Kaitlin M McGrew1, Hélène Carabin2, Tabitha Garwe3, S Reza Jafarzadeh4, Mary B Williams5, Yan Daniel Zhao6, Douglas A Drevets7. 1. Department of Biostatistics & Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, United States. 2. Department of Biostatistics & Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, United States; Département de pathologie et de microbiologie, Faculté de Médecine vétérinaire-Université de Montréal, Saint-Hyacinthe, Québec, J2S 2M2, Canada; Département de médecine sociale et preventive, École de Santé Publique, Université de Montréal, Montréal, Québec, H3N 1X9, Canada; Centre de Recherche en Santé Publique (CReSP), Université de Montréal, Montréal, Québec, Canada. Electronic address: helene.carabin@umontreal.ca. 3. Department of Biostatistics & Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, United States. Electronic address: Tabitha-Garwe@ouhsc.edu. 4. Department of Medicine, Boston University School of Medicine, Boston, MA 02118, United States. Electronic address: srjafarz@bu.edu. 5. Department of Biostatistics & Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, United States; Department of Family and Community Medicine, OU-TU School of Community Medicine, Tulsa, OK 74135, United States. Electronic address: Mary-Williams@ouhsc.edu. 6. Department of Biostatistics & Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, United States. Electronic address: daniel-zhao@ouhsc.edu. 7. Department of Internal Medicine- Infectious Diseases, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, United States; Medical Services, Department of Veterans Affairs Medical Center, Oklahoma City, OK 73104, United States. Electronic address: Douglas-Drevets@ouhsc.edu.
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.
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.
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