Background: The chaotic nature of blood glucose creates a formidable clinical challenge for diabetes healthcare. The recent discovery of recurrent endocrine cycles offers the advantage of advanced-prediction (proactive) health care.
Methods: Historical studies covering 111 patients and 1 subject collected several months of glucose readings and their daily metrics. Phase portraits and phase analytics can detect recurrent metric cycles and test their ability to anticipate serious glycemic conditions.
Results: Recurrent patterns were detected having a rate of ~7 days per complete cycle. Plots and risk models based on these cycles produced advanced alerts for acute glycemia, capturing greater than 96% of true-positive days with a 5% false-positive rate.
Conclusions: This method can be implemented graphically and functionally within a BG monitoring system to warn doctors and patients of impending serious glycemic levels.
Keywords: diabetes; dynamics; hyperglycemia; hypoglycemia; prediction; recurrent cycles.