Development and external validation of a clinical prediction model to aid coeliac disease diagnosis in primary care: An observational study
Summary: 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 model...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-04-01
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| Series: | EClinicalMedicine |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589537022001067 |
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| author | Martha M.C. Elwenspoek, PhD Rachel O'Donnell, MSc Joni Jackson, MSc Hazel Everitt, PhD Peter Gillett, MBChB Alastair D. Hay, FRCGP Hayley E. Jones, PhD Gerry Robins, MD Jessica C. Watson, PhD Sue Mallett, DPhil Penny Whiting, PhD |
| author_facet | Martha M.C. Elwenspoek, PhD Rachel O'Donnell, MSc Joni Jackson, MSc Hazel Everitt, PhD Peter Gillett, MBChB Alastair D. Hay, FRCGP Hayley E. Jones, PhD Gerry Robins, MD Jessica C. Watson, PhD Sue Mallett, DPhil Penny Whiting, PhD |
| author_sort | Martha M.C. Elwenspoek, PhD |
| collection | DOAJ |
| description | Summary: 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) |
| first_indexed | 2024-04-14T06:00:08Z |
| format | Article |
| id | doaj.art-a718830b907a4bb4885391bb542dc8cd |
| institution | Directory Open Access Journal |
| issn | 2589-5370 |
| language | English |
| last_indexed | 2024-04-14T06:00:08Z |
| publishDate | 2022-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | EClinicalMedicine |
| spelling | doaj.art-a718830b907a4bb4885391bb542dc8cd2022-12-22T02:08:48ZengElsevierEClinicalMedicine2589-53702022-04-0146101376Development and external validation of a clinical prediction model to aid coeliac disease diagnosis in primary care: An observational studyMartha M.C. Elwenspoek, PhD0Rachel O'Donnell, MSc1Joni Jackson, MSc2Hazel Everitt, PhD3Peter Gillett, MBChB4Alastair D. Hay, FRCGP5Hayley E. Jones, PhD6Gerry Robins, MD7Jessica C. Watson, PhD8Sue Mallett, DPhil9Penny Whiting, PhD10The National Institute for Health Research Applied Research Collaboration West (NIHR ARC West), University Hospitals Bristol NHS Foundation Trust, Bristol, BS1 2NT, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2PS, UK; Corresponding author. Martha M.C. Elwenspoek, 9th Floor, Whitefriars, Lewins Mead, Bristol, BS1 2NT. Tel: +44/0 117 3427689.The National Institute for Health Research Applied Research Collaboration West (NIHR ARC West), University Hospitals Bristol NHS Foundation Trust, Bristol, BS1 2NT, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2PS, UKThe National Institute for Health Research Applied Research Collaboration West (NIHR ARC West), University Hospitals Bristol NHS Foundation Trust, Bristol, BS1 2NT, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2PS, UKPrimary Care Research Centre, University of Southampton, Southampton SO16 5ST, UKPaediatric Gastroenterology, Hepatology and Nutrition Department, Royal Hospital for Sick Children, Edinburgh EH9 1LF, Scotland, UKPopulation Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2PS, UKPopulation Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2PS, UKDepartment of Gastroenterology, York Teaching Hospital NHS Foundation Trust, York, YO31 8HE, UKPopulation Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2PS, UKCentre for Medical Imaging, University College London, 2nd Floor, Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UKPopulation Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2PS, UKSummary: 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)http://www.sciencedirect.com/science/article/pii/S2589537022001067Coeliac diseasePrediction modelClinical prediction ruleCPRD |
| spellingShingle | Martha M.C. Elwenspoek, PhD Rachel O'Donnell, MSc Joni Jackson, MSc Hazel Everitt, PhD Peter Gillett, MBChB Alastair D. Hay, FRCGP Hayley E. Jones, PhD Gerry Robins, MD Jessica C. Watson, PhD Sue Mallett, DPhil Penny Whiting, PhD Development and external validation of a clinical prediction model to aid coeliac disease diagnosis in primary care: An observational study EClinicalMedicine Coeliac disease Prediction model Clinical prediction rule CPRD |
| title | Development and external validation of a clinical prediction model to aid coeliac disease diagnosis in primary care: An observational study |
| title_full | Development and external validation of a clinical prediction model to aid coeliac disease diagnosis in primary care: An observational study |
| title_fullStr | Development and external validation of a clinical prediction model to aid coeliac disease diagnosis in primary care: An observational study |
| title_full_unstemmed | Development and external validation of a clinical prediction model to aid coeliac disease diagnosis in primary care: An observational study |
| title_short | Development and external validation of a clinical prediction model to aid coeliac disease diagnosis in primary care: An observational study |
| title_sort | development and external validation of a clinical prediction model to aid coeliac disease diagnosis in primary care an observational study |
| topic | Coeliac disease Prediction model Clinical prediction rule CPRD |
| url | http://www.sciencedirect.com/science/article/pii/S2589537022001067 |
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