Deferio Joseph J, Levin Tomer T, Cukor Judith, Banerjee Samprit, Abdulrahman Rozan, Sheth Amit, Mehta Neel, Pathak Jyotishman
Department of Healthcare Policy and Research, Weill Cornell Medicine, Cornell University, New York, New York, USA.
Beacon Health Options, New York, New York, USA.
JAMIA Open. 2018 Oct;1(2):233-245. doi: 10.1093/jamiaopen/ooy037. Epub 2018 Sep 24.
To characterize nonpsychiatric prescription patterns of antidepressants according to drug labels and evidence assessments (on-label, evidence-based, and off-label) using structured outpatient electronic health record (EHR) data.
A retrospective analysis was conducted using deidentified EHR data from an outpatient practice at a New York City-based academic medical center. Structured "medication-diagnosis" pairs for antidepressants from 35 325 patients between January 2010 and December 2015 were compared to the latest drug product labels and evidence assessments.
Of 140 929 antidepressant prescriptions prescribed by primary care providers (PCPs) and nonpsychiatry specialists, 69% were characterized as "on-label/evidence-based uses." Depression diagnoses were associated with 67 233 (48%) prescriptions in this study, while pain diagnoses were slightly less common (35%). Manual chart review of "off-label use" prescriptions revealed that on-label/evidence-based diagnoses of depression (39%), anxiety (25%), insomnia (13%), mood disorders (7%), and neuropathic pain (5%) were frequently cited as prescription indication despite lacking ICD-9/10 documentation.
The results indicate that antidepressants may be prescribed for off-label uses, by PCPs and nonpsychiatry specialists, less frequently than believed. This study also points to the fact that there are a number of off-label uses that are efficacious and widely accepted by expert clinical opinion but have not been included in drug compendia. Despite the fact that diagnosis codes in the outpatient setting are notoriously inaccurate, our approach demonstrates that the correct codes are often documented in a patient's recent diagnosis history. Examining both structured and unstructured data will help to further validate findings. Routinely collected clinical data in EHRs can serve as an important resource for future studies in investigating prescribing behaviors in outpatient clinics.
利用结构化门诊电子健康记录（EHR）数据，根据药物标签和证据评估（标签内、循证及超说明书用药）来描述抗抑郁药的非精神科处方模式。
使用纽约市一家学术医疗中心门诊的去识别化EHR数据进行回顾性分析。将2010年1月至2015年12月期间35325例患者的抗抑郁药结构化“药物 - 诊断”对与最新的药品标签和证据评估进行比较。
在初级保健提供者（PCP）和非精神科专科医生开出的140929张抗抑郁药处方中，69%被归类为“标签内/循证用药”。在本研究中，抑郁症诊断与67233张（48%）处方相关，而疼痛诊断则稍少见（35%）。对“超说明书用药”处方的人工病历审查显示，尽管缺乏ICD - 9/10记录，但抑郁症（39%）、焦虑症（25%）、失眠症（13%）、情绪障碍（7%）和神经性疼痛（5%）的标签内/循证诊断经常被列为处方指征。
结果表明，初级保健提供者和非精神科专科医生开具抗抑郁药超说明书用药的频率可能低于预期。本研究还指出，有一些超说明书用药是有效的，并且被专家临床意见广泛接受，但尚未纳入药品汇编。尽管门诊环境中的诊断代码 notoriously不准确，但我们的方法表明正确的代码通常记录在患者最近的诊断历史中。检查结构化和非结构化数据将有助于进一步验证研究结果。EHR中常规收集的临床数据可作为未来研究门诊处方行为的重要资源。
