Davis GM(1), Faulds E(2), Walker T(3), Vigliotti D(4), Rabinovich M(4), Hester J(5), Peng L(6), McLean B(7), Hannon P(7), Poindexter N(7), Saunders P(7), Perez-Guzman C(1), Tekwani SS(8), Martin GS(8), Umpierrez G(1), Agarwal S(9), Dungan K(2), Pasquel FJ(10). Author information:
(1)Division of Endocrinology, Metabolism, and Lipids, Emory University School of
Medicine, Atlanta, GA.
(2)Division of Endocrinology, The Ohio State University, Columbus, OH.
(3)Information Technology, Grady Health System, Atlanta, GA.
(4)Department of Pharmacy, Grady Health System, Atlanta, GA.
(5)Department of Medicine, Morehouse School of Medicine, Atlanta, GA.
(6)Department of Biostatistics and Bioinformatics, Rollins School of Public
Health, Emory University, Atlanta, GA.
(7)Division of Critical Care, Grady Health System, Atlanta, GA.
(8)Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Emory
University School of Medicine, Atlanta, GA.
(9)Fleischer Institute for Diabetes and Metabolism, New York Regional Center for
Diabetes Translational Research, Albert Einstein College of Medicine, Bronx, NY.
(10)Division of Endocrinology, Metabolism, and Lipids, Emory University School
of Medicine, Atlanta, GA [Email]
OBJECTIVE: The use of remote real-time continuous glucose monitoring (CGM) in the hospital has rapidly emerged to preserve personal protective equipment and reduce potential exposures during coronavirus disease 2019 (COVID-19). RESEARCH DESIGN AND METHODS: We linked a hybrid CGM and point-of-care (POC) glucose testing protocol to a computerized decision support system for continuous insulin infusion and integrated a validation system for sensor glucose values into the electronic health record. We report our proof-of-concept experience in a COVID-19 intensive care unit. RESULTS: All nine patients required mechanical ventilation and corticosteroids. During the protocol, 75.7% of sensor values were within 20% of the reference POC glucose with an associated average reduction in POC of 63%. Mean time in range (70-180 mg/dL) was 71.4 ± 13.9%. Sensor accuracy was impacted by mechanical interferences in four patients. CONCLUSIONS: A hybrid protocol integrating real-time CGM and POC is helpful for managing critically ill patients with COVID-19 requiring insulin infusion.
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