Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota, USA.
Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA.
J Am Med Inform Assoc. 2014 Sep-Oct;21(5):785-91. doi: 10.1136/amiajnl-2014-002699. Epub 2014 Apr 29.
We validated an algorithm designed to identify new or prevalent users of antidepressant medications via population-based drug prescription records.
We obtained population-based drug prescription records for the entire Olmsted County, Minnesota, population from 2011 to 2012 (N=149,629) using the existing electronic medical records linkage infrastructure of the Rochester Epidemiology Project (REP). We selected electronically a random sample of 200 new antidepressant users stratified by age and sex. The algorithm required the exclusion of antidepressant use in the 6 months preceding the date of the first qualifying antidepressant prescription (index date). Medical records were manually reviewed and adjudicated to calculate the positive predictive value (PPV). We also manually reviewed the records of a random sample of 200 antihistamine users who did not meet the case definition of new antidepressant user to estimate the negative predictive value (NPV).
161 of the 198 subjects electronically identified as new antidepressant users were confirmed by manual record review (PPV 81.3%). Restricting the definition of new users to subjects who were prescribed typical starting doses of each agent for treating major depression in non-geriatric adults resulted in an increase in the PPV (90.9%). Extending the time windows with no antidepressant use preceding the index date resulted in only modest increases in PPV. The manual abstraction of medical records of 200 antihistamine users yielded an NPV of 98.5%.
Our study confirms that REP prescription records can be used to identify prevalent and incident users of antidepressants in the Olmsted County, Minnesota, population.
我们通过基于人群的药物处方记录验证了一种用于识别新的或现患抗抑郁药物使用者的算法。
我们利用罗切斯特流行病学项目（REP）现有的电子病历链接基础设施，从 2011 年至 2012 年获取了整个明尼苏达州奥姆斯特德县的基于人群的药物处方记录（N=149629）。我们通过年龄和性别分层选择了 200 名新抗抑郁药使用者的随机电子样本。该算法要求排除在首次合格抗抑郁药处方（索引日期）前 6 个月内使用抗抑郁药。我们对医疗记录进行了手动审查和裁决，以计算阳性预测值（PPV）。我们还对 200 名不符合新抗抑郁药使用者病例定义的抗组胺药使用者的随机样本记录进行了手动审查，以估计阴性预测值（NPV）。
通过电子方式识别的 198 名新抗抑郁药使用者中的 161 名经手动记录审查得到确认（PPV 81.3%）。将新使用者的定义限制为为非老年成年人治疗重度抑郁症而开处每种药物典型起始剂量的使用者，可提高 PPV（90.9%）。扩大索引日期前无抗抑郁药使用的时间窗口只会适度提高 PPV。对 200 名抗组胺药使用者的医疗记录进行手动提取，得到 NPV 为 98.5%。
我们的研究证实，REP 处方记录可用于识别明尼苏达州奥姆斯特德县的抗抑郁药物现患和新使用者。
