anticoagulant Bivalirudin Administered to Cardiac Surgical Patients.
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DOI：
10.1109/cdc.2013.6759869
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Paschalidis IC
中科院分区：
文献类型：
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作者：
Zhao Q;Edrich T;Paschalidis IC
比伐卢定用于肝素诱导的血小板减少症患者，是一种直接凝血酶抑制剂。由于它是一种很少使用的药物，其给药的临床经验很少。我们开发了一个模型，预测比伐卢定的影响，测量部分凝血活酶时间（PTT）的基础上，其过去的融合率。我们通过解决使用患者数据训练集的非线性优化问题来学习整个人群的模型参数。更有趣的是，我们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，因为它既降低了平均误差，又大大降低了每个患者的误差方差。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 is used in patients with heparin-induced thrombocytopenia and is a direct thrombin inhibitor. Since it is a rarely used drug, clinical experience with its dosing is sparse. We develop a model that predicts the effect of bivalirudin, measured by the Partial Thromboplastin Time (PTT), based on its past fusion rates. We learn population-wide model parameters by solving a nonlinear optimization problem that uses a training set of patient data. More interestingly, we 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 reduces both the mean error and, drastically, the per-patient error variance. The model accuracy we demonstrate on actual patient measurements is sufficient to be useful in guiding optimal therapy.