Orlando Buendia
Is it possible to implement a rare disease case-finding tool in primary care? A UK-based pilot study
Buendia, Orlando; Shankar, Sneha; Mahon, Hadley; Toal, Connor; Menzies, Lara; Ravichandran, Pradeep; Roper, Jane; Takhar, Jag; Benfredj, Rudy; Evans, Will
Authors
Sneha Shankar
Hadley Mahon
Connor Toal
Lara Menzies
Pradeep Ravichandran
Jane Roper
Jag Takhar
Rudy Benfredj
Will Evans
Abstract
Introduction: This study implemented MendelScan, a primary care rare disease case-finding tool, into a UK National Health Service population. Rare disease diagnosis is challenging due to disease complexity and low physician awareness. The 2021 UK Rare Diseases Framework highlights as a key priority the need for faster diagnosis to improve clinical outcomes. Methods and results: A UK primary care locality with 68,705 patients was examined. MendelScan encodes diagnostic/screening criteria for multiple rare diseases, mapping clinical terms to appropriate SNOMED CT codes (UK primary care standardised clinical terminology) to create digital algorithms. These algorithms were applied to a pseudo-anonymised structured data extract of the electronic health records (EHR) in this locality to "flag" at-risk patients who may require further evaluation. All flagged patients then underwent internal clinical review (a doctor reviewing each EHR flagged by the algorithm, removing all cases with a clear diagnosis/diagnoses that explains the clinical features that led to the patient being flagged); for those that passed this review, a report was returned to their GP. 55 of 76 disease criteria flagged at least one patient. 227 (0.33%) of the total 68,705 of EHR were flagged; 18 EHR were already diagnosed with the disease (the highlighted EHR had a diagnostic code for the same RD it was screened for, e.g. Behcet’s disease algorithm identifying an EHR with a SNOMED CT code Behcet's disease). 75/227 (33%) EHR passed our internal review. Thirty-six reports were returned to the GP. Feedback was available for 28/36 of the reports sent. GP categorised nine reports as "Reasonable possible diagnosis" (advance for investigation), six reports as "diagnosis has already been excluded", ten reports as "patient has a clear alternative aetiology", and three reports as "Other" (patient left study locality, unable to re-identify accurately). All the 9 cases considered as "reasonable possible diagnosis" had further evaluation. Conclusions: This pilot demonstrates that implementing such a tool is feasible at a population level. The case-finding tool identified credible cases which were subsequently referred for further investigation. Future work includes performance-based validation studies of diagnostic algorithms and the scalability of the tool.
Journal Article Type | Article |
---|---|
Acceptance Date | Feb 6, 2022 |
Online Publication Date | Feb 16, 2022 |
Publication Date | Feb 16, 2022 |
Deposit Date | Oct 1, 2023 |
Publicly Available Date | Nov 27, 2023 |
Journal | Orphanet Journal of Rare Diseases |
Electronic ISSN | 1750-1172 |
Publisher | Springer Verlag |
Peer Reviewed | Peer Reviewed |
Volume | 17 |
Article Number | 54 |
DOI | https://doi.org/10.1186/s13023-022-02216-w |
Public URL | https://nottingham-repository.worktribe.com/output/20281387 |
Publisher URL | https://ojrd.biomedcentral.com/articles/10.1186/s13023-022-02216-w |
Files
Is it possible to implement a rare disease case-finding tool
(2 Mb)
PDF
Publisher Licence URL
https://creativecommons.org/licenses/by/4.0/
Downloadable Citations
About Repository@Nottingham
Administrator e-mail: discovery-access-systems@nottingham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search