Institute of General Practice, Goethe University Frankfurt, Frankfurt am Main, Hessen, Germany
Institute of Medical Biometry and Informatics, University of Heidelberg, Heidelberg, Baden-Württemberg, Germany.
BMJ Open. 2020 Oct 22;10(10):e039747. doi: 10.1136/bmjopen-2020-039747.
Polypharmacy interventions are resource-intensive and should be targeted to those at risk of negative health outcomes. Our aim was to develop and internally validate prognostic models to predict health-related quality of life (HRQoL) and the combined outcome of falls, hospitalisation, institutionalisation and nursing care needs, in older patients with multimorbidity and polypharmacy in general practices.
: two independent data sets, one comprising health insurance claims data (n=592 456), the other data from the PRIoritising MUltimedication in Multimorbidity (PRIMUM) cluster randomised controlled trial (n=502). : ≥60 years, ≥5 drugs, ≥3 chronic diseases, excluding dementia. : combined outcome of falls, hospitalisation, institutionalisation and nursing care needs (after 6, 9 and 24 months) (claims data); and HRQoL (after 6 and 9 months) (trial data). : age, sex, morbidity-related variables (disease count), medication-related variables (European Union-Potentially Inappropriate Medication list (EU-PIM list)) and health service utilisation. : additional socio-demographics, morbidity-related variables (Cumulative Illness Rating Scale, depression), Medication Appropriateness Index (MAI), lifestyle, functional status and HRQoL (EuroQol EQ-5D-3L). : mixed regression models, combined with stepwise variable selection, 10-fold cross validation and sensitivity analyses.
Most important predictors of EQ-5D-3L at 6 months in best model (Nagelkerke's R² 0.507) were depressive symptoms (-2.73 (95% CI: -3.56 to -1.91)), MAI (-0.39 (95% CI: -0.7 to -0.08)), baseline EQ-5D-3L (0.55 (95% CI: 0.47 to 0.64)). Models based on claims data and those predicting long-term outcomes based on both data sets produced low R² values. In claims data-based model with highest explanatory power (R²=0.16), previous falls/fall-related injuries, previous hospitalisations, age, number of involved physicians and disease count were most important predictor variables.
Best trial data-based model predicted HRQoL after 6 months well and included parameters of well-being not found in claims. Performance of claims data-based models and models predicting long-term outcomes was relatively weak. For generalisability, future studies should refit models by considering parameters representing well-being and functional status.
多药治疗干预措施资源密集，应针对有负面健康结果风险的患者。我们的目的是开发并内部验证预测模型，以预测患有多种疾病和多种药物的老年患者的健康相关生活质量（HRQoL）和跌倒、住院、机构化和护理需求的综合结果。
使用两个独立的数据集，一个包含健康保险索赔数据（n=592456），另一个来自 PRIoritising MUltimedication in Multimorbidity（PRIMUM）集群随机对照试验（n=502）。纳入标准：≥60 岁，≥5 种药物，≥3 种慢性疾病，不包括痴呆症。主要结局：跌倒、住院、机构化和护理需求的综合结果（6、9 和 24 个月后）（索赔数据）；和 HRQoL（6 和 9 个月后）（试验数据）。预测因素：年龄、性别、与疾病相关的变量（疾病数量）、与药物相关的变量（欧盟潜在不适当药物清单（EU-PIM 清单））和卫生服务利用情况。其他社会人口统计学、与疾病相关的变量（累积疾病评分、抑郁）、药物适宜性指数（MAI）、生活方式、功能状态和 HRQoL（EuroQol EQ-5D-3L）。
最佳模型（Nagelkerke 的 R² 0.507）6 个月时 EQ-5D-3L 的最重要预测因子为抑郁症状（-2.73（95%CI：-3.56 至-1.91））、MAI（-0.39（95%CI：-0.7 至-0.08））、基线 EQ-5D-3L（0.55（95%CI：0.47 至 0.64））。基于索赔数据的模型和基于两个数据集预测长期结果的模型产生的 R² 值较低。在具有最高解释能力的基于索赔数据的模型中（R²=0.16），先前的跌倒/与跌倒相关的伤害、先前的住院、年龄、涉及的医生数量和疾病数量是最重要的预测变量。
最佳基于试验数据的模型很好地预测了 6 个月后的 HRQoL，并且包含了在索赔中未发现的幸福感参数。基于索赔数据的模型和预测长期结果的模型的性能相对较弱。为了推广，未来的研究应通过考虑代表幸福感和功能状态的参数来重新拟合模型。
