Remote Patient Monitoring for Youth with Type 1 Diabetes (T1D) Predicted to Experience a Rise in Hemoglobin A1C (A1c)
Authors: David D. Williams, MPH; Diana Ferro, PhD; Brent Lockee, BS; Shelby Carrothers, MPH; Mitchell S. Barnes, BS; Emily DeWit, MASL; Sarah Albert, BS; Britaney Spartz, NONE; Ryan McDonough, DO; Mark A. Clements, MD, PhD David D. Williams Children’s Mercy Kansas City 2401 Gillham Road | Kansas City, MO 64108 913-980-3283 ddwilliams@cmh.edu Background/Objective: One in […]
Schema and Data Validation of T1D Exchange Mapped Data Using Pandera Framework
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 […]
Implementation of a Transition Readiness Assessment and Transition Discussion Documentation in a Type 1 Diabetes Clinic
Authors: Sonalee Ravi, MD; Jaimie Contreras, BSN, RN, CDECS; Julie Kincheloe, BSN, RN, CDECS; Heather Feingold, LSCSW, LCSW; Lydia Sailor BSN, RN, CDECS; Callie Chagas, MS, RD, CDECS; Rebekah Elliott BS, Katelyn Evans, LCSW, LMSW; Diana Ferro, PhD; Emily DeWit, MSAL; Ryan McDonough, DO; Mark Clements, MD PhD Children’s Mercy Kansas City Kansas City, Missouri, […]
Implication of Device Disengagement on Glycemic Control and Diabetic Ketoacidosis in Youth with T1D
Authors: Diana Ferro, PhD; David Williams, MPH; Brent Lockee; Colin Mullaney; Jacob Redel, MD; Lydia Skrabonja; Mark A. Clements MD, PhD; Ryan J. McDonough, DO Children Mercy Research Institute, Kansas City, Missouri, USA dferro@cmh.edu +1 (816) 7317242 Background/Objective: The use of continuous glucose monitors (CGM), insulin pumps (PUMP), and hybrid closed loop systems has been […]
Abstract: Artificial Intelligence And Disparities In Pediatric Type 1 Diabetes Care: Predictive Model Performance Varies By Age And Sex
Authors: Diana Ferro, David D. Williams, Susana R. Patton, Ryan McDonough, Mark A. Clements We can use predictive models to intensify care among youth with type 1 diabetes (T1D) who are predicted to experience a rise in hemoglobin A1c (HbA1c), yet little is known about the impact of age and sex on the performance of […]
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
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)
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
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 […]