Article Text

Original research
Prediction of type 2 diabetes risk in people with non-diabetic hyperglycaemia: model derivation and validation using UK primary care data
  1. Briana Coles1,2,
  2. Kamlesh Khunti1,2,
  3. Sarah Booth3,
  4. Francesco Zaccardi1,2,
  5. Melanie J Davies2,
  6. Laura J Gray3
  1. 1Leicester Real World Evidence Unit, Diabetes Research Centre, University of Leicester, Leicester, UK
  2. 2Diabetes Research Centre, University of Leicester, Leicester General Hospital, Leicester, UK
  3. 3Department of Health Sciences, University of Leicester, Leicester, UK
  1. Correspondence to Ms Briana Coles; bc188{at}leicester.ac.uk

Abstract

Objective Using primary care data, develop and validate sex-specific prognostic models that estimate the 10-year risk of people with non-diabetic hyperglycaemia developing type 2 diabetes.

Design Retrospective cohort study.

Setting Primary care.

Participants 154 705 adult patients with non-diabetic hyperglycaemia.

Primary outcome Development of type 2 diabetes.

Methods This study used data routinely collected in UK primary care from general practices contributing to the Clinical Practice Research Datalink. Patients were split into development (n=109 077) and validation datasets (n=45 628). Potential predictor variables, including demographic and lifestyle factors, medical and family history, prescribed medications and clinical measures, were included in survival models following the imputation of missing data. Measures of calibration at 10 years and discrimination were determined using the validation dataset.

Results In the development dataset, 9332 patients developed type 2 diabetes during 293 238 person-years of follow-up (31.8 (95% CI 31.2 to 32.5) per 1000 person-years). In the validation dataset, 3783 patients developed type 2 diabetes during 115 113 person-years of follow-up (32.9 (95% CI 31.8 to 33.9) per 1000 person-years). The final prognostic models comprised 14 and 16 predictor variables for males and females, respectively. Both models had good calibration and high levels of discrimination. The performance statistics for the male model were: Harrell’s C statistic of 0.700 in the development and 0.701 in the validation dataset, with a calibration slope of 0.974 (95% CI 0.905 to 1.042) in the validation dataset. For the female model, Harrell’s C statistics were 0.720 and 0.718, respectively, while the calibration slope was 0.994 (95% CI 0.931 to 1.057) in the validation dataset.

Conclusion These models could be used in primary care to identify those with non-diabetic hyperglycaemia most at risk of developing type 2 diabetes for targeted referral to the National Health Service Diabetes Prevention Programme.

  • general diabetes
  • primary care
  • epidemiology
  • health policy
  • risk management
  • diabetes & endocrinology
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Footnotes

  • Funding This research was supported by a grant from National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care (CLAHRC) East Midlands. This research was also funded in part by the NIHR Leicester Biomedical Research Centre and NIHR Applied Research Collaboration.

  • Disclaimer The funding body had no role in the study design, data collection, analysis, or interpretation; in the writing of the manuscript; or in the decision to submit the manuscript. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care.

  • Competing interests BC, LG, FZ and SB: none. MJD has acted as consultant, advisory board member and speaker for Novo Nordisk, Sanofi-Aventis, Lilly, Merck Sharp & Dohme, Boehringer Ingelheim, AstraZeneca and Janssen, an advisory board member for Servier and as a speaker for Mitsubishi Tanabe Pharma Corporation and Takeda Pharmaceuticals International Inc. She has received grants in support of investigator and investigator initiated trials from Novo Nordisk, Sanofi-Aventis, Lilly, Boehringer Ingelheim and Janssen. She was a member of the NICE public health guideline for prevention of Type 2 diabetes (NICE PH 38). KK has acted as a consultant and speaker for Novartis, Novo Nordisk, Sanofi-Aventis, Lilly and Merck Sharp and Dohme. He has received grants in support of investigator and investigator-initiated trials from Novartis, Novo Nordisk, Sanofi-Aventis, Lilly, Pfizer, Boehringer Ingelheim and Merck Sharp & Dohme. He is a member of the External Reference Group of the NHS DPP and was Chair of the NICE public health guideline for prevention of Type 2 diabetes (NICE PH 38).

  • Patient consent for publication Not required.

  • Ethics approval This research was approved by the Independent Scientific Advisory Committee (ISAC) for Medicines and Healthcare products Regulatory Agency Database Research (protocol 18_238).

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability statement No data are available. Patient-level electronic health records obtained from CPRD cannot be shared. However, the authors will share programming code and aggregate statistics if requested. A list of medcodes used to define Type 2 diabetes, pre-existing type 1 diabetes, and medical and family history as well as product codes used to identify current medication is available at github.com/bc188/Prognostic-model-codes.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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