摘要：AbstractNeonatal hypoglycaemia is common in at-risk infants and can cause adverse neurologic outcomes in later life. Continuous glucose monitoring (CGM) technology offers a way to continuously monitor patient condition, helping to detect hypoglycaemia as well as provide insight into the general glycaemic state of the patient. Characterising Glycaemic States can be easily done by eye, but no simple, clinically relevant algorithm exists to do this characterisation analytically or computationally. This paper presents such an algorithm to characterise Glycaemic States and detect State Changes. This algorithm was developed on a cohort of 366 infants, using a total of 12356 hours of CGM sensor data. State Changes were defined as an intersection between a 6-hour rolling average of the CGM trace and the average of the whole interstitial glucose CGM trace, with a 5 hour minimum crossover threshold defining a single State. The majority of infants were found to have experienced less than 2 State Changes in the first 48 hours of birth (279 of 366 patients, 76%). The median number of State Changes per day was 0.68 [IQR: 0.60, 1.14], while the median absolute change in IG over a State Change was 0.6 mmol/L [IQR: 0.4, 0.9 mmol/L]. Visually, the majority of algorithmically characterised State Changes matched CGM traces characterised by eye. Future use of the algorithm could associate the State Changes with clinical outcomes.
关键词：KeywordsBiosignal analysisprocessinginterpretationMedical technologyMetabolic system
