BACKGROUND: Glucose control in artificial pancreas (AP) studies is commonly assessed by metrics such as the percentage of time with blood glucose (BG) concentration below 70 mg/dL or in the nearly normal range 70-180 mg/dL (in brief, time in hypoglycemia and time in target, respectively). In outpatient studies these control metrics can be computed only from continuous glucose monitoring (CGM) sensor data, with the risk of an unfair assessment because of their inaccuracy. The aim of the present article is to show that the control metrics can be much more accurately determined if CGM data are preprocessed by a recently proposed retrofitting algorithm. SUBJECTS AND METHODS: Data from 47 type 1 diabetes subjects are considered. Subjects were studied in a closed-loop control trial prescribing three 24-h admissions. Glucose concentration was monitored using the Dexcom(®) (San Diego, CA) SEVEN(®) Plus CGM sensor. Frequent BG reference values were collected in parallel with the YSI analyzer (Yellow Springs Instrument, Yellow Springs, OH). To simulate the few reference values available in outpatient conditions, we down-sampled the YSI data and provided to the retrofitting algorithm only the reference values that would have been collected in outpatient protocols. Time in hypoglycemia, time in target, mean, and SD of glucose profile were computed on the basis of both the original and the retrofitted CGM traces and compared with those computed using the frequently obtained YSI data. RESULTS: Using the retrofitted traces, the average error affecting the estimation of time in hypoglycemia and time in target was approximately halved with respect to the original CGM traces (from 4.5% to 1.9% and from 8.7% to 4.4%, respectively). Error in mean and SD was reduced even further, from 10.0 mg/dL to 3.5 mg/dL and from 8.6 mg/dL to 2.9 mg/dL, respectively. CONCLUSIONS: The improved accuracy of retrofitted CGM with respect to the original CGM traces allows a more reliable assessment of glucose control in outpatient AP studies.
BACKGROUND:Glucose control in artificial pancreas (AP) studies is commonly assessed by metrics such as the percentage of time with blood glucose (BG) concentration below 70 mg/dL or in the nearly normal range 70-180 mg/dL (in brief, time in hypoglycemia and time in target, respectively). In outpatient studies these control metrics can be computed only from continuous glucose monitoring (CGM) sensor data, with the risk of an unfair assessment because of their inaccuracy. The aim of the present article is to show that the control metrics can be much more accurately determined if CGM data are preprocessed by a recently proposed retrofitting algorithm. SUBJECTS AND METHODS: Data from 47 type 1 diabetes subjects are considered. Subjects were studied in a closed-loop control trial prescribing three 24-h admissions. Glucose concentration was monitored using the Dexcom(®) (San Diego, CA) SEVEN(®) Plus CGM sensor. Frequent BG reference values were collected in parallel with the YSI analyzer (Yellow Springs Instrument, Yellow Springs, OH). To simulate the few reference values available in outpatient conditions, we down-sampled the YSI data and provided to the retrofitting algorithm only the reference values that would have been collected in outpatient protocols. Time in hypoglycemia, time in target, mean, and SD of glucose profile were computed on the basis of both the original and the retrofitted CGM traces and compared with those computed using the frequently obtained YSI data. RESULTS: Using the retrofitted traces, the average error affecting the estimation of time in hypoglycemia and time in target was approximately halved with respect to the original CGM traces (from 4.5% to 1.9% and from 8.7% to 4.4%, respectively). Error in mean and SD was reduced even further, from 10.0 mg/dL to 3.5 mg/dL and from 8.6 mg/dL to 2.9 mg/dL, respectively. CONCLUSIONS: The improved accuracy of retrofitted CGM with respect to the original CGM traces allows a more reliable assessment of glucose control in outpatient AP studies.
Authors: Stamatina Zavitsanou; Joon Bok Lee; Jordan E Pinsker; Mei Mei Church; Francis J Doyle; Eyal Dassau Journal: J Diabetes Sci Technol Date: 2017-10-16
Authors: Ahmed H El-Laboudi; Shivani Misra; Marcus Martineau; Paramjeet Deol; Alison Sanders; Nick Oliver Journal: J Diabetes Sci Technol Date: 2015-04-07
Authors: James S Krinsley; J Geoffrey Chase; Jan Gunst; Johan Martensson; Marcus J Schultz; Fabio S Taccone; Jan Wernerman; Julien Bohe; Christophe De Block; Thomas Desaive; Pierre Kalfon; Jean-Charles Preiser Journal: Crit Care Date: 2017-07-31 Impact factor: 9.097
Authors: David M Maahs; Bruce A Buckingham; Jessica R Castle; Ali Cinar; Edward R Damiano; Eyal Dassau; J Hans DeVries; Francis J Doyle; Steven C Griffen; Ahmad Haidar; Lutz Heinemann; Roman Hovorka; Timothy W Jones; Craig Kollman; Boris Kovatchev; Brian L Levy; Revital Nimri; David N O'Neal; Moshe Philip; Eric Renard; Steven J Russell; Stuart A Weinzimer; Howard Zisser; John W Lum Journal: Diabetes Care Date: 2016-07 Impact factor: 19.112