Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA.
Indian J Pharmacol. 2012 Mar;44(2):234-7. doi: 10.4103/0253-7613.93856.
Mathematical models are valuable for optimizing drug dose and dosing regimens.
To compare the precision and bias of three a-priori methods in the prediction of serum level of lithium in patients with bipolar disorder, and to determine their sensitivity and specificity in detecting serum lithium levels outside the therapeutic range.
Hospital-based, retrospective study.
In a retrospective study of 31 in-patients, the serum level of lithium was calculated using three different a-priori methods. Mean Prediction Error was used as a measure of bias while Mean Absolute Error and Root Mean Squared Error were used as a measure of precision. The sensitivity and specificity of the methods was calculated.
All three models underestimated serum lithium level. Precision was best with the model described by Pepin et al., while bias of prediction was the least with the method of Abou Auda et al. The formula by Pepin et al. was able to predict serum lithium level with a mean error of 36.57%. The sensitivity and specificity of the models in identifying serum lithium levels outside the therapeutic range was 80% and 76.19% for Pepin et al., 90% and 74.19% for Zetin et al., and 90% and 66.67% for Abou-Auda et al., respectively.
The study demonstrates the difference in precision and bias of three a-priori methods, with no one method being superior to the other in the prediction of serum concentration.
数学模型对于优化药物剂量和给药方案非常有价值。
比较三种先验方法在预测双相情感障碍患者血清锂水平中的精度和偏差，并确定它们在检测治疗范围外血清锂水平方面的敏感性和特异性。
基于医院的回顾性研究。
在一项对 31 名住院患者的回顾性研究中，使用三种不同的先验方法计算血清锂水平。平均预测误差用于衡量偏差，而平均绝对误差和均方根误差用于衡量精度。计算了方法的敏感性和特异性。
所有三种模型均低估了血清锂水平。Pepin 等人描述的模型具有最佳的精度，而 Abou Auda 等人的方法预测偏差最小。Pepin 等人的公式能够预测血清锂水平，平均误差为 36.57%。Pepin 等人、Zetin 等人和 Abou-Auda 等人的模型在识别治疗范围外的血清锂水平方面的敏感性和特异性分别为 80%和 76.19%、90%和 74.19%以及 90%和 66.67%。
该研究表明，三种先验方法在精度和偏差方面存在差异，没有一种方法在预测血清浓度方面优于其他方法。
