OBJECTIVE: Although developed to be a management tool for individuals with diabetes, continuous glucose monitoring (CGM) also has potential value for the assessment of outcomes in clinical studies. We evaluated using CGM as such an outcome measure. RESEARCH DESIGN AND METHODS: Data were analyzed from six previously completed inpatient studies in which both CGM (Freestyle Navigator™ [Abbott Diabetes Care, Alameda, CA] or Guardian(®) [Medtronic, Northridge, CA]) and reference glucose measurements were available. The analyses included 97 days of data from 93 participants with type 1 diabetes (age range, 5-57 years; mean, 18 ± 12 years). RESULTS: Mean glucose levels per day were similar for the CGM and reference measurements (median, 148 mg/dL vs. 143 mg/dL, respectively; P = 0.92), and the correlation of the two was high (r = 0.89). Similarly, most glycemia metrics showed no significant differences comparing CGM and reference values, except that the nadir glucose tended to be slightly lower and peak glucose slightly higher with reference measurements than CGM measurements (respective median, 59 mg/dL vs. 66 mg/dL [P = 0.05] and 262 mg/dL vs. 257 mg/dL [P = 0.003]) and glucose variability as measured with the coefficient of variation was slightly lower with CGM than reference measurements (respective median, 31% vs. 35%; P<0.001). CONCLUSIONS: A reasonably high degree of concordance exists when comparing outcomes based on CGM measurements with outcomes based on reference blood glucose measurements. CGM inaccuracy and underestimation of the extremes of hyperglycemia and hypoglycemia can be accounted for in a clinical trial's study design. Thus, in appropriate settings, CGM can be a very meaningful and feasible outcome measure for clinical trials.
OBJECTIVE: Although developed to be a management tool for individuals with diabetes, continuous glucose monitoring (CGM) also has potential value for the assessment of outcomes in clinical studies. We evaluated using CGM as such an outcome measure. RESEARCH DESIGN AND METHODS: Data were analyzed from six previously completed inpatient studies in which both CGM (Freestyle Navigator™ [Abbott Diabetes Care, Alameda, CA] or Guardian(®) [Medtronic, Northridge, CA]) and reference glucose measurements were available. The analyses included 97 days of data from 93 participants with type 1 diabetes (age range, 5-57 years; mean, 18 ± 12 years). RESULTS: Mean glucose levels per day were similar for the CGM and reference measurements (median, 148 mg/dL vs. 143 mg/dL, respectively; P = 0.92), and the correlation of the two was high (r = 0.89). Similarly, most glycemia metrics showed no significant differences comparing CGM and reference values, except that the nadir glucose tended to be slightly lower and peak glucose slightly higher with reference measurements than CGM measurements (respective median, 59 mg/dL vs. 66 mg/dL [P = 0.05] and 262 mg/dL vs. 257 mg/dL [P = 0.003]) and glucose variability as measured with the coefficient of variation was slightly lower with CGM than reference measurements (respective median, 31% vs. 35%; P<0.001). CONCLUSIONS: A reasonably high degree of concordance exists when comparing outcomes based on CGM measurements with outcomes based on reference blood glucose measurements. CGM inaccuracy and underestimation of the extremes of hyperglycemia and hypoglycemia can be accounted for in a clinical trial's study design. Thus, in appropriate settings, CGM can be a very meaningful and feasible outcome measure for clinical trials.
Authors: Darrell M Wilson; Roy W Beck; William V Tamborlane; Mariya J Dontchev; Craig Kollman; Peter Chase; Larry A Fox; Katrina J Ruedy; Eva Tsalikian; Stuart A Weinzimer Journal: Diabetes Care Date: 2007-01 Impact factor: 19.112
Authors: William V Tamborlane; Roy W Beck; Bruce W Bode; Bruce Buckingham; H Peter Chase; Robert Clemons; Rosanna Fiallo-Scharer; Larry A Fox; Lisa K Gilliam; Irl B Hirsch; Elbert S Huang; Craig Kollman; Aaron J Kowalski; Lori Laffel; Jean M Lawrence; Joyce Lee; Nelly Mauras; Michael O'Grady; Katrina J Ruedy; Michael Tansey; Eva Tsalikian; Stuart Weinzimer; Darrell M Wilson; Howard Wolpert; Tim Wysocki; Dongyuan Xing Journal: N Engl J Med Date: 2008-09-08 Impact factor: 91.245
Authors: Roman Hovorka; Kavita Kumareswaran; Julie Harris; Janet M Allen; Daniela Elleri; Dongyuan Xing; Craig Kollman; Marianna Nodale; Helen R Murphy; David B Dunger; Stephanie A Amiel; Simon R Heller; Malgorzata E Wilinska; Mark L Evans Journal: BMJ Date: 2011-04-13
Authors: Roy W Beck; Irl B Hirsch; Lori Laffel; William V Tamborlane; Bruce W Bode; Bruce Buckingham; Peter Chase; Robert Clemons; Rosanna Fiallo-Scharer; Larry A Fox; Lisa K Gilliam; Elbert S Huang; Craig Kollman; Aaron J Kowalski; Jean M Lawrence; Joyce Lee; Nelly Mauras; Michael O'Grady; Katrina J Ruedy; Michael Tansey; Eva Tsalikian; Stuart A Weinzimer; Darrell M Wilson; Howard Wolpert; Tim Wysocki; Dongyuan Xing Journal: Diabetes Care Date: 2009-05-08 Impact factor: 19.112
Authors: Mattia Zanon; Martin Mueller; Pavel Zakharov; Mark S Talary; Marc Donath; Werner A Stahel; Andreas Caduff Journal: J Diabetes Sci Technol Date: 2017-11-16
Authors: Oliver Schnell; Katharine Barnard; Richard Bergenstal; Emanuele Bosi; Satish Garg; Bruno Guerci; Thomas Haak; Irl B Hirsch; Linong Ji; Shashank R Joshi; Maarten Kamp; Lori Laffel; Chantal Mathieu; William H Polonsky; Frank Snoek; Philip Home Journal: Diabetes Technol Ther Date: 2017-05-22 Impact factor: 6.118
Authors: Roy W Beck; Richard M Bergenstal; Tonya D Riddlesworth; Craig Kollman; Zhaomian Li; Adam S Brown; Kelly L Close Journal: Diabetes Care Date: 2018-10-23 Impact factor: 19.112