902-P: Introducing Continuous Glucose Monitoring Technology at Diagnosis in Pediatric Type 1 Diabetes: A Proof-of-Concept Study

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 902-P
Author(s):  
PAUL HIERS ◽  
JANEY ADAMS ◽  
STEPHANIE L. FILIPP ◽  
MICHAEL J. HALLER
Keyword(s):  
Type 1 Diabetes ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Proof Of Concept ◽  
Monitoring Technology ◽  
Concept Study ◽  
Pediatric Type 1 Diabetes

scholarly journals Prediction of Nocturnal Hypoglycemia From Continuous Glucose Monitoring Data in People With Type 1 Diabetes: A Proof-of-Concept Study

2019 ◽  
Vol 14 (2) ◽  
pp. 250-256 ◽  
Author(s):  
Morten H. Jensen ◽  
Claus Dethlefsen ◽  
Peter Vestergaard ◽  
Ole Hejlesen
Keyword(s):  
Type 1 Diabetes ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Feature Subset ◽  
Proof Of Concept ◽  
Forward Selection ◽  
Operating Characteristics ◽  
Concept Study ◽  
Nocturnal Hypoglycemia

Background: Intensive insulin therapy has documented benefits but may also come at the expense of a higher risk of hypoglycemia. Hypoglycemia is associated with higher all-cause mortality and nocturnal hypoglycemia has been associated with the sudden dead-in-bed syndrome. This proof-of-concept study sought to investigate if nocturnal hypoglycemia can be predicted. Method: Continuous glucose monitoring, meal, insulin, and demographics data from 463 people with type 1 diabetes were obtained from a clinical trial. A total of 4721 nights without or with hypoglycemia (429) were available including data from three consecutive days before the night. Thirty-two features were calculated based on these data. Data were split into 20% participants for evaluation and 80% for training. The optimal feature subset was found from forward selection of the 80% participants with linear discriminant analysis as basis for the classifier. Results: The forward selection resulted in a feature subset of four features. The evaluation resulted in an area under the receiver operating characteristics curve (ROC-AUC) of 0.79 leading to a sensitivity and a specificity of, e.g., 75% and 70%. Conclusions: It was possible to predict nocturnal hypoglycemic episodes with a ROC-AUC of 0.79. A warning at bedtime about nocturnal hypoglycemia could be of great help for people with diabetes to enable preventive actions. Further development of the proposed algorithm is needed for implementation in everyday practice.

scholarly journals Lag Time Remains with Newer Real-Time Continuous Glucose Monitoring Technology During Aerobic Exercise in Adults Living with Type 1 Diabetes

2019 ◽  
Vol 21 (6) ◽  
pp. 313-321 ◽  
Author(s):  
Dessi P. Zaharieva ◽  
Kamuran Turksoy ◽  
Sarah M. McGaugh ◽  
Rubin Pooni ◽  
Todd Vienneau ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Aerobic Exercise ◽  
Real Time ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Lag Time ◽  
Monitoring Technology

Prevalence of Nocturnal Hypoglycemia in Pediatric Type 1 Diabetes: A Pilot Study Using Continuous Glucose Monitoring

2011 ◽  
Vol 159 (2) ◽  
pp. 297-302.e1 ◽  
Author(s):  
Alexandra Ahmet ◽  
Simon Dagenais ◽  
Nick J. Barrowman ◽  
Catherine J. Collins ◽  
Margaret L. Lawson
Keyword(s):  
Type 1 Diabetes ◽  
Pilot Study ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Nocturnal Hypoglycemia ◽  
Pediatric Type 1 Diabetes
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