BACKGROUND: Closed-loop control of blood glucose levels in people with type 1 diabetes offers the potential to reduce the incidence of diabetes complications and reduce the patients' burden, particularly if meals do not need to be announced. We therefore tested a closed-loop algorithm that does not require meal announcement. MATERIALS AND METHODS: A multiple model probabilistic predictive controller (MMPPC) was assessed on four patients, revised to improve performance, and then assessed on six additional patients. Each inpatient admission lasted for 32 h with five unannounced meals containing approximately 1 g/kg of carbohydrate per admission. The system used an Abbott Diabetes Care (Alameda, CA) Navigator(®) continuous glucose monitor (CGM) and Insulet (Bedford, MA) Omnipod(®) insulin pump, with the MMPPC implemented through the artificial pancreas system platform. The controller was initialized only with the patient's total daily dose and daily basal pattern. RESULTS: On a 24-h basis, the first cohort had mean reference and CGM readings of 179 and 167 mg/dL, respectively, with 53% and 62%, respectively, of readings between 70 and 180 mg/dL and four treatments for glucose values <70 mg/dL. The second cohort had mean reference and CGM readings of 161 and 142 mg/dL, respectively, with 63% and 78%, respectively, of the time spent euglycemic. There was one controller-induced hypoglycemic episode. For the 30 unannounced meals in the second cohort, the mean reference and CGM premeal, postmeal maximum, and 3-h postmeal values were 139 and 132, 223 and 208, and 168 and 156 mg/dL, respectively. CONCLUSIONS: The MMPPC, tested in-clinic against repeated, large, unannounced meals, maintained reasonable glycemic control with a mean blood glucose level that would equate to a mean glycated hemoglobin value of 7.2%, with only one controller-induced hypoglycemic event occurring in the second cohort.
BACKGROUND: Closed-loop control of blood glucose levels in people with type 1 diabetes offers the potential to reduce the incidence of diabetes complications and reduce the patients' burden, particularly if meals do not need to be announced. We therefore tested a closed-loop algorithm that does not require meal announcement. MATERIALS AND METHODS: A multiple model probabilistic predictive controller (MMPPC) was assessed on four patients, revised to improve performance, and then assessed on six additional patients. Each inpatient admission lasted for 32 h with five unannounced meals containing approximately 1 g/kg of carbohydrate per admission. The system used an Abbott Diabetes Care (Alameda, CA) Navigator(®) continuous glucose monitor (CGM) and Insulet (Bedford, MA) Omnipod(®) insulin pump, with the MMPPC implemented through the artificial pancreas system platform. The controller was initialized only with the patient's total daily dose and daily basal pattern. RESULTS: On a 24-h basis, the first cohort had mean reference and CGM readings of 179 and 167 mg/dL, respectively, with 53% and 62%, respectively, of readings between 70 and 180 mg/dL and four treatments for glucose values <70 mg/dL. The second cohort had mean reference and CGM readings of 161 and 142 mg/dL, respectively, with 63% and 78%, respectively, of the time spent euglycemic. There was one controller-induced hypoglycemic episode. For the 30 unannounced meals in the second cohort, the mean reference and CGM premeal, postmeal maximum, and 3-h postmeal values were 139 and 132, 223 and 208, and 168 and 156 mg/dL, respectively. CONCLUSIONS: The MMPPC, tested in-clinic against repeated, large, unannounced meals, maintained reasonable glycemic control with a mean blood glucose level that would equate to a mean glycated hemoglobin value of 7.2%, with only one controller-induced hypoglycemic event occurring in the second cohort.
Authors: Boris Kovatchev; Claudio Cobelli; Eric Renard; Stacey Anderson; Marc Breton; Stephen Patek; William Clarke; Daniela Bruttomesso; Alberto Maran; Silvana Costa; Angelo Avogaro; Chiara Dalla Man; Andrea Facchinetti; Lalo Magni; Giuseppe De Nicolao; Jerome Place; Anne Farret Journal: J Diabetes Sci Technol Date: 2010-11-01
Authors: Marc Breton; Anne Farret; Daniela Bruttomesso; Stacey Anderson; Lalo Magni; Stephen Patek; Chiara Dalla Man; Jerome Place; Susan Demartini; Simone Del Favero; Chiara Toffanin; Colleen Hughes-Karvetski; Eyal Dassau; Howard Zisser; Francis J Doyle; Giuseppe De Nicolao; Angelo Avogaro; Claudio Cobelli; Eric Renard; Boris Kovatchev Journal: Diabetes Date: 2012-06-11 Impact factor: 9.461
Authors: S D Patek; L Magni; E Dassau; C Karvetski; C Toffanin; G De Nicolao; S Del Favero; M Breton; C Dalla Man; E Renard; H Zisser; F J Doyle; C Cobelli; B P Kovatchev Journal: IEEE Trans Biomed Eng Date: 2012-04-03 Impact factor: 4.538
Authors: Gregory P Forlenza; Faye M Cameron; Trang T Ly; David Lam; Daniel P Howsmon; Nihat Baysal; Georgia Kulina; Laurel Messer; Paula Clinton; Camilla Levister; Stephen D Patek; Carol J Levy; R Paul Wadwa; David M Maahs; B Wayne Bequette; Bruce A Buckingham Journal: Diabetes Technol Ther Date: 2018-04-16 Impact factor: 6.118
Authors: Eyal Dassau; Jordan E Pinsker; Yogish C Kudva; Sue A Brown; Ravi Gondhalekar; Chiara Dalla Man; Steve Patek; Michele Schiavon; Vikash Dadlani; Isuru Dasanayake; Mei Mei Church; Rickey E Carter; Wendy C Bevier; Lauren M Huyett; Jonathan Hughes; Stacey Anderson; Dayu Lv; Elaine Schertz; Emma Emory; Shelly K McCrady-Spitzer; Tyler Jean; Paige K Bradley; Ling Hinshaw; Alejandro J Laguna Sanz; Ananda Basu; Boris Kovatchev; Claudio Cobelli; Francis J Doyle Journal: Diabetes Care Date: 2017-10-13 Impact factor: 19.112
Authors: Daniel P Howsmon; Nihat Baysal; Bruce A Buckingham; Gregory P Forlenza; Trang T Ly; David M Maahs; Tatiana Marcal; Lindsey Towers; Eric Mauritzen; Sunil Deshpande; Lauren M Huyett; Jordan E Pinsker; Ravi Gondhalekar; Francis J Doyle; Eyal Dassau; Juergen Hahn; B Wayne Bequette Journal: J Diabetes Sci Technol Date: 2018-02-01
Authors: Patricio H Colmegna; Ricardo S Sanchez-Pena; Ravi Gondhalekar; Eyal Dassau; Frank J Doyle Journal: IEEE Trans Biomed Eng Date: 2015-10-05 Impact factor: 4.538