title: A novel algorithm for prediction and detection of hypoglycemia based on continuous glucose monitoring and heart rate variability in patients with type 1 diabetes.
abstract: Hypoglycemia is a common and serious side effect of insulin therapy in patients with diabetes. Early detection and prediction of hypoglycemia may improve treatment and avoidance of serious complications. Continuous glucose monitoring (CGM) has previously been used for detection of hypoglycemia, but with a modest accuracy. Therefore, our aim was to investigate whether a novel algorithm that adds information of the complex dynamic/pattern of heart rate variability (HRV) could improve the accuracy of hypoglycemia as detected by a CGM device. Data from 10 patients with type 1 diabetes studied during insulin-induced hypoglycemia were obtained. Blood glucose samples were used as reference. HRV patterns and CGM data were combined in a mathematical prediction algorithm. Detection of hypoglycemic periods, performed by the algorithm, was treated as a pattern recognition problem and features/patterns derived from HRV and CGM prior to each blood glucose sample were used to decide if that particular point in time was below the hypoglycemic threshold of 3.9 mmol/L. A total of 903 samples were analyzed by the novel algorithm, which yielded a sensitivity of 79% and a specificity of 99%. The algorithm was able to detect 16/16 hypoglycemic events with no false positives and had a lead time of 22 minutes as compared to the CGM device. Detection accuracy and lead time were significantly improved by the novel algorithm compared to that of CGM alone.
authors.affiliation: Department of Endocrinology and Internal Medicine and Medical Research Laboratory, Aarhus University Hospital, Aarhus, Denmark Department of Health Science and Technology, Aalborg University, Aalborg, Denmark simcich@hst.aau.dk.
authors.forename: Simon Lebech
authors.initials: SL
authors.lastname: Cichosz
authors.affiliation: Department of Endocrinology and Internal Medicine and Medical Research Laboratory, Aarhus University Hospital, Aarhus, Denmark.
authors.forename: Jan
authors.initials: J
authors.lastname: Frystyk
authors.affiliation: Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.
authors.forename: Ole K
authors.initials: OK
authors.lastname: Hejlesen
authors.affiliation: Department of Clinical Epidemiology, Aarhus University and Nordsjaellands Hospitaler Hilleroed, Aarhus, Denmark.
authors.forename: Lise
authors.initials: L
authors.lastname: Tarnow
authors.affiliation: Department of Endocrinology and Internal Medicine and Medical Research Laboratory, Aarhus University Hospital, Aarhus, Denmark.
authors.forename: Jesper
authors.initials: J
authors.lastname: Fleischer
mesh_terms: D000328:Adult; D000465:Algorithms; D015190:Blood Glucose Self-Monitoring; D003922:Diabetes Mellitus, Type 1 / Q000150:complications* / Q000503:physiopathology; D004562:Electrocardiography; D006339:Heart Rate; D006801:Humans; D007003:Hypoglycemia / Q000139:chemically induced / Q000175:diagnosis*; D007004:Hypoglycemic Agents / Q000009:adverse effects; D007328:Insulin / Q000009:adverse effects; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D011237:Predictive Value of Tests
chemical_list: D007004:Hypoglycemic Agents; D007328:Insulin
keywords: continuous glucose monitoring; diabetes; heart rate variability; hypoglycemia