Combining information of autonomic modulation and CGM measurements enables prediction and improves detection of spontaneous hypoglycemic events
- Authors:
- 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. 132-137
- 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
