Article ; Online: Developing electronic health record algorithms that accurately identify patients with juvenile idiopathic arthritis.
Seminars in arthritis and rheumatism
2023 Volume 59, Page(s) 152167
Abstract: Background: The objective of this study was to develop an algorithm that accurately identifies juvenile idiopathic arthritis (JIA) patients in the electronic health record (EHR).: Methods: Algorithms were developed in a de-identified EHR by searching ...
Abstract | Background: The objective of this study was to develop an algorithm that accurately identifies juvenile idiopathic arthritis (JIA) patients in the electronic health record (EHR). Methods: Algorithms were developed in a de-identified EHR by searching for a priori JIA ICD-9 (International Classification of Diseases, Ninth Revision) and ICD-10-CM (International Classification of Diseases, Tenth Revision, Clinical Modification) codes and JIA-related keywords. Exclusion criteria were selected to remove other autoimmune diseases. A training set of 200 patients was randomly selected from patients containing ≥1 occurrence of a JIA ICD-9 or ICD-10-CM code. Case status was determined by a rheumatology clinic note documenting a JIA diagnosis before age 20. For each algorithm, positive predictive value (PPV), sensitivity, and F-measure were determined using the training set. Results: We developed 103 algorithms using combinations of ICD codes, keywords, and exclusion criteria. The algorithm requiring 4 or more counts of JIA ICD-9 or ICD-10-CM codes, keywords "enthesitis" and "uveitis", and exclusion of ICD-9 or ICD-10-CM codes for systemic lupus erythematosus, dermatomyositis, polymyositis, and dermatopolymyositis had the highest PPV of 97% in the training set with an F-measure of 87%. There were 1,131 JIA cases returned by this algorithm. We validated the highest performing algorithm in a separate cohort from the training set with a PPV of 92% and an F-measure of 75%. Conclusion: We developed and validated JIA EHR algorithms with ICD-9 and ICD-10-CM codes to accurately identify a JIA cohort. Three algorithms achieved PPVs of 97%, each with different algorithm criteria, allowing for users to select an algorithm to best fit their research needs. |
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MeSH term(s) | Humans ; Young Adult ; Adult ; Arthritis, Juvenile ; Electronic Health Records ; Predictive Value of Tests ; Algorithms ; Lupus Erythematosus, Systemic/diagnosis |
Language | English |
Publishing date | 2023-01-18 |
Publishing country | United States |
Document type | Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't |
ZDB-ID | 120247-9 |
ISSN | 1532-866X ; 0049-0172 |
ISSN (online) | 1532-866X |
ISSN | 0049-0172 |
DOI | 10.1016/j.semarthrit.2023.152167 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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