Risk models and scores for type 2 diabetes: systematic review
BMJ 2011; 343 doi: https://doi.org/10.1136/bmj.d7163 (Published 28 November 2011) Cite this as: BMJ 2011;343:d7163- Douglas Noble, lecturer1,
- Rohini Mathur, research fellow1,
- Tom Dent, consultant2,
- Catherine Meads, senior lecturer1,
- Trisha Greenhalgh, professor1
- 1Centre for Primary Care and Public Health, Barts and the London School of Medicine and Dentistry, London E1 2AT, UK
- 2PHG Foundation, Cambridge, UK
- Correspondence to: D Noble d.noble{at}qmul.ac.uk
- Accepted 5 October 2011
Abstract
Objective To evaluate current risk models and scores for type 2 diabetes and inform selection and implementation of these in practice.
Design Systematic review using standard (quantitative) and realist (mainly qualitative) methodology.
Inclusion criteria Papers in any language describing the development or external validation, or both, of models and scores to predict the risk of an adult developing type 2 diabetes.
Data sources Medline, PreMedline, Embase, and Cochrane databases were searched. Included studies were citation tracked in Google Scholar to identify follow-on studies of usability or impact.
Data extraction Data were extracted on statistical properties of models, details of internal or external validation, and use of risk scores beyond the studies that developed them. Quantitative data were tabulated to compare model components and statistical properties. Qualitative data were analysed thematically to identify mechanisms by which use of the risk model or score might improve patient outcomes.
Results 8864 titles were scanned, 115 full text papers considered, and 43 papers included in the final sample. These described the prospective development or validation, or both, of 145 risk prediction models and scores, 94 of which were studied in detail here. They had been tested on 6.88 million participants followed for up to 28 years. Heterogeneity of primary studies precluded meta-analysis. Some but not all risk models or scores had robust statistical properties (for example, good discrimination and calibration) and had been externally validated on a different population. Genetic markers added nothing to models over clinical and sociodemographic factors. Most authors described their score as “simple” or “easily implemented,” although few were specific about the intended users and under what circumstances. Ten mechanisms were identified by which measuring diabetes risk might improve outcomes. Follow-on studies that applied a risk score as part of an intervention aimed at reducing actual risk in people were sparse.
Conclusion Much work has been done to develop diabetes risk models and scores, but most are rarely used because they require tests not routinely available or they were developed without a specific user or clear use in mind. Encouragingly, recent research has begun to tackle usability and the impact of diabetes risk scores. Two promising areas for further research are interventions that prompt lay people to check their own diabetes risk and use of risk scores on population datasets to identify high risk “hotspots” for targeted public health interventions.
Footnotes
We thank Helen Elwell, librarian at the British Medical Association Library, for help with the literature search; Samuel Rigby for manually removing duplicates; and Sietse Wieringa, Kaveh Memarzadeh, and Nicholas Swetenham for help with translation of non-English papers. BMJ reviewers Wendy Hu and John Furler provided helpful comments on an earlier draft.
Contributors: DN conceptualised the study, managed the project, briefed and supported all researchers, assisted with developing the search strategy and ran the search, scanned all titles and abstracts, extracted quantitative data on half the papers, citation tracked all papers, checked a one third sample of the qualitative data extraction, and cowrote the paper. TG conceptualised the qualitative component of the study, extracted qualitative data on all papers, independently citation tracked all papers, and led on writing the paper. RM independently scanned all titles and abstracts of the electronic search, extracted quantitative data from some papers, assisted with other double checking, and helped revise drafts of the paper. TD helped revise and refine the study aims, independently double checked quantitative data extraction from all papers, and helped revise drafts of the paper. CM advised on systematic review methodology, helped develop the search strategy, extracted quantitative data from some papers, and helped revise drafts of the paper. TG acts as guarantor.
Funding: This study was funded by grants from Tower Hamlets, Newham, and City and Hackney primary care trusts, by a National Institute of Health Research senior investigator award for TG, and by internal funding for staff time from Barts and the London School of Medicine and Dentistry. The funders had no input into the selection or analysis of data or the content of the final manuscript.
Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declare: no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; and no other relationships or activities that could appear to have influenced the submitted work.
Ethical approval: Not required.
Data sharing: No additional data available.
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