Kaitlin M McGrew1, Juell B Homco2, Tabitha Garwe3, Hanh Dung Dao4, Mary B Williams5, Douglas A Drevets6, S Reza Jafarzadeh7, Yan Daniel Zhao8, Hélène Carabin9. 1. Department of Biostatistics & Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, United States. Electronic address: Kaitlin-McGrew@ouhsc.edu. 2. 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. Electronic address: Juell-Homco@ouhsc.edu. 3. Department of Biostatistics & Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, United States. Electronic address: Tabitha-Garwe@ouhsc.edu. 4. Department of Biostatistics & Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, United States. Electronic address: HanhDung-Dao@ouhsc.edu. 5. Department of Biostatistics & Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, United States; Department of Family and Community Medicine, OU-TU School of Community Medicine, Tulsa, Oklahoma, United States. Electronic address: Mary-Williams@ouhsc.edu. 6. Department of Internal Medicine- Infectious Diseases, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, United States; Medical Services, Department of Veterans Affairs Medical Center, Oklahoma City, Oklahoma, United States. Electronic address: Douglas-Drevets@ouhsc.edu. 7. Department of Medicine, Boston University School of Medicine, Boston, Massachusetts, United States. Electronic address: srjafarz@bu.edu. 8. Department of Biostatistics & Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, United States. Electronic address: daniel-zhao@ouhsc.edu. 9. Department of Biostatistics & Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, United States; Département de pathologie et de microbiologie, Faculté de Médecine vétérinaire, Université de Montréal, Saint-Hyacinthe, Québec, Canada; Département de médecine sociale et préventive, École de Santé Publique, Université de Montréal, Montréal, Québec, Canada; Centre de Recherche en Santé Publique (CReSP), Université de Montréal, Montréal, Québec, Canada. Electronic address: helene.carabin@umontreal.ca.
Abstract
BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
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