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Abstract ContextYouth with type 1 diabetes (T1D) struggle to meet and sustain hemoglobin A1c (HbA1c) targets. Youth enrolled in the Pilot 4T Study improved HbA1c by 0.5% at 1 year, compared to historical controls. ObjectiveTo assess 3 years of glycemic outcomes in the Pilot 4T Study. MethodsThe Pilot 4T Extension cohort was prospectively followed to determine changes in HbA1c and continuous glucose monitoring (CGM) metrics over 3 years at the Stanford Medicine Children's Health Diabetes Clinic. Youth with T1D in the Pilot 4T Study enrolled in the extension phase started CGM in the first month of diabetes diagnosis, received intensified education and remote patient monitoring (RPM) weekly for the first year of diabetes diagnosis, and monthly RPM in the extension phase. HbA1c and CGM metrics were evaluated over the first 3 years of diagnosis. ResultsIn the Pilot 4T cohort, 78.5% (n = 102) of participants enrolled in the study extension phase and were followed through 3 years. The adjusted difference in HbA1c at 3 years was 1.2% (95% CI 0.7%-1.7%) lower in the Pilot 4T cohort than in the Historical cohort. In the Pilot 4T cohort, 68% and 37% met the <7.5% and <7% HbA1c targets at 3 years, respectively, compared to 37% and 20% in the Historical cohort. ConclusionYouth with T1D in the Pilot 4T extension phase sustained improvements in HbA1c over 3 years. Focusing resources on intensive management during the first year after T1D diagnosis may impact long-term glycemia. 

Abstract AimsPsychosocial impacts of early continuous glucose monitoring (CGM) initiation in youth soon after type 1 diabetes diagnosis are underexplored. We report parent/guardian and youth patient‐reported outcomes (PROs) that measure psychosocial states for families in 4T Study 1. Materials and MethodsOf the 133 families in the 4T Study 1, 132 parent/guardian and 66 youth (≥11 years) were eligible to complete PROs. PROs evaluated included diabetes distress, global health, diabetes technology attitudes and CGM benefits/burden scales. Temporal trends of PROs were assessed via generalised linear mixed effects regression. Sociodemographic and clinical characteristics associated with PROs were evaluated. Psychosocial associations were evaluated by regressing parental distress on youth distress. ResultsPRO completion rates were 85.6% and varied between parent/guardian and youth. Throughout the study, parent/guardian and youth distress remained low and youth had increased technology acceptance (p = 0.046). Each additional month of CGM use was associated with a 14% decrease in the odds of experiencing diabetes distress (aOR = 0.86, 95% CI [0.76, 0.99],p = 0.029). Additionally, higher time‐in‐range was associated with decreased diabetes distress (p = 0.048). Age, diabetic ketoacidosis at diagnosis, gender, ethnicity, insurance status and language spoken were not associated with PROs. ConclusionsInitiation of CGM shortly after type 1 diabetes diagnosis does not have unintended negative psychological consequences. Longer duration of CGM use was associated with decreased youth distress and technology acceptance increased throughout the study. 

Hybrid models composing mechanistic ODE- based dynamics with flexible and expressive neural network components have grown rapidly in popularity, especially in scientific domains where such ODE-based modeling offers important interpretability and validated causal grounding (e.g., for counterfactual reasoning). The incorporation of mechanistic models also provides inductive bias in standard blackbox modeling approaches, critical when learning from small datasets or partially observed, complex systems. Unfortunately, as the hybrid models become more flexible, the causal grounding provided by the mechanistic model can quickly be lost. We address this problem by leveraging another common source of domain knowledge: ranking of treatment effects for a set of interventions, even if the precise treatment effect is unknown. We encode this information in a causal loss that we combine with the standard predictive loss to arrive at a hybrid loss that biases our learning towards causally valid hybrid models. We demonstrate our ability to achieve a win-win, state-of-the-art predictive performance and causal validity, in the challenging task of modeling glucose dynamics post-exercise in individuals with type 1 diabetes. 

Abstract Introduction Algorithm‐enabled remote patient monitoring (RPM) programs pose novel operational challenges. For clinics developing and deploying such programs, no standardized model is available to ensure capacity sufficient for timely access to care. We developed a flexible model and interactive dashboard of capacity planning for whole‐population RPM‐based care for T1D. Methods Data were gathered from a weekly RPM program for 277 paediatric patients with T1D at a paediatric academic medical centre. Through the analysis of 2 years of observational operational data and iterative interviews with the care team, we identified the primary operational, population, and workforce metrics that drive demand for care providers. Based on these metrics, an interactive model was designed to facilitate capacity planning and deployed as a dashboard. Results The primary population‐level drivers of demand are the number of patients in the program, the rate at which patients enrol and graduate from the program, and the average frequency at which patients require a review of their data. The primary modifiable clinic‐level drivers of capacity are the number of care providers, the time required to review patient data and contact a patient, and the number of hours each provider allocates to the program each week. At the institution studied, the model identified a variety of practical operational approaches to better match the demand for patient care. Conclusion We designed a generalizable, systematic model for capacity planning for a paediatric endocrinology clinic providing RPM for T1D. We deployed this model as an interactive dashboard and used it to facilitate expansion of a novel care program (4 T Study) for newly diagnosed patients with T1D. This model may facilitate the systematic design of RPM‐based care programs. 

Continuous glucose monitoring (CGM) use soon after T1D diagnosis in the 4T Study was associated with improved glycemic outcomes. We evaluated participant factors associated with elevated versus in target A1c for youth in the 4T Study. All youth from the 4T Study 1 (n=133) were evaluated. In this analysis, the 110 youth who had a final A1c between 6-13 months were included in a complete case analysis. These 110 youth were comparable to the 133 4T Study 1 youth by race/ethnicity, insurance, preferred language, and age. Group differences by non-ordered A1c categories were evaluated for categorical (race/ethnicity, insurance, gender, and language) and continuous (age and time from CGM start) variables via chi-square and ANOVA, respectively. A majority of youth in the 4T Study 1 met glycemic targets (65% with A1c ≤7% between 6-13 months post-diagnosis). Age, race/ethnicity, and insurance status were significantly associated with A1c categories (p=0.02 for all; Table). Higher A1c categories were more likely to include Hispanic youth and youth with public insurance. In the 4T Study 1, Hispanic youth and youth with public insurance had higher A1c categories despite similar CGM access and training. These findings suggest the need to address additional drivers of disparities in addition to CGM access. Approaches focused on health equity are required to improve glycemic outcomes in all youth newly diagnosed with T1D. Disclosure J. Kim: None. D. P. Zaharieva: Advisory Panel; Dexcom, Inc., Research Support; Hemsley Charitable Trust, International Society for Pediatric and Adolescent Diabetes, Insulet Corporation, Speaker's Bureau; American Diabetes Association, Ascensia Diabetes Care, Medtronic. F. K. Bishop: None. D. Scheinker: None. R. Johari: None. M. Desai: None. K. K. Hood: Consultant; Cecelia Health. D. M. Maahs: Advisory Panel; Medtronic, LifeScan Diabetes Institute, MannKind Corporation, Consultant; Abbott, Research Support; Dexcom, Inc. A. Addala: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases (K23DK13134201, R18DK122422)