0
LONDON — A machine learning model designed to predict inpatient hypoglycemic events using only capillary blood glucose (CBG) showed excellent performance, according to results of an artificial intelligence study.
In a separate analysis, researchers used the model to assess the relative importance of different glycemic features in predicting inpatient hypoglycemia , concluding that extreme and variable CBG measurements had the greatest prognostic value.
Chris Sainsbury, MD, consultant in diabetes and endocrinology at Gartnavel General Hospital in Glasgow, Scotland, presented the results at this year’s Diabetes UK Professional Conference (DUKPC) 2024 , alongside his hospital colleagues Greg Jones, MD, diabetes consultant, and Deborah Morrison, MD, a general practitioner with diabetes specialist training.
"We’ve shown the model has very good predictive power for hypoglycemic events," Sainsbury told Medscape Medical News . "Now, we need to show that staff can act on these alerts efficiently and that this will translate into significant clinical impact."
The model’s accuracy in predicting a hypoglycemic event was assessed using the area under the receiver operating characteristic curve. The model produced a number between zero and one, which predicted the patient’s risk of having a hypoglycemic event in the next 24 hours and was shown to increase to a maximum at day 7.
"It increased from around 0.78 at day 2 to 0.85 at day 7 and then stayed at around 0.85 out to day 31, with increasing confidence intervals as the number of admissions of each duration reduces," Sainsbury said.The model is designed for use in hospitalized patients by assessing […]
Get Creative with Cannabis: The Ultimate Guide to Painting and Pottery Workshops
Creative with Cannabis Cannabis and creativity go hand in hand—especially
