title: Combining information of autonomic modulation and CGM measurements enables prediction and improves detection of spontaneous hypoglycemic events.
abstract: We have previously tested, in a laboratory setting, a novel algorithm that enables prediction of hypoglycemia. The algorithm integrates information of autonomic modulation, based on heart rate variability (HRV), and data based on a continuous glucose monitoring (CGM) device. Now, we investigate whether the algorithm is suitable for prediction of hypoglycemia and for improvement of hypoglycemic detection during normal daily activities. Twenty-one adults (13 men) with T1D prone to hypoglycemia were recruited and monitored with CGM and a Holter device while they performed normal daily activities. We used our developed algorithm (a pattern classification method) to predict spontaneous hypoglycemia based on CGM and HRV. We compared 3 different models; (i) a model containing raw data from the CGM device; (ii) a CGM* model containing data derived from the CGM device signal; and (iii) a CGM+HRV model-combining model (ii) with HRV data. A total of 12 hypoglycemic events (glucose levels < 3.9 mmol/L, 70 mg/dL) and 237 euglycemic measurements were included. For a 20-minute prediction, model (i) resulted in a ROC AUC of 0.69. If a high sensitivity of 100% was chosen, the corresponding specificity was 69%. (ii) The CGM* model yielded a ROC AUC of 0.92 with a corresponding sensitivity of 100% and specificity of 71%. (iii) The CGM+HRV model yielded a ROC AUC of 0.96 with a corresponding sensitivity of 100% and specificity of 91%. Data shows that adding information of autonomic modulation to CGM measurements enables prediction and improves the detection of hypoglycemia.
authors.affiliation: Department of Endocrinology and Internal Medicine and Medical Research Laboratory, Aarhus University Hospital, Denmark Department of Health Science and Technology, Aalborg University, 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, Denmark.
authors.forename: Jan
authors.initials: J
authors.lastname: Frystyk
authors.affiliation: Steno Diabetes Center, Department of Clinical Epidemiology, Aarhus University and Nordsjaellands Hospitaler Hilleroed, 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, Denmark.
authors.forename: Jesper
authors.initials: J
authors.lastname: Fleischer
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D001341:Autonomic Nervous System / Q000503:physiopathology*; D015190:Blood Glucose Self-Monitoring; D003922:Diabetes Mellitus, Type 1 / Q000097:blood* / Q000175:diagnosis / Q000503:physiopathology*; D005260:Female; D006339:Heart Rate; D006801:Humans; D007003:Hypoglycemia / Q000175:diagnosis* / Q000503:physiopathology; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D012680:Sensitivity and Specificity
keywords: continuous glucose monitoring; diabetes; heart rate variability; hypoglycemia; prediction