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The American Diabetes Association (ADA) “Standards of Care in Diabetes” includes the ADA’s current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA’s clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

The American Diabetes Association (ADA) “Standards of Care in Diabetes” includes the ADA’s current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA’s clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

The American Diabetes Association (ADA) “Standards of Care in Diabetes” includes the ADA’s current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA’s clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

The American Diabetes Association (ADA) “Standards of Care in Diabetes” includes the ADA’s current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA’s clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

The American Diabetes Association (ADA) “Standards of Care in Diabetes” includes the ADA’s current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA’s clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

The American Diabetes Association (ADA) “Standards of Care in Diabetes” includes the ADA’s current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA’s clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

The American Diabetes Association (ADA) “Standards of Care in Diabetes” includes the ADA’s current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA’s clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

The American Diabetes Association (ADA) “Standards of Care in Diabetes” includes the ADA’s current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA’s clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

The American Diabetes Association (ADA) “Standards of Care in Diabetes” includes the ADA’s current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA’s clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

During the COVID-19 pandemic, fewer in-person clinic visits resulted in fewer point-of-care (POC) HbA1c measurements. In this sub-study, we assessed the performance of alternative glycemic measures that can be obtained remotely, such as HbA1c home kits and Glucose Management Indicator (GMI) values from Dexcom Clarity. Home kit HbA1c (n = 99), GMI, (n = 88), and POC HbA1c (n = 32) were collected from youth with T1D (age 9.7 ± 4.6 years). Bland–Altman analyses and Lin’s concordance correlation coefficient (𝜌c) were used to characterize the agreement between paired HbA1c measures. Both the HbA1c home kit and GMI showed a slight positive bias (mean difference 0.18% and 0.34%, respectively) and strong concordance with POC HbA1c (𝜌c = 0.982 [0.965, 0.991] and 0.823 [0.686, 0.904], respectively). GMI showed a slight positive bias (mean difference 0.28%) and fair concordance (𝜌c = 0.750 [0.658, 0.820]) to the HbA1c home kit. In conclusion, the strong concordance of GMI and home kits to POC A1c measures suggest their utility in telehealth visits assessments. Although these are not candidates for replacement, these measures can facilitate telehealth visits, particularly in the context of other POC HbA1c measurements from an individual.

The coronavirus disease 2019 (COVID-19) pandemic necessitated using telehealth to bridge the clinical gap, but could increase health disparities. This article reports on a chart review of diabetes telehealth visits occurring before COVID-19, during shelter-in-place orders, and during the reopening period. Visits for children with public insurance and for those who were non-English speaking were identified. Telehealth visits for children with public insurance increased from 26.2% before COVID-19 to 37.3% during shelter-in-place orders and 34.3% during reopening. Telehealth visits for children who were non-English speaking increased from 3.5% before COVID-19 to 17.5% during shelter-in-place orders and remained at 15.0% during reopening. Pandemic-related telehealth expansion included optimization of workflows to include patients with public insurance and those who did not speak English. Increased participation by those groups persisted during the reopening phase, indicating that prioritizing inclusive telehealth workflows can reduce disparities in access to care.

STRUCTURED ABSTRACTObjectivesPost-acute sequalae of SARS-CoV-2 infection (PASC) is not well defined in pediatrics given its heterogeneity of presentation and severity in this population. The aim of this study is to use novel methods that rely on data mining approaches rather than clinical experience to detect conditions and symptoms associated with pediatric PASC.Materials and MethodsWe used a propensity-matched cohort design comparing children identified using the new PASC ICD10CM diagnosis code (U09.9) (N=1309) to children with (N=6545) and without (N=6545) SARS-CoV-2 infection. We used a tree-based scan statistic to identify potential condition clusters co-occurring more frequently in cases than controls.ResultsWe found significant enrichment among children with PASC in cardiac, respiratory, neurologic, psychological, endocrine, gastrointestinal, and musculoskeletal systems, the most significant related to circulatory and respiratory such as dyspnea, difficulty breathing, and fatigue and malaise.DiscussionOur study addresses methodological limitations of prior studies that rely on pre-specified clusters of potential PASC-associated diagnoses driven by clinician experience. Future studies are needed to identify patterns of diagnoses and their associations to derive clinical phenotypes.ConclusionWe identified multiple conditions and body systems associated with pediatric PASC. Because we rely on a data-driven approach, several new or under-reported conditions and symptoms were detected that warrant further investigation.LAY SUMMARYPediatric long COVID in children does not currently have a precise clinical definition, in part due to its widely varying presentation in kids. By comparing children diagnosed with long COVID to children who had COVID-19 but were not diagnosed with long COVID, this study identified several groups of symptoms and conditions that are associated with pediatric long COVID. These findings can be used towards developing a precise definition of long COVID in children for use in future studies.

The American Diabetes Association (ADA) “Standards of Medical Care in Diabetes” includes the ADA’s current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee (https://doi.org/10.2337/dc22-SPPC), are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA’s clinical practice recommendations, please refer to the Standards of Care Introduction (https://doi.org/10.2337/dc22-SINT). Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

ObjectiveThis study was conducted to identify rates of pediatric nirmatrelvir/ritonavir (Paxlovid) prescriptions overall and by patient characteristics.MethodsPatients up to 23 years old with a clinical encounter and a nirmatrelvir/ritonavir (Paxlovid, n/r) prescription in a PEDSnet-affiliated institution between December 1, 2021 and September 14, 2022 were identified using electronic health record (EHR) data.ResultsOf the 1,496,621 patients with clinical encounters during the study period, 920 received a nirmatrelvir/ritonavir prescription (mean age 17.2 years; SD 2.76 years). 40% (367/920) of prescriptions were provided to individuals aged 18-23, and 91% (838/920) of prescriptions occurred after April 1, 2022. The majority of patients (70%; 648/920) had received at least one COVID-19 vaccine dose at least 28 days before nirmatrelvir/ritonavir prescription. Only 40% (371/920) of individuals had documented COVID-19 within the 0 to 6 days prior to receiving a nirmatrelvir/ritonavir prescription. 53% (485/920) had no documented COVID-19 infection in the EHR. Among nirmatrelvir/ritonavir prescription recipients, 64% (586/920) had chronic or complex chronic disease and 9% (80/920) had malignant disease. 38/920 (4.5%) were hospitalized within 30 days of receiving nirmatrelvir/ritonavir.ConclusionClinicians prescribe nirmatrelvir/ritonavir infrequently to children. While individuals receiving nirmatrelvir/ritonavir generally have significant chronic disease burden, a majority are receiving nirmatrelvir/ritonavir prescriptions without an EHR-recorded COVID-19 positive test or diagnosis. Development and implementation of concerted pediatric nirmatrelvir/ritonavir prescribing workflows can help better capture COVID-19 presentation, response, and adverse events at the population level.

The American Diabetes Association (ADA) “Standards of Care in Diabetes” includes the ADA’s current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA’s clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

The American Diabetes Association (ADA) “Standards of Care in Diabetes” includes the ADA’s current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA’s clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

The American Diabetes Association (ADA) “Standards of Care in Diabetes” includes the ADA’s current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA’s clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

The American Diabetes Association (ADA) “Standards of Care in Diabetes” includes the ADA’s current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA’s clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

The American Diabetes Association (ADA) “Standards of Care in Diabetes” includes the ADA’s current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA’s clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Although advances in diabetes technology and pharmacology have significantly and positively impacted diabetes management and health outcomes for some, diabetes care remains burdensome and can be challenging to balance with other life priorities. The purpose of this article is to review the rationale for assessment of psychosocial domains in diabetes care settings and strategies for the implementation of psychosocial screening into routine practice. Survey data from the Type 1 Diabetes Exchange Quality Improvement Network is highlighted.Implementation of psychosocial screening requires identifying the population; selecting validated tools to assess target domains; determining frequency of screening and mode of survey delivery; and scoring, interpreting, documenting, and facilitating referrals such that these processes are part of clinical workflows. Recognizing the influence of psychosocial factors for people with diabetes (PWD), professional society guidelines for comprehensive diabetes care recommend the integration of psychosocial screening into routine care.

This article is the work product of the Continuous Ketone Monitoring Consensus Panel, which was organized by Diabetes Technology Society and met virtually on April 20, 2021. The panel consisted of 20 US-based experts in the use of diabetes technology, representing adult endocrinology, pediatric endocrinology, advanced practice nursing, diabetes care and education, clinical chemistry, and bioengineering. The panelists were from universities, hospitals, freestanding research institutes, government, and private practice. Panelists reviewed the medical literature pertaining to ten topics: (1) physiology of ketone production, (2) measurement of ketones, (3) performance of the first continuous ketone monitor (CKM) reported to be used in human trials, (4) demographics and epidemiology of diabetic ketoacidosis (DKA), (5) atypical hyperketonemia, (6) prevention of DKA, (7) non-DKA states of fasting ketonemia and ketonuria, (8) potential integration of CKMs with pumps and automated insulin delivery systems to prevent DKA, (9) clinical trials of CKMs, and (10) the future of CKMs. The panelists summarized the medical literature for each of the ten topics in this report. They also developed 30 conclusions (amounting to three conclusions for each topic) about CKMs and voted unanimously to adopt the 30 conclusions. This report is intended to support the development of safe and effective continuous ketone monitoring and to apply this technology in ways that will benefit people with diabetes.

BackgroundPost-acute sequelae of SARS-Co-V-2 infection (PASC) is associated with worsening diabetes trajectory. It is unknown whether PASC in children with type 1 diabetes (T1D) manifests as worsening diabetes trajectory.ObjectiveTo explore the association between SARS-CoV-2 infection (COVID-19) and T1D-related healthcare utilization (for diabetic ketoacidosis [DKA] or severe hypoglycemia [SH]) or Hemoglobin (Hb) A1c trajectory.MethodsWe included children ResultsFrom 03/01/2020-06/22/2022, 2,404 and 1,221 youth met entry criteria for the Broad and Narrow cohorts, respectively. The hazard ratio for utilization was (HR 1.45 [95%CI,0.97,2.16]). In the Narrow Cohort, the rate of change (slope) of HbA1c increased 91-180 days after cohort entry for those with COVID-19 (0.138 vs. -0.002, p=0.172). Beyond 180 days, greater declines in HbA1c were observed in the positive cohort (-0.104 vs. 0.008 per month, p=0.024).ConclusionWhile a trend towards worse outcomes following COVID-19 in T1D patients was observed, these findings were not statistically significant. Continued clinical monitoring of youth with T1D following COVID-19 is warranted.Authorship StatementAuthorship has been determined according to ICMJE recommendationsDisclaimerThe content is solely the responsibility of the authors and does not necessarily represent the official views of the RECOVER Program, the NIH or other funders.Funding Source⍰This research was funded by the National Institutes of Health (NIH) Agreement OT2HL161847-01 as part of the Researching COVID to Enhance Recovery (RECOVER) program of research.

ContextEarly initiation of continuous glucose monitoring (CGM) is advocated for youth with type 1 diabetes (T1D). Data to guide CGM use on time-in-range (TIR), hypoglycemia, and the role of partial clinical remission (PCR) are limited.ObjectiveOur aims were to assess whether 1) an association between increased TIR and hypoglycemia exists, and 2) how time in hypoglycemia varies by PCR status.MethodsWe analyzed 80 youth who were started on CGM shortly after T1D diagnosis and were followed for up to 1-year post diagnosis. TIR and hypoglycemia rates were determined by CGM data and retrospectively analyzed. PCR was defined as (visit glycated hemoglobin A1c) + (4*units/kg/day) less than 9.ResultsYouth were started on CGM 8.0 (interquartile range, 6.0-13.0) days post diagnosis. Time spent at less than 70 mg/dL remained low despite changes in TIR (highest TIR 74.6 ± 16.7%, 2.4 ± 2.4% hypoglycemia at 1 month post diagnosis; lowest TIR 61.3 ± 20.3%, 2.1 ± 2.7% hypoglycemia at 12 months post diagnosis). No events of severe hypoglycemia occurred. Hypoglycemia was rare and there was minimal difference for PCR vs non-PCR youth (54-70 mg/dL: 1.8% vs 1.2%, P = .04; ConclusionAs TIR gradually decreased over 12 months post diagnosis, hypoglycemia was limited with no episodes of severe hypoglycemia. Hypoglycemia rates did not vary in a clinically meaningful manner by PCR status. With CGM being started earlier, consideration needs to be given to modifying CGM hypoglycemia education, including alarm settings. These data support a trial in the year post diagnosis to determine alarm thresholds for youth who wear CGM.

The American Diabetes Association (ADA) “Standards of Care in Diabetes” includes the ADA’s current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA’s clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

BackgroundChronic medical conditions are a risk factor for moderate or severe COVID-19 in children, but little is known about post-acute sequelae of SARS-CoV-2 infection (PASC) in children with chronic medical conditions (CMCs). To understand whether SARS-CoV-2 infection led to potential exacerbation of underlying chronic disease in children, we explored whether children with CMCs had increased healthcare utilization in the post-acute (28 days after infection) period compared to children with CMCs without SARS-CoV-2 infection.MethodsWe conducted a retrospective, matched-cohort study using electronic health record data collected from 8 pediatric health care systems participating in the PEDSnet network. We included children ResultsWe identified 748,692 patients with at least one chronic condition, 78,744 of whom met inclusion criteria for the COVID-19 cohort. 96% of patients from the positive cohort were matched. Cohorts were well-balanced for chronic condition clusters, total number of conditions, time since first diagnosis, baseline utilization, cohort entry period, age, sex, race/ethnicity and test location. We found that among children with chronic medical conditions, those with COVID-19 had higher healthcare utilization than those with no recorded COVID-19 diagnosis or positive test, with utilization rate ratio of 1.21 (95% CI: 1.18-1.24). The utilization was highest for inpatient care with utilization rate ratio of 2.03 (95% CI: 1.85-2.23) but the utilization was increased across all settings. Hazard ratios estimated in time-to-first-utilization analysis mirrored these results. Patients with severe or moderate acute COVID-19 illness had greater increases in utilization in all settings than those with mild or asymptomatic disease.ConclusionsWe found that care utilization in all settings was increased following COVID-19 in children with chronic medical conditions in the post-acute period, particularly in the inpatient setting. Increased utilization was correlated with more severe COVID-19. Additional research is needed to better understand the reasons for higher care utilization by studying condition-specific outcomes in children with chronic disease.

Background: The T1D Exchange Quality Improvement Collaborative (T1Dx-QI) is a learning network of over forty US type 1 diabetes clinics. Clinics share best-practice ideas for quality improvement and use data for population health research. This study benchmarks continuous glucose monitor (CGM) use across pediatric clinics in the T1Dx-QI. Method: T1Dx-QI clinics report monthly on critical metrics, including the percentage of patients with hemoglobin A1c (HbA1c) Results: Across 17 T1Dx-QI clinics, the percentage of patients using CGM ranged from 23% to 85% (Figure 1; mean and median CGM use were 61.3% and 61.7%, respectively) . Median HbA1c values ranged from 7.4% to 9.2%. There were no observed differences in CGM use by patient population size. Conclusion: The T1Dx-QI benchmarks key clinical metrics to support quality improvement, share best practices, and promote learning across clinics. Disclosure A. Garrity: None. A. Mungmode: None. K. Cossen: None. M. Gallagher: None. A.J. Feuer: None. D. Brimberry: None. P. Prahalad: None. M.L. Scott: None. O. Ebekozien: None. D. Eng: None. Funding The Leona M. and Harry B. Helmsley Charitable Trust

T1DX-QI is a network of over 40 pediatric and adult centers that share best practices to improve outcomes for people with T1D. Guidelines for comprehensive diabetes care recommend integration of psychosocial screening; however, variable resources and clinical structures influence the degree to which practices can implement these recommendations. A 22-item survey was administered to T1DX-QI sites in 2021 (n= 25 pediatric and 8 adult) . Data from items relevant to psychosocial screening were analyzed with Rstudio. Fisher tests were used to compare the percentage of psychosocial domains assessed by pediatric vs. adult clinics. Overall, 96% of pediatric centers report using at least one screening tool, 79% report > 2; 43% of adult centers use one or more screening tools. The most common psychosocial domain in both settings is depression; pediatric (96%) , adult (38%) (Figure) . Screening for social determinants of health and diabetes-related distress were reported in both settings, and unique to pediatric is assessment of readiness to transition to adult care. Varying degrees of psychosocial screening is present in the T1DX-QI network. Centers endorse benefits, as well as common challenges, including time constraints, integration into workflow, and how best to follow up responses. T1DX-QI sites are working together to overcome barriers that contribute to gaps between guidelines and practice. Disclosure S.Corathers: None. J.C.Kichler: None. O.Ebekozien: None. E.L.Ospelt: None. S.Rompicherla: None. P.Prahalad: None. A.J.Roberts: None. C.E.Muñoz: None. M.Basina: None. L.B.Smith: None. D.N.Williford: None. Funding The Leona M. and Harry B. Helmsley Charitable Trust

We previously examined a type 1 diabetes care model based on the use of a remote patient monitoring (RPM) tool that analyzes CGM data. The care model was associated with improved glucose management and reduced provider effort. We compare the financial feasibility of this care model in value-based care (VBC) and fee-for-service (FFS) settings. We set parameters based on the current deployment of the care model. In the FFS setting, we estimated Medicare reimbursement rates and labor costs for in-person diabetes education. We compared these to costs in a VBC setting where providers make prospective payments for RPM services. We evaluated the financial impact of the care model across a variety of parameters (Fig) . In a FFS setting, we estimated that in-person care incurs net costs of $1per patient. Under a VBC contract, we estimated that prospective payments are offset by reduced labor costs, incurring net costs of $65 per patient (i.e., 49% savings) . These results are sensitive to input parameters (Fig) . We identified financially viable payment models for an algorithm-enabled telemedicine-based care model associated with improved T1D glucose management. The analysis may inform operational planning at the clinic level as well as reimbursement policy design. Disclosure R.Leonard pei: None. P.Dupenloup: None. P.Prahalad: None. R.Johari: None. A.Addala: None. D.P.Zaharieva: Research Support; Insulet Corporation, International Society for Pediatric and Adolescent Diabetes, Leona M. and Harry B. Helmsley Charitable Trust. D.M.Maahs: Advisory Panel; Abbott Diabetes, Eli Lilly and Company, Medtronic, Novo Nordisk, Sanofi, Consultant; Aditx Therapeutics, Inc., Biospex. D.Scheinker: None. Funding Helmsley Charitable Trust.ISPAD-JDRF Fellowship.R18DK122422.SDRC.LPCH Auxiliaries.The Stanford REDCap platform (http://redcap.stanford.edu) is developed and operated by Stanford Medicine Research IT team. The REDCap platform services at Stanford are subsidized by a) Stanford School of Medicine Research Office, and b) the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health, through grant UL1 TR001085.Stanford Maternal and Child Health Research Institute.

We previously showed that the use of a tool for remote analysis of continuous glucose monitor data, Timely Interventions for Diabetes Excellence (TIDE) , was associated with improved glucose management for patients with type 1 diabetes. To facilitate the deployment of TIDE at two partner institutions, we developed an operational planning tool. We designed an interactive dashboard to match capacity and demand with a variety of operational, population, and workforce parameters. To improve the dashboard’s accessibility, simplicity, and interpretability, we performed iterative design based on feedback from users and two partner institutions. We set parameter values based on the first deployment of TIDE and conducted sensitivity analyses of the capacity-demand match. The dashboard (Figure 1) contains seven modifiable parameters; generates a table illustrating the match between capacity and demand; displays a timeseries plot of capacity and demand; and calculates three alternative capacity plans that satisfy annual patient demand. It is available online (www.bit.ly/surf-tide) . Small absolute reductions in per-patient review time produced the most significant changes in clinic capacity. We developed an interactive dashboard that facilitates data-driven operational planning for clinics seeking to deploy algorithm-enabled telemedicine-based diabetes care. Figure 1. Capacity Planning Dashboard Disclosure A.Chang: None. M.Z.Gao: None. J.Ferstad: None. D.M.Maahs: Advisory Panel; Abbott Diabetes, Eli Lilly and Company, Medtronic, Novo Nordisk, Sanofi, Consultant; Aditx Therapeutics, Inc., Biospex. P.Prahalad: None. R.Johari: None. D.Scheinker: None. Funding NIH R18DK122422

In the Pilot 4T study (n=135) , remote patient monitoring (RPM) was associated with improved time in range (TIR) and lower A1c. Measuring differences in how patients respond to messages from the care team is essential for the design of effective, personalized care models. We analyzed electronic health record (EHR) and continuous glucose monitor (CGM) data to estimate the week-over-week impact of RPM messages on patients’ TIR. We applied statistical clustering methods to divide patients who received messages into two groups based on their changes in TIR and compared the characteristics of these groups. Receiving a message was associated with a greater mean week-to-week improvement in TIR after a low-TIR week [4.9 percentage points (pp) after a message vs. 2.5pp without a message; p < 0.001]. A patient’s TIR improvement after receiving a message is greater on average if the same patient’s TIR improved following previous messages. We identified two groups of patients with significantly different responses to messages. The group with greater mean TIR improvement after messages contains a larger proportion of non-white, non-English speaking, and publicly insured patients. We found that messages were associated with different magnitudes of TIR improvement across two clusters of patients. Identifying patients who benefit more from RPM could facilitate the personalization of management strategies. Disclosure J.Ferstad: None. P.Prahalad: None. D.M.Maahs: Advisory Panel; Abbott Diabetes, Eli Lilly and Company, Medtronic, Novo Nordisk, Sanofi, Consultant; Aditx Therapeutics, Inc., Biospex. E.Fox: None. R.Johari: None. D.Scheinker: None. Funding Helmsley Charitable Trust, ISPAD-JDRF Fellowship,NIH R18DK122422,Stanford Diabetes Research Center,LPCH Auxiliaries,Stanford Maternal and Child Health Research Institute, The Stanford REDCap platform (http://redcap.stanford.edu) is developed and operated by Stanford Medicine Research IT team. The REDCap platform services at Stanford are subsidized by a) Stanford School of Medicine Research Office, and b) the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health, through grant UL1 TR001085.

The California government imposed a shelter-in-place (SIP) order on 3/15/2020 to slow the spread of COVID-19. For many children and adolescents, particularly youth with type 2 diabetes (T2D) , school closures led to major changes in daily routines, affecting physical activity levels and dietary choices. We performed a retrospective descriptive study on youth ages 7 to 21 years with T2D who were seen at our health care system at least one time in the year preceding SIP and again in the year after, allowing us to calculate change in BMI over time during SIP. Utilizing databases from the United States government, we examined the effects of living in an area with at least one risk factor (“food deserts” with a paucity of healthy food, “food swamps” with an abundance of unhealthy food, or rural regions) on BMI change compared to low-risk neighborhoods. We included 78 youth with T2D and 46% lived in at-risk areas. Overall, youth had a slight increase in BMI during SIP (0.± 0.2 kg/m2/month) . Youth living in at-risk areas had a rise in BMI during this time period (0.02 ± 0.2 kg/m2/month) , whereas youth in low-risk areas had a small drop in BMI (-0.± 0.2 kg/m2/month) . As this study was not powered to detect group differences, further investigation of neighborhood risk factors is needed to aid in tailoring community-level interventions to combat obesity in youth with T2D. Disclosure J.Ni: None. P.Prahalad: None. A.Addala: None.