What clinical criteria predict Type 1 diabetes (as defined by absolute insulin deficiency)?
Our search approach will be systematic and exhaustive. We propose searching for relevant literature in a variety of ways so as to ensure sensitivity and depth of retrieval. We intend to search not only for peer-reviewed publications but also grey literature. Although we do not anticipate finding a great volume of grey literature (a point which has been confirmed in scoping) given the nature of the topic, we consider there may be work in clinical guidelines (e.g. RCGP) and know of at least one conference abstract on the subject, so therefore anticipate that there may be more. Our search strategy will include:
1. Electronic Databases
2. Websites - We propose searching web-sites as tools for gathering grey, or difficult to locate, literature. Given the clinical nature of our topic, it is unlikely these searches will yield much information.
3. Pearl growing, Snowballing and Hand-searching – In addition to searching electronic databases, we propose citation chasing and contacting the authors of included studies. These approaches have been shown to yield additional information. Forward citation chasing will be conducted in Web of Science (ISI) and bibliographies of included studies will be pursued by the review team to further enhance our ability to capture all relevant literature. We will hand-search the core journals identified by the number of includes in our search, most likely to be Diabetes Care and Diabetic Medicine as the key clinical diabetes journals.
Search Limits: Searches will be limited to the date range 1979-Current, which reflects the original classification scheme proposed by the National Diabetes Data Group. The searches will also be limited to human only populations. We will not be restricting the search by language.
Search Recording: The exported files from the literature searching will be uploaded and de-duplicated in Endnote X4 (Thompson Reuters). Where an export is not possible, for example from a resource without RIS functionality, the data will be exported to a word file and saved. All files will be copied and saved for record. The searches will be recorded using PRISMA guidelines.
Update Searches: Towards the end of the review time-line, we will repeat the search process to bring our review up-to-date.
(b) Articles published from 1979 onwards (when the original classification scheme was proposed).
(c) Any clinical predictors which can be measued and replicated.
(d) Any measurements of c-peptide to determine insulin deficiency.
(e) All age groups.
(f) All racial groups.
(g) All common forms of diabetes.
Known rare genetic, syndromic or secondary forms of diabetes.
Condition or domain being studied
Diabetes mellitus. Specifically clinical criteria to predict Type 1 diabetes.
Patients diagnosed with diabetes with evidence of insulin deficiency, as determined by negative C-peptide results (“Type 1 diabetes”). Type 2 diabetes will be the exclusion of Type 1 and other subtypes where the aetiology is known (e.g. secondary diabetes or monogenic diabetes). The focus of the literature search will be on patients with the more common forms of diabetes (i.e. known rare genetic, syndromic or secondary forms of diabetes will be excluded). All ages and racial groups will be considered.
Predictors: Preliminary scoping has indicated that the major predictors will be age at diagnosis and BMI, so the primary focus will be on assessing their diagnostic accuracy and the optimal thresholds of these for discriminating the two types of diabetes. Any clinical features that can be measured and replicated will be considered. Some criteria will be subject to clinician judgement (e.g. time to insulin treatment, which is confounded by the clinician considering their patient has Type 1 diabetes). A distinction will be made between those that can be used for classification at diagnosis and those that can be used after diagnosis (e.g. time to insulin treatment cannot be used at diagnosis).
Reference standard: For “Type 1 diabetes”, measurement of C-peptide to determine insulin deficiency. All measurements will be considered including different types of sample (urine or blood), different types of stimulus (fasting, random, glucose-stimulated, mixed meal-stimulated, post-glucagon stimulated), and timing after stimulation (peak or area-under-curve of numerous measurements, 90-minute, 2-hour)). The definition of insulin deficiency used will be recorded and evaluated.
(i) The clinical features assessed to predict C-peptide levels, (ii) The sensitivity and specificity of clinical features as measured against insulin deficiency as determined by C-peptide.
Data extraction, (selection and coding)
A standard data extraction form will be developed and applied to all included studies. This data extraction form will be piloted at the beginning of the research to ensure it is sufficient and captures all relevant information. The information extracted from each study will include: setting (e.g. country, year), general patient demographics (e.g. age range, gender, racial group, type of diabetes), methods (e.g. sensitivity/specificity, ROC curves), results (e.g. predictors of C-peptide, magnitude of effect, statistical significance, sensitivity and specificity of predictor) and conclusions.
Risk of bias (quality) assessment
The QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies) tool will be used to assess the quality of the diagnostic accuracy studies included in the systematic review. This tool will help identify potential biases in the primary studies so that the conclusions of the systematic review appropriately reflect the possible impact of such biases.
Strategy for data synthesis
It is anticipated that the data will be analysed at the aggregate level, using a quantitative synthesis. In particular, hierarachical summary ROC curve models will be used to determine the overall diagnostic value of clinical characteristics to predict type 1 diabetes while accounting for between-study heterogeneity. Where papers have reported the combined use of two clinical criteria, linked ROC plots will be used to display how the variables perform in combination. Between-study heterogeneity will be explored by including covariates in the hierarchical summary ROC curve models. Evidence of reporting and/or publication bias will be undertaken using funnel plots and a related test, if deemed appropriate.
Analysis of subgroups or subsets
As different clinical criteria may apply to different subgroups of individuals, age (e.g. children v adults) and racial group will form two subgroups for exploration during the analysis.
Results from the systematic review will inform a validation study. The results of both of these pieces of work will be presented to local, regional, national and international audiences via presentations (e.g. to the Diabetes UK Annual Professional Conference), reports (e.g. to the World Health Organisation committee for the report into classification and diagnosis of diabetes) and publications (e.g. submitted to internationally recognised peer-reviewed journals).
Contact details for further information
NIHR Exeter Clinical Research Facility
University of Exeter Medical School
Organisational affiliation of the review
NIHR Exeter Clinical Research Facility, University of Exeter. Peninsula Technology Assessment Group (PenTAG), University of Exeter
http://www.peninsulacrf.org/ and http://sites.pcmd.ac.uk/pentag/
Dr Beverley Shields, NIHR Exeter Clinical Research Facility, University of Exeter Medical School, University of Exeter Dr Jaime Peters, PenTAG, University of Exeter Medical School, University of Exeter Mr Chris Cooper, PenTAG, University of Exeter Medical School, University of Exeter Dr Roy Powell, Research Design Service (RDS) South West Dr Bea Knight, NIHR Exeter Clinical Research Facility, University of Exeter Medical School, University of Exeter Professor Chris Hyde, PenTAG, University of Exeter Medical School, University of Exeter Professor Andrew Hattersley, NIHR Exeter Clinical Research Facility, University of Exeter Medical School, University of Exeter
Anticipated or actual start date
01 September 2012
Anticipated completion date
31 August 2013
Research for Patient Benefit (project number PB-PG-0711-25111)
Conflicts of interest
Subject index terms status
Subject indexing assigned by CRD
Subject index terms
Diabetes Mellitus, Type 1; Humans; Insulin; "Predictive Value of Tests"
Date of registration in PROSPERO
28 September 2012
Date of publication of this revision
25 October 2012
Stage of review at time of this submission
Piloting of the study selection process
Formal screening of search results against eligibility criteria
Risk of bias (quality) assessment
PROSPERO This information has been provided by the named contact for this review. CRD has accepted this information in good faith and registered the review in PROSPERO. CRD bears no responsibility or liability for the content of this registration record, any associated files or external websites.