A validation study of a new classification algorithm to identify rheumatoid arthritis using administrative health databases: Case-control and cohort diagnostic accuracy studies. Results from the RECord linkage on Rheumatic Diseases study of the Italian Society for Rheumatology

BMJ Open

Volumn 5, Issue 1, 2015, Pages

  Carrara, Greta ^a   Scirè, Carlo A ^a   Zambon, Antonella ^b   Cimmino, Marco A ^c   Cerra, Carlo ^d   Caprioli, Marta ^e   Cagnotto, Giovanni ^f   Nicotra, Federica ^b   Arfè, Andrea ^b   Migliazza, Simona ^d   Corrao, Giovanni ^b   Minisola, Giovanni ^g   Montecucco, Carlomaurizio ^f  

^a Epidemiology Unit   (Italy)

^b UNIVERSITY OF MILANO BICOCCA   (Italy)

^c UNIVERSITY OF GENOA   (Italy)

^d Local Health Authority   (Italy)

^e Department of Surgery   (Italy)

^f FONDAZIONE IRCCS POLICLINICO SAN MATTEO   (Italy)

^g SAN CAMILLO FORLANINI HOSPITAL   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

ABATACEPT; DISEASE MODIFYING ANTIRHEUMATIC DRUG; GLUCOCORTICOID; LEFLUNOMIDE; METHOTREXATE; PRESCRIPTION DRUG; TOCILIZUMAB;

ADMINISTRATIVE HEALTH DATABASE; ARTICLE; CERTIFICATION; CLASSIFICATION ALGORITHM; COHORT ANALYSIS; CONTROLLED STUDY; CORTICOSTEROID THERAPY; DATA BASE; DIAGNOSTIC ACCURACY; DIAGNOSTIC ERROR; DIAGNOSTIC TEST ACCURACY STUDY; FEMALE; HOSPITAL BASED CASE CONTROL STUDY; HUMAN; MAJOR CLINICAL STUDY; MALE; MEDICAL SPECIALIST; PREDICTIVE VALUE; PRESCRIPTION; PREVALENCE; RANDOMIZED CONTROLLED TRIAL (TOPIC); RHEUMATOID ARTHRITIS; RHEUMATOLOGY; SENSITIVITY AND SPECIFICITY; VALIDATION STUDY; ADULT; AGED; ALGORITHM; ARTHRITIS, RHEUMATOID; CASE CONTROL STUDY; ELECTRONIC MEDICAL RECORD; FACTUAL DATABASE; ITALY; MEDICAL RECORD; MIDDLE AGED; STATISTICS AND NUMERICAL DATA; VERY ELDERLY;

ADULT; AGED; AGED, 80 AND OVER; ALGORITHMS; ARTHRITIS, RHEUMATOID; CASE-CONTROL STUDIES; COHORT STUDIES; DATABASES, FACTUAL; FEMALE; HUMANS; ITALY; MALE; MEDICAL RECORD LINKAGE; MEDICAL RECORDS SYSTEMS, COMPUTERIZED; MIDDLE AGED; PREVALENCE; RHEUMATOLOGY; SENSITIVITY AND SPECIFICITY;

EID: 84921897316     PISSN: None     EISSN: 20446055     Source Type: Journal    
DOI: 10.1136/bmjopen-2014-006029     Document Type: Article

References (32)

1. Woolf AD, Pfleger B. Burden of major musculoskeletal conditions. Bull World Health Organ 2003;81:646-56.
2. Woolf AD, Vos T, March L. How to measure the impact of musculoskeletal conditions. Best Pract Res Clin Rheumatol 2010;24:723-32.
3. Kim SY, Solomon DH. Use of administrative claims data for comparative effectiveness research of rheumatoid arthritis treatments. Arthritis Res Ther 2011;13:129.
4. Corrao G, Cesana G, Merlino L. Pharmacoepidemiological research and the linking of electronic healthcare databases available in the Italian region of Lombardy. Bio Med Stat Clin Epidemiol 2008;2:117-25.
5. Scotti L, Arfè A, Zambon A, et al. Cost-effectiveness of enhancing adherence with oral bisphosphonates treatment in osteoporotic women: an empirical approach based on healthcare utilisation databases. BMJ Open 2014;4:e003758.
6. Gabriel SE. The sensitivity and specificity of computerized databases for the diagnosis of rheumatoid arthritis. Arthritis Rheum 1994;37:821-3.
7. Katz JN, Barrett J, Liang MH, et al. Sensitivity and positive predictive value of Medicare Part B physician claims for rheumatologic diagnoses and procedures. Arthritis Rheum 1997;40:1594-600.
8. MacLean CH, Park GS, Traina SB, et al. Positive predictive value (PPV) of an administrative data-based algorithm for the identification of patients with rheumatoid arthritis (RA). Arthritis Rheum 2001;44:1-520.
9. Losina E, Barrett J, Baron JA, et al. Accuracy of Medicare claims data for rheumatologic diagnoses in total hip replacement recipients. J Clin Epidemiol 2003;56:515-19.
10. Singh JA, Holmgren AR, Noorbaloochi S. Accuracy of Veterans Administration databases for a diagnosis of rheumatoid arthritis. Arthritis Rheum 2004;51:952-7.
11. Pedersen M, Klarlund M, Jacobsen S, et al. Validity of rheumatoid arthritis diagnoses in the Danish National Patient Registry. Eur J Epidemiol 2004;19:1097-103.
12. Thomas SL, Edwards CJ, Smeeth L, et al. How accurate are diagnoses for rheumatoid arthritis and juvenile idiopathic arthritis in the general practice research database? Arthritis Rheum 2008;59:1314-21.
13. Kim SY, Servi A, Polinski JM, et al. Validation of rheumatoid arthritis diagnoses in health care utilization data. Arthritis Res Ther 2011;13:R32.
14. Widdifield J, Bernatsky S, Paterson JM, et al. Accuracy of Canadian health administrative databases in identifying patients with rheumatoid arthritis: a validation study using the medical records of rheumatologists. Arthritis Care Res (Hoboken) 2013;65:1582-91.
15. Widdifield J, Labrecque J, Lix L, et al. Systematic review and critical appraisal of validation studies to identify rheumatic diseases in health administrative databases. Arthritis Care Res 2013;65:1490-503.
16. Chung CP, Rohan P, Krishnaswami S, et al. A systematic review of validated methods for identifying patients with rheumatoid arthritis using administrative or claims data. Vaccine 2013;31(Supplement 10):K41-61.
17. Benchimol EI, Manuel DG, To T, et al. Development and use of reporting guidelines for assessing the quality of validation studies of health administrative data. J Clin Epidemiol 2011;64:821-9.
18. Bossuyt PM, Reitsma JB, Bruns DE, et al. The STARD Statement for Reporting Studies of Diagnostic Accuracy: explanation and elaboration. Ann Intern Med 2003;138:W1-12.
19. Biesheuvel CJ, Vergouwe Y, Oudega R, et al. Advantages of the nested case-control design in diagnostic research. BMC Med Res Methodol 2008;8:48.
20. Steinberg DM, Fine J, Chappell R. Sample size for positive and negative predictive value in diagnostic research using case-control designs. Biostatistics 2009;10:94-105.
21. Steyerberg EW. Clinical prediction models - A practical approach to development, validation, and updating. New York: Springer, 2009.
22. MacGregor AJ, Bamber S, Silman AJ. A comparison of the performance of different methods of disease classification for rheumatoid arthritis. Results of an analysis from a nationwide twin study. J Rheumatol 1994;21:1420-6.
23. Ng B, Aslam F, Petersen NJ, et al. Identification of rheumatoid arthritis patients using an administrative database: a Veterans Affairs study. Arthritis Care Res (Hoboken) 2012;64:1490-6.
24. Rogan WJ, Gladen B. Estimating prevalence from the results of a screening test. Am J Epidemiol 1978;107:71-6.
25. Joseph L, Gyorkos TW, Coupal L. Bayesian estimation of disease prevalence and the parameters of diagnostic tests in the absence of a gold standard. Am J Epidemiol 1995;141:263-72.
26. Cimmino MA, Parisi M, Moggiana G, et al. Prevalence of rheumatoid arthritis in Italy: the Chiavari Study. Ann Rheum Dis 1998;57:315-18.
27. Salaffi F, De AR, Grassi W. Prevalence of musculoskeletal conditions in an Italian population sample: results of a regional community-based study. I. The MAPPING study. Clin Exp Rheumatol 2005;23:819-28.
28. Marotto D, Nieddu ME, Cossu A, et al. [Prevalence of rheumatoid arthritis in North Sardinia: the Tempio Pausania's study]. Reumatismo 2005;57:273-6.
29. Della Rossa A, Neri R, Talarico R, et al. Diagnosis and referral of rheumatoid arthritis by primary care physician: results of a pilot study on the city of Pisa, Italy. Clin Rheumatol 2010;29:71-81.
30. Lunn DJ, Thomas A, Best N, et al. WinBUGS - a Bayesian modelling framework: concepts, structure, and extensibility. Stat Comput 2000;10:325-37.
31. Gladen B, Rogan WJ. Misclassification and the design of environmental studies. Am J Epidemiol 1979;109:607-16.
32. Sakellariou G, Scire CA, Zambon A, et al. Performance of the 2010 classification criteria for rheumatoid arthritis: a systematic literature review and a meta-analysis. PLoS ONE 2013;8:e56528.