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

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

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).

CPRD, Clinical prediction rule, Coeliac disease, Prediction model
2589-5370
Elwenspoek, Martha M C
6fa131b1-b60f-4eb8-a6cb-f3dab87b845c
O'Donnell, Rachel
91c00d95-529a-43aa-b27e-b43cf98b47be
Jackson, Joni
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Everitt, Hazel
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Gillett, Peter
5057d58f-fbdc-48e2-bcd7-f085418adbf3
Hay, Alastair D
bfae9e44-ae9b-473c-923f-1dea50747023
Jones, Hayley E
22849f0d-6153-40f9-87a9-8a62f6d3b045
Robins, Gerry
e7370277-583f-48ab-a778-66dc8aabfd16
Watson, Jessica C
da587a34-93ab-49ae-ba53-2fd1d1b9a2ca
Mallett, Sue
a86967fb-42a1-474c-8095-b764769c26a4
Whiting, Penny
13470a2b-00dd-4f25-88c6-7607e641c184
Elwenspoek, Martha M C
6fa131b1-b60f-4eb8-a6cb-f3dab87b845c
O'Donnell, Rachel
91c00d95-529a-43aa-b27e-b43cf98b47be
Jackson, Joni
1f1810c9-e2d2-4584-81ea-1f446c29db95
Everitt, Hazel
80b9452f-9632-45a8-b017-ceeeee6971ef
Gillett, Peter
5057d58f-fbdc-48e2-bcd7-f085418adbf3
Hay, Alastair D
bfae9e44-ae9b-473c-923f-1dea50747023
Jones, Hayley E
22849f0d-6153-40f9-87a9-8a62f6d3b045
Robins, Gerry
e7370277-583f-48ab-a778-66dc8aabfd16
Watson, Jessica C
da587a34-93ab-49ae-ba53-2fd1d1b9a2ca
Mallett, Sue
a86967fb-42a1-474c-8095-b764769c26a4
Whiting, Penny
13470a2b-00dd-4f25-88c6-7607e641c184

Elwenspoek, Martha M C, O'Donnell, Rachel, Jackson, Joni, Everitt, Hazel, Gillett, Peter, Hay, Alastair D, Jones, Hayley E, Robins, Gerry, Watson, Jessica C, Mallett, Sue and Whiting, Penny (2022) Development and external validation of a clinical prediction model to aid coeliac disease diagnosis in primary care: An observational study. EClinicalMedicine, 46, [101376]. (doi:10.1016/j.eclinm.2022.101376).

Record type: Article

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).

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Accepted/In Press date: 21 March 2022
e-pub ahead of print date: 7 April 2022
Additional Information: Funding National Institute for Health Research Health Technology Assessment Programme (grant number NIHR129020) All authors report funding from the National Institute for Health Research (NIHR) Health Technology Assessment Programme grant (NIHR129020). This publica- tion presents independent research funded by the NIHR.
Keywords: CPRD, Clinical prediction rule, Coeliac disease, Prediction model

Identifiers

Local EPrints ID: 467360
URI: http://eprints.soton.ac.uk/id/eprint/467360
ISSN: 2589-5370
PURE UUID: 149185c2-b763-42c6-8167-0124e91848cf
ORCID for Hazel Everitt: ORCID iD orcid.org/0000-0001-7362-8403

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Date deposited: 07 Jul 2022 16:50
Last modified: 02 Sep 2022 01:36

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Contributors

Author: Martha M C Elwenspoek
Author: Rachel O'Donnell
Author: Joni Jackson
Author: Hazel Everitt ORCID iD
Author: Peter Gillett
Author: Alastair D Hay
Author: Hayley E Jones
Author: Gerry Robins
Author: Jessica C Watson
Author: Sue Mallett
Author: Penny Whiting

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