Abstract
The standard formula (SF) used in bolus calculators (BCs) determines meal insulin bolus using "static" measurement of blood glucose concentration (BG) obtained by self-monitoring of blood glucose (SMBG) fingerprick device. Some methods have been proposed to improve efficacy of SF using "dynamic" information provided by continuous glucose monitoring (CGM), and, in particular, glucose rate of change (ROC). This article compares, in silico and in an ideal framework limiting the exposition to possibly confounding factors (such as CGM noise), the performance of three popular techniques devised for such a scope, that is, the methods of Buckingham et al (BU), Scheiner (SC), and Pettus and Edelman (PE). Using the UVa/Padova Type 1 diabetes simulator we generated data of 100 virtual subjects in noise-free, single-meal scenarios having different preprandial BG and ROC values. Meal insulin bolus was computed using SF, BU, SC, and PE. Performance was assessed with the blood glucose risk index (BGRI) on the 9 hours after meal. On average, BU, SC, and PE improve BGRI compared to SF. When BG is rapidly decreasing, PE obtains the best performance. In the other ROC scenarios, none of the considered methods prevails in all the preprandial BG con...Continue Reading
References
Jun 28, 2003·Diabetes Technology & Therapeutics·Todd M GrossSuzanne Juth
Jan 29, 2008·Pediatric Diabetes·UNKNOWN Diabetes Research In Children Network (DirecNet) Study GroupRoy Beck
Sep 10, 2008·The New England Journal of Medicine·William V TamborlaneDongyuan Xing
Jan 23, 2009·Endocrine Practice : Official Journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists·Paul C DavidsonBruce W Bode
Apr 23, 2009·The Journal of Clinical Endocrinology and Metabolism·Irl B Hirsch
Oct 8, 2011·Diabetic Medicine : a Journal of the British Diabetic Association·S MardenD Kerr
Oct 16, 2012·Journal of Diabetes Science and Technology·David C Klonoff
May 31, 2014·Journal of Diabetes Science and Technology·Signe Schmidt, Kirsten Nørgaard
May 31, 2014·Journal of Diabetes Science and Technology·Chiara Dalla ManClaudio Cobelli
Aug 16, 2015·Journal of Diabetes Science and Technology·Chiara FabrisMarc D Breton
Jan 20, 2016·Diabetes Technology & Therapeutics·Jeremy Pettus, Steven V Edelman
Aug 18, 2016·Journal of Diabetes Science and Technology·Jeremy Pettus, Steven V Edelman
Sep 16, 2016·Journal of Diabetes Science and Technology·Steven V Edelman
Jan 19, 2018·Journal of the Endocrine Society·Grazia AleppoDennis R Harris
Mar 2, 2018·Journal of Diabetes Science and Technology·Giacomo CapponGiovanni Sparacino
Citations
Jul 22, 2019·Sensors·Giacomo CapponPau Herrero
Mar 8, 2020·Diabetes Care·Chiara FabrisMarc D Breton
Jul 16, 2020·Sensors·Martina VettorettiGiovanni Sparacino
Aug 24, 2019·Diabetes & Metabolism Journal·Giacomo CapponAndrea Facchinetti
Dec 11, 2019·Sensors·Martina VettorettiAndrea Facchinetti
Apr 3, 2021·Journal of Diabetes Science and Technology·Basak OzaslanMarc D Breton
Jul 20, 2021·Journal of the American Medical Informatics Association : JAMIA·Minh NguyenJonathan H Chen
Sep 7, 2021·Journal of Diabetes Science and Technology·Giulia NoaroAndrea Facchinetti