152-LB: Decision Tree Modeling Identifies Factors Associated with Increased Time-in-Range (TIR) during Inpen Smart Insulin Pen and Continuous Glucose Monitor (CGM) Use In Youth

Background: Previous InPen decision tree modeling (DTM) in adults indicated that missed meal dosing was a major modifiable factor associated with TIR (70-180mg/dL) . We used DTM to find characteristics/use-behaviors associated with TIR for InPen+CGM users <18 years of age. Methods: InPen+CGM data...

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Published inDiabetes (New York, N.Y.) Vol. 71; no. Supplement_1
Main Authors SMITH, MADISON, HEUNGYONG IM, GLEN, GAETANO, ANGELA, THANASEKARAN, SNEHA, MACLEOD, JANICE, VIGERSKY, ROBERT A.
Format Journal Article
LanguageEnglish
Published New York American Diabetes Association 01.06.2022
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Summary:Background: Previous InPen decision tree modeling (DTM) in adults indicated that missed meal dosing was a major modifiable factor associated with TIR (70-180mg/dL) . We used DTM to find characteristics/use-behaviors associated with TIR for InPen+CGM users <18 years of age. Methods: InPen+CGM data from youth (N=883) with T1D starting InPen between 7/20 and 6/21 were used to identify characteristics (prior delivery method, diabetes duration) and use-behaviors (frequency of missed/correction doses, priming/bolus count, meal therapy mode, InPen bolus calculator use, app reminders, time settings, and report generation) associated with TIR. A variance inflation factor >10 was used to reduce multicollinearity for feature importance. Those with insufficient CGM and/or bolus data and lack of consistent insulin therapy settings were excluded. Results: DTM of users determined 1) diabetes duration, 2) frequency of missed meal doses, 3) bolus count and 4) correction frequency as the top variables associated with TIR (Figure) . Highest average TIR (68.0%) was observed in newly diagnosed (≤1 year) users. Users with >2 years diabetes duration, <5.5 boluses/day, and >35.1% missed dose frequency demonstrated lowest average TIR (35%) . Conclusions: DTM is a novel application for understanding glycemic outcomes due to modifiable and non-modifiable factors. Disclosure M. Smith: Employee; Medtronic. G. Im: Employee; Medtronic. A. Gaetano: None. S. Thanasekaran: None. J. Macleod: Employee; Medtronic. R. A. Vigersky: Employee; Medtronic.
ISSN:0012-1797
1939-327X
DOI:10.2337/db22-152-LB