Risk of Nondherence to Diabetes Medications Among Medicare Advantage Enrollees: Development of a Validated Risk Prediction Tool
Risk of Nondherence to Diabetes Medications Among Medicare Advantage Enrollees: Development of a Validated Risk Prediction Tool
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医疗保险优势参与者不遵守糖尿病药物的风险：开发经过验证的风险预测工具
DOI：
10.18553/jmcp.2016.22.11.1293
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发表时间：
2016
影响因子：
2.1
通讯作者：
PhD Sujit S. Sansgiry
中科院分区：
文献类型：
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作者：
PhD Shivani K. Mhatre;PharmD Omar Serna;PhD Shubhada Sansgiry;P. M. Marc L. Fleming;MD DrPH E. James Essien;PhD Sujit S. Sansgiry
背景技术背景：在医疗保险人群中，口服降糖药（OAD）的依从性较低可能会大大降低医疗保险和医疗补助服务中心（CMS）对管理式医疗机构（MCO）的星星评级。目的：开发并验证风险评估工具（糖尿病药物处方药依从性预测工具[RxAPT-D]），以使用Medicare索赔数据预测OAD的不依从性。方法：在这项回顾性观察性研究中，使用了在德克萨斯州休斯顿参加医疗保险优势处方药（MA-PD）计划的成员的索赔数据。2012年（基线期）的数据用于确定预测2013年（随访期）依从性的关键变量。研究纳入了年龄≥ 65岁、诊断为糖尿病、至少有1种OAD处方（双胍类、磺脲类、噻唑烷二酮类、二肽基肽酶-4抑制剂或氯茴苯酸类）且连续入组2年的成员。接受胰岛素处方的患者从队列中排除。研究结果，2013年的不依从性定义为覆盖天数比例（PDC）<80%。多变量logistic模型使用200个bootstrap重复（替换）确定与不依从相关的因素。对最终模型进行了鉴别和校准统计测试，并使用10倍交叉验证进行了内部验证。使用预测因子的加权β系数，创建RxAPT-D来对不依从风险进行分层，并对灵敏度、特异性、阳性预测值和阴性预测值进行检验。该工具的预测能力进行了比较，与过去的PDC值使用净重新分类改进（NRI）和综合判别改进（IDI）指数。结果：来自7，028名MA-PD成员的数据用于工具开发。从logistic模型中确定了≥ 50%的自助样本中具有统计学显著性的7个预测因素（年龄、OAD再填充总量、OAD填充总量、最后一次填充OAD的供应天数、药丸负担、最后一次填充OAD的覆盖率和既往依从性）。最终模型显示出良好的区分度（c-统计量= 0.75）和校准（Hosmer-Lemeshow拟合优度P < 0.05）统计量，具有良好的内部效度（曲线下面积= 0.73）。RxAPT-D显示了充分的灵敏度统计：灵敏度= 0.73，特异性= 0.63，阳性预测值= 0.74，阴性预测值= 0.62。与使用既往依从性指标相比，RxAPT-D具有更高的预测能力，相对IDI = 2.09，用户定义的NRI = 0.16，24%的事件正确重新分类。结论：RxAPT是一种有效的工具，可用于识别在随访年内可能不依从OAD的患者。MCO中的药剂师可以使用该工具识别预期不依从OAD的患者，并制定有针对性的干预计划，以帮助提高MCO CMS星星评级。
BACKGROUND: Low adherence to oral antidiabetic drugs (OADs) in the Medicare population can greatly reduce Centers for Medicare & Medicaid Services (CMS) star ratings for managed care organizations (MCOs). OBJECTIVE: To develop and validate a risk assessment tool (Prescription Medication Adherence Prediction Tool for Diabetes Medications [RxAPT-D]) to predict nonadherence to OADs using Medicare claims data. METHODS: In this retrospective observational study, claims data for members enrolled in a Medicare Advantage Prescription Drug (MA-PD) program in Houston, Texas, were used. Data from 2012 (baseline period) were used to identify key variables to predict adherence in 2013 (follow-up period). Members aged 65 years and older with a diabetes diagnosis, at least 1 prescription for OADs (biguanides, sulfonylureas, thiazolidinediones, dipeptidyl peptidase-4 inhibitors, or meglitinides), and continuously enrolled for both years were included in the study. Patients with insulin prescriptions were excluded from the cohort. The study outcome, nonadherence in 2013, was defined as proportion of days covered (PDC) < 80%. Multivariable logistic models using 200 bootstrap replications (with replacement) identified factors associated with nonadherence. The final model was tested for discrimination and calibration statistics and internally validated using 10-fold cross-validation. Using weighted beta coefficients of the predictors, the RxAPT-D was created to stratify nonadherence risk and was tested for sensitivity, specificity, positive prediction value, and negative prediction value. The predictive ability of the tool was compared with that of past PDC values using net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indices. RESULTS: Data from 7,028 MA-PD members were used for tool development. Seven predictors (age, total OAD refills, total OAD classes filled, days supply of last filled OAD, pill burden, coverage of last filled OAD, and past adherence) statistically significant in ≥ 50% of the bootstrapped samples were identified from the logistic models. The final model demonstrated good discrimination (c-statistics = 0.75) and calibration (Hosmer-Lemeshow goodness-of-fit P < 0.05) statistics, with good internal validity (area under the curve = 0.73). The RxAPT-D demonstrated adequate sensitivity statistics: sensitivity = 0.73, specificity = 0.63, positive prediction value = 0.74, and negative prediction value = 0.62. Compared with use of past adherence measures, the RxAPT-D had higher prediction ability, relative IDI = 2.09, and user defined NRI = 0.16 with 24% events correctly reclassified. CONCLUSIONS: The RxAPT is an effective tool to identify patients who are likely to become nonadherent to OADs in the follow-up year. Pharmacists in MCOs can use this tool to identify patients expected to be nonadherent to OADs and develop targeted intervention programs to assist in improving MCO CMS star ratings.