High incidence of glucocorticoid-induced hyperglycaemia in inflammatory bowel disease: metabolic and clinical predictors identified by machine learning

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.

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