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Inflammatory bowel disease
High incidence of glucocorticoid-induced hyperglycaemia in inflammatory bowel disease: metabolic and clinical predictors identified by machine learning
  1. Martin McDonnell1,2,
  2. Richard J Harris1,
  3. Florina Borca3,4,
  4. Tilly Mills1,
  5. Louise Downey1,
  6. Suranga Dharmasiri1,
  7. Mayank Patel5,
  8. Benjamin Zare1,
  9. Matt Stammers1,6,
  10. Trevor R Smith1,
  11. Richard Felwick1,
  12. J R Fraser Cummings1,2,
  13. Hang T T Phan3,4,
  14. Markus Gwiggner1
  1. 1Department of Gastroenterology, University Hospital Southampton NHS Foundation Trust, Southampton, UK
  2. 2Human Health and Development, University of Southampton Faculty of Medicine, Southampton, UK
  3. 3NIHR Southampton Biomedical Research Centre, University Hospital Southampton NHS Foundation Trust, Southampton, UK
  4. 4Clinical Informatics Research Unit, University of Southampton Faculty of Medicine, Southampton, UK
  5. 5Department of Diabetes and Endocrinology, University Hospital Southampton NHS Foundation Trust, Southampton, UK
  6. 6NIHR Biomedical Research Facility, University of Southampton, Southampton, UK
  1. Correspondence to Dr Martin McDonnell; m.j.mcdonnell{at}soton.ac.uk

Abstract

Background Glucocorticosteroids (GC) are long-established, widely used agents for induction of remission in inflammatory bowel disease (IBD). Hyperglycaemia is a known complication of GC treatment with implications for morbidity and mortality. Published data on prevalence and risk factors for GC-induced hyperglycaemia in the IBD population are limited. We prospectively characterise this complication in our cohort, employing machine-learning methods to identify key predictors of risk.

Methods We conducted a prospective observational study of IBD patients receiving intravenous hydrocortisone (IVH). Electronically triggered three times daily capillary blood glucose (CBG) monitoring was recorded alongside diabetes mellitus (DM) history, IBD biomarkers, nutritional and IBD clinical activity scores. Hyperglycaemia was defined as CBG ≥11.1 mmol/L and undiagnosed DM as glycated haemoglobin ≥48 mmol/mol. Random forest (RF) regression models were used to extract predictor-patterns present within the dataset.

Results 94 consecutive IBD patients treated with IVH were included. 60% (56/94) of the cohort recorded an episode of hyperglycaemia, including 57% (50/88) of those with no history of DM, of which 19% (17/88) and 5% (4/88) recorded a CBG ≥14 mmol/L and ≥20 mmol/L, respectively. The RF models identified increased C-reactive protein (CRP) followed by a longer IBD duration as leading risk predictors for significant hyperglycaemia.

Conclusion Hyperglycaemia is common in IBD patients treated with intravenous GC. Therefore, CBG monitoring should be included in routine clinical practice. Machine learning methods can identify key risk factors for clinical complications. Steroid-sparing treatment strategies may be considered for those IBD patients with higher admission CRP and greater disease duration, who appear to be at the greatest risk of hyperglycaemia.

  • inflammatory bowel disease
  • diabetes mellitus
  • adverse drug reactions
  • drug toxicity
http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

http://dx.doi.org/10.1136/bmjgast-2020-000532

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    Footnotes

    • MM and RJH are joint first authors.

    • Twitter @martinjmcd

    • Contributors MM, RJH, SD and MG were responsible for the original concept and planning of the study. MM, RJH, TM, SD, BZ and LD were responsible for clinical data collection and analysis. FB and HTTP were responsible for data extraction, analysis and modelling. RJH and MM contributed equally to this work and drafted the manuscript, which RF, TS, MP, JRFC, HTTP, MS and MG critically reviewed and revised.

    • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

    • Competing interests MM: received non-financial support from Falk, MSD, Janssen and Takeda. RJH: personal fees from AbbVie and Janssen; non-financial support from Falk. LD: non-financial support from Janssen. SD: personal fees and non-financial support from Janssen; personal fees from Falk, MSD and AbbVie. JRFC: personal fees and research and/or educational support from Abbot, AbbVie, Amgen, Astra Zeneca, Biogen, Celltrion, GlaxoSmithKline, Janssen, Norgine, Pfizer, Pharmacosmos, Samsung, Shield Therapeutics, Shire, Takeda and Vifor. MG: personal fees from AbbVie, MSD and Takeda; non-financial support from AbbVie and Takeda.

    • Patient consent for publication Not required.

    • Ethics approval The protocol was reviewed and approved by the UK Health Research Authority - reference 19/HRA/0033.

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

    • Data availability statement Anonymised data and random forrest code can be made available upon request.

    • 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.