Pratt Nicole L, Kerr Mhairi, Barratt John D, Kemp-Casey Anna, Kalisch Ellett Lisa M, Ramsay Emmae, Roughead Elizabeth Ellen
Quality Use of Medicines and Pharmacy Research Centre, University of South Australia, Adelaide, South Australia, Australia.
BMJ Open. 2018 Apr 13;8(4):e021122. doi: 10.1136/bmjopen-2017-021122.
To provide a map of Anatomical Therapeutic Chemical (ATC) Classification System codes to individual Rx-Risk comorbidities and to validate the Rx-Risk Comorbidity Index.
The 46 comorbidities in the Rx-Risk Index were mapped to dispensing's indicative of each condition using ATC codes. Prescription dispensing claims in 2014 were used to calculate the Rx-Risk. A baseline logistic regression model was fitted using age and gender as covariates. Rx-Risk was added to the base model as an (1) unweighted score, (2) weighted score and as (3) individual comorbidity categories indicating the presence or absence of each condition. The Akaike information criterion and c-statistic were used to compare the models.
Models were developed in the Australian Government Department of Veterans' Affairs health claims data, and external validation was undertaken in a 10% sample of the Australian Pharmaceutical Benefits Scheme Data.
Subjects aged 65 years or older.
Death within 1 year (eg, 2015).
Compared with the base model (c-statistic 0.738, 95% CI 0.734 to 0.742), including Rx-Risk improved prediction of mortality; unweighted score 0.751, 95% CI 0.747 to 0.754, weighted score 0.786, 95% CI 0.782 to 0.789 and individual comorbidities 0.791, 95% CI 0.788 to 0.795. External validation confirmed the utility of the weighted index (c-statistic=0.833).
The updated Rx-Risk Comorbidity Score was predictive of 1-year mortality and may be useful in practice to adjust for confounding in observational studies using medication claims data.
提供解剖治疗化学(ATC)分类系统代码与个体Rx-Risk共病的映射关系,并验证Rx-Risk共病指数。
使用ATC代码将Rx-Risk指数中的46种共病映射到每种疾病的配药指标。2014年的处方配药数据用于计算Rx-Risk。使用年龄和性别作为协变量拟合基线逻辑回归模型。将Rx-Risk作为(1)未加权分数、(2)加权分数以及(3)表明每种疾病存在或不存在的个体共病类别添加到基础模型中。使用赤池信息准则和c统计量比较模型。
模型是在澳大利亚政府退伍军人事务部健康索赔数据中开发的,并在澳大利亚药品福利计划数据的10%样本中进行了外部验证。
65岁及以上的受试者。
1年内(如2015年)死亡。
与基础模型(c统计量为0.738,95%可信区间为0.734至0.742)相比,纳入Rx-Risk可改善对死亡率的预测;未加权分数为0.751,95%可信区间为0.747至0.754,加权分数为0.786,95%可信区间为0.