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在医院环境中能否轻松生成与老年人使用长效苯二氮䓬类药物相关的临床实践指标?

Can clinical practice indicator relating to long-acting benzodiazepine use in the elderly be easily generated in a hospital setting?

作者信息

Kadri Badria, Teixeira Antonio, Duteil Christelle, Tan Annabelle, Barreteau Hélène, Segouin Christophe, Troude Pénélope

机构信息

Public Health and Health Economics Department, AP-HP, Saint-Louis-Lariboisière Fernand-Widal Hospital Group, Paris, France.

Pharmacy, AP-HP, Saint-Louis-Lariboisière Fernand-Widal Hospital Group, Paris, France.

出版信息

Eur J Clin Pharmacol. 2018 Feb;74(2):233-241. doi: 10.1007/s00228-017-2371-7. Epub 2017 Nov 16.

Abstract

BACKGROUND

To improve the appropriate use of long-acting benzodiazepine (la.bzd) prescriptions in the elderly, the Haute Autorité de Santé (HAS) has developed clinical practice indicators (CPI). The alert indicator (AI) evaluates the prevalence of la.bzd prescription among older people. The mastering indicator (MI) corresponds to the prevalence of elderly with a justified, i.e., appropriate, la.bzd prescription among all the elderly with la.bzd prescriptions.

OBJECTIVE

The objective of this study was to test the feasibility of routine generation of CPI regarding la.bzd prescriptions among the elderly in the hospital setting.

DESIGN

This was a retrospective study.

SETTING

The study was conducted in two university hospitals located in Paris.

SUBJECT

Eligible cases were patients aged 65 years and older who were hospitalized in acute care units from January to June 2014.

METHOD

The AI calculation was based on information extracted from medical databases from these hospitals. The appropriateness of la.bzd prescription was assessed by a physician and a pharmacist and was based on review of computerized patient records and prescriptions, using an ad hoc algorithm. The MI was then calculated. Variation in the level of indicators was explored according to the characteristics of patients and of their hospitalization using chi test. Factors associated with a potentially inappropriate prescription (PIP) of la.bzd were studied using univariate and multivariate logistic regression.

RESULT

Among the 4167 patients included in the study, 362 had la.bzd prescriptions, i.e., the AI was 9%. Prescriptions were found to be appropriate for 83 patients, i.e., the MI was 23% and PIP was 77%. The MI varied between 13 and 31% according to characteristics of patients and of hospitalization. In multivariate analysis, factors associated with PIP were age, number of comorbidities, type of care unit, and concurrent prescription of a neuroleptic or hypnotic.

CONCLUSION

Generation of the AI was routinely possible but only for acute care units with computerized prescriptions, corresponding to 78% of patients. Production of the MI has required medical record review for all patients with a la.bzd prescription and cannot be automated. However, difficulties in generating the MI have identified areas for significant improvement. Moreover, strategies to improve the care of older people with a la.bzd prescription could be targeted using characteristics of patients and of hospitalization associated with PIP. The future deployment of a single electronic medical record in all care departments would make it easier to mine the data and make possible automated production of CPI.

摘要

背景

为提高老年人长效苯二氮䓬类药物(la.bzd)处方的合理使用率,法国卫生高级管理局(HAS)制定了临床实践指标(CPI)。警示指标(AI)评估老年人中la.bzd处方的流行率。掌握指标(MI)对应于在所有开具la.bzd处方的老年人中,有合理(即适当)la.bzd处方的老年人的流行率。

目的

本研究的目的是测试在医院环境中常规生成老年人la.bzd处方CPI的可行性。

设计

这是一项回顾性研究。

地点

研究在位于巴黎的两家大学医院进行。

研究对象

符合条件的病例为2014年1月至6月在急性护理病房住院的65岁及以上患者。

方法

AI的计算基于从这些医院的医疗数据库中提取的信息。la.bzd处方的适当性由一名医生和一名药剂师评估,基于对计算机化患者记录和处方的审查,使用专门算法。然后计算MI。使用卡方检验根据患者及其住院特征探讨指标水平的差异。使用单因素和多因素逻辑回归研究与la.bzd潜在不适当处方(PIP)相关的因素。

结果

在纳入研究的4167名患者中,362人开具了la.bzd处方,即AI为9%。发现83名患者的处方是适当的,即MI为23%,PIP为77%。根据患者和住院特征,MI在13%至31%之间变化。在多因素分析中,与PIP相关的因素是年龄、合并症数量、护理单元类型以及同时开具抗精神病药或催眠药。

结论

常规生成AI是可行的,但仅适用于有计算机化处方的急性护理病房,对应78%的患者。生成MI需要对所有开具la.bzd处方的患者进行病历审查,且无法自动化。然而,生成MI的困难已确定了显著改进的领域。此外,可利用与PIP相关的患者和住院特征,制定针对改善开具la.bzd处方老年人护理的策略。在所有护理部门未来部署单一电子病历将便于挖掘数据并实现CPI的自动化生成。

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