Development and external validation of a clinical prediction model to aid coeliac disease diagnosis in primary care: an observational study

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Abstract

Background
Coeliac disease (CD) affects approximately 1% of the population, although only a fraction of patients are diagnosed. Our objective was to develop diagnostic prediction models to help decide who should be offered testing for CD in primary care.

Methods
Logistic regression models were developed in Clinical Practice Research Datalink (CPRD) GOLD (between Sep 9, 1987 and Apr 4, 2021, n=107,075) and externally validated in CPRD Aurum (between Jan 1, 1995 and Jan 15, 2021, n=227,915), two UK primary care databases, using (and controlling for) 1:4 nested case-control designs. Candidate predictors included symptoms and chronic conditions identified in current guidelines and using a systematic review of the literature. We used elastic-net regression to further refine the models.

Findings
The prediction model included 24, 24, and 21 predictors for children, women, and men, respectively. For children, the strongest predictors were type 1 diabetes, Turner syndrome, IgA deficiency, or first-degree relatives with CD. For women and men, these were anaemia and first-degree relatives. In the development dataset, the models showed good discrimination with a c-statistic of 0·84 (95% CI 0·83–0·84) in children, 0·77 (0·77–0·78) in women, and 0·81 (0·81–0·82) in men. External validation discrimination was lower, potentially because ‘first-degree relative’ was not recorded in the dataset used for validation. Model calibration was poor, tending to overestimate CD risk in all three groups in both datasets.

Interpretation
These prediction models could help identify individuals with an increased risk of CD in relatively low prevalence populations such as primary care. Offering a serological test to these patients could increase case finding for CD. However, this involves offering tests to more people than is currently done. Further work is needed in prospective cohorts to refine and confirm the models and assess clinical and cost effectiveness.

Funding
National Institute for Health Research Health Technology Assessment Programme (grant number NIHR129020).

Keywords
Coeliac disease, prediction model, clinical prediction rule, CPRD.
Original languageEnglish
Article number101376
JournalEClinicalMedicine
Volume46
DOIs
Publication statusPublished - 7 Apr 2022

Bibliographical note

Funding Information:
All authors report funding from the National Institute for Health Research (NIHR) Health Technology Assessment Programme grant (NIHR129020). This publication presents independent research funded by the NIHR. The views expressed in this article are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. Sue Mallett receives funding from the NIHR UCL/UCLH Biomedical Research Centre.

Funding Information:
This study will be published as a chapter in the Health Technology Assessment journal series as part of a larger project on improving case finding for CD. We thank Jo Stubbs and Debbie Lane for their feedback from a patient perspective at the study design stage. We would also like to thank James McKernon and Tim Jones for extracting the CPRD data and performing case-control matching. This research was also supported by the National Institute for Health Research (NIHR) Applied Research Collaboration West (NIHR ARC West).

Funding Information:
National Institute for Health Research Health Technology Assessment Programme (grant number NIHR129020).

Funding Information:
This study will be published as a chapter in the Health Technology Assessment journal series as part of a larger project on improving case finding for CD. We thank Jo Stubbs and Debbie Lane for their feedback from a patient perspective at the study design stage. We would also like to thank James McKernon and Tim Jones for extracting the CPRD data and performing case-control matching. This research was also supported by the National Institute for Health Research (NIHR) Applied Research Collaboration West (NIHR ARC West). National Institute for Health Research Health Technology Assessment Programme (grant number NIHR129020). PW conceptualised the project and obtained funding together with ME, HE, PG, AH, HJ, DL, SM, GR, JS, and JW. ME drafted and all authors commented on the ISAC protocol. ME, ROD, JW, AH, and HE developed medical code lists. ME, PW, HJ and SM designed the statistical analysis plan. HE, PG, AH, GR, and JW provided clinical perspectives and context. ME performed the analysis supervised by PW, SM, and HJ. ME and JJ verified the underlying data. ME drafted the initial manuscript. All authors reviewed the manuscript, had access to the aggregated data in the study, and accept responsibility to submit for publication. The statistical analysis plan has been published online (osf.io/q5gyc/). The code list for the predictors developed for the analysis are available upon request to the corresponding author. We used anonymised data on individual patients provided by CPRD. Only the authors have had access to the data during the study in accordance with the relevant licence agreements. However, the relevant data can be obtained directly from CPRD (https://www.cprd.com/).

Publisher Copyright:
© 2022 The Authors

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