Abstract：
Frailty is an important predictor of mortality, health care costs and utilization, and health outcomes. Validated measures of frailty are not consistently collected during clinical encounters, making comparisons across populations challenging. However, several claims-based algorithms have been developed to predict frailty and related concepts. This study compares performance of three such algorithms among Medicare beneficiaries. Claims data from 12-month continuous enrollment periods were selected during 2014-2016. Frailty scores, calculated using previously developed algorithms from Faurot, Kim, and RAND, were added to baseline regression models to predict claims-based outcomes measured in the following year. Root mean square error and area under the receiver operating characteristic curve were calculated for each model and outcome combination and tested in subpopulations of interest. Overall, Kim models performed best across most outcomes, metrics, and subpopulations. Kim frailty scores may be used by health systems and researchers for risk adjustment or targeting interventions.
摘要：
衰弱是死亡率的重要预测指标，医疗保健成本和利用率，和健康结果。在临床接触期间，未收集到验证的虚弱措施，进行不同人群的比较具有挑战性。然而,已经开发了几种基于索赔的算法来预测脆弱和相关概念。本研究比较了医疗保险受益人中三种此类算法的性能。在2014-2016年期间，选择了来自12个月连续注册期的索赔数据。脆弱的分数，使用Faurot先前开发的算法计算，Kim,还有兰德,被添加到基线回归模型中，以预测下一年测量的基于索赔的结果。计算每个模型和结果组合的均方根误差和受试者工作特征曲线下的面积，并在感兴趣的亚群中进行测试。总的来说,Kim模型在大多数结果中表现最好，指标、和亚群。卫生系统和研究人员可以使用Kim脆弱分数进行风险调整或有针对性的干预措施。
