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Elwenspoek MM, Thom H, Sheppard AL, Keeney E, O'Donnell R, Jackson J, Roadevin C, Dawson S, Lane D, Stubbs J, Everitt H, Watson JC, Hay AD, Gillett P, Robins G, Jones HE, Mallett S, Whiting PF. Defining the optimum strategy for identifying adults and children with coeliac disease: systematic review and economic modelling. Health Technol Assess 2022; 26:1-310. [PMID: 36321689 PMCID: PMC9638887 DOI: 10.3310/zuce8371] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Coeliac disease is an autoimmune disorder triggered by ingesting gluten. It affects approximately 1% of the UK population, but only one in three people is thought to have a diagnosis. Untreated coeliac disease may lead to malnutrition, anaemia, osteoporosis and lymphoma. OBJECTIVES The objectives were to define at-risk groups and determine the cost-effectiveness of active case-finding strategies in primary care. DESIGN (1) Systematic review of the accuracy of potential diagnostic indicators for coeliac disease. (2) Routine data analysis to develop prediction models for identification of people who may benefit from testing for coeliac disease. (3) Systematic review of the accuracy of diagnostic tests for coeliac disease. (4) Systematic review of the accuracy of genetic tests for coeliac disease (literature search conducted in April 2021). (5) Online survey to identify diagnostic thresholds for testing, starting treatment and referral for biopsy. (6) Economic modelling to identify the cost-effectiveness of different active case-finding strategies, informed by the findings from previous objectives. DATA SOURCES For the first systematic review, the following databases were searched from 1997 to April 2021: MEDLINE® (National Library of Medicine, Bethesda, MD, USA), Embase® (Elsevier, Amsterdam, the Netherlands), Cochrane Library, Web of Science™ (Clarivate™, Philadelphia, PA, USA), the World Health Organization International Clinical Trials Registry Platform ( WHO ICTRP ) and the National Institutes of Health Clinical Trials database. For the second systematic review, the following databases were searched from January 1990 to August 2020: MEDLINE, Embase, Cochrane Library, Web of Science, Kleijnen Systematic Reviews ( KSR ) Evidence, WHO ICTRP and the National Institutes of Health Clinical Trials database. For prediction model development, Clinical Practice Research Datalink GOLD, Clinical Practice Research Datalink Aurum and a subcohort of the Avon Longitudinal Study of Parents and Children were used; for estimates for the economic models, Clinical Practice Research Datalink Aurum was used. REVIEW METHODS For review 1, cohort and case-control studies reporting on a diagnostic indicator in a population with and a population without coeliac disease were eligible. For review 2, diagnostic cohort studies including patients presenting with coeliac disease symptoms who were tested with serological tests for coeliac disease and underwent a duodenal biopsy as reference standard were eligible. In both reviews, risk of bias was assessed using the quality assessment of diagnostic accuracy studies 2 tool. Bivariate random-effects meta-analyses were fitted, in which binomial likelihoods for the numbers of true positives and true negatives were assumed. RESULTS People with dermatitis herpetiformis, a family history of coeliac disease, migraine, anaemia, type 1 diabetes, osteoporosis or chronic liver disease are 1.5-2 times more likely than the general population to have coeliac disease; individual gastrointestinal symptoms were not useful for identifying coeliac disease. For children, women and men, prediction models included 24, 24 and 21 indicators of coeliac disease, respectively. The models showed good discrimination between patients with and patients without coeliac disease, but performed less well when externally validated. Serological tests were found to have good diagnostic accuracy for coeliac disease. Immunoglobulin A tissue transglutaminase had the highest sensitivity and endomysial antibody the highest specificity. There was little improvement when tests were used in combination. Survey respondents (n = 472) wanted to be 66% certain of the diagnosis from a blood test before starting a gluten-free diet if symptomatic, and 90% certain if asymptomatic. Cost-effectiveness analyses found that, among adults, and using serological testing alone, immunoglobulin A tissue transglutaminase was most cost-effective at a 1% pre-test probability (equivalent to population screening). Strategies using immunoglobulin A endomysial antibody plus human leucocyte antigen or human leucocyte antigen plus immunoglobulin A tissue transglutaminase with any pre-test probability had similar cost-effectiveness results, which were also similar to the cost-effectiveness results of immunoglobulin A tissue transglutaminase at a 1% pre-test probability. The most practical alternative for implementation within the NHS is likely to be a combination of human leucocyte antigen and immunoglobulin A tissue transglutaminase testing among those with a pre-test probability above 1.5%. Among children, the most cost-effective strategy was a 10% pre-test probability with human leucocyte antigen plus immunoglobulin A tissue transglutaminase, but there was uncertainty around the most cost-effective pre-test probability. There was substantial uncertainty in economic model results, which means that there would be great value in conducting further research. LIMITATIONS The interpretation of meta-analyses was limited by the substantial heterogeneity between the included studies, and most included studies were judged to be at high risk of bias. The main limitations of the prediction models were that we were restricted to diagnostic indicators that were recorded by general practitioners and that, because coeliac disease is underdiagnosed, it is also under-reported in health-care data. The cost-effectiveness model is a simplification of coeliac disease and modelled an average cohort rather than individuals. Evidence was weak on the probability of routine coeliac disease diagnosis, the accuracy of serological and genetic tests and the utility of a gluten-free diet. CONCLUSIONS Population screening with immunoglobulin A tissue transglutaminase (1% pre-test probability) and of immunoglobulin A endomysial antibody followed by human leucocyte antigen testing or human leucocyte antigen testing followed by immunoglobulin A tissue transglutaminase with any pre-test probability appear to have similar cost-effectiveness results. As decisions to implement population screening cannot be made based on our economic analysis alone, and given the practical challenges of identifying patients with higher pre-test probabilities, we recommend that human leucocyte antigen combined with immunoglobulin A tissue transglutaminase testing should be considered for adults with at least a 1.5% pre-test probability of coeliac disease, equivalent to having at least one predictor. A more targeted strategy of 10% pre-test probability is recommended for children (e.g. children with anaemia). FUTURE WORK Future work should consider whether or not population-based screening for coeliac disease could meet the UK National Screening Committee criteria and whether or not it necessitates a long-term randomised controlled trial of screening strategies. Large prospective cohort studies in which all participants receive accurate tests for coeliac disease are needed. STUDY REGISTRATION This study is registered as PROSPERO CRD42019115506 and CRD42020170766. FUNDING This project was funded by the National Institute for Health and Care Research ( NIHR ) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 26, No. 44. See the NIHR Journals Library website for further project information.
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Affiliation(s)
- Martha Mc Elwenspoek
- National Institute for Health and Care Research Applied Research Collaboration West, University Hospitals Bristol NHS Foundation Trust, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Howard Thom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Athena L Sheppard
- National Institute for Health and Care Research Applied Research Collaboration West, University Hospitals Bristol NHS Foundation Trust, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Department of Health Sciences, University of Leicester, Leicester, UK
| | - Edna Keeney
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Rachel O'Donnell
- National Institute for Health and Care Research Applied Research Collaboration West, University Hospitals Bristol NHS Foundation Trust, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Joni Jackson
- National Institute for Health and Care Research Applied Research Collaboration West, University Hospitals Bristol NHS Foundation Trust, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Cristina Roadevin
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Sarah Dawson
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | | | | | - Hazel Everitt
- Primary Care Research Centre, Population Sciences and Medical Education, University of Southampton, Southampton, UK
| | - Jessica C Watson
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Alastair D Hay
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Peter Gillett
- Paediatric Gastroenterology, Hepatology and Nutrition Department, Royal Hospital for Sick Children, Edinburgh, UK
| | - Gerry Robins
- Department of Gastroenterology, York Teaching Hospital NHS Foundation Trust, York, UK
| | - Hayley E Jones
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Sue Mallett
- Centre for Medical Imaging, University College London, London, UK
| | - Penny F Whiting
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
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Shahramian I, Bazi A, Sargazi A. An Overview of Celiac Disease in Childhood Type 1 Diabetes. Int J Endocrinol Metab 2018; 16:e66801. [PMID: 30214462 PMCID: PMC6119207 DOI: 10.5812/ijem.66801] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2018] [Revised: 06/09/2018] [Accepted: 06/13/2018] [Indexed: 02/08/2023] Open
Abstract
CONTEXT Celiac disease (CD) is a common phenomenon in children with Type 1 diabetes (T1D). In the present review, we have discussed the pathogenesis, diagnostic biomarkers, risk factors, and prognosis of CD in the context of pediatric T1D. EVIDENCE ACQUISITION Literature published in Web of Science, PubMed, Scopus, Google Scholar, and Cochrane Library were scrutinized up to the end of 2017. The keywords of celiac disease, Type 1 diabetes, children, and pediatric were used in different combinations. RESULTS Immune cytotoxic reactions along with dampen immune regulatory functions contribute to CD in the context of pediatric T1D. Many children with simultaneous CD and T1D do not represent with the clinical signs of the enteropathy rendering a diagnostic challenge. The most common screening tests in these children are routine serological tests of CD, anti - endomysial, anti - transglutaminase, and anti - deamidated gliadin peptide antibodies. Typing for human leukocyte antigens of DQ - 2 and DQ - 8 may assist in the diagnosis of silent CD in children with T1D. The most significant shared non - HLA genetic loci of CD and T1D comprise CTLA - 4, TAGAP, IL - 18RAP, PTPN2, RGS1, SH2B3, CCR5. Interactions between these loci can be important in susceptibility to CD in T1D. Some new biomarkers have been suggested for diagnosis of CD including ischemia-modified albumin (IMA), soluble syndecan-1 (SSDC-1), regenerating gene Iα (REG-Iα), Neurotensin, and Zonulin, which can be useful for diagnosis and screening of CD in childhood T1D. CONCLUSIONS Overall, active seropositive CD seems to be of clinical importance in T1D with significant impacts on the quality of life and predisposition to diabetes associated complications. It is important to detect CD in the context of T1D to prevent potential risks contributing to morbidities and mortalities associated with either CD or T1D.
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Affiliation(s)
- Iraj Shahramian
- Pediatric Ward, Amir - Al - Momenin Hospital, Zabol University of Medical Sciences, Zabol, Iran
| | - Ali Bazi
- Clinical Research Development Unit, Amir - Al - Momenin Hospital, Zabol University of Medical Sciences, Zabol, Iran
- Corresponding author: Ali Bazi, Clinical Research Development Unit, Amir - Al - Momenin Hospital, Zabol University of Medical Sciences, Zabol, Iran. Tel/Fax: +98-5432232166, E-mail:
| | - Alireza Sargazi
- Student Research Committee, Zabol University of Medical Sciences, Zabol, Iran
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