Validity of ICD-based algorithms to estimate the prevalence of injection drug use among infective endocarditis hospitalizations in the absence of a reference standard. (1st April 2020)

• Record Type: Journal Article
• Authors: McGrew, Kaitlin M.; Carabin, Hélène; Garwe, Tabitha; Jafarzadeh, S. Reza; Williams, Mary B.; Zhao, Yan Daniel; Drevets, Douglas A.
• Is Part Of: Drug and alcohol dependence. Volume 209(2020)
• Publication Date: 2020-04-01
• Subjects: Validation studies -- Endocarditis -- Substance abuse -- Intravenous -- International Classification of Diseases -- Diagnostic error -- Bayesian analysis; Drug abuse -- Periodicals; Alcoholism -- Periodicals; 616.86
• DOI: 10.1016/j.drugalcdep.2020.107906
• Languages: English
• ISSNs: 0376-8716

Abstract

Highlights: IDU misclassification varies across ICD algorithms and revisions. IDU prevalence was overestimated with a combination drug/Hepatitis C algorithm. ICD-10 codes for drug use had decent sensitivity and nearly perfect specificity. Analyses of administrative health databases should adjust for misclassification. Abstract: Background: International Classification of Diseases (ICD) code algorithms are routinely used to estimate the frequency of illicit injection drug use (IDU)-associated hospitalizations in administrative health datasets despite a lack of evidence regarding their validity. We aimed to measure the sensitivity and specificity of ICD code algorithms used to estimate the prevalence of current/recent IDU among infective endocarditis (IE) hospitalizations without a reference standard. Methods: We reviewed medical records of 321 patients aged 18–64 years old from an urban academic hospital with an IE diagnosis between 2007 and 2017. Diagnostic tests for IDU included self-reported IDU in medical records; a drug use, abuse and dependence (UAD) ICD algorithm; a Hepatitis C Virus (HCV) ICD algorithm; and a combination drug UAD/HCV ICD algorithm. Sensitivity, specificity and the misclassification error (ME)-adjusted IDU prevalence were estimated using Bayesian latent class models. Results: The combination algorithm had the highest sensitivity and lowest specificity. Sensitivity increased for the drug UAD algorithm in the ICD-10 period compared to the ICD-9 period. The ME-adjusted current/recent IDU prevalence estimated using the drug UAD and HCV algorithms was 23 % (95 % Bayesian credible interval: 16 %, 31 %). The unadjusted prevalence estimate from the drug UAD algorithm underestimated the ME-adjusted prevalence, while the combination algorithm overestimated it. Conclusion: The validity of ICD code algorithms for IDU among IE hospitalizations is imperfect and differs between ICD-9 and ICD-10. Commonly used ICD-based algorithms could lead to substantially biased prevalence estimates in IDU-associated hospitalizations when using administrative health data.