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开发和验证一种药物优化工具,以保护患有严重精神疾病的人的身体健康(OPTIMISE)。

The development and validation of a medicines optimisation tool to protect the physical health of people with severe mental illness (OPTIMISE).

机构信息

Saint John of God Hospital, Stillorgan, Co. Dublin, Ireland.

School of Pharmacy and Biomolecular Science, Royal College of Surgeons Ireland, 123 St Stephen's Green, Dublin 2, Dublin, Ireland.

出版信息

BMC Psychiatry. 2022 Sep 3;22(1):585. doi: 10.1186/s12888-022-04235-0.

Abstract

BACKGROUND

The life expectancy of people with severe mental illness (SMI) is shorter than those without SMI, with multimorbidity and poorer physical health contributing to health inequality. Screening tools could potentially assist the optimisation of medicines to protect the physical health of people with SMI. The aim of our research was to design and validate a medicines optimisation tool (OPTIMISE) to help clinicians to optimise physical health in people with SMI.

METHODS

A review of existing published guidelines, PubMed and Medline was carried out. Literature was examined for medicines optimisation recommendations and also for reference to the management of physical illness in people with mental illness. Potential indicators were grouped according to physiological system. A multidisciplinary team with expertise in mental health and the development of screening tools agreed that 83 indicators should be included in the first draft of OPTIMISE. The Delphi consensus technique was used to develop and validate the contents. A 17-member multidisciplinary panel of experts from the UK and Ireland completed 2 rounds of Delphi consensus, rating their level of agreement to 83 prescribing indicators using a 5-point Likert scale. Indicators were accepted for inclusion in the OPTIMISE tool after achieving a median score of 1 or 2, where 1 indicated strongly agree and 2 indicated agree, and 75 centile value of ≤ 2. Interrater reliability was assessed among 4 clinicians across 20 datasets and the chance corrected level of agreement (kappa) was calculated. The kappa statistic was interpreted as poor if 0.2 or less, fair if 0.21-0.4, moderate if 0.41-0.6, substantial if 0.61-0.8, and good if 0.81-1.0.

RESULTS

Consensus was achieved after 2 rounds of Delphi for 62 prescribing indicators where 53 indicators were accepted after round 1 and a further 9 indicators were accepted after round 2. Interrater reliability of OPTIMISE between physicians and pharmacists indicated a substantial level of agreement with a kappa statistic of 0.75.

CONCLUSIONS

OPTIMISE is a 62 indicator medicines optimisation tool designed to assist decision making in those treating adults with SMI. It was developed using a Delphi consensus methodology and interrater reliability is substantial. OPTIMISE has the potential to improve medicines optimisation by ensuring preventative medicines are considered when clinically indicated. Further research involving the implementation of OPTIMISE is required to demonstrate its true benefit.

TRIAL REGISTRATION

This article does not report the results of a health care intervention on human participants.

摘要

背景

严重精神疾病(SMI)患者的预期寿命短于非 SMI 患者,合并症和较差的身体健康状况导致健康不平等。筛查工具可能有助于优化药物,以保护 SMI 患者的身体健康。我们的研究旨在设计和验证一种药物优化工具(OPTIMISE),以帮助临床医生优化 SMI 患者的身体健康。

方法

对已发表的指南、PubMed 和 Medline 进行了回顾。检查文献中关于药物优化建议的内容,以及关于精神疾病患者躯体疾病管理的内容。根据生理系统将潜在指标进行分组。一个具有精神健康和筛查工具开发专业知识的多学科团队认为,83 个指标应包含在 OPTIMISE 的初稿中。采用德尔菲共识技术对内容进行开发和验证。来自英国和爱尔兰的 17 名多学科专家组成员完成了 2 轮德尔菲共识,使用 5 分李克特量表对 83 个处方指标的同意程度进行评分。只有在获得中位数得分为 1 或 2 分后,指标才被接受纳入 OPTIMISE 工具,其中 1 表示强烈同意,2 表示同意,且 75%的值为≤2。4 名临床医生在 20 个数据集上评估了组内一致性,计算了校正后机会一致性水平(kappa)。kappa 统计量如果为 0.2 或以下,则表示一致性差,如果为 0.21-0.4,则表示一致性一般,如果为 0.41-0.6,则表示一致性中等,如果为 0.61-0.8,则表示一致性较好,如果为 0.81-1.0,则表示一致性好。

结果

经过 2 轮德尔菲共识,有 62 个处方指标达成共识,其中 53 个指标在第一轮达成共识,第二轮又有 9 个指标达成共识。医生和药剂师之间 OPTIMISE 的组内一致性显示,kappa 统计量为 0.75,具有中度一致性。

结论

OPTIMISE 是一种 62 项指标的药物优化工具,旨在帮助治疗 SMI 成人的临床医生做出决策。它是使用德尔菲共识方法开发的,组内一致性良好。OPTIMISE 有可能通过确保在临床指征出现时考虑预防性药物来改善药物优化。需要进一步研究实施 OPTIMISE,以证明其真正的益处。

试验注册

本文不报告人类参与者健康护理干预的结果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b591/9441032/8562b5fd774d/12888_2022_4235_Fig1_HTML.jpg

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