A predictive model to identify Parkinson disease from administrative claims data
A predictive model to identify Parkinson disease from administrative claims data
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DOI：
10.1212/wnl.0000000000004536
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发表时间：
2017-10-03
期刊：
影响因子：
9.9
通讯作者：
Racette, Brad A.
中科院分区：
文献类型：
--
作者：
Nielsen, Susan Searles;Warden, Mark N.;Racette, Brad A.
目的：使用行政医疗索赔数据来识别诊断前的帕金森病（PD）患者。方法：使用2009年66-90岁医疗保险受益人中发生PD的基于人群的病例对照研究（89,790例，118,095例对照）和弹性网络算法，我们开发了一个交叉验证模型，仅使用人口统计学数据和2004-2009年医疗保险索赔数据来预测PD。然后，我们比较了这个模型更基本的模型，只包含人口统计学数据和诊断代码便秘，味觉/嗅觉障碍，REM睡眠行为障碍，使用每个模型的接收器操作员特征曲线下面积（AUC）。结果：我们观察到所有已建立的关联PD和年龄，性别，种族/民族，吸烟，和上述医疗条件。具有这些预测因子的模型的AUC仅为0.670（95%置信区间[CI] 0.668-0.673）。相比之下，具有536个诊断和程序代码的预测模型的AUC为0.857（95% CI 0.855-0.859）。在最佳分界点，敏感性为73.5%，特异性为83.2%。结论：仅使用人口统计学数据和行政索赔数据中现成的选定诊断和程序代码，就有可能识别出最终被诊断为PD的高概率个体。
Objective: To use administrative medical claims data to identify patients with incident Parkinson disease (PD) prior to diagnosis.Methods: Using a population-based case-control study of incident PD in 2009 among Medicare beneficiaries aged 66-90 years (89,790 cases, 118,095 controls) and the elastic net algorithm, we developed a cross-validated model for predicting PD using only demographic data and 2004-2009 Medicare claims data. We then compared this model to more basic models containing only demographic data and diagnosis codes for constipation, taste/smell disturbance, and REM sleep behavior disorder, using each model's receiver operator characteristic area under the curve (AUC).Results: We observed all established associations between PD and age, sex, race/ethnicity, tobacco smoking, and the above medical conditions. A model with those predictors had an AUC of only 0.670 (95% confidence interval [CI] 0.668-0.673). In contrast, the AUC for a predictive model with 536 diagnosis and procedure codes was 0.857 (95% CI 0.855-0.859). At the optimal cut point, sensitivity was 73.5% and specificity was 83.2%.Conclusions: Using only demographic data and selected diagnosis and procedure codes readily available in administrative claims data, it is possible to identify individuals with a high probability of eventually being diagnosed with PD.