David C Klonoff1, Jing Wang2, David Rodbard3, Michael A Kohn4, Chengdong Li2, Dorian Liepmann5, David Kerr6, David Ahn7, Anne L Peters8, Guillermo E Umpierrez9, Jane Jeffrie Seley10, Nicole Y Xu11, Kevin T Nguyen11, Gregg Simonson12, Michael S D Agus13, Mohammed E Al-Sofiani14,15, Gustavo Armaiz-Pena16, Timothy S Bailey17, Ananda Basu18, Tadej Battelino19, Sewagegn Yeshiwas Bekele20, Pierre-Yves Benhamou21, B Wayne Bequette22, Thomas Blevins23, Marc D Breton18, Jessica R Castle24, James Geoffrey Chase25, Kong Y Chen26, Pratik Choudhary27, Mark A Clements28, Kelly L Close29, Curtiss B Cook30, Thomas Danne31, Francis J Doyle32, Angela Drincic33, Kathleen M Dungan34, Steven V Edelman35, Niels Ejskjaer36, Juan C Espinoza37, G Alexander Fleming38, Gregory P Forlenza39, Guido Freckmann40, Rodolfo J Galindo9, Ana Maria Gomez41, Hanna A Gutow29, Lutz Heinemann42, Irl B Hirsch43, Thanh D Hoang44, Roman Hovorka45, Johan H Jendle46, Linong Ji47, Shashank R Joshi48, Michael Joubert49, Suneil K Koliwad4, Rayhan A Lal50, M Cecilia Lansang51,52, Wei-An Andy Lee53, Lalantha Leelarathna54, Lawrence A Leiter55, Marcus Lind56, Michelle L Litchman57, Julia K Mader58, Katherine M Mahoney29, Boris Mankovsky59, Umesh Masharani4, Nestoras N Mathioudakis15, Alexander Mayorov60, Jordan Messler61, Joshua D Miller62, Viswanathan Mohan63,64, James H Nichols65, Kirsten Nørgaard66, David N O'Neal67, Francisco J Pasquel9, Athena Philis-Tsimikas68, Thomas Pieber58, Moshe Phillip69, William H Polonsky70, Rodica Pop-Busui71, Gerry Rayman72, Eun-Jung Rhee73, Steven J Russell74, Viral N Shah39, Jennifer L Sherr75, Koji Sode76,77, Elias K Spanakis78, Deborah J Wake79, Kayo Waki80, Amisha Wallia81, Melissa E Weinberg82, Howard Wolpert83, Eugene E Wright84, Mihail Zilbermint15,85, Boris Kovatchev18. 1. Diabetes Research Institute, Mills-Peninsula Medical Center, San Mateo, CA, USA. 2. Florida State University College of Nursing, Tallahassee, FL, USA. 3. Biomedical Informatics Consultants LLC, Potomac, MD, USA. 4. University of California, San Francisco, San Francisco, CA, USA. 5. University of California, Berkeley, Berkeley, CA, USA. 6. Sansum Diabetes Research Institute, Santa Barbara, CA, USA. 7. Hoag Memorial Hospital Presbyterian, Newport Beach, CA, USA. 8. University of Southern California, Los Angeles, CA, USA. 9. Emory University, Atlanta, GA, USA. 10. Weill Cornell Medicine, New York, NY, USA. 11. Diabetes Technology Society, Burlingame, CA, USA. 12. International Diabetes Center, Minneapolis, MN, USA. 13. Boston Children's Hospital, Harvard Medical School, Boston, MA, USA. 14. King Saud University, Riyadh, Saudi Arabia. 15. Johns Hopkins University, Baltimore, MD, USA. 16. UT Health San Antonio, San Antonio, TX, USA. 17. AMCR Institute, Escondido, CA, USA. 18. University of Virginia, Charlottesville, VA, USA. 19. Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia. 20. Addis Ababa University, Addis Ababa, Ethiopia. 21. Centre Hospitalier Universitaire de Grenoble, Grenoble, France. 22. Rensselaer Polytechnic Institute, Troy, NY, USA. 23. Texas Diabetes and Endocrinology, Austin, TX, USA. 24. Oregon Health & Science University, Portland, OR, USA. 25. University of Canterbury, Christchurch, New Zealand. 26. National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, USA. 27. University of Leicester, Leicester, UK. 28. Children's Mercy Hospital, Kansas City, MO, USA. 29. Close Concerns, San Francisco, CA, USA. 30. Mayo Clinic Arizona, Scottsdale, AZ, USA. 31. Diabetes Center Auf der Bult, Hannover Medical School, Hannover, Germany. 32. Harvard University, Cambridge, MA, USA. 33. University of Nebraska, Omaha, NE, USA. 34. The Ohio State University, Columbus, OH, USA. 35. University of California, San Diego, San Diego, CA, USA. 36. Steno Diabetes Center North Denmark, Aalborg University Hospital, Aalborg, Denmark. 37. Children's Hospital Los Angeles, University of Southern California, Los Angeles, CA, USA. 38. Kinexum, Harpers Ferry, WV, USA. 39. Barbara Davis Center for Diabetes, University of Colorado, Aurora, CO, USA. 40. Institute for Diabetes-Technology GmbH, Ulm, Germany. 41. Pontificia Universidad Javeriana, Bogotá, Colombia. 42. Science Consulting in Diabetes GmbH, Kaarst, Germany. 43. University of Washington, Seattle, WA, USA. 44. Walter Reed National Military Medical Center, Bethesda, MD, USA. 45. University of Cambridge, Cambridge, UK. 46. Örebro University, Örebro, Sweden. 47. Peking University People's Hospital, Peking University Diabetes Center, Beijing, China. 48. Joshi Clinic, Mumbai, India. 49. Caen University Hospital, Caen, France. 50. Stanford University, Stanford, CA, USA. 51. Cleveland Clinic, Cleveland, OH, USA. 52. Cleveland Clinic Lerner College of Medicine, Cleveland, OH, USA. 53. LAC + USC Medical Center, Los Angeles County Department of Health Service, Los Angeles, CA, USA. 54. Manchester University NHS Foundation Trust and The University of Manchester, Manchester, UK. 55. Li Ka Shing Knowledge Institute, St. Michael's Hospital and University of Toronto, Toronto, ON, Canada. 56. University of Gothenburg, Gothenburg, Sweden. 57. The University of Utah College of Nursing, Salt Lake City, UT, USA. 58. Medical University of Graz, Graz, Austria. 59. National Healthcare University of Ukraine, Kiev, Ukraine. 60. Endocrinology Research Centre, Moscow, Russian Federation. 61. Morton Plant Hospital, Clearwater, FL, USA. 62. Stony Brook University, Stony Brook, NY, USA. 63. Dr. Mohan's Diabetes Specialities Centre, Chennai, India. 64. Madras Diabetes Research Foundation, Chennai, India. 65. Vanderbilt University Medical Center, Nashville, TN, USA. 66. Steno Diabetes Center Copenhagen, Herlev, Denmark. 67. University of Melbourne, Melbourne, VIC, Australia. 68. Scripps Whittier Diabetes Institute, San Diego, CA, USA. 69. Institute for Endocrinology and Diabetes, Schneider Children's Medical Center of Israel and Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv, Israel. 70. Behavioral Diabetes Institute, San Diego, CA, USA. 71. University of Michigan, Ann Arbor, MI, USA. 72. Ipswich Hospital, East Suffolk and North Essex Foundation Trust and University of East Anglia, Ipswich, UK. 73. Kangbuk Samsung Hospital, Sungkyunkwan University, Seoul, Korea. 74. Massachusetts General Hospital Diabetes Research Center, Boston, MA, USA. 75. Yale University, New Haven, CT, USA. 76. The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. 77. North Carolina State University, Raleigh, NC, USA. 78. University of Maryland, Baltimore, MD, USA. 79. The University of Edinburgh, Edinburgh, UK. 80. The University of Tokyo, Tokyo, Japan. 81. Northwestern University, Chicago, IL, USA. 82. California Pacific Medical Center, San Francisco, CA, USA. 83. Boston Medical Center, Boston, MA, USA. 84. Charlotte AHEC, Charlotte, NC, USA. 85. Johns Hopkins Community Physicians, Bethesda, MD, USA.
Abstract
BACKGROUND: A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data. METHODS: We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low-glucose and low-glucose hypoglycemia; very high-glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation. RESULTS: The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals. CONCLUSION: The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.
BACKGROUND: A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data. METHODS: We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low-glucose and low-glucose hypoglycemia; very high-glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation. RESULTS: The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals. CONCLUSION: The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.
Entities:
Keywords:
ambulatory glucose profile; composite metric; continuous glucose monitor; diabetes; glycemia risk index; hyperglycemia; hypoglycemia; time in range
Authors: Nicole Y Xu; Kevin T Nguyen; Ashley Y DuBord; John Pickup; Jennifer L Sherr; Hazhir Teymourian; Eda Cengiz; Barry H Ginsberg; Claudio Cobelli; David Ahn; Riccardo Bellazzi; B Wayne Bequette; Laura Gandrud Pickett; Linda Parks; Elias K Spanakis; Umesh Masharani; Halis K Akturk; John S Melish; Sarah Kim; Gu Eon Kang; David C Klonoff Journal: J Diabetes Sci Technol Date: 2022-05-02
Authors: Nicole Y Xu; Kevin T Nguyen; Ashley Y DuBord; David C Klonoff; Julian M Goldman; Shahid N Shah; Elias K Spanakis; Charisse Madlock-Brown; Siavash Sarlati; Azhar Rafiq; Axel Wirth; David Kerr; Raman Khanna; Scott Weinstein; Juan Espinoza Journal: J Diabetes Sci Technol Date: 2022-05-09