scholarly journals Prolonged Nocturnal Hypoglycemia Is Common During 12 Months of Continuous Glucose Monitoring in Children and Adults With Type 1 Diabetes

Diabetes Care ◽  
2010 ◽  
Vol 33 (5) ◽  
pp. 1004-1008 ◽  
Author(s):  
Keyword(s):  
Type 1 Diabetes ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Nocturnal Hypoglycemia

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

Nocturnal hypoglycemia detected with the continuous glucose monitoring system in pediatric patients with type 1 diabetes

2002 ◽  
Vol 141 (5) ◽  
pp. 625-630 ◽  
Author(s):  
Francine Ratner Kaufman ◽  
Juliana Austin ◽  
Aaron Neinstein ◽  
Lily Jeng ◽  
Mary Halvorson ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Monitoring System ◽  
Continuous Glucose Monitoring ◽  
Pediatric Patients ◽  
Glucose Monitoring ◽  
Continuous Glucose Monitoring System ◽  
Nocturnal Hypoglycemia

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 Prediction of Nocturnal Hypoglycemia in Adults with Type 1 Diabetes under Multiple Daily Injections Using Continuous Glucose Monitoring and Physical Activity Monitor

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1705 ◽  
Author(s):  
Arthur Bertachi ◽  
Clara Viñals ◽  
Lyvia Biagi ◽  
Ivan Contreras ◽  
Josep Vehí ◽  
...  
Keyword(s):  
Physical Activity ◽  
Machine Learning ◽  
Type 1 Diabetes ◽  
Continuous Glucose Monitoring ◽  
Prediction Models ◽  
Glucose Monitoring ◽  
Supervised Machine Learning ◽  
Nocturnal Hypoglycemia ◽  
Activity Tracker

(1) Background: nocturnal hypoglycemia (NH) is one of the most challenging side effects of multiple doses of insulin (MDI) therapy in type 1 diabetes (T1D). This work aimed to investigate the feasibility of a machine-learning-based prediction model to anticipate NH in T1D patients on MDI. (2) Methods: ten T1D adults were studied during 12 weeks. Information regarding T1D management, continuous glucose monitoring (CGM), and from a physical activity tracker were obtained under free-living conditions at home. Supervised machine-learning algorithms were applied to the data, and prediction models were created to forecast the occurrence of NH. Individualized prediction models were generated using multilayer perceptron (MLP) and a support vector machine (SVM). (3) Results: population outcomes indicated that more than 70% of the NH may be avoided with the proposed methodology. The predictions performed by the SVM achieved the best population outcomes, with a sensitivity and specificity of 78.75% and 82.15%, respectively. (4) Conclusions: our study supports the feasibility of using ML techniques to address the prediction of nocturnal hypoglycemia in the daily life of patients with T1D on MDI, using CGM and a physical activity tracker.

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