Validity of early-onset dementia diagnoses in VA electronic medical record administrative data
Validity of early-onset dementia diagnoses in VA electronic medical record administrative data
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DOI：
10.1080/13854046.2019.1679889
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发表时间：
2019-10-22
影响因子：
3.9
通讯作者：
Pugh, Mary Jo
中科院分区：
文献类型：
--
作者：
Marceaux, Janice C.;Soble, Jason R.;Pugh, Mary Jo
目的：为了确定从使用管理数据的算法中获得的早发性痴呆（EOD）诊断的有效性，我们检查了退伍军人健康管理局（VHA）的电子病历（EMR）。方法：一种以前使用的使用行政数据识别痴呆症病例的方法被应用于176例65岁以下的9/11后部署退伍军人的随机样本。回顾性，横断面检查的EMR进行，使用的行政数据，图表摘要，并审查/共识的董事会认证的神经心理学家的组合。结果：在整个样本中，使用现有算法识别的约73%的EOD诊断被识别为假阳性。这一比例在有精神健康状况的人中增加到约76%，在轻度创伤性脑损伤（TBI;即脑震荡）的人中增加到约85%。与提高诊断准确性相关的因素包括更严重的TBI，诊断临床医生类型，神经影像学数据的存在，没有共病的心理健康状况诊断，以及诊断时年龄较大。结论：先前使用VHA管理数据检测痴呆的算法不支持用于年轻成人样本，并导致不可接受的高数量假阳性。根据这些调查结果，人们担心在使用类似算法确定退伍军人爆炸物处理率的人口研究中可能会出现错误分类。此外，我们还提出了建议，以开发一种增强型算法，在年轻人群中进行更准确的痴呆症监测。
Objective: To determine the validity of diagnoses indicative of early-onset dementia (EOD) obtained from an algorithm using administrative data, we examined Veterans Health Administration (VHA) electronic medical records (EMRs). Method: A previously used method of identifying cases of dementia using administrative data was applied to a random sample of 176 cases of Post-9/11 deployed veterans under 65 years of age. Retrospective, cross-sectional examination of EMRs was conducted, using a combination of administrative data, chart abstraction, and review/consensus by board-certified neuropsychologists. Results: Approximately 73% of EOD diagnoses identified using existing algorithms were identified as false positives in the overall sample. This increased to approximately 76% among those with mental health conditions and approximately 85% among those with mild traumatic brain injury (TBI; i.e. concussion). Factors related to improved diagnostic accuracy included more severe TBI, diagnosing clinician type, presence of neuroimaging data, absence of a comorbid mental health condition diagnosis, and older age at time of diagnosis. Conclusions: A previously used algorithm for detecting dementia using VHA administrative data was not supported for use in the younger adult samples and resulted in an unacceptably high number of false positives. Based on these findings, there is concern for possible misclassification in population studies using similar algorithms to identify rates of EOD among veterans. Further, we provide suggestions to develop an enhanced algorithm for more accurate dementia surveillance among younger populations.