基于索赔的算法开发，以识别潜在未确诊的慢性偏头痛患者。
Development of a claims-based algorithm to identify potentially undiagnosed chronic migraine patients.
机构信息
1 Montefiore Headache Center, Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA.
2 Allergan plc, Irvine, CA, USA.
出版信息
Cephalalgia. 2019 Apr;39(4):465-476. doi: 10.1177/0333102418825373. Epub 2019 Mar 9.
OBJECTIVE
To develop a claims-based algorithm to identify undiagnosed chronic migraine among patients enrolled in a healthcare system.
METHODS
An observational study using claims and patient survey data was conducted in a large medical group. Eligible patients had an International Classification of Diseases, Ninth/Tenth Revision (ICD-9/10) migraine diagnosis, without a chronic migraine diagnosis, in the 12 months before screening and did not have a migraine-related onabotulinumtoxinA claim in the 12 months before enrollment. Trained clinicians administered a semi-structured diagnostic interview, which served as the gold standard to diagnose chronic migraine, to enrolled patients. Potential claims-based predictors of chronic migraine that differentiated semi-structured diagnostic interview-positive (chronic migraine) and semi-structured diagnostic interview-negative (non-chronic migraine) patients were identified in bivariate analyses for inclusion in a logistic regression model.
RESULTS
The final sample included 108 patients (chronic migraine = 64; non-chronic migraine = 44). Four significant predictors for chronic migraine were identified using claims in the 12 months before enrollment: ≥15 versus <15 claims for acute treatment of migraine, including opioids (odds ratio = 5.87 [95% confidence interval: 1.34-25.63]); ≥24 versus <24 healthcare visits (odds ratio = 2.80 [confidence interval: 1.08-7.25]); female versus male sex (odds ratio = 9.17 [confidence interval: 1.26-66.50); claims for ≥2 versus 0 unique migraine preventive classes (odds ratio = 4.39 [confidence interval: 1.19-16.22]). Model sensitivity was 78.1%; specificity was 72.7%.
CONCLUSIONS
The claims-based algorithm identified undiagnosed chronic migraine with sufficient sensitivity and specificity to have potential utility as a chronic migraine case-finding tool using health claims data. Research to further validate the algorithm is recommended.
目的
开发一种基于索赔的算法，以识别参加医疗保健系统的患者中未经诊断的慢性偏头痛。
方法
在一个大型医疗集团中进行了一项基于观察索赔和患者调查数据的研究。合格的患者在筛选前的 12 个月内具有国际疾病分类，第九/第十版（ICD-9/10）偏头痛诊断，但在筛选前的 12 个月内没有慢性偏头痛诊断，并且在入组前的 12 个月内没有偏头痛相关的 onabotulinumtoxinA 索赔。经过培训的临床医生对入组患者进行了半结构化诊断访谈，作为诊断慢性偏头痛的金标准。在二元分析中确定了区分半结构化诊断访谈阳性（慢性偏头痛）和半结构化诊断访谈阴性（非慢性偏头痛）患者的慢性偏头痛潜在索赔预测因素，以纳入逻辑回归模型。
结果
最终样本包括 108 名患者（慢性偏头痛= 64；非慢性偏头痛= 44）。在入组前的 12 个月内使用索赔确定了四个慢性偏头痛的显著预测因素：≥15 次与<15 次偏头痛急性治疗的索赔，包括阿片类药物（优势比= 5.87 [95%置信区间：1.34-25.63]）；≥24 次与<24 次医疗就诊（优势比= 2.80 [置信区间：1.08-7.25]）；女性与男性性别（优势比= 9.17 [置信区间：1.26-66.50]）；索赔≥2 次与 0 次独特偏头痛预防类别（优势比= 4.39 [置信区间：1.19-16.22]）。模型灵敏度为 78.1%；特异性为 72.7%。
结论
该基于索赔的算法能够识别未经诊断的慢性偏头痛，具有足够的敏感性和特异性，可作为使用健康索赔数据发现慢性偏头痛的潜在工具。建议进一步验证该算法的研究。
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