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Clinical Targets for Continuous Glucose Monitoring Data Interpretation: Recommendations From the International Consensus on Time in Range.

Tadej BattelinoThomas DanneRichard M BergenstalStephanie A AmielRoy W BeckTorben BiesterEmanuele BosiBruce A BuckinghamWilliam T CefaluKelly L CloseClaudio CobelliEyal DassauJ Hans DeVriesKim C DonaghueKlemen DovcFrancis J Doyle IiiSatish GargGeorge GrunbergerSimon R HellerLutz HeinemannIrl B HirschRoman HovorkaWeiping JiaOlga KordonouriBoris P KovatchevAaron J KowalskiLori M B LaffelBrian LevineAlexander MayorovChantal MathieuHelen R MurphyRevital NimriKirsten NørgaardChristopher G ParkinPr Eric RenardDavid RodbardBanshi SabooDesmond A SchatzKeaton StonerTatsuhiko UrakamiStuart A WeinzimerMoshe Phillip
Published in: Diabetes care (2019)
Improvements in sensor accuracy, greater convenience and ease of use, and expanding reimbursement have led to growing adoption of continuous glucose monitoring (CGM). However, successful utilization of CGM technology in routine clinical practice remains relatively low. This may be due in part to the lack of clear and agreed-upon glycemic targets that both diabetes teams and people with diabetes can work toward. Although unified recommendations for use of key CGM metrics have been established in three separate peer-reviewed articles, formal adoption by diabetes professional organizations and guidance in the practical application of these metrics in clinical practice have been lacking. In February 2019, the Advanced Technologies & Treatments for Diabetes (ATTD) Congress convened an international panel of physicians, researchers, and individuals with diabetes who are expert in CGM technologies to address this issue. This article summarizes the ATTD consensus recommendations for relevant aspects of CGM data utilization and reporting among the various diabetes populations.
Keyphrases
  • clinical practice
  • type diabetes
  • glycemic control
  • cardiovascular disease
  • electronic health record
  • emergency department
  • deep learning
  • adipose tissue
  • artificial intelligence
  • skeletal muscle