多药疗法评估评分的开发和验证规程。

Jung Yin Tsang, Matthew Sperrin, Thomas Blakeman, Rupert A Payne, Darren M Ashcroft
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引用次数: 0

摘要

背景:越来越多的人每天使用多种药物,即 "多重用药"。造成这一现象的原因是人口老龄化、多病症的增加以及以单一疾病为重点的指导方针。药物治疗的好处显而易见,但同时多重用药也会带来不良后果,包括药物不良事件、药物与药物、药物与疾病之间的相互作用、患者体验不佳以及资源浪费。有问题的多种药物治疗是指 "不适当地开具多种药物处方,或无法实现预期疗效"。识别有问题的多种药物治疗患者非常复杂,因为多种药物可能适用于需要更多治疗的多种慢性病患者。因此,多种药物治疗往往可能存在问题,而并非总是不合适,这取决于临床环境和个人的获益与风险。我们有必要改进识别和评估这些患者的方法,不仅仅是简单地计算药物数量,还要考虑个人因素和长期病情:分析将分三部分进行:1.以观察到的用药次数为因变量,以年龄、性别和长期病症为自变量,建立预测模型。然后,通过计算观察到的处方药数量与预期的处方药数量之间的差异,建立 "多重药瘾评估分数",从而突出显示处方药数量出乎意料的人群。第 2 部分和第 3 部分将对 "多药方评估分数 "的有效性的不同方面进行研究: 2. 为了评估 "构建有效性",横断面分析将使用不适当处方的显性(STOPP/START 标准)和隐性(用药适当性指数)测量方法,对根据一系列 "多药方评估分数 "界定的人群中的高风险处方进行评估。3.3. 为了评估 "预测有效性",一项回顾性队列研究将探讨不同评分之间在临床结果(药物不良反应、非计划住院和全因死亡率)方面的差异:讨论:开发一种跨领域的多药滥用测量方法,可使医护人员利用不寻常的处方水平对多药滥用患者进行优先排序和风险分层。这将改进目前使用简单分界线或狭窄处方标准的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Protocol for the development and validation of a Polypharmacy Assessment Score.

Background: An increasing number of people are using multiple medications each day, named polypharmacy. This is driven by an ageing population, increasing multimorbidity, and single disease-focussed guidelines. Medications carry obvious benefits, yet polypharmacy is also linked to adverse consequences including adverse drug events, drug-drug and drug-disease interactions, poor patient experience and wasted resources. Problematic polypharmacy is 'the prescribing of multiple medicines inappropriately, or where the intended benefits are not realised'. Identifying people with problematic polypharmacy is complex, as multiple medicines can be suitable for people with several chronic conditions requiring more treatment. Hence, polypharmacy is often potentially problematic, rather than always inappropriate, dependent on clinical context and individual benefit vs risk. There is a need to improve how we identify and evaluate these patients by extending beyond simple counts of medicines to include individual factors and long-term conditions.

Aim: To produce a Polypharmacy Assessment Score to identify a population with unusual levels of prescribing who may be at risk of potentially problematic polypharmacy.

Methods: Analyses will be performed in three parts: 1. A prediction model will be constructed using observed medications count as the dependent variable, with age, gender and long-term conditions as independent variables. A 'Polypharmacy Assessment Score' will then be constructed through calculating the differences between the observed and expected count of prescribed medications, thereby highlighting people that have unexpected levels of prescribing. Parts 2 and 3 will examine different aspects of validity of the Polypharmacy Assessment Score: 2. To assess 'construct validity', cross-sectional analyses will evaluate high-risk prescribing within populations defined by a range of Polypharmacy Assessment Scores, using both explicit (STOPP/START criteria) and implicit (Medication Appropriateness Index) measures of inappropriate prescribing. 3. To assess 'predictive validity', a retrospective cohort study will explore differences in clinical outcomes (adverse drug reactions, unplanned hospitalisation and all-cause mortality) between differing scores.

Discussion: Developing a cross-cutting measure of polypharmacy may allow healthcare professionals to prioritise and risk stratify patients with polypharmacy using unusual levels of prescribing. This would be an improvement from current approaches of either using simple cutoffs or narrow prescribing criteria.

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