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

• Published in: Open forum infectious diseases 2020-06, Vol.7 (6), p.ofaa201-ofaa201
• Main Authors: Kobayashi, Takaaki, Beck, Brice, Miller, Aaron, Polgreen, Philip, O’Shea, Amy M J, Ohl, Michael E
• Format: Article
• Language: English
• Subjects: Major
• ISSN: 2328-8957; 2328-8957
• DOI: 10.1093/ofid/ofaa201

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.