Lalantha Leelarathna1,2, Hood Thabit1,2, Malgorzata E Wilinska3,4, Lia Bally3,5, Julia K Mader6, Thomas R Pieber6, Carsten Benesch7, Sabine Arnolds7, Terri Johnson8, Lutz Heinemann7,9, Norbert Hermanns10,11, Mark L Evans3,12, Roman Hovorka3,4. 1. Manchester Diabetes Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK. 2. Division of Diabetes, Endocrinology and Gastroenterology, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK. 3. Wellcome Trust-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, UK. 4. Department of Paediatrics, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK. 5. Department of Diabetes, Endocrinology, Clinical Nutrition and Metabolism, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland. 6. Division of Endocrinology and Diabetology, Department of Internal Medicine, Medical University of Graz, Graz, Austria. 7. Profil Institut für Stoffwechselforschung GmbH, Neuss, Germany. 8. Dexcom Inc, San Diego, CA, USA. 9. Science-Consulting in Diabetes GmBH, Dusseldorf, Germany. 10. Research Institute Diabetes of the Diabetes Academy Mergentheim (FIDAM), Mergentheim, Germany. 11. Department of Clinical Psychology and Psychotherapy, University of Bamberg, Bamberg, Germany. 12. Wolfson Diabetes & Endocrinology Clinic, Addenbrookes Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
Abstract
OBJECTIVE: The objective was to describe a novel composite continuous glucose monitoring index (COGI) and to evaluate its utility, in adults with type 1 diabetes, during hybrid closed-loop (HCL) therapy and multiple daily injections (MDI) therapy combined with real-time continuous glucose monitoring (CGM). METHODS: COGI consists of three key components of glucose control as assessed by CGM: Time in range (TIR), time below range (TBR), and glucose variability (GV) (weighted by 50%, 35% and 15%). COGI ranges from 0 to 100, where 1% increase of time <3.9 mmol/L (<70 mg/dl) is equivalent to 4.7% reduction of TIR between 3.9-10 mmol/L (70-180 mg/dl), and 0.5 mmol/L (9 mg/dl) increase in standard deviation is equivalent to 3% reduction in TIR. RESULTS: Continuous subcutaneous insulin infusion (CSII) users with HbA1c >7.5-10%, had significantly higher COGI during 12 weeks of HCL compared to sensor-augmented pump therapy, mean (SD), 60.3 (8.6) versus 69.5 (6.9), P < .001. Similarly, in CSII users with HbA1c <7.5%, HCL improved COGI from 59.9 (11.2) to 74.8 (6.6), P < .001. In MDI users with HbA1c >7.5% to 9.9%, use of real-time CGM led to improved COGI, 49.8 (14.2) versus 58.2 (9.1), P < .0001. In MDI users with impaired awareness of hypoglycemia, use of real-time CGM led to improved COGI, 53.4 (12.2) versus 66.7 (11.1), P < .001. CONCLUSIONS: COGI summarizes three key aspects of CGM data into a concise metric that could be utilized to evaluate the quality of glucose control and to demonstrate the incremental benefit of a wide range of treatment modalities.
OBJECTIVE: The objective was to describe a novel composite continuous glucose monitoring index (COGI) and to evaluate its utility, in adults with type 1 diabetes, during hybrid closed-loop (HCL) therapy and multiple daily injections (MDI) therapy combined with real-time continuous glucose monitoring (CGM). METHODS: COGI consists of three key components of glucose control as assessed by CGM: Time in range (TIR), time below range (TBR), and glucose variability (GV) (weighted by 50%, 35% and 15%). COGI ranges from 0 to 100, where 1% increase of time <3.9 mmol/L (<70 mg/dl) is equivalent to 4.7% reduction of TIR between 3.9-10 mmol/L (70-180 mg/dl), and 0.5 mmol/L (9 mg/dl) increase in standard deviation is equivalent to 3% reduction in TIR. RESULTS: Continuous subcutaneous insulin infusion (CSII) users with HbA1c >7.5-10%, had significantly higher COGI during 12 weeks of HCL compared to sensor-augmented pump therapy, mean (SD), 60.3 (8.6) versus 69.5 (6.9), P < .001. Similarly, in CSII users with HbA1c <7.5%, HCL improved COGI from 59.9 (11.2) to 74.8 (6.6), P < .001. In MDI users with HbA1c >7.5% to 9.9%, use of real-time CGM led to improved COGI, 49.8 (14.2) versus 58.2 (9.1), P < .0001. In MDI users with impaired awareness of hypoglycemia, use of real-time CGM led to improved COGI, 53.4 (12.2) versus 66.7 (11.1), P < .001. CONCLUSIONS: COGI summarizes three key aspects of CGM data into a concise metric that could be utilized to evaluate the quality of glucose control and to demonstrate the incremental benefit of a wide range of treatment modalities.
Entities:
Keywords:
closed-loop insulin delivery; continuous glucose monitoring; type 1 diabetes
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