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1051-P: Improving Glycemic Outcomes in T1D Using an Automated Decision Support Recommender System: Evaluation In Silico and Compared with Physician Recommendations.

Authors :
TYLER, NICHOLE S.
MOSQUERA-LOPEZ, CLARA M.
WILSON, LEAH M.
RESALAT, NAVID
DODIER, ROBERT
BRANIGAN, DEBORAH
GABO, VIRGINIA
YOUSSEF, JOSEPH EL
CASTLE, JESSICA R.
JACOBS, PETER G.
Source :
Diabetes. 2019 Supplement, Vol. 68, pN.PAG-N.PAG. 1p.
Publication Year :
2019

Abstract

Most people with type 1 diabetes use multiple daily injections (MDI) therapy, yet there are limited decision support tools for this population. We designed and evaluated a K-nearest-neighbors decision support system (KNN-DSS) that utilizes continuous glucose data and Bluetooth-enabled insulin dose capture devices to detect problematic glycemic patterns and recommend insulin dosage adjustments to maximize time-in-range (70-180 mg/dL) and minimize hypoglycemia (< 70 mg/dL). The KNN-DSS employs distance and class weighting, a carb ratio aggressiveness algorithm, and an expert-opinion quality-control algorithm to filter erroneous recommendations. The KNN-DSS was evaluated both in silico and in comparison with physician recommendations. In silico virtual patients (n=30) of varying weight, total daily insulin requirement, insulin sensitivity, and compliance with care were evaluated in a 1-year study whereby they received weekly recommendations. The primary outcome measures were the % time-in-range and % time-in-hypoglycemia. The KNN-DSS was further evaluated for accuracy compared with endocrinologist recommendations collected during a 4-week, at-home clinical trial of CGM-augmented MDI therapy (N = 12, 7 female, 30.8 ± 5 years, 79.6 ± 18.9 kg), and compared using Dice similarity coefficient. In silico, the KNN-DSS improved time-in-range from 66.8% at baseline to 81.0% (21% relative improvement, p <0.005), and reduced time-in-hypoglycemia from 3.14% at baseline to 1.37% (56% relative improvement, p < 0.005) after one year. Compared to endocrinologists, the KNN-DSS recommendations to real-world patients were within acceptable agreement 75% of the time, 6% contraindicated, and 19% whereby KNN-DSS gave a recommendation when the physician gave no recommendation. KNN-DSS improves glycemic outcomes in silico and exhibits acceptable agreement with physician recommendations. Future clinical studies are planned. Disclosure: N.S. Tyler: None. C.M. Mosquera-Lopez: None. L.M. Wilson: None. N. Resalat: None. R. Dodier: None. D. Branigan: None. V. Gabo: None. J. El Youssef: None. J.R. Castle: Advisory Panel; Self; Novo Nordisk Inc., Zealand Pharma A/S. Consultant; Self; Dexcom, Inc. Research Support; Self; Dexcom, Inc., Xeris Pharmaceuticals, Inc. P.G. Jacobs: Stock/Shareholder; Self; Pacific Diabetes Technologies. Funding: The Leona M. and Harry B. Helmsley Charitable Trust (2018PG-T1D001) [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00121797
Volume :
68
Database :
Academic Search Index
Journal :
Diabetes
Publication Type :
Academic Journal
Accession number :
152324936
Full Text :
https://doi.org/10.2337/db19-1051-P