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华法林的贝叶斯个体化剂量方法。

A Bayesian dose-individualization method for warfarin.

机构信息

School of Pharmacy, University of Otago, PO Box 56, Dunedin, 9054, New Zealand.

出版信息

Clin Pharmacokinet. 2013 Jan;52(1):59-68. doi: 10.1007/s40262-012-0017-6.

Abstract

BACKGROUND

Warfarin is a difficult drug to dose accurately and safely due to large inter-individual variability in dose requirements. Current dosing strategies appear to be sub-optimal, with reports indicating that patients achieve international normalized ratios (INRs) within the therapeutic range only 40-65 % of the time. The consequences of poor INR control are potentially severe with INRs below 2 carrying an increased risk of clotting while INRs >4 increase the risk of major bleeding events. Bayesian forecasting methods have the potential to improve INR control.

AIMS

The aims of this study were to (1) prospectively assess the predictive performance of a Bayesian dosing method for warfarin implemented in TCIWorks; and (2) determine the expected time in the therapeutic range (TTR) of INRs predicted using TCIWorks.

METHODS

Patients who were initiating warfarin therapy were prospectively recruited from Dunedin Hospital, Dunedin, New Zealand. Warfarin doses were entered into TCIWorks from the first day of therapy until a stable steady-state INR (INR(ss)) was achieved. The predicted INR(ss) values were determined using the first zero to six serially collected INR observations. Observed and predicted INR(ss) values were compared using measures of bias (mean prediction error [MPE]) and imprecision (root mean square error [RMSE]). The TTR was determined by calculating the percentage of predicted INR(ss) values between 2 and 3 when zero to six serially collected INR observations were available.

RESULTS

A total of 55 patients were recruited between March and November 2011. When no observed INR values were available the resulting INR(ss) predictions were positively biased (MPE 0.52 [95 % CI 0.30, 0.73]); however, this disappeared once observed INR values were entered into TCIWorks. The precision of the predicted INR(ss) values improved dramatically once three or more observed INR values were available (RMSE <0.53) compared with no INRs (RMSE 0.96). These results suggest that TCIWorks will be effective at maintaining the INR within the therapeutic INR range (2-3) 65 % of the time when three INR measurements are available and 80 % of the time when six INR measurements are available.

CONCLUSION

The TCIWorks warfarin dosing method produced accurate and precise INR(ss) predictions. We predict that the method will provide an INR value within the therapeutic range 65-80 % of the time once three or more INR observations are available, making this a useful tool for clinicians and warfarin clinics. Further research to assess the impact of this method on long-term INR control is warranted.

摘要

背景

华法林由于剂量需求在个体间存在较大差异,因此准确且安全地给药较为困难。目前的给药策略似乎并不理想,有报道称患者只有 40-65%的时间能将国际标准化比值(INR)控制在治疗范围内。INR 控制不佳的后果可能很严重,INR<2 会增加血栓形成的风险,而 INR>4 则会增加大出血事件的风险。贝叶斯预测方法有可能改善 INR 控制。

目的

本研究旨在(1)前瞻性评估 TCIWorks 中实施的华法林贝叶斯给药方法的预测性能;(2)确定使用 TCIWorks 预测的 INR 治疗范围内时间(TTR)的预期时间。

方法

从新西兰达尼丁医院招募正在开始华法林治疗的患者。从治疗的第一天开始,将华法林剂量输入 TCIWorks,直到达到稳定的稳态 INR(INR(ss))。使用前六个连续采集的 INR 观察值来确定预测的 INR(ss)值。使用偏倚(平均预测误差[MPE])和不精确性(均方根误差[RMSE])来比较观察到的和预测的 INR(ss)值。当有零到六个连续采集的 INR 观察值时,通过计算预测 INR(ss)值在 2 到 3 之间的百分比来确定 TTR。

结果

2011 年 3 月至 11 月期间共招募了 55 名患者。当没有观察到 INR 值时,得到的 INR(ss)预测值呈正偏倚(MPE 0.52 [95%CI 0.30, 0.73]);然而,一旦将观察到的 INR 值输入 TCIWorks,这种情况就会消失。当有三个或更多观察到的 INR 值时,预测的 INR(ss)值的精度显著提高(RMSE<0.53),而没有 INR 值时,精度则较差(RMSE 0.96)。这些结果表明,当有三个 INR 测量值时,TCIWorks 将能够有效地将 INR 维持在治疗范围内(2-3)的 65%的时间内,而当有六个 INR 测量值时,TCIWorks 将能够有效地将 INR 维持在治疗范围内(2-3)的 80%的时间内。

结论

TCIWorks 华法林给药方法产生了准确且精确的 INR(ss)预测值。我们预测,一旦有三个或更多的 INR 观察值,该方法将有 65-80%的时间能提供治疗范围内的 INR 值,这使其成为临床医生和华法林诊所的有用工具。需要进一步研究来评估该方法对长期 INR 控制的影响。

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