Department of Biostatistics & Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, United States.
Department of Biostatistics & Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, United States; Department of Medical Informatics, OU-TU School of Community Medicine, Tulsa, Oklahoma, United States.
Drug Alcohol Depend. 2020 Mar 1;208:107825. doi: 10.1016/j.drugalcdep.2019.107825. Epub 2019 Dec 23.
The twenty-first century opioid crisis has spurred interest in using International Classification of Diseases (ICD) code algorithms to identify patients using illicit drugs from administrative healthcare data. We conducted a systematic review of studies that validated ICD code algorithms for illicit drug use against a reference standard of medical record data.
Systematic searches of MEDLINE, EMBASE, PsycINFO, and Web of Science were conducted for studies published between 1980 and 2018 in English, French, Italian, or Spanish. We included validation studies of ICD-9 or ICD-10 code algorithms for an illicit drug use target condition (e.g., illicit drug use, abuse, or dependence (UAD), illicit drug use-related complications) given the sensitivity or specificity was reported or could be calculated. Bias was assessed with the Quality Assessment of Diagnostic Accuracy Studies Version 2 (QUADAS-2) tool.
Six of the 1210 articles identified met the inclusion criteria. For validation studies of broad UAD (n = 4), the specificity was nearly perfect, but the sensitivity ranged from 47% to 83%, with higher sensitivities tending to occur in higher prevalence populations. For validation studies of injection drug use (IDU)-associated infective endocarditis (n = 2), sensitivity and specificity were poor due to the lack of an ICD code for IDU. For all six studies, the risk of bias for the QUADAS-2 "reference standard" and "flow/timing domains" was scored as "unclear" due to insufficient reporting.
Few studies have validated ICD code algorithms for illicit drug use target conditions, and available evidence is challenging to interpret due to inadequate reporting. PROSPERO Registration: CRD42019118401.
21 世纪的阿片类药物危机促使人们有兴趣使用国际疾病分类(ICD)代码算法,从医疗保健管理数据中识别使用非法药物的患者。我们对验证 ICD 代码算法用于非法药物使用的研究进行了系统评价,这些研究以病历数据的参考标准为对照。
系统检索了 1980 年至 2018 年间发表的英文、法文、意大利文或西班牙文的 MEDLINE、EMBASE、PsycINFO 和 Web of Science 数据库,以查找验证 ICD-9 或 ICD-10 代码算法的研究。纳入了报告了非法药物使用目标疾病(如非法药物使用、滥用或依赖(UAD)、非法药物使用相关并发症)的 ICD 诊断代码算法的验证研究,或者可以计算出其敏感性或特异性。使用诊断准确性研究的质量评估工具(QUADAS-2)评估偏倚。
在确定的 1210 篇文章中,有 6 篇符合纳入标准。在针对广泛的 UAD 的验证研究(n=4)中,特异性几乎为完美,但敏感性范围为 47%至 83%,在高流行人群中,敏感性更高。在针对与注射吸毒(IDU)相关的感染性心内膜炎的 IDU 验证研究(n=2)中,由于缺乏 IDU 的 ICD 代码,敏感性和特异性均较差。对于所有 6 项研究,由于报告不足,QUADAS-2“参考标准”和“流程/时间域”的偏倚风险评分均为“不明确”。
很少有研究验证了 ICD 代码算法用于非法药物使用的目标条件,并且由于报告不足,现有证据难以解释。PROSPERO 注册号:CRD42019118401。