Cohort identification of axial spondyloarthritis in a large healthcare dataset: current and future methods
Abstract Background Big data research is important for studying uncommon diseases in real-world settings. Most big data studies in axial spondyloarthritis (axSpA) have been limited to populations identified with billing codes for ankylosing spondylitis (AS). axSpA is a more inclusive concept, and re...
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| Format: | Article |
| Language: | English |
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BMC
2018-09-01
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| Series: | BMC Musculoskeletal Disorders |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12891-018-2211-7 |
| _version_ | 1818313279878987776 |
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| author | Jessica A. Walsh Shaobo Pei Gopi K. Penmetsa Jianwei Leng Grant W. Cannon Daniel O. Clegg Brian C. Sauer |
| author_facet | Jessica A. Walsh Shaobo Pei Gopi K. Penmetsa Jianwei Leng Grant W. Cannon Daniel O. Clegg Brian C. Sauer |
| author_sort | Jessica A. Walsh |
| collection | DOAJ |
| description | Abstract Background Big data research is important for studying uncommon diseases in real-world settings. Most big data studies in axial spondyloarthritis (axSpA) have been limited to populations identified with billing codes for ankylosing spondylitis (AS). axSpA is a more inclusive concept, and reliance on AS codes does not produce a comprehensive axSpA study population. The first objective was to describe our process for establishing an appropriate sample of patients with and without axSpA for developing accurate axSpA identification methods. The second objective was to determine the classification performance of AS billing codes against the chart-reviewed axSpA reference standard. Methods Veteran Health Affairs clinical and administrative data, between January 2005 and June 2015, were used to randomly select patients with clinical phenotypes that represented high, moderate, and low likelihoods of an axSpA diagnosis. With chart review, the sampled patients were classified as Yes axSpA, No axSpA or Uncertain axSpA, and these classification assignments were used as the reference standard for determining the positive predictive value (PPV) and sensitivity of AS ICD-9 codes for axSpA. Results Six hundred patients were classified as Yes axSpA (26.8%), No axSpA (68.3%), or Uncertain axSpA (4.8%). The PPV and sensitivity of an AS ICD-9 code for axSpA were 83.3% and 57.3%, respectively. Conclusions Standard methods of identifying axSpA patients in a large dataset lacked sensitivity. An appropriate sample of patients with and without axSpA was established and characterized for developing novel axSpA identification methods that are anticipated to enable previously impractical big data research. |
| first_indexed | 2024-12-13T08:31:14Z |
| format | Article |
| id | doaj.art-44415f8ef4824a949d15557a33c7195c |
| institution | Directory Open Access Journal |
| issn | 1471-2474 |
| language | English |
| last_indexed | 2024-12-13T08:31:14Z |
| publishDate | 2018-09-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Musculoskeletal Disorders |
| spelling | doaj.art-44415f8ef4824a949d15557a33c7195c2022-12-21T23:53:45ZengBMCBMC Musculoskeletal Disorders1471-24742018-09-011911710.1186/s12891-018-2211-7Cohort identification of axial spondyloarthritis in a large healthcare dataset: current and future methodsJessica A. Walsh0Shaobo Pei1Gopi K. Penmetsa2Jianwei Leng3Grant W. Cannon4Daniel O. Clegg5Brian C. Sauer6Division of Rheumatology School of MedicineGeorge E. Wahlen Veteran Affairs Medical CenterDivision of Rheumatology School of MedicineGeorge E. Wahlen Veteran Affairs Medical CenterGeorge E. Wahlen Veteran Affairs Medical CenterDivision of Rheumatology School of MedicineGeorge E. Wahlen Veteran Affairs Medical CenterAbstract Background Big data research is important for studying uncommon diseases in real-world settings. Most big data studies in axial spondyloarthritis (axSpA) have been limited to populations identified with billing codes for ankylosing spondylitis (AS). axSpA is a more inclusive concept, and reliance on AS codes does not produce a comprehensive axSpA study population. The first objective was to describe our process for establishing an appropriate sample of patients with and without axSpA for developing accurate axSpA identification methods. The second objective was to determine the classification performance of AS billing codes against the chart-reviewed axSpA reference standard. Methods Veteran Health Affairs clinical and administrative data, between January 2005 and June 2015, were used to randomly select patients with clinical phenotypes that represented high, moderate, and low likelihoods of an axSpA diagnosis. With chart review, the sampled patients were classified as Yes axSpA, No axSpA or Uncertain axSpA, and these classification assignments were used as the reference standard for determining the positive predictive value (PPV) and sensitivity of AS ICD-9 codes for axSpA. Results Six hundred patients were classified as Yes axSpA (26.8%), No axSpA (68.3%), or Uncertain axSpA (4.8%). The PPV and sensitivity of an AS ICD-9 code for axSpA were 83.3% and 57.3%, respectively. Conclusions Standard methods of identifying axSpA patients in a large dataset lacked sensitivity. An appropriate sample of patients with and without axSpA was established and characterized for developing novel axSpA identification methods that are anticipated to enable previously impractical big data research.http://link.springer.com/article/10.1186/s12891-018-2211-7SpondyloarthropathyAnkylosing spondylitisDatabasesHealth services research |
| spellingShingle | Jessica A. Walsh Shaobo Pei Gopi K. Penmetsa Jianwei Leng Grant W. Cannon Daniel O. Clegg Brian C. Sauer Cohort identification of axial spondyloarthritis in a large healthcare dataset: current and future methods BMC Musculoskeletal Disorders Spondyloarthropathy Ankylosing spondylitis Databases Health services research |
| title | Cohort identification of axial spondyloarthritis in a large healthcare dataset: current and future methods |
| title_full | Cohort identification of axial spondyloarthritis in a large healthcare dataset: current and future methods |
| title_fullStr | Cohort identification of axial spondyloarthritis in a large healthcare dataset: current and future methods |
| title_full_unstemmed | Cohort identification of axial spondyloarthritis in a large healthcare dataset: current and future methods |
| title_short | Cohort identification of axial spondyloarthritis in a large healthcare dataset: current and future methods |
| title_sort | cohort identification of axial spondyloarthritis in a large healthcare dataset current and future methods |
| topic | Spondyloarthropathy Ankylosing spondylitis Databases Health services research |
| url | http://link.springer.com/article/10.1186/s12891-018-2211-7 |
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