Epilepsy Among Elderly Medicare Beneficiaries A Validated Approach to Identify Prevalent and Incident Epilepsy
Epilepsy Among Elderly Medicare Beneficiaries A Validated Approach to Identify Prevalent and Incident Epilepsy
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DOI：
10.1097/mlr.0000000000001072
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发表时间：
2019-04-01
期刊：
影响因子：
3
通讯作者：
Hsu, John
中科院分区：
文献类型：
--
作者：
Moura, Lidia M. V. R.;Smith, Jason R.;Hsu, John
背景资料：健康保险索赔和其他大型数据集内癫痫诊断的不确定有效性阻碍了在人群水平上研究和监测护理的努力。目的：使用纵向医疗保险管理数据开发和验证预测模型，以在诊断中识别实际癫痫患者。研究设计，受试者，测量：我们使用相关的电子健康记录和医疗保险管理数据，包括索赔来预测癫痫状态。一名神经科医生审查了电子健康记录数据，以评估2012年1月至2014年12月期间65岁以上医疗保险受益人分层随机样本的癫痫状态。然后，我们使用逆概率抽样权重重建完整样本。我们开发了预测模型，使用纵向医疗保险数据，然后在一个单独的样本中评估每个模型的预测性能，例如，在接受者工作特征曲线（AUROC），灵敏度和specificity.Results下的面积：20,945例患者在重建的样本，2.1%已确认癫痫。识别流行性癫痫的最佳预测模型需要癫痫诊断，至少间隔60天的多次索赔，以及癫痫特定药物索赔：AUROC=0.93 [95%置信区间（CI），0.90-0.96]，诊断阈值为80%，灵敏度=87.8%（95% CI，80.4%-93.2%），特异性= 98.4%（95% CI，98.2%-98.5%）。一个类似的模型也表现良好，在预测癫痫发作（k=0.79; 95%CI，0.66-0.92）。结论：预测模型，使用纵向医疗保险数据进行准确预测癫痫发作和流行状态。
Background: Uncertain validity of epilepsy diagnoses within health insurance claims and other large datasets have hindered efforts to study and monitor care at the population level.Objectives: To develop and validate prediction models using longitudinal Medicare administrative data to identify patients with actual epilepsy among those with the diagnosis.Research Design, Subjects, Measures: We used linked electronic health records and Medicare administrative data including claims to predict epilepsy status. A neurologist reviewed electronic health record data to assess epilepsy status in a stratified random sample of Medicare beneficiaries aged 65+ years between January 2012 and December 2014. We then reconstructed the full sample using inverse probability sampling weights. We developed prediction models using longitudinal Medicare data, then in a separate sample evaluated the predictive performance of each model, for example, area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity.Results: Of 20,945 patients in the reconstructed sample, 2.1% had confirmed epilepsy. The best-performing prediction model to identify prevalent epilepsy required epilepsy diagnoses with multiple claims at least 60 days apart, and epilepsy-specific drug claims: AUROC=0.93 [95% confidence interval (CI), 0.90-0.96], and with an 80% diagnostic threshold, sensitivity=87.8% (95% CI, 80.4%-93.2%), specificity= 98.4% (95% CI, 98.2%-98.5%). A similar model also performed well in predicting incident epilepsy (k=0.79; 95% CI, 0.66-0.92).Conclusions: Prediction models using longitudinal Medicare data perform well in predicting incident and prevalent epilepsy status accurately.