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计算机生成的住院老年患者 STOPP/START 建议:SENATOR 试验中临床相关性与实施率之间关系的评估。

Computer-generated STOPP/START recommendations for hospitalised older adults: evaluation of the relationship between clinical relevance and rate of implementation in the SENATOR trial.

机构信息

Pharmaceutical Care Research Group, School of Pharmacy, University College Cork, Cork, Ireland.

Department of Medicine, University College Cork, Cork, Ireland.

出版信息

Age Ageing. 2020 Jul 1;49(4):615-621. doi: 10.1093/ageing/afaa062.

Abstract

BACKGROUND

findings from a recent qualitative study indicate that the perceived clinical relevance of computer-generated STOPP/START recommendations was a key factor affecting their implementation by physician prescribers caring for hospitalised older adults in the SENATOR trial.

AIM

to systematically evaluate the clinical relevance of these recommendations and to establish if clinical relevance significantly affected the implementation rate.

METHODS

a pharmacist-physician pair retrospectively reviewed the case records for all SENATOR trial intervention patients at Cork University Hospital and assigned a degree of clinical relevance for each STOPP/START recommendation based on a previously validated six-point scale. The chi-square test was used to quantify the differences in prescriber implementation rates between recommendations of varying clinical relevance, with statistical significance set at P < 0.05.

RESULTS

in 204 intervention patients, the SENATOR software produced 925 STOPP/START recommendations. Nearly three quarters of recommendations were judged to be clinically relevant (73.6%); however, nearly half of these were deemed of 'possibly low relevance' (320/681; 47%). Recommendations deemed of higher clinical relevance were significantly more likely to be implemented than those of lower clinical relevance (P < 0.05).

CONCLUSIONS

a large proportion (61%) of the computer-generated STOPP/START recommendations provided were of potential 'adverse significance', of 'no clinical relevance' or of 'possibly low relevance'. The adjudicated clinical relevance of computer-generated medication recommendations significantly affects their implementation. Meticulous software refinement is required for future interventions of this type to increase the proportion of recommendations that are of high clinical relevance. This should facilitate their implementation, resulting in prescribing optimisation and improved clinical outcomes for multimorbid older adults.

摘要

背景

最近的一项定性研究结果表明,在 SENATOR 试验中,临床医生在为住院老年患者开处方时,感知到计算机生成的 STOPP/START 建议的临床相关性是影响其实施的关键因素。

目的

系统评估这些建议的临床相关性,并确定临床相关性是否显著影响实施率。

方法

药剂师-医生二人组回顾性地审查了科克大学医院所有 SENATOR 试验干预患者的病历,并根据先前验证的六点量表为每个 STOPP/START 建议分配一定程度的临床相关性。卡方检验用于量化不同临床相关性建议的处方实施率之间的差异,统计显著性水平设置为 P < 0.05。

结果

在 204 名干预患者中,SENATOR 软件生成了 925 项 STOPP/START 建议。近四分之三的建议被认为具有临床相关性(73.6%);然而,其中近一半被认为具有“可能低相关性”(320/681;47%)。具有较高临床相关性的建议被实施的可能性明显高于具有较低临床相关性的建议(P < 0.05)。

结论

计算机生成的 STOPP/START 建议中相当大比例(61%)具有潜在的“不利意义”、“无临床相关性”或“可能低相关性”。计算机生成的药物建议的临床相关性显著影响其实施。对于此类未来干预措施,需要进行细致的软件改进,以增加具有高临床相关性的建议比例。这将有助于其实施,从而优化处方并改善多病老年患者的临床结局。

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