Premium
Systematic Review and Critical Appraisal of Validation Studies to Identify Rheumatic Diseases in Health Administrative Databases
Author(s) -
Widdifield Jessica,
Labrecque Jeremy,
Lix Lisa,
Paterson J. Michael,
Bernatsky Sasha,
Tu Karen,
Ivers Noah,
Bombardier Claire
Publication year - 2013
Publication title -
arthritis care and research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.032
H-Index - 163
eISSN - 2151-4658
pISSN - 2151-464X
DOI - 10.1002/acr.21993
Subject(s) - medicine , medical diagnosis , critical appraisal , medline , medical record , gold standard (test) , diagnosis code , population , database , data mining , pathology , alternative medicine , surgery , computer science , environmental health , political science , law
Objective To evaluate the quality of the methods and reporting of published studies that validate administrative database algorithms for rheumatic disease case ascertainment. Methods We systematically searched MEDLINE, Embase, and the reference lists of articles published from 1980 to 2011. We included studies that validated administrative data algorithms for rheumatic disease case ascertainment using medical record or patient‐reported diagnoses as the reference standard. Each study was evaluated using published standards for the reporting and quality assessment of diagnostic accuracy, which informed the development of a methodologic framework to help critically appraise and guide research in this area. Results Twenty‐three studies met the inclusion criteria. Administrative database algorithms to identify cases were most frequently validated against diagnoses in medical records (83%). Almost two‐thirds of the studies (61%) used diagnosis codes in administrative data to identify potential cases and then reviewed medical records to confirm the diagnoses. The remaining studies did the reverse, identifying patients using a reference standard and then testing algorithms to identify cases in administrative data. Many authors (61%) described the patient population, but few (26%) reported key measures of diagnostic accuracy (sensitivity, specificity, and positive and negative predictive values). Only one‐third of studies reported disease prevalence in the validation study sample. Conclusion The methods used in administrative data validation studies of rheumatic diseases are highly variable. Few studies reported key measures of diagnostic accuracy despite their importance for drawing conclusions about the validity of administrative database algorithms. We developed a methodologic framework and recommendations for validation study conduct and reporting.