Validation of an Algorithm to Identify Infective Endocarditis in People Who Inject Drugs

Bibliographic Data

ID9101390
AuthorsLaura Ball (0009-0000-6214-3267, Western University, corresponding author), Laura J Ball (0000-0003-2465-3279, Schulich School of Medicine and Dentistry, Western University), Adeel Sherazi (Schulich School of Medicine and Dentistry, Western University, corresponding author), Dora Laczko (Schulich School of Medicine and Dentistry, Western University, corresponding author), Kaveri Gupta (Department of Medicine, Division of Infectious Diseases, Schulich School of Medicine & Dentistry, Western University), Sharon Koivu (Department of Family Medicine, Schulich School of Medicine & Dentistry, Western Centre for Public Health and Family Medicine, Western University), Matthew A Weir (0000-0003-3099-0124, Department of Medicine, Division of Nephrology), Tina Mele (0000-0002-6617-9790, Department of Surgery, Division of General Surgery, London Health Sciences Centre, University Hospital), Rommel Tirona (Department of Physiology and Pharmacology, University Hospital), Rommel G Tirona (0000-0001-5455-3960), John K McCormick (0000-0003-3742-7289, Department of Microbiology and Immunology, Western University), Michael Silverman (0000-0002-2904-597X, Schulich School of Medicine and Dentistry, Western University, corresponding author)
Year2018
Volume56
Issue10
Pagese70-e75
Publication date2018-10-01
Open AccessNo
TypeARTICLE
VenueMedical Care (JOURNAL)
Journal identifiersISSN: 0025-7079 • E-ISSN: 1537-1948
PublisherOvid Technologies (Wolters Kluwer Health) (PUBLISHER)
DOI10.1097/mlr.0000000000000838
PMID29200131
OpenAlexW2772989883
LanguageEN
Citations received3
References cited14

INTRODUCTION: Infective endocarditis is associated with high morbidity and mortality. Currently, there is concern that the incidence of infective endocarditis associated with people who inject drugs (PWID) is increasing. However, it is difficult to monitor population-wide trends in PWID-associated infective endocarditis, as there is no International Statistical Classification of Diseases, 10th Revision (ICD-10) code for injection drug use. To address this barrier, we sought to develop a validated algorithm using ICD-10 discharge diagnosis codes. MATERIALS AND METHODS: We constructed a cohort of patients whose hospital discharge diagnosis included infective endocarditis. We reviewed 100 patients with incident infective endocarditis from 2014 to 2016 for their infective endocarditis and injection drug use status. We calculated the operating characteristics for algorithms constructed using permutations of ICD-10 codes associated with injection drug use. We repeated this analysis in a cohort of 100 patients with incident infective endocarditis from 2009 to 2011 to examine the temporal stability of the operating characteristics of each algorithm. RESULTS: We found that a combination of hepatitis C virus, drug use, and mental/behavioral disorder codes yielded the highest sensitivity (93%) and positive predictive value (83%) of the algorithms analyzed. DISCUSSION: We have described the first algorithm, validated against chart review data, for identifying PWID-associated infective endocarditis cases using ICD-10 codes. The high sensitivity and positive predictive value indicate that this algorithm can be used for surveillance and research with confidence. CONCLUSIONS: This algorithm will enable researchers to examine epidemiological trends in PWID-associated infective endocarditis

Algorithm · Endocarditis · Infective endocarditis · Intensive care medicine · Clostridium difficile and Clostridium perfringens research · Computer Science · HIV, Drug Use, Sexual Risk · Infective Endocarditis Diagnosis and Management · Internal Medicine · Medicine

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