Zeinab Mahmoudi1, Mette Dencker Johansen1, Hanne Holdflod Nørgaard2, Steen Andersen2, Ulrik Pedersen-Bjergaard2, Lise Tarnow3, Jens Sandahl Christiansen4, Ole Hejlesen5. 1. Department of Health Science and Technology, Aalborg University, Aalborg, Denmark. 2. Department of Endocrinology, Nordsjaellands University Hospital, Hillerød, Denmark. 3. Department of Endocrinology, Nordsjaellands University Hospital, Hillerød, Denmark Aarhus University, Aarhus, Denmark. 4. Department of Endocrinology and Diabetes, Aarhus University Hospital, Aarhus, Denmark. 5. Department of Health Science and Technology, Aalborg University, Aalborg, Denmark Department of Health and Nursing Science, University of Agder, Agder, Norway Department of Computer Science, University of Tromsø, Tromsø, Norway okh@hst.aau.dk.
Abstract
BACKGROUND: The use of continuous glucose monitoring (CGM) in clinical decision making in diabetes could be limited by the inaccuracy of CGM data when compared to plasma glucose measurements. The aim of the present study is to investigate the impact of CGM numerical accuracy on the precision of diabetes treatment adjustments. METHOD: CGM profiles with maximum 5-day duration from 12 patients with type 1 diabetes treated with a basal-bolus insulin regimen were processed by 2 CGM algorithms, with the accuracy of algorithm 2 being higher than the accuracy of algorithm 1, using the median absolute relative difference (MARD) as the measure of accuracy. During 2 separate and similar occasions over a 1-month interval, 3 clinicians reviewed the processed CGM profiles, and adjusted the dose level of basal and prandial insulin. The precision of the dosage adjustments were defined in terms of the interclinician agreement and the intraclinician reproducibility of the decisions. The Cohen's kappa coefficient was used to assess the precision of the decisions. The study was based on retrospective and blind CGM data. RESULTS: For the interclinician agreement, in the first occasion, the kappa of algorithm 1 was .32, and that of algorithm 2 was .36. For the interclinician agreement, in the second occasion, the kappas of algorithms 1 and 2 were .17 and .22, respectively. For the intraclinician reproducibility of the decisions, the kappas of algorithm 1 were .35, .22, and .80 and the kappas of algorithm 2 were .44, .52, and .32, for the 3 clinicians, respectively. For the interclinician agreement, the relative kappa change from algorithm 1 to algorithm 2 was 86.06%, and for the intraclinician reproducibility, the relative kappa change from algorithm 1 to algorithm 2 was 53.99%. CONCLUSIONS: Results indicated that the accuracy of CGM algorithms might potentially affect the precision of the CGM-based insulin adjustments for type 1 diabetes patients. However, a larger study with several clinical centers, with higher number of clinicians and patients is required to validate the impact of CGM accuracy on decisions precision.
BACKGROUND: The use of continuous glucose monitoring (CGM) in clinical decision making in diabetes could be limited by the inaccuracy of CGM data when compared to plasma glucose measurements. The aim of the present study is to investigate the impact of CGM numerical accuracy on the precision of diabetes treatment adjustments. METHOD: CGM profiles with maximum 5-day duration from 12 patients with type 1 diabetes treated with a basal-bolus insulin regimen were processed by 2 CGM algorithms, with the accuracy of algorithm 2 being higher than the accuracy of algorithm 1, using the median absolute relative difference (MARD) as the measure of accuracy. During 2 separate and similar occasions over a 1-month interval, 3 clinicians reviewed the processed CGM profiles, and adjusted the dose level of basal and prandial insulin. The precision of the dosage adjustments were defined in terms of the interclinician agreement and the intraclinician reproducibility of the decisions. The Cohen's kappa coefficient was used to assess the precision of the decisions. The study was based on retrospective and blind CGM data. RESULTS: For the interclinician agreement, in the first occasion, the kappa of algorithm 1 was .32, and that of algorithm 2 was .36. For the interclinician agreement, in the second occasion, the kappas of algorithms 1 and 2 were .17 and .22, respectively. For the intraclinician reproducibility of the decisions, the kappas of algorithm 1 were .35, .22, and .80 and the kappas of algorithm 2 were .44, .52, and .32, for the 3 clinicians, respectively. For the interclinician agreement, the relative kappa change from algorithm 1 to algorithm 2 was 86.06%, and for the intraclinician reproducibility, the relative kappa change from algorithm 1 to algorithm 2 was 53.99%. CONCLUSIONS: Results indicated that the accuracy of CGM algorithms might potentially affect the precision of the CGM-based insulin adjustments for type 1 diabetespatients. However, a larger study with several clinical centers, with higher number of clinicians and patients is required to validate the impact of CGM accuracy on decisions precision.
Authors: C Choleau; J C Klein; G Reach; B Aussedat; V Demaria-Pesce; G S Wilson; R Gifford; W K Ward Journal: Biosens Bioelectron Date: 2002-08 Impact factor: 10.618