Positive Predictive Values of 2 Algorithms for Identifying Patients with Intravenous Drug Use-Associated Endocarditis Using Administrative Data

Takaaki Kobayashi; Brice Beck; Aaron Miller; Philip Polgreen; Amy M. J. O'Shea; Michael E. Ohl

doi:10.1093/ofid/ofaa201

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Positive Predictive Values of 2 Algorithms for Identifying Patients with Intravenous Drug Use-Associated Endocarditis Using Administrative Data

Journal article

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Positive Predictive Values of 2 Algorithms for Identifying Patients with Intravenous Drug Use-Associated Endocarditis Using Administrative Data

Takaaki Kobayashi, Brice Beck, Aaron Miller, Philip Polgreen, Amy M. J. O'Shea and Michael E. Ohl

Open forum infectious diseases, Vol.7(6), pp.ofaa201-ofaa201

06/01/2020

DOI: 10.1093/ofid/ofaa201

PMCID: PMC7307439

PMID: 32607386

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Abstract

Background. Prior studies have used International Classification of Disease (ICD) diagnosis codes in administrative data to identify patients with infective endocarditis (IE) associated with intravenous drug use (IVDU). Little is known about the accuracy of ICD codes for IVDU-IE. Methods. We used 2 previously described algorithms to identify patients with potential IVDU-IE admitted to 125 Veterans Administration hospitals from January 2010 through December 2018. Algorithm A identified patients with concurrent ICD-9/10 codes for IE and drug use during the same admission. Algorithm B identified patients with drug use coded either during the IE admission or during outpatient or other visits within 6 months of admission. We reviewed 400 randomly selected patient charts to determine the positive predictive value (PPV) of each algorithm for clinical documentation of IE, any drug use, IVDU, and IVDU-IE, respectively. Results. Algorithm A identified 788 patients, and B identified 1314 patients, a 68% increase. PPVs were high for clinical documentation of diagnoses of IE (86.5% for A and 82.6% for B) and any drug use (99.0% and 96.3%). PPVs were lower for documented IVDU (74.5% and 64.1%) and combined diagnoses of IVDU-IE (65.0% and 55.2%), partly because of a lack of ICD codes specific to IVDU. Among patients identified by algorithm B but not A, 72% had clinical documentation of drug use during the IE admission, indicating a failure of algorithm A to capture cases due to incomplete recording of inpatient ICD codes for drug use. Conclusions. There is need for improved algorithms for IVDU-IE surveillance during the ongoing opioid epidemic.

Immunology

Infectious Diseases

Life Sciences & Biomedicine

Microbiology

Science & Technology

Details

Title: Subtitle: Positive Predictive Values of 2 Algorithms for Identifying Patients with Intravenous Drug Use-Associated Endocarditis Using Administrative Data
Creators: Takaaki Kobayashi - University of Iowa
Brice Beck - Iowa City VA Health Care System
Aaron Miller - University of Iowa
Philip Polgreen - University of Iowa
Amy M. J. O'Shea - University of Iowa
Michael E. Ohl - University of Iowa
Resource Type: Journal article
Publication Details: Open forum infectious diseases, Vol.7(6), pp.ofaa201-ofaa201
DOI: 10.1093/ofid/ofaa201
PMID: 32607386
PMCID: PMC7307439
NLM abbreviation: Open Forum Infect Dis
ISSN: 2328-8957
eISSN: 2328-8957
Publisher: Oxford University Press
Number of pages: 7
Grant note: 7351 / Veterans Affairs Office of Rural Health
Language: English
Date published: 06/01/2020
Academic Unit: Psychiatry; Infectious Diseases; Epidemiology; Injury Prevention Research Center; General Internal Medicine; Internal Medicine
Record Identifier: 9984359576502771

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