Diagnostic accuracy of administrative data algorithms in the diagnosis of osteoarthritis: a systematic review

• Published in: BMC medical informatics and decision making Vol. 16; no. 1; p. 82
• Main Authors: Shrestha, Swastina, Dave, Amish J., Losina, Elena, Katz, Jeffrey N.
• Format: Journal Article
• Language: English
• Published: London BioMed Central 07.07.2016; BioMed Central Ltd; Springer Nature B.V
• Subjects: Algorithms; Care and treatment; Clinical decision-making; Diagnosis; Health Informatics; Health maintenance organizations; HMOs; Humans; Information Systems and Communication Service; knowledge support systems; Management of Computing and Information Systems; Medicine; Medicine & Public Health; Osteoarthritis; Osteoarthritis - diagnosis; Practice Guidelines as Topic - standards; Primary care; Research Article; Studies; theory; United States; Administrative data; Systematic review; Osteoarthritis; Diagnostic accuracy
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/s12911-016-0319-y

Background Administrative health care data are frequently used to study disease burden and treatment outcomes in many conditions including osteoarthritis (OA). OA is a chronic condition with significant disease burden affecting over 27 million adults in the US. There are few studies examining the performance of administrative data algorithms to diagnose OA. The purpose of this study is to perform a systematic review of administrative data algorithms for OA diagnosis; and, to evaluate the diagnostic characteristics of algorithms based on restrictiveness and reference standards. Methods Two reviewers independently screened English-language articles published in Medline, Embase, PubMed, and Cochrane databases that used administrative data to identify OA cases. Each algorithm was classified as restrictive or less restrictive based on number and type of administrative codes required to satisfy the case definition. We recorded sensitivity and specificity of algorithms and calculated positive likelihood ratio (LR+) and positive predictive value (PPV) based on assumed OA prevalence of 0.1, 0.25, and 0.50. Results The search identified 7 studies that used 13 algorithms. Of these 13 algorithms, 5 were classified as restrictive and 8 as less restrictive. Restrictive algorithms had lower median sensitivity and higher median specificity compared to less restrictive algorithms when reference standards were self-report and American college of Rheumatology (ACR) criteria. The algorithms compared to reference standard of physician diagnosis had higher sensitivity and specificity than those compared to self-reported diagnosis or ACR criteria. Conclusions Restrictive algorithms are more specific for OA diagnosis and can be used to identify cases when false positives have higher costs e.g. interventional studies. Less restrictive algorithms are more sensitive and suited for studies that attempt to identify all cases e.g. screening programs.