Validity of International Classification of Diseases codes in identifying illicit drug use target conditions using medical record data as a reference standard: A systematic review.


Journal

Drug and alcohol dependence
ISSN: 1879-0046
Titre abrégé: Drug Alcohol Depend
Pays: Ireland
ID NLM: 7513587

Informations de publication

Date de publication:
01 03 2020
Historique:
received: 02 08 2019
revised: 08 12 2019
accepted: 11 12 2019
pubmed: 27 1 2020
medline: 20 1 2021
entrez: 27 1 2020
Statut: ppublish

Résumé

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.

Sections du résumé

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.

Identifiants

pubmed: 31982637
pii: S0376-8716(19)30602-7
doi: 10.1016/j.drugalcdep.2019.107825
pmc: PMC9533471
mid: NIHMS1839011
pii:
doi:

Substances chimiques

Illicit Drugs 0

Types de publication

Journal Article Systematic Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

107825

Subventions

Organisme : NIAMS NIH HHS
ID : P30 AR072571
Pays : United States
Organisme : NIA NIH HHS
ID : R03 AG060272
Pays : United States
Organisme : NIAMS NIH HHS
ID : R21 AR074578
Pays : United States
Organisme : NIGMS NIH HHS
ID : U54 GM104938
Pays : United States

Informations de copyright

Copyright © 2019 Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest No conflict declared.

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Auteurs

Kaitlin M McGrew (KM)

Department of Biostatistics & Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, United States. Electronic address: Kaitlin-McGrew@ouhsc.edu.

Juell B Homco (JB)

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.

Tabitha Garwe (T)

Department of Biostatistics & Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, United States. Electronic address: Tabitha-Garwe@ouhsc.edu.

Hanh Dung Dao (HD)

Department of Biostatistics & Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, United States. Electronic address: HanhDung-Dao@ouhsc.edu.

Mary B Williams (MB)

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.

Douglas A Drevets (DA)

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.

S Reza Jafarzadeh (SR)

Department of Medicine, Boston University School of Medicine, Boston, Massachusetts, United States. Electronic address: srjafarz@bu.edu.

Yan Daniel Zhao (YD)

Department of Biostatistics & Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, United States. Electronic address: daniel-zhao@ouhsc.edu.

Hélène Carabin (H)

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.

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Classifications MeSH