UniSA Clinical and Health Sciences, University of South Australia, Frome Road, Adelaide, SA, Australia.
Department of Pharmacy, Royal Adelaide Hospital, Port Road, Adelaide, SA, Australia.
Drugs Aging. 2020 Oct;37(10):767-776. doi: 10.1007/s40266-020-00794-7.
Polypharmacy has been associated with increased mortality but the contribution of different medication-related factors to this is unknown.
The aim of this study was to identify demographic and medication-related predictors of mortality in the older population. Given the intrinsic link between polypharmacy and multimorbidity, the secondary aim was to examine if the medicines or underlying diseases predicted mortality.
Patients aged ≥ 65 years from an outpatient multimorbidity clinic were included. Medication-related factors included the medicines count, high-risk medicines, inappropriate medicines duplication, and potential drug-drug and drug-disease interactions. Logistic regression was used to identify mortality predictors within a year of clinic discharge from the outpatient clinic. Patients attend the clinic until medications and comorbidity management have been optimised, at which point they are discharged from the clinic, and their General Practitioner provides ongoing care.
A total of 584 patients were included (median age 80.0 years) and 9.9% (n = 58) died within a year of discharge. Demographics, namely age (adjusted odds ratio [aOR] 1.05; 95% CI 1.01-1.09; p = 0.018) and being male (aOR 5.10; 95% CI 2.63-9.88; p < 0.001); chronic disease, namely heart failure (aOR 3.36; 95% CI 1.78-6.35; p < 0.001); and medication-related factors, namely the number of sedative and anticholinergic medicines (aOR 1.66; 95% CI 1.19-2.33; p = 0.003) predicted mortality in the study population.
Whilst polypharmacy has been defined using the number of medicines in the literature, a combination of demographics, chronic disease and medications predicted mortality in our study. This provides guidance for the development of future tools and guidelines regarding the inclusion of key factors for identifying high-risk patients at risk of adverse health outcomes such as mortality.
多种药物治疗与死亡率增加有关，但不同药物相关因素对此的贡献尚不清楚。
本研究旨在确定老年人群中与死亡率相关的人口统计学和药物相关预测因素。鉴于多种药物治疗与多种疾病之间的内在联系，次要目的是检查药物或潜在疾病是否可以预测死亡率。
纳入了来自门诊多病种诊所的年龄≥65 岁的患者。药物相关因素包括药物数量、高危药物、不适当的药物重复使用以及潜在的药物-药物和药物-疾病相互作用。使用逻辑回归确定门诊诊所出院后一年内的死亡率预测因素。患者在门诊接受治疗，直到药物和合并症管理得到优化，此时他们从诊所出院，他们的全科医生提供持续护理。
共纳入 584 例患者（中位年龄 80.0 岁），9.9%（n=58）在出院后一年内死亡。人口统计学因素，即年龄（调整后的优势比[aOR] 1.05；95%置信区间[CI] 1.01-1.09；p=0.018）和男性（aOR 5.10；95%CI 2.63-9.88；p<0.001）；慢性疾病，即心力衰竭（aOR 3.36；95%CI 1.78-6.35；p<0.001）；以及药物相关因素，即镇静剂和抗胆碱能药物的数量（aOR 1.66；95%CI 1.19-2.33；p=0.003），预测了研究人群的死亡率。
虽然文献中已经使用药物数量来定义多种药物治疗，但在我们的研究中，人口统计学因素、慢性疾病和药物联合预测了死亡率。这为开发未来的工具和指南提供了指导，以确定具有不良健康结局（如死亡率）风险的高危患者的关键因素。
