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一个更简单、更准确的预测每日锂剂量的方程式。

A simpler and more accurate equation to predict daily lithium dose.

作者信息

Terao T, Okuno K, Okuno T, Nakano H, Shinkai T, Ohmori O, Yoshimura R, Suzuki T, Abe K

机构信息

Department of Psychiatry, University of Occupational and Environmental Health, Kitakyushu, Japan.

出版信息

J Clin Psychopharmacol. 1999 Aug;19(4):336-40. doi: 10.1097/00004714-199908000-00009.

DOI:10.1097/00004714-199908000-00009
PMID:10440461
Abstract

Recently, the authors suggested that the lithium dose prediction equation created by Zetin and associates cannot always accurately predict a required lithium dose and that the inclusion of renal function data may improve the accuracy of the equation. The charts of 70 patients were reviewed to obtain data regarding factors thought to affect serum lithium concentrations, including renal function, and an equation to estimate the dose intended to achieve an expected concentration was derived by stepwise multiple linear regression. The equation was also applied to 30 other patients to evaluate its accuracy. The authors obtained the following equation: daily lithium carbonate dose (in milligrams) = 100.5 + 752.7 x (expected lithium concentration in millimoles per liter) - 3.6 x (age in years) + 7.2 x (weight in kilograms) - 13.7 x (blood urea nitrogen [BUN] in milligrams per deciliter). When the equation was applied to 30 patients, the mean +/- SD of deviations from the expected concentration was 0.15 +/- 0.30 mmol/L, and 19 patients (63%) had deviations of less than 0.20 mmol/L. On the other hand, when the equation set forth by Zetin and associates was applied to the same patients, the mean +/- SD of deviations from the expected concentration was 0.52 +/- 0.42 mmol/L, and only 6 patients (20%) had deviations of less than 0.20 mmol/L. Although it is necessary to measure BUN levels before starting lithium, this equation may be simpler and more accurate than that offered by Zetin and associates.

摘要

最近,作者指出,泽廷及其同事创建的锂剂量预测方程并不能总是准确预测所需的锂剂量,纳入肾功能数据可能会提高该方程的准确性。回顾了70例患者的病历,以获取有关被认为会影响血清锂浓度的因素的数据,包括肾功能,并通过逐步多元线性回归得出了一个用于估计达到预期浓度所需剂量的方程。该方程还应用于另外30例患者以评估其准确性。作者得出了以下方程:每日碳酸锂剂量(毫克)= 100.5 + 752.7×(预期锂浓度,毫摩尔/升)- 3.6×(年龄,岁)+ 7.2×(体重,千克)- 13.7×(血尿素氮[BUN],毫克/分升)。当该方程应用于30例患者时,与预期浓度的偏差的平均值±标准差为0.15±0.30毫摩尔/升,19例患者(63%)的偏差小于0.20毫摩尔/升。另一方面,当将泽廷及其同事提出的方程应用于相同患者时,与预期浓度的偏差的平均值±标准差为0.52±0.42毫摩尔/升,只有6例患者(20%)的偏差小于0.20毫摩尔/升。虽然在开始使用锂之前有必要测量BUN水平,但该方程可能比泽廷及其同事提供的方程更简单、更准确。

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A simpler and more accurate equation to predict daily lithium dose.一个更简单、更准确的预测每日锂剂量的方程式。
J Clin Psychopharmacol. 1999 Aug;19(4):336-40. doi: 10.1097/00004714-199908000-00009.
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Application of the Cockcroft-Gault method to estimate lithium dosage requirement.应用Cockcroft-Gault方法估算锂剂量需求。
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引用本文的文献

1
Predicting Serum Levels of Lithium-Treated Patients: A Supervised Machine Learning Approach.预测锂治疗患者的血清水平:一种监督式机器学习方法。
Biomedicines. 2021 Oct 28;9(11):1558. doi: 10.3390/biomedicines9111558.
2
Comparison of three a-priori models in the prediction of serum lithium concentration.三种先验模型在预测血清锂浓度中的比较。
Indian J Pharmacol. 2012 Mar;44(2):234-7. doi: 10.4103/0253-7613.93856.
3
Lithium: updated human knowledge using an evidence-based approach. Part II: Clinical pharmacology and therapeutic monitoring.
锂:采用循证方法更新人类知识。第二部分:临床药理学与治疗监测。
CNS Drugs. 2009;23(4):331-49. doi: 10.2165/00023210-200923040-00005.