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Combining Information of Autonomic Modulation and CGM Measurements Enables Prediction and Improves Detection of Spontaneous Hypoglycemic Events

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Authors:
  • Cichosz, Simon Lebech ;
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    Department of Clinical Medicine - The Department of Endocrinology and Diabetes, Department of Clinical Medicine, Health, Aarhus University
  • Frystyk, Jan ;
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    Department of Clinical Medicine - Medical Research Laboratory, Department of Clinical Medicine, Health, Aarhus University
  • Tarnow, Lise ;
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    Department of Clinical Medicine - Department of Clinical Epidemiology, Department of Clinical Medicine, Health, Aarhus University
  • Fleischer, Jesper
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    Department of Clinical Medicine - The Department of Endocrinology and Diabetes, Department of Clinical Medicine, Health, Aarhus University
DOI:
10.1177/1932296814549830
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.
Type:
Journal article
Language:
English
Published in:
Journal of Diabetes Science and Technology, 2015, Vol 9, Issue 1, p. 132-7
Keywords:
Clinical Trial; Comparative Study; Journal Article; Research Support, Non-U.S. Gov't
Main Research Area:
Medical science
Publication Status:
Published
Review type:
Peer Review
Submission year:
2015
Scientific Level:
Scientific
ID:
271002501

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