William Evans
Opportunities and challenges for identifying undiagnosed Rare Disease patients through analysis of primary care records: long QT syndrome as a test case
Evans, William; Akyea, Ralph K.; Simms, Alex; Kai, Joe; Qureshi, Nadeem
Authors
Dr RALPH AKYEA RALPH.AKYEA1@NOTTINGHAM.AC.UK
Senior Research Fellow
Alex Simms
Professor JOE KAI joe.kai@nottingham.ac.uk
Professor of Primary Care
Professor NADEEM QURESHI nadeem.qureshi@nottingham.ac.uk
Clinical Professor
Abstract
Background
Patients with rare genetic diseases frequently experience significant diagnostic delays. Routinely collected data in the electronic health record (EHR) may be used to help identify patients at risk of undiagnosed conditions. Long QT syndrome (LQTS) is a rare inherited cardiac condition associated with significant morbidity and premature mortality. In this study, we examine LQTS as an exemplar disease to assess if clinical features recorded in the primary care EHR can be used to develop and validate a predictive model to aid earlier detection.
Methods
1495 patients with an LQTS diagnostic code and 7475 propensity-score matched controls were identified from 10.5 million patients’ electronic primary care records in the UK’s Clinical Practice Research Datalink (CPRD). Associated clinical features recorded before diagnosis (with p < 0.05) were incorporated into a multivariable logistic regression model, the final model was determined by backwards regression and validated by bootstrapping to determine model optimism.
Results
The mean age at LQTS diagnosis was 58.4 (SD 19.41). 18 features were included in the final model. Discriminative accuracy, assessed by area under the curve (AUC), was 0.74, (95% CI 0.73, 0.75) (optimism 6%). Features occurring at significantly greater frequency before diagnosis included: epilepsy, palpitations, syncope, collapse, mitral valve disease and irritable bowel syndrome.
Conclusion
This study demonstrates the potential to develop primary care prediction models for rare conditions, like LQTS, in routine primary care records and highlights key considerations including disease suitability, finding an appropriate linked dataset, the need for accurate case ascertainment and utilising an approach to modelling suitable for rare events.
Citation
Evans, W., Akyea, R. K., Simms, A., Kai, J., & Qureshi, N. (2024). Opportunities and challenges for identifying undiagnosed Rare Disease patients through analysis of primary care records: long QT syndrome as a test case. Journal of Community Genetics, https://doi.org/10.1007/s12687-024-00742-7
Journal Article Type | Article |
---|---|
Acceptance Date | Oct 2, 2024 |
Online Publication Date | Oct 15, 2024 |
Publication Date | Oct 15, 2024 |
Deposit Date | Oct 21, 2024 |
Publicly Available Date | Oct 21, 2024 |
Journal | Journal of Community Genetics |
Print ISSN | 1868-310X |
Electronic ISSN | 1868-6001 |
Publisher | Springer Verlag |
Peer Reviewed | Peer Reviewed |
DOI | https://doi.org/10.1007/s12687-024-00742-7 |
Keywords | Prolonged QT interval; Long QT syndrome; Genetics; Clinical prediction; Rare disease; Primary care |
Public URL | https://nottingham-repository.worktribe.com/output/40705889 |
Publisher URL | https://link.springer.com/article/10.1007/s12687-024-00742-7 |
Additional Information | Received: 28 March 2024; Accepted: 2 October 2024; First Online: 15 October 2024; : ; : Financial: WE is a Consultant and former employee of Mendelian. Non-Financial: NQ was an advisor to the Genomics England Rare Disease Consortium on identifying rare diseases in primary care electronic health records. RKA, AS and JK have no relevant financial or non-financial interests to disclose. |
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s12687-024-00742-7
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Publisher Licence URL
https://creativecommons.org/licenses/by/4.0/
Copyright Statement
© The Author(s) 2024
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