Department of Clinical Neurosciences, University of Calgary, Canada.
Neurology. 2012 Sep 4;79(10):1049-55. doi: 10.1212/WNL.0b013e3182684707. Epub 2012 Aug 22.
Administrative health data are frequently used for large population-based studies. However, the validity of these data for identifying neurologic conditions is uncertain.
This article systematically reviews the literature to assess the validity of administrative data for identifying patients with neurologic conditions. Two reviewers independently assessed for eligibility all abstracts and full-text articles identified through a systematic search of Medline and Embase. Study data were abstracted on a standardized abstraction form to identify ICD code-based case definitions and corresponding sensitivity, specificity, positive predictive values (PPVs), and negative predictive values (NPVs).
Thirty full-text articles met the eligibility criteria. These included 8 studies for Alzheimer disease/dementia (sensitivity: 8-86.5, specificity: 56.3-100, PPV: 60-97.9, NPV: 68.0-98.9), 2 for brain tumor (sensitivity: 54.0-100, specificity: 97.0-99.0, PPV: 91.0-98.0), 4 for epilepsy (sensitivity: 98.8, specificity: 69.6, PPV: 62.0-100, NPV: 89.5-99.1), 4 for motor neuron disease (sensitivity: 78.9-93.0, specificity: 99.0-99.9, PPV: 38.0-90.0, NPV: 99), 2 for multiple sclerosis (sensitivity: 85-92.4, specificity: 55.9-92.6, PPV: 74.5-92.7, NPV: 70.8-91.9), 4 for Parkinson disease/parkinsonism (sensitivity: 18.7-100, specificity: 0-99.9, PPV: 38.6-81.0, NPV: 46.0), 3 for spinal cord injury (sensitivity: 0.9-90.6, specificity: 31.9-100, PPV: 27.3-100), and 3 for traumatic brain injury (sensitivity: 45.9-78.0 specificity: 97.8, PPV: 23.7-98.0, NPV: 99.2). No studies met eligibility criteria for cerebral palsy, dystonia, Huntington disease, hydrocephalus, muscular dystrophy, spina bifida, or Tourette syndrome.
To ensure the accurate interpretation of population-based studies with use of administrative health data, the accuracy of case definitions for neurologic conditions needs to be taken into consideration.
行政健康数据常用于基于人群的大型研究。然而，这些数据在识别神经疾病方面的有效性尚不确定。
本文系统地综述了文献，以评估行政数据识别神经疾病患者的有效性。两名审查员独立评估通过系统搜索 Medline 和 Embase 确定的所有摘要和全文文章的合格性。研究数据在标准化的摘录表格上进行摘录，以确定基于 ICD 代码的病例定义以及相应的敏感性、特异性、阳性预测值（PPV）和阴性预测值（NPV）。
30 篇全文文章符合入选标准。其中包括 8 项阿尔茨海默病/痴呆症研究（敏感性：8-86.5，特异性：56.3-100，PPV：60-97.9，NPV：68.0-98.9），2 项脑肿瘤研究（敏感性：54.0-100，特异性：97.0-99.0，PPV：91.0-98.0，NPV：89.5-99.1），4 项癫痫研究（敏感性：98.8，特异性：69.6，PPV：62.0-100，NPV：89.5-99.1），4 项运动神经元疾病研究（敏感性：78.9-93.0，特异性：99.0-99.9，PPV：38.0-90.0，NPV：99），2 项多发性硬化症研究（敏感性：85-92.4，特异性：55.9-92.6，PPV：74.5-92.7，NPV：70.8-91.9），4 项帕金森病/帕金森症研究（敏感性：18.7-100，特异性：0-99.9，PPV：38.6-81.0，NPV：46.0），3 项脊髓损伤研究（敏感性：0.9-90.6，特异性：31.9-100，PPV：27.3-100）和 3 项创伤性脑损伤研究（敏感性：45.9-78.0，特异性：97.8，PPV：23.7-98.0，NPV：99.2）。没有研究符合脑瘫、肌张力障碍、亨廷顿病、脑积水、肌肉营养不良、脊柱裂或妥瑞氏症的入选标准。
为确保使用行政健康数据准确解释基于人群的研究，需要考虑神经疾病病例定义的准确性。
