February 2022

Background: where we started In 2020, we launched a new effort called the Rising T1DE Alliance (formerly known as the Rapid Learning Lab), funded by a 3-year, $8.5M grant from The Leona M. & Harry B. Helmsley Charitable Trust. This exciting effort is led by Dr. Mark Clements at Children’s Mercy, in collaboration with Dr. […]

Remote Patient Monitoring for Youth with Type 1 Diabetes (T1D) Predicted to Experience a Rise in Hemoglobin A1C (A1c)

graphic: teen girl in a blue cap and gown at graduation

Authors: David D. Williams, MPH; Diana Ferro, PhD; Brent Lockee, BS; Shelby Carrothers; Mitchell S. Barnes, BS; Emily DeWit, MASL; Sarah Albert, BS; Britaney Spartz; Ryan McDonough, DO; Mark A. Clements, MD, PhD David D. WilliamsChildren’s Mercy Kansas City2401 Gillham Road | Kansas City, MO 64108913-980-3283ddwilliams@cmh.edu Background/Objective: One in five youth with T1D experience worsening […]

Schema and Data Validation of T1D Exchange Mapped Data Using Pandera Framework

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Authors: Brent Lockee; Mitchell Barnes; Emily Dewit; Mark C. Clements MD, Ph.D; Diana Ferro, Ph.D.    Children’s Mercy Hospitals Kansas City, Missouri, United States bclockee@cmh.edu   Background/Objective: Data registries, such as T1D Exchange, advance data-driven innovation by compiling data from centers across the nation and using them to answer complex questions and develop strategies to improve patient outcomes. That […]

Abstract: Direct-to-consumer telehealth to support youth with Type 1 Diabetes (T1D) predicted to experience a rise in hemoglobin A1c (A1c): A pragmatic trial

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Authors: Emily L. DeWit, David D. Williams, Susana R. Patton, Colin Mullaney, Diana Ferro, Katie Noland, Lydia Skrabonja, Britaney Spartz, Rebekah Elliott, Robin L. Kenyon, Ryan McDonough, Sanjeev Mehta, Leonard D’Avolio, Mark A. Clements Background and aims: One in five youth with T1D experience worsening HbA1c values between quarterly visits. We evaluated the effectiveness of […]

Abstract: Comparative performance of a recurrent neural network (RNN) and logistic regression (LR) model to predict diabetic ketoacidosis (DKA) among youth with established type 1 diabetes (T1D)

little girl holding book over her head

Authors: David D. Williams, Sarina Dass, Jon Bass, Susana Patton, Sanjeev Mehta, Ryan McDonough, Colin Mullaney, Leonard D’Avolio, Mark Clements     Preventing dangerous and costly episodes of DKA is a goal of diabetes care, but clinicians lack tools to predict DKA events. We sought to compare performance characteristics of an RNN model with that of a […]

Remote Patient Monitoring

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Youth in the United States collectively exhibit deterioration in glycemic control between the ages of 8 and 18, with small improvements beginning to show from ages 18 to 30. Evidence suggests that suboptimal control early in the course of disease has an irrevocable impact on the risk of developing future health complications. To combat this […]