TY - JOUR
T1 - Evaluation of an algorithm for retrospective hypoglycemia detection using professional continuous glucose monitoring data
AU - Jensen, Morten Hasselstrøm
AU - Mahmoudi, Zeinab
AU - Christensen, Toke Folke
AU - Tarnow, Lise
AU - Seto, Edmund
AU - Johansen, Mette Dencker
AU - Hejlesen, Ole Kristian
PY - 2014/1
Y1 - 2014/1
N2 - Background: People with type 1 diabetes (T1D) are unable to produce insulin and thus rely on exogenous supply to lower their blood glucose. Studies have shown that intensive insulin therapy reduces the risk of late-diabetic complications by lowering average blood glucose. However, the therapy leads to increased incidence of hypoglycemia. Although inaccurate, professional continuous glucose monitoring (PCGM) can be used to identify hypoglycemic events, which can be useful for adjusting glucose-regulating factors. New pattern classification approaches based on identifying hypoglycemic events through retrospective analysis of PCGM data have shown promising results. The aim of this study was to evaluate a new pattern classification approach by comparing the performance with a newly developed PCGM calibration algorithm. Methods: Ten male subjects with T1D were recruited and monitored with PCGM and self-monitoring blood glucose during insulin-induced hypoglycemia. A total of 19 hypoglycemic events occurred during the sessions. Results: The pattern classification algorithm detected 19/19 hypoglycemic events with 1 false positive, while the PCGM with the new calibration algorithm detected 17/19 events with 2 false positives. Conclusions: We can conclude that even after the introduction of new calibration algorithms, the pattern classification approach is still a valuable addition for improving retrospective hypoglycemia detection using PCGM.
AB - Background: People with type 1 diabetes (T1D) are unable to produce insulin and thus rely on exogenous supply to lower their blood glucose. Studies have shown that intensive insulin therapy reduces the risk of late-diabetic complications by lowering average blood glucose. However, the therapy leads to increased incidence of hypoglycemia. Although inaccurate, professional continuous glucose monitoring (PCGM) can be used to identify hypoglycemic events, which can be useful for adjusting glucose-regulating factors. New pattern classification approaches based on identifying hypoglycemic events through retrospective analysis of PCGM data have shown promising results. The aim of this study was to evaluate a new pattern classification approach by comparing the performance with a newly developed PCGM calibration algorithm. Methods: Ten male subjects with T1D were recruited and monitored with PCGM and self-monitoring blood glucose during insulin-induced hypoglycemia. A total of 19 hypoglycemic events occurred during the sessions. Results: The pattern classification algorithm detected 19/19 hypoglycemic events with 1 false positive, while the PCGM with the new calibration algorithm detected 17/19 events with 2 false positives. Conclusions: We can conclude that even after the introduction of new calibration algorithms, the pattern classification approach is still a valuable addition for improving retrospective hypoglycemia detection using PCGM.
KW - Calibration
KW - Continuous glucose monitoring
KW - Diabetes
KW - Evaluation
KW - Hypoglycemia
KW - Machine learning
KW - Retrospective
UR - http://www.scopus.com/inward/record.url?scp=84900545806&partnerID=8YFLogxK
U2 - 10.1177/1932296813511744
DO - 10.1177/1932296813511744
M3 - Article
AN - SCOPUS:84900545806
SN - 1932-2968
VL - 8
SP - 117
EP - 122
JO - Journal of Diabetes Science and Technology
JF - Journal of Diabetes Science and Technology
IS - 1
ER -