Early clinical features in Systemic Lupus Erythematosus: can they be used to achieve earlier diagnosis?: a risk prediction model
OBJECTIVES: 1) To compare the primary care consulting behaviour prior to diagnosis of people with Systemic Lupus Erythematosus (SLE) with controls, 2) to develop and validate a risk prediction model to aid earlier SLE diagnosis. METHODS: 1,739 incident SLE cases practice-matched to 6,956 control...
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| Format: | Article |
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Wiley
2017
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| Online Access: | https://eprints.nottingham.ac.uk/41662/ |
| _version_ | 1848796325799264256 |
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| author | Rees, Frances Doherty, Michael Lanyon, Peter Davenport, Graham Riley, Richard D. Zhang, Weiya Grainge, Matthew J. |
| author_facet | Rees, Frances Doherty, Michael Lanyon, Peter Davenport, Graham Riley, Richard D. Zhang, Weiya Grainge, Matthew J. |
| author_sort | Rees, Frances |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | OBJECTIVES: 1) To compare the primary care consulting behaviour prior to diagnosis of people with Systemic Lupus Erythematosus (SLE) with controls, 2) to develop and validate a risk prediction model to aid earlier SLE diagnosis.
METHODS: 1,739 incident SLE cases practice-matched to 6,956 controls from the UK Clinical Practice Research Datalink. Odds ratios were calculated for age, gender, consultation rates, selected presenting clinical features and previous diagnoses in the 5 years preceding diagnosis date using logistic regression. A risk prediction model was developed from pre-selected variables using backward stepwise logistic regression. Model discrimination and calibration were tested in an independent validation cohort of 1,831,747 patients.
RESULTS: People with SLE had a significantly higher consultation rate than controls (median 9.2 vs 3.8/year) which was in part attributable to clinical features that occur in SLE. The final risk prediction model included the variables age, gender, consultation rate, arthralgia or arthritis, rash, alopecia, sicca, Raynaud's, serositis and fatigue. The model discrimination and calibration in the validation sample was good (Receiver operator characteristic curve: 0.75, 95% CI 0.73-0.78). However, absolute risk predictions for SLE were typically less than 1% due to the rare nature of SLE.
CONCLUSIONS: People with SLE consult their GP more frequently and with clinical features attributable to SLE in the five years preceding diagnosis, suggesting that there are potential opportunities to reduce diagnostic delay in primary care. A risk prediction model was developed and validated which may be used to identify people at risk of SLE in future clinical practice. This article is protected by copyright. All rights reserved. |
| first_indexed | 2025-11-14T19:46:12Z |
| format | Article |
| id | nottingham-41662 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:46:12Z |
| publishDate | 2017 |
| publisher | Wiley |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-416622020-05-04T19:57:07Z https://eprints.nottingham.ac.uk/41662/ Early clinical features in Systemic Lupus Erythematosus: can they be used to achieve earlier diagnosis?: a risk prediction model Rees, Frances Doherty, Michael Lanyon, Peter Davenport, Graham Riley, Richard D. Zhang, Weiya Grainge, Matthew J. OBJECTIVES: 1) To compare the primary care consulting behaviour prior to diagnosis of people with Systemic Lupus Erythematosus (SLE) with controls, 2) to develop and validate a risk prediction model to aid earlier SLE diagnosis. METHODS: 1,739 incident SLE cases practice-matched to 6,956 controls from the UK Clinical Practice Research Datalink. Odds ratios were calculated for age, gender, consultation rates, selected presenting clinical features and previous diagnoses in the 5 years preceding diagnosis date using logistic regression. A risk prediction model was developed from pre-selected variables using backward stepwise logistic regression. Model discrimination and calibration were tested in an independent validation cohort of 1,831,747 patients. RESULTS: People with SLE had a significantly higher consultation rate than controls (median 9.2 vs 3.8/year) which was in part attributable to clinical features that occur in SLE. The final risk prediction model included the variables age, gender, consultation rate, arthralgia or arthritis, rash, alopecia, sicca, Raynaud's, serositis and fatigue. The model discrimination and calibration in the validation sample was good (Receiver operator characteristic curve: 0.75, 95% CI 0.73-0.78). However, absolute risk predictions for SLE were typically less than 1% due to the rare nature of SLE. CONCLUSIONS: People with SLE consult their GP more frequently and with clinical features attributable to SLE in the five years preceding diagnosis, suggesting that there are potential opportunities to reduce diagnostic delay in primary care. A risk prediction model was developed and validated which may be used to identify people at risk of SLE in future clinical practice. This article is protected by copyright. All rights reserved. Wiley 2017-06 Article PeerReviewed Rees, Frances, Doherty, Michael, Lanyon, Peter, Davenport, Graham, Riley, Richard D., Zhang, Weiya and Grainge, Matthew J. (2017) Early clinical features in Systemic Lupus Erythematosus: can they be used to achieve earlier diagnosis?: a risk prediction model. Arthritis Care & Research, 69 (6). pp. 833-841. ISSN 2151-4658 Clinical Practice Research Datalink; Systemic Lupus Erythematosus; early diagnosis; risk prediction http://onlinelibrary.wiley.com/doi/10.1002/acr.23021/abstract doi:10.1002/acr.23021 doi:10.1002/acr.23021 |
| spellingShingle | Clinical Practice Research Datalink; Systemic Lupus Erythematosus; early diagnosis; risk prediction Rees, Frances Doherty, Michael Lanyon, Peter Davenport, Graham Riley, Richard D. Zhang, Weiya Grainge, Matthew J. Early clinical features in Systemic Lupus Erythematosus: can they be used to achieve earlier diagnosis?: a risk prediction model |
| title | Early clinical features in Systemic Lupus Erythematosus: can they be used to achieve earlier diagnosis?: a risk prediction model |
| title_full | Early clinical features in Systemic Lupus Erythematosus: can they be used to achieve earlier diagnosis?: a risk prediction model |
| title_fullStr | Early clinical features in Systemic Lupus Erythematosus: can they be used to achieve earlier diagnosis?: a risk prediction model |
| title_full_unstemmed | Early clinical features in Systemic Lupus Erythematosus: can they be used to achieve earlier diagnosis?: a risk prediction model |
| title_short | Early clinical features in Systemic Lupus Erythematosus: can they be used to achieve earlier diagnosis?: a risk prediction model |
| title_sort | early clinical features in systemic lupus erythematosus: can they be used to achieve earlier diagnosis?: a risk prediction model |
| topic | Clinical Practice Research Datalink; Systemic Lupus Erythematosus; early diagnosis; risk prediction |
| url | https://eprints.nottingham.ac.uk/41662/ https://eprints.nottingham.ac.uk/41662/ https://eprints.nottingham.ac.uk/41662/ |