How accurate are diagnoses for rheumatoid arthritis and juvenile idiopathic arthritis in the general practice research database?
How accurate are diagnoses for rheumatoid arthritis and juvenile idiopathic arthritis in the general practice research database?
Objective. To identify characteristics that predict a valid rheumatoid arthritis (RA) or juvenile idiopathic arthritis (JIA) diagnosis among RA- and JIA-coded individuals in the General Practice Research Database (GPRD), and to assess limitations of this type of diagnostic validation. Methods. Four RA and 2 JIA diagnostic groups were created with differing strengths of evidence of RA/JIA (Group 1 = strongest evidence), based oil RA/JIA medical codes. Individuals were sampled from each group and clinical and 0 prescription data were extracted from anonymized hospital/practice correspondence and electronic records. American College of Rheumatology and International League of Associations for Rheumatology diagnostic criteria were, used to validate diagnoses. A data- derived diagnostic algorithm that maximized sensitivity and specificity was identified using logistic regression. Results. Among 223 RA-coded individuals. the diagnostic algorithm classified individuals as having RA if they had an appropriate GPRD disease-modifying antirheumatic drug prescription or 3 other GPRD characteristics: >1 RA code during followup, RA diagnostic Group 1 or 2, and no later alternative diagnostic code. This algorithm had >80% sensitivity and specificity when applied to a test data set. Among 101 JIA-coded individuals, the strongest predictor of a valid diagnosis was a Group 1 diagnostic code (>90% sensitivity and specificity). Conclusion. Validity of an RA diagnosis among RA-coded GPRD individuals appears high for patients with specific characteristics. The findings are important for both interpreting results Of Published GPRD studies and identifying RA/JIA patients for future GPRD-based research. However, several limitations were identified, and further debate is needed on how best to validate chronic disease diagnoses in the GPRD
diagnosis, validity, chronic disease, risk, sensitivity and specificity, fracture, england, rheumatoid arthritis, classification, united-kingdom, methods, disease
1314-1321
Thomas, S. L.
2da6f265-5207-438e-80d3-70b8bb393d75
Edwards, C. J.
97e65c42-4286-4ce4-b354-010169b72143
Smeeth, L.
9961ba34-78b0-4419-8210-3e4bafae9b14
Cooper, C.
e05f5612-b493-4273-9b71-9e0ce32bdad6
Hall, A. J.
7f4dacde-d823-41a6-825b-d61289d5f3c9
2008
Thomas, S. L.
2da6f265-5207-438e-80d3-70b8bb393d75
Edwards, C. J.
97e65c42-4286-4ce4-b354-010169b72143
Smeeth, L.
9961ba34-78b0-4419-8210-3e4bafae9b14
Cooper, C.
e05f5612-b493-4273-9b71-9e0ce32bdad6
Hall, A. J.
7f4dacde-d823-41a6-825b-d61289d5f3c9
Thomas, S. L., Edwards, C. J., Smeeth, L., Cooper, C. and Hall, A. J.
(2008)
How accurate are diagnoses for rheumatoid arthritis and juvenile idiopathic arthritis in the general practice research database?
Arthritis and Rheumatism, 59 (9), .
Abstract
Objective. To identify characteristics that predict a valid rheumatoid arthritis (RA) or juvenile idiopathic arthritis (JIA) diagnosis among RA- and JIA-coded individuals in the General Practice Research Database (GPRD), and to assess limitations of this type of diagnostic validation. Methods. Four RA and 2 JIA diagnostic groups were created with differing strengths of evidence of RA/JIA (Group 1 = strongest evidence), based oil RA/JIA medical codes. Individuals were sampled from each group and clinical and 0 prescription data were extracted from anonymized hospital/practice correspondence and electronic records. American College of Rheumatology and International League of Associations for Rheumatology diagnostic criteria were, used to validate diagnoses. A data- derived diagnostic algorithm that maximized sensitivity and specificity was identified using logistic regression. Results. Among 223 RA-coded individuals. the diagnostic algorithm classified individuals as having RA if they had an appropriate GPRD disease-modifying antirheumatic drug prescription or 3 other GPRD characteristics: >1 RA code during followup, RA diagnostic Group 1 or 2, and no later alternative diagnostic code. This algorithm had >80% sensitivity and specificity when applied to a test data set. Among 101 JIA-coded individuals, the strongest predictor of a valid diagnosis was a Group 1 diagnostic code (>90% sensitivity and specificity). Conclusion. Validity of an RA diagnosis among RA-coded GPRD individuals appears high for patients with specific characteristics. The findings are important for both interpreting results Of Published GPRD studies and identifying RA/JIA patients for future GPRD-based research. However, several limitations were identified, and further debate is needed on how best to validate chronic disease diagnoses in the GPRD
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Published date: 2008
Keywords:
diagnosis, validity, chronic disease, risk, sensitivity and specificity, fracture, england, rheumatoid arthritis, classification, united-kingdom, methods, disease
Organisations:
Infection Inflammation & Immunity, Dev Origins of Health & Disease
Identifiers
Local EPrints ID: 70555
URI: http://eprints.soton.ac.uk/id/eprint/70555
ISSN: 0004-3591
PURE UUID: 52793f81-9d70-48aa-890b-131fa282799c
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Date deposited: 10 Feb 2010
Last modified: 18 Mar 2024 02:44
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Contributors
Author:
S. L. Thomas
Author:
C. J. Edwards
Author:
L. Smeeth
Author:
A. J. Hall
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