Objective: To conduct a systematic review of the literature on the validation of algorithms identifying infections in administrative data for future use in populations with rheumatic diseases.
Methods: Medline and EMBase were searched using the themes "administrative data" and "infection" between 1950 and October 2012. Inclusion criteria consisted of validation studies of administrative data identifying infections in adult populations. Article quality was assessed using a validated tool.
Results: A total of 5,941 articles were identified, 90 articles underwent detailed review, and 24 studies were included. The majority (17 of 24) examined bacterial infections and 9 examined opportunistic infections. Eighteen studies were from the US and all but 4 studies used International Classification of Diseases, Ninth Revision codes. Rheumatoid arthritis patients were studied in 6 of 24 articles. The studies on bacterial infections in general reported highly variable sensitivity and positive predictive value (PPV) for the diagnosis of infections using administrative data (sensitivity range 4.4-100%, PPV range 21.7-100%). Algorithms to identify opportunistic infections similarly had a highly variable sensitivity (range 20-100%) and PPV (range 1.3-100%). Thirteen studies compared the diagnostic accuracy of different algorithms, which revealed that strategies including a comprehensive algorithm using a greater number of diagnostic codes or codes in any position had the highest sensitivity for the diagnosis of infection. Algorithms that incorporated microbiologic or pharmacy data in combination with diagnostic codes had improved PPV for identification of tuberculosis.
Conclusion: Algorithms for identifying infections using administrative data should be selected based on the purpose of the study, with careful consideration as to whether a high sensitivity or PPV is required.
Copyright © 2013 by the American College of Rheumatology.