Background: Hybrid Closed-Loop (HCL) systems aid individuals with type 1 diabetes in improving glycemic control; however, sustained use over time has not been consistent for all users. This study developed and validated prognostic models for successful 12-month use of the first commercial HCL system based on baseline and 1- or 3-month data. Methods and Materials: Data from participants at the Barbara Davis Center (N = 85) who began use of the MiniMed 670G HCL were used to develop prognostic models using logistic regression and Lasso model selection. Candidate factors included sex, age, duration of diabetes, baseline hemoglobin A1c (HbA1c), race, ethnicity, insurance status, history of insulin pump and continuous glucose monitor use, 1- or 3-month Auto Mode use, boluses per day, and time in range (TIR; 70-180 mg/dL), and scores on behavioral questionnaires. Successful use of HCL was predefined as Auto Mode use ≥60%. The 3-month model was then externally validated against a sample from Stanford University (N = 55). Results: Factors in the final model included baseline HbA1c, sex, ethnicity, 1- or 3-month Auto Mode use, Boluses per Day, and TIR. The 1- and 3-month prognostic models had very good predictive ability with area under the curve values of 0.894 and 0.900, respectively. External validity was acceptable with an area under the curve of 0.717. Conclusions: Our prognostic models use clinically accessible baseline and early device-use factors to identify risk for failure to succeed with 670G HCL technology. These models may be useful to develop targeted interventions to promote success with new technologies.
Background: Hybrid Closed-Loop (HCL) systems aid individuals with type 1 diabetes in improving glycemic control; however, sustained use over time has not been consistent for all users. This study developed and validated prognostic models for successful 12-month use of the first commercial HCL system based on baseline and 1- or 3-month data. Methods and Materials: Data from participants at the Barbara Davis Center (N = 85) who began use of the MiniMed 670G HCL were used to develop prognostic models using logistic regression and Lasso model selection. Candidate factors included sex, age, duration of diabetes, baseline hemoglobin A1c (HbA1c), race, ethnicity, insurance status, history of insulin pump and continuous glucose monitor use, 1- or 3-month Auto Mode use, boluses per day, and time in range (TIR; 70-180 mg/dL), and scores on behavioral questionnaires. Successful use of HCL was predefined as Auto Mode use ≥60%. The 3-month model was then externally validated against a sample from Stanford University (N = 55). Results: Factors in the final model included baseline HbA1c, sex, ethnicity, 1- or 3-month Auto Mode use, Boluses per Day, and TIR. The 1- and 3-month prognostic models had very good predictive ability with area under the curve values of 0.894 and 0.900, respectively. External validity was acceptable with an area under the curve of 0.717. Conclusions: Our prognostic models use clinically accessible baseline and early device-use factors to identify risk for failure to succeed with 670G HCL technology. These models may be useful to develop targeted interventions to promote success with new technologies.
Authors: J T Markowitz; L K Volkening; D A Butler; J Antisdel-Lomaglio; B J Anderson; L M B Laffel Journal: Diabet Med Date: 2012-04 Impact factor: 4.359
Authors: Sue A Brown; Roy W Beck; Dan Raghinaru; Bruce A Buckingham; Lori M Laffel; R Paul Wadwa; Yogish C Kudva; Carol J Levy; Jordan E Pinsker; Eyal Dassau; Francis J Doyle; Louise Ambler-Osborn; Stacey M Anderson; Mei Mei Church; Laya Ekhlaspour; Gregory P Forlenza; Camilla Levister; Vinaya Simha; Marc D Breton; Craig Kollman; John W Lum; Boris P Kovatchev Journal: Diabetes Care Date: 2020-05-29 Impact factor: 19.112
Authors: Martin Tauschmann; Hood Thabit; Lia Bally; Janet M Allen; Sara Hartnell; Malgorzata E Wilinska; Yue Ruan; Judy Sibayan; Craig Kollman; Peiyao Cheng; Roy W Beck; Carlo L Acerini; Mark L Evans; David B Dunger; Daniela Elleri; Fiona Campbell; Richard M Bergenstal; Amy Criego; Viral N Shah; Lalantha Leelarathna; Roman Hovorka Journal: Lancet Date: 2018-10-03 Impact factor: 202.731
Authors: J Weissberg-Benchell; J B Shapiro; K Hood; L M Laffel; D Naranjo; K Miller; K Barnard Journal: Diabet Med Date: 2019-03-20 Impact factor: 4.359
Authors: Gregory P Forlenza; Orit Pinhas-Hamiel; David R Liljenquist; Dorothy I Shulman; Timothy S Bailey; Bruce W Bode; Michael A Wood; Bruce A Buckingham; Kevin B Kaiserman; John Shin; Suiying Huang; Scott W Lee; Francine R Kaufman Journal: Diabetes Technol Ther Date: 2018-12-26 Impact factor: 6.118