PDF(Pubmed)
Abstract：
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.
OBJECTIVE: 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.
CONCLUSIONS: 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.
OBJECTIVE: 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.
CONCLUSIONS: 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.
摘要：
背景：越来越多的人每天使用多种药物，名为复方药。这是由人口老龄化驱动的，增加多浊度，和单一疾病聚焦指南。药物有明显的好处，然而，多重用药也与包括不良药物事件在内的不良后果有关，药物-药物和药物-疾病相互作用，患者经验不足，资源浪费。有问题的多重用药是不适当地开出多种药物的处方，或未实现预期收益的情况。识别有问题的多重用药的人很复杂，因为多种药物可以适用于需要更多治疗的多种慢性疾病的人。因此，多重用药通常是潜在的问题，而不是总是不合适的，取决于临床情况和个体获益与风险。有必要通过超越简单的药物计数，包括个体因素和长期条件来改善我们识别和评估这些患者的方式。
目的：产生多重用药评估评分，以确定处方水平异常的人群，他们可能面临潜在问题多重用药的风险。
方法：分析将分三个部分进行：1。将使用观察到的药物计数作为因变量来构建预测模型，随着年龄,性别和长期条件为自变量。然后将通过计算观察到的和预期的处方药物计数之间的差异来构建“多药房评估评分”。从而突出了处方水平出乎意料的人。第2部分和第3部分将检查多重药学评估得分的有效性的不同方面：2。要评估“构造有效性”，横断面分析将评估由一系列多药房评估分数定义的人群中的高风险处方，使用显式（STOPP/START标准）和隐式（药物适当性指数）测量不适当的处方。3.要评估“预测有效性”，一项回顾性队列研究将探讨临床结局的差异(药物不良反应，计划外住院和全因死亡率)在不同评分之间。
结论：制定多重用药的交叉措施可能会使医疗保健专业人员使用不寻常的处方水平对多重用药患者进行优先排序和风险分层。这将是对使用简单截止或狭窄处方标准的当前方法的改进。
目的：产生多重用药评估评分，以确定处方水平异常的人群，他们可能面临潜在问题多重用药的风险。
方法：分析将分三个部分进行：1。将使用观察到的药物计数作为因变量来构建预测模型，随着年龄,性别和长期条件为自变量。然后将通过计算观察到的和预期的处方药物计数之间的差异来构建“多药房评估评分”。从而突出了处方水平出乎意料的人。第2部分和第3部分将检查多重药学评估得分的有效性的不同方面：2。要评估“构造有效性”，横断面分析将评估由一系列多药房评估分数定义的人群中的高风险处方，使用显式（STOPP/START标准）和隐式（药物适当性指数）测量不适当的处方。3.要评估“预测有效性”，一项回顾性队列研究将探讨临床结局的差异(药物不良反应，计划外住院和全因死亡率)在不同评分之间。
结论：制定多重用药的交叉措施可能会使医疗保健专业人员使用不寻常的处方水平对多重用药患者进行优先排序和风险分层。这将是对使用简单截止或狭窄处方标准的当前方法的改进。
