The goal of the 4T program is to implement proven methods and emerging diabetes technology into clinical practice to sustain tight glucose control from the onset of type 1 diabetes (T1D) and optimize patient-reported and psychosocial outcomes. The investigators will expand the 4T (Teamwork, Targets, Technology, and Tight Control) program to all patients seen at Stanford Pediatric Diabetes Endocrinology as the standard of care. Disseminating the 4T program as the standard of care will optimize the benefits of diabetes technology by lowering HbA1c, improving PROs, and reducing disparities.
The investigators propose a multi-disciplinary diabetes team approach to utilize existing and emerging diabetes technologies and education strategies to implement scalable diabetes care with the goal of maintaining tight control for newly diagnosed Pediatric T1D patients. Successful T1D care is more than glucose control; the 4T program will closely monitor psychosocial and patient-reported outcomes (PROs) through the established psychosocial screening and treatment program. The 4T program will utilize automated approaches to analyze glucose profiles developed in conjunction with the Stanford SURF (Systems Utilization Research For Stanford Medicine) team. The 4T program will tailor intervention to those patients most in need of team support employing a broad range of behavioral and technology supports, strengths of the Stanford Pediatric Diabetes program. The 4T program approach more precisely adapts care to patient needs and optimizes glucose and psychosocial outcomes. Therefore, the investigators propose to expand the diabetes team approach (Teamwork, Targets, and Technology for Tight Control: 4T), which utilizes existing diabetes technologies and education strategies to implement and standardize goal-oriented diabetes care for newly diagnosed pediatric T1D patients, to a standard of care program at Stanford pediatric diabetes clinics. The Stanford diabetes team has revised the diabetes education approach to set clear and tighter targets at new-onset through the early phase of T1D and to be aggressive with the intensification of control as insulin needs and care demands increase. The 4T Sustainability program will further standardize T1D team care while allowing for personalization based on patient/family needs with the goal of optimizing glucose and psychosocial outcomes. (NOTE: the current protocol \[IRB #52812\] focuses on early initiation of CGM as the key technology for glucose monitoring. The 4T program is designed to be flexible to incorporate emerging technologies as they become available for patient care. For example, automated insulin delivery systems will be supported as part of the program). To make the 4T Sustainability Program a true standard of care program, remote patient monitoring (RPM) of the CGM data and CDCES-tailored patient contacts will be billed using RPM billing codes. These RPM billing codes have received hospital compliance approval, and billing workflows will be available in LPCH EPIC. Personalized Goals and Automated Identification for Need for Insulin Changes in New Onsets: Data collected by continuous glucose monitoring (CGM) can improve the quality of patient care; facilitate the use of telemedicine to reduce costs and improve convenience; and improve the understanding of how daily behaviors shape long-term outcomes. Modern Electronic Health Records (EHRs) can potentially be used to reduce the workload necessary to care for patients; reduce error rates by automating alerts; and facilitate continuous evaluation and improvement of provider adherence to best-practices. To be practical and scalable, these systems must provide actionable information and require relatively few inputs. The investigators have developed stand-alone tools that analyze CGM time series data to set personalized care goals and to determine when glucose values increase, or insulin dose adjustments and other care adaptations are required. To allow Stanford and the broader community to realize the full potential of such tools, the investigators will implement and refine their use in our clinics: 1. Implement and refine analytical methods and software tools to interpret and improve the quality of time-series CGM data, set personalized goals, and generate automated notifications for care providers. 2. A data-driven intervention to improve long-term outcomes for early onset patients including a hospital- server-based monitoring system to evaluate the intervention. \[Note: identification is the goal of the system and medical advice will be solely given by trained healthcare professionals.\]
Study Type
OBSERVATIONAL
Enrollment
5,000
1\. Implement the 4T program as standard of care at Stanford Diabetes clinics, including Continuous Glucose Monitoring (CGM) and Remote Patient Monitoring (RPM) within the first 30 days after T1D diagnosis to reduce the rise in HbA1c trajectory observed 4-12 months post-diagnosis.
Lucile Packard Children's Hospital
Palo Alto, California, United States
HbA1c trajectory observed 4-12 months post-diagnosis
Implement 4T program as standard of care, including Continuous Glucose Monitoring (CGM) and Remote Patient Monitoring (RPM) within the first 30 days after T1D diagnosis to reduce the rise in HbA1c trajectory observed 4-12 months post-diagnosis.To address Aim 1, the investigators will use generalized linear mixed effects regression techniques that allows for two piecewise linear slopes of HbA1c levels to be estimated from diagnosis to 4 months and from 4 to 12 months post-diagnosis to determine the effect of the 4T diabetes intervention on changes in HbA1c between 4- and 12-months post-diagnosis and compare observed increases to those in our internal and external contemporaneous controls via separate models. Each mixed effects model will include a subject-specific random effect to account for the correlation of HbA1c within a person over time, and these models will be adjusted for sex, age, ethnicity, and insurance type at diagnosis.
Time frame: 4-12 months post TID diagnosis
Diabetes distress measured at baseline and 12 months post-diagnosis.
The investigators will utilize generalized linear mixed effects models to address Aim 2. More specifically, the investigators will regress diabetes distress index on use of CGM. Such a model will include a subject-specific random effect and an indicator of whether the patient utilized CGM technologies. Assuming the ratio of using CGM technology is 0.6 and SD of diabetes distress score is 3, we have 90% power to detect a two-unit reduction on mean diabetes distress score.
Time frame: baseline and 12 months post Type 1 Diabetes diagnosis
This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.