bluuebunny/crossref_metadata_2025_split huggingface dataset:
1056-P: Cost-Effectiveness of the MiniMed 780G System vs. Multiple Daily Injections (MDI) Used with the Dexcom G6 Continuous Glucose Monitoring (CGM) System
Authors: KOMMAREDDI MALLIKA , VIGERSKY ROBERT A. 
Published: June 2024

Objective: A simulation study assessed cost-effectiveness of the MiniMed™ 780G system (MM780G) compared to MDI used with Dexcom G6 CGM (MDI+G6) in people with T1D in the United States.
Methods: The IQVIA Core Diabetes Model was used to estimate costs and quality of life over a lifetime horizon. Demographic and clinical characteristics were sourced from a 6-month randomized controlled trial1. The cohort had an age (mean±SD) of 45.4±13.3 years, was 62.5% male, and had an A1C of 8.9%±0.5% at baseline. Total daily dose (TDD) of insulin at baseline differed between groups (52.8±29.1 for MM780G vs. 79.2±47.1 for MDI+G6). After 6 months, MM780G reduced A1C by 1.7±1.0% and MDI+G6 by 0.6±1.3%. Rates of hypoglycemia with MDI + real-time CGM therapy were taken from the literature and, also, used as rates of hypoglycemia for the MM780G. One-way sensitivity analyses were performed to assess the impact of model assumptions, and cost-effectiveness acceptability curves were used to find the probability of cost-effectiveness at a commonly accepted willingness-to-pay (WTP) threshold ($150,000).
Results: The base case results show that MM780G was associated with increased quality-adjusted life expectancy (QALE) compared to MDI+G6 (11.31±0.15 years for MM780G vs. 10.34±0.13 for MDI+G6), as well as increased costs ($453,962 ± $10,858 for MM780G vs. $363,470 ± $10,736 for MDI+G6). The incremental cost-effectiveness ratio (ICER) was $92,917 per QALE, which has a 96.3% probability of being cost-effective at the WTP threshold. Assuming the same TDD between groups, analyses determined that MM780G was associated with an ICER of $136,189 per QALE and a 67.7% probability of being cost effective.
Conclusions: At a WTP threshold of $150,000, MM780G is cost effective in T1D versus therapy with MDI + Dexcom G6, in the United States.
Reference:
1. van den Heuvel T, et al. J Diabetes Sci Technol. 2023; doi:10.1177/19322968231161320.

Disclosure
M. Kommareddi: Employee; Medtronic. R.A. Vigersky: Employee; Medtronic.
1077-P: Optimizing In-Clinic Conversations Using Data Science and AI to Promote Medication Adherence
Authors: MALLABADI RAJSHRI , KALRA SANJAY 
Published: June 2024

Introduction and Objective: Limited time for meaningful conversations between the healthcare providers (HCP) and persons with diabetes (PWD) results in non-adherence to prescribed treatment in 60% of the cases. We intend to deploy an artificial intelligence (AI)-based model/tool that suggests positive words/ language to promote biopsychosocial wellbeing of PWD and this improve medication adherence.
Methods: The AI model has been developed using psychologically safe words as a base. The front end of the tool is a simple interface that acts as an in-clinic companion for HCPs and can be integrated into the existing electronic health record platforms.
Results: The proof of concept suggested appropriate conversation cues for a pilot of 25 PWD. The most impactful words in the context of improving medication adherence were “vigilant,” “thrive,” “nourishing,” “conquer” and “transform.” The least impactful words included “uncontrolled,” “avoid,” “negligent,” “sedentary” and “compromise.” The game changing conversational sentence was “Your vigilance is helping you thrive in this transformative journey.” All 25 PWD agreed that a good conversation with their HCP motivated them to focus on self-care more actively.
Conclusion: Using data science and AI to optimize in-clinic conversations has the potential to solve a pressing problem in diabetes care - that of using the right language for the right PWD. The proposed AI tool has multi-lingual capabilities, is convenient and simple to use and could prove to be a significant addition to the in-clinic armamentarium for HCPs. The tool may also help in easing cognitive burden for care providers.

Disclosure
R. Mallabadi: None. S. Kalra: Speaker's Bureau; Abbott, Bayer Inc., Novo Nordisk, Sanofi.
1085-P: Implementation of a MAR-Based Insulin Calculator at the University of California Davis Health
Authors: LEE RACHAEL O. , SCHULMEISTER CAROLINE , SCHAUER DANNIKA , BAGLEY BERIT M. 
Published: June 2024

Insulin is a high-risk medication and can lead to significant morbidity and mortality, especially in young children who require smaller, weight- based dosing. It is also one of the most common medications listed in incident reports within inpatient pediatric units at University of California Davis Children’s Hospital (UCDCH) due to manual calculation and entry errors. We sought to answer, does the use of a MAR based insulin calculator reduce the number subcutaneous insulin dose errors for an inpatient children’s hospital? A MAR based insulin calculator was configured into Epic Hyperspace, insulin order sets were updated, nursing policies were updated, communication tools were delivered to advise staff of new calculator workflow, and inpatient pediatric nurses were assigned an electronic learning module. Nurse champions were trained on the calculator and were utilized for unit-based education and real time support. From January 2020 through January 2022, there were 53 subcutaneous insulin dosing errors in the inpatient pediatric setting at UCDCH. After implementation of the MAR based calculator there were 2 errors over an eight-week period. A MAR based insulin calculator reduced the number of subcutaneous insulin dose errors by 50% in a children’s hospital. A MAR based insulin calculator suggests safer inpatient use of insulin although more time is needed to evaluate the full scope of error reduction.

Disclosure
R.O. Lee: None. C. Schulmeister: None. D. Schauer: None. B.M. Bagley: None.
1089-P: Educational Intervention on the Implementation of the Six Core Elements of Health Care Transition and Teach-Back Method in Type 1 Diabetes
Authors: DE ALMAGRO CAROLINA M. , CARRILLO ADRIANA , VERME JACQUELYN S. , GOLDIN DEANA S. 
Published: June 2024

Introduction & Objective: Structured healthcare transition (S-HCT), the process of moving from pediatric to adult healthcare services, promotes optimal health outcomes in adolescents diagnosed with Type 1 diabetes mellitus (T1DM). This study assessed for changes in clinicians’ knowledge, attitudes, and behaviors before and after an educational seminar that included evidence to support a S-HCT process for youth diagnosed with T1DM, the Six Core Elements of Healthcare Transition (6CEoHCT) framework, the teach-back method for communication and a roadmap to operationalize the process in a hospital’s endocrinology department.
Methods: After IRB approval, clinicians’ knowledge, attitudes, and behaviors regarding a S-HCT process were assessed using a pre-implementation survey. Then an educational seminar that included the 6CEoHCT, the teach-back method as a communication strategy and a visual guide to support clinicians was delivered. Three weeks later, post-implementation surveys were administered to those that attended the seminar. Descriptive data analysis was used to assess data. Wilcoxon test was used to assess statistical significance.
Results: Among the twenty-one (N=21) clinicians that completed both the pre- and post-test surveys, 43% reported over ten years of experience. The educational intervention was associated with significant increases in average scores from pre-surveys in clinician knowledge (M=1.43) to the post-survey (M=2.43), p &lt; 0.05, clinician attitudes (M= 24.95) to the post-survey (M = 28.86), p &lt; 0.05, and clinician behavior subscales (M=21.71) to the post-survey (M = 29.29), p &lt; 0.05, respectively.
Conclusion: The results suggest that providing S-HCT education and support for clinicians was successful in increasing clinician knowledge, attitudes, and behaviors. Future research should explore sustained changes in clinicians 6-months and 1-year post-education.

Disclosure
C.M. de Almagro: None. A. Carrillo: None. J.S. Verme: None. D.S. Goldin: None.
1090-P: RCT Design Risks Excluding Key Groups in Pediatric–Adult Transition Studies
Authors: LIU YING-TING , MORGAN MARCI , SHAH SEJAL , HSU LIANA , DESAI DIMPI , SUH BAILEY , CHEN JULIE , LAL RAYHAN , HUGHES MICHAEL S. 
Published: June 2024

Introduction: Importance of the patient-provider relationship in pediatric to adult clinic transitions is well recognized. Yet, there is a lack of evidence from randomized controlled trials (RCTs). We tested a new approach to moving endocrinology patients to adult care in an RCT. However, consent requirements of the RCT posed challenges. We therefore examined whether this skewed enrollment, inadvertently precluding the very patients we sought to assist.
Methods: Participants age ≥16 with a chronic endocrine condition transferring from Stanford's pediatric to adult endocrinology clinics were randomized 2:1 to "Guided Transfer" (starting care with an adult physician in the pediatric clinic before moving) or "Direct Referral" (beginning adult care in adult clinic). To identify potential biases after enrollment ceased in Nov 2023, we reviewed enrollment efforts, compared participants' demographics to the age-matched population of patients in the 4 most commonly referring clinics, and surveyed referring providers. Data collection on the primary outcome, attendance in two adult clinic visits, is ongoing.
Results: Of 119 candidates, 34% were unreachable despite 256 cumulative contact attempts. 45 enrollees were randomized to GT and 21 to DR; 82% had diabetes. Compared to the age-matched clinic population (n=2169), enrollees were significantly more white (59% vs 38%, p&lt;0.001) and privately insured (73% vs 43%, p&lt;0.001). Among providers (n=13), 85% felt GT provided a benefit to patients. Key themes regarding improvement areas included eliminating consent needs, including all participants in GT, and enhancing monitoring and communication methods.
Conclusion: RCTs are prone to recruitment and enrollment bias in this population, missing those who might benefit most from the intervention, such as harder to contact or underserved individuals. Providers recognized RCT challenges but still favored the GT approach. A single-arm QI-based study may reduce barriers and improve demographic representation.

Disclosure
Y. Liu: None. M. Morgan: None. S. Shah: None. L. Hsu: None. D. Desai: None. B. Suh: None. J. Chen: Advisory Panel; Bayer Inc., Immunovant, Biomarin, The Dedham Group. R. Lal: Consultant; Abbott, Adaptyx Biosciences, Biolinq, Capillary Biomedical, Inc., Deep Valley Labs, Gluroo, PhysioLogic Devices, Portal Insulin, Tidepool. Advisory Panel; Lilly Diabetes. M.S. Hughes: Consultant; Dexcom, Inc.

Funding
National Institutes of Health (5K12DK122550, 1K23DK122017, T32DK007217, P30DK116074)
1112-P: Sustaining Show Rates in the Diabetes Clinic
Authors: GUNASEKARAN UMA 
Published: June 2024

The Parkland Diabetes Clinic provides specialty diabetes care to the indigent people of Dallas County with diabetes. Previously, we showed that using an internally scheduling patients, rather than a third-party call center, and personally calling scheduled patients to remind them of their appointments improved our clinic no-show rates in 2021, compared to 2019, from 32% to 22%. We have continued these interventions but questioned whether these results have been sustained. Three years later, we reviewed 1014 charts from a typical month at the clinic to determine how many patients completed their appointments compared to whether they received a reminder phone call, specifically focusing on demographic and glycemic data as mitigating factors. The average no-show rate has risen to ~26% even though the previous solutions were still in place. Approximately 66% of patients received reminder phone calls with 57% of these calls resulting in a direct conversation with the patient confirming the appointment. 30% of patients who directly spoke to a staff member confirmed attendance but did not show for their appointment. There were no statistically significant differences in demographics or visit type for patients who completed an appointment, but there was a difference in their last Hba1c prior to the scheduled appointment (9.3% in patients who completed their appointment vs. 10.0% in patient who did not attend the appointment, p = 0.003). Contrary to published data that calling patients to confirm their appointments improves show rates, the benefit of this is limited in our population. With 30% of patients confirming attendance to an appointment 1-3 days prior but not attending shows that there are other factors which can influence no-show rates. Furthermore, patient with poorer glycemic control at the prior visit tended to show less. In the indigent population, there are more complicating factors than reminder phone calls that prevent patients from attending their clinic appointment which should be researched further to address barriers to care.

Disclosure
U. Gunasekaran: None.
1127-P: Diabetes Advice Lines Prevent Emergency Room and Outpatient Attendance
Authors: LEWIS GEMMA A. , MITCHELL LESLEY J. , HARDY KEVIN J. 
Published: June 2024

Introduction and Objective: Type 2 diabetes care is underpinned by patient education, evidence-based therapy, complication surveillance and timely intervention. Patients sometimes need urgent specialist advice to prevent emergency room (ER) and extra outpatient (OP) attendance and for reassurance. We offer OP care, website support, and 3 lines: Emergency Advice Line (EAL) - immediate specialist advice 8AM - 10PM weekdays and 8AM - 4PM weekends, and Non-urgent Patient/Carer (NU-P) and Non-urgent Clinician (NU-C) advice within 24-hr. Objective - to assess impact of clinical support lines on ER and extra OP attendance.
Methods: We assessed line activity and impact October 2021 to November 2023 in 5,809 contacts (mean 232 pcm): EAL (452 [7%]) - 18 pcm, NU-P (2531 [44%]) - 101 pcm and NU-C (2826 [49%]) - 113 pcm. After contact, diabetes specialists gauged whether consultation likely prevented ER or extra OP attendance. We used hospital records to test specialist judgements in 100 consecutive patients and found specialists accurately predicted 28-day ER prevention and OP referral prevention in 98% and 93% patients respectively.
Results: For EAL, 75% calls were patients and carers and 25% non-specialist clinicians. Over 25-months, EAL calls fell 17%, but NU-P and NU-C grew by 63% and 36% respectively.

Conclusion: Timely advice and reassurance are invaluable, but our experience suggests advice lines may also prevent ER and extra OP attendances.

Disclosure
G.A. Lewis: None. L.J. Mitchell: None. K.J. Hardy: Other Relationship; AstraZeneca, Sanofi.
1129-P: Effect of Discharge Day on Post-discharge Glycemic Control
Authors: KAPLAN LEAH , LEVISTER CAMILLA , SHAH NIRALI , LEVY CAROL J. , ROUVIERE MADELEINE , TAMRU ABIGAIL , IPEK ASLIHAN , OGYAADU SELASSIE J. , CHERÑAVVSKY DANIEL R. , O'MALLEY GRENYE 
Published: June 2024

Introduction & Objective: Hospital readmission rates are nearly two times higher in patients with diabetes. Risk of readmission for severe dysglycemia is sixfold higher in patients with a prior hospitalization for severe dysglycemia. The DIREMOS study aimed to assess the use of continuous glucose monitoring upon hospital discharge. This subanalysis compares glycemic metrics of patients discharged on weekdays versus weekends.
Methods: Forty hospitalized patients with diabetes (type I, type II, steroid induced) who experienced inpatient dysglycemia were enrolled in a pilot feasibility study using a real time remote patient monitoring system for thirty days after hospital discharge. In this secondary analysis of thirty-nine participants (1 dropped), time in range (TIR) (70-180 mg/dl) and time with severe hyperglycemia (&gt;250 mg/dL) for the three days post-discharge (day 0, 1, 2, 3) are compared in participants discharged on weekdays (Monday-Thursday) versus weekends (Friday-Sunday or holiday).
Results: Twenty-five participants were discharged on weekdays, and 14 participants were discharged on weekends. Mean post-discharge TIR was 47.7±26% in the weekday group and 42.0±25% in the weekend group. Median time with severe hyperglycemia was 9% (IQR 2-28%) in the weekday group and 14% (IQR 9-36%) in the weekend group. Four (16%) of weekday and 3 (21%) of weekend participants reported difficulty with medication access.
Conclusion: Three-day post-discharge average TIR was lower and time above 250 mg/dL was higher in participants discharged on weekends. This data suggests that weekend discharges have additional factors that negatively impact post-discharge glycemic control. More research and quality improvement initiatives are needed on the impact of weekday vs weekend discharges on glycemic control and the effect of time in range on readmission risk.

Disclosure
L. Kaplan: None. C. Levister: Research Support; Dexcom, Inc., Tandem Diabetes Care, Inc. N. Shah: None. C.J. Levy: Research Support; Dexcom, Inc. Consultant; Dexcom, Inc. Research Support; MannKind Corporation, T1D Exchange, Tandem Diabetes Care, Inc., Abbott, Insulet Corporation. M. Rouviere: None. A. Tamru: None. A. Ipek: None. S.J. Ogyaadu: None. D.R. Cherñavvsky: None. G. O'Malley: Research Support; Dexcom, Inc., Insulet Corporation, Abbott, Tandem Diabetes Care, Inc., MannKind Corporation.

Funding
Dexcom, Inc
1130-P: Enhancing Safety—Managing Individuals on SGLT-2 Inhibitors during the Perioperative Period
Authors: ALIMATHUNISA ABDUL KATHER FIRDHOUS , IYENGAR JENNIFER J. , ESFANDIARI NAZANENE H. , BLANK ROSS , SABERI SIMA , KUMAR SATHISH S. , GARCIA DAVID J. , BUTLER SIMONA , JANKE ELLEN L. , CIARKOWSKI SCOTT L. , ANG LYNN 
Published: June 2024

Introduction & Objective: SGLT-2 inhibitors are crucial for managing diabetes, heart failure, and CKD but increase euglycemic diabetic ketoacidosis (eDKA) risk by altering insulin secretion and glucagon levels, especially during surgical stress and fasting. The FDA recommends holding SGLT-2 inhibitors for 3-4 days before surgery. Our single-center academic medical center has formulated guidelines to mitigate the risk of eDKA in high-risk individuals. Simultaneously, the guidelines strive to avoid cancellations for low-risk cases, particularly when SGLT-2 inhibitors have not been or cannot be discontinued.
Methods: A multidisciplinary team consisting of anesthesiology, endocrinology, and pharmacy was convened. A literature review was conducted to identify risk factors for DKA/eDKA to guide algorithm development and a survey was conducted of anesthesia faculty to assess baseline adherence patterns with FDA recommendations and comfort level with diagnosis and treatment of eDKA.
Results: In the past 3 months, 55% of anesthesiologists (n=55) canceled a case due to SGLT-2 inhibitor use, and 65% proceeded without holding the SGLT-2 inhibitor. Only 22% felt confident in eDKA management. Consensus recommendations were developed to 1) risk stratify an individual’s eDKA risk 2) standardize guidelines for elective and urgent procedures 3) establish robust post-operative monitoring protocols. These steps form a comprehensive framework to enhance perioperative safety in the context of SGLT-2 inhibitor usage.
Conclusion: With a multidisciplinary team, we implemented a tailored approach to managing individuals on SGLT-2 inhibitors during surgical procedures, aligning with the local care environment and emphasizing patient safety. Future work should evaluate guideline implementation/adherence and audit clinical practice and assess the effects on institutional rates of case cancellation and eDKA.

Disclosure
F. Abdul Kather: None. J.J. Iyengar: None. N.H. Esfandiari: None. R. Blank: None. S. Saberi: None. S.S. Kumar: Research Support; Edwards life sciences, PCORI grant, Hemosonics. D.J. Garcia: None. S. Butler: None. E.L. Janke: None. S.L. Ciarkowski: None. L. Ang: None.
1132-P: Understanding Patients New to Insulin—Baseline Evaluation of Participants in the “Implementation and Testing of a Diabetes Discharge Intervention to Improve Safety during Transitions of Care” Study
Authors: LEE KRISTEN , SMYRNIOTIS COLLEEN M. , CLINGAN CAROLINE A. , CARTHY KEVIN M.V. , COVENTRY CHRISTINA , DAVIS KA'DERRICKA , FREEMAN STEPHEN J. , LEE JUNGWHA J. , SONG JING , HOLL JANE L. , BAILEY STACY , WALLIA AMISHA 
Published: June 2024

Objective: Describe baseline patient characteristics, needs, and stress of new to insulin at hospital discharge.
Methods: Single site RCT to evaluate a DM Discharge Toolkit on mean 30-day glucose is ongoing among English speaking patients (pts), 21 - 80 years, with expected survival &gt; 90 days, new to insulin at discharge.
Results: Among 97 eligible, 67 approached, and 40 enrolled (consent rate 60%). Mean age was 51.9 years +11.4; 55% were male; 35% had public insurance; 52% identified as black, 30% white, and &gt;22% Hispanic/Latino, with 23 (58%) prior DM. Mean glucose was 374 &lt;u&gt;+&lt;/u&gt;213 and median HbA1C 11.4 [9.6, 13.3]. Nearly 30% had limited literacy (Single Item Literacy Screener, &gt;2), though &gt; 90% had home internet and reported its use for health information. Acceptability of research continuous glucose meter (CGM) at discharge was high (48%). Stress was also high, over 60% reported elevated perceived stress (Perceived Stress Scale &lt;u&gt;&gt;&lt;/u&gt;14), 35% with prior DM reported elevated DM distress (Diabetes Distress Score &lt;u&gt;&gt; &lt;/u&gt;2.0). A majority (60%) had &lt;u&gt;&gt;&lt;/u&gt; 1 health-related social need (HRSN), 12.5% had &gt; 3; food insecurity (22.5%), transportation (15%) and medication affordability (12.5%) were highest.
Conclusion: Our medically/socially complex cohort of DM patients consented to RCT, including many of minority race/ethnicity. These patients have high levels of disease and non-disease specific stress and multiple HRSN that could affect safe insulin delivery. Interestingly, most accessed and used technology/internet, and were open to DM technology (CGM). Achieving better glycemic control in our sickest patients demands comprehensive, multi-modal interventions which can leverage DM technology to enhance care delivery.

Disclosure
K. Lee: None. C.M. Smyrniotis: None. C.A. Clingan: None. K.M.V. Carthy: None. C. Coventry: None. K. Davis: None. S.J. Freeman: None. J.J. Lee: None. J. Song: None. J.L. Holl: None. S. Bailey: Consultant; Gilead Sciences, Inc. Research Support; Gilead Sciences, Inc., Pfizer Inc., Merck Sharp & Dohme Corp., Lundbeck. Consultant; Lundbeck. A. Wallia: Research Support; UnitedHealth Group, Novo Nordisk.

Funding
R18 HS026143, NIH/AHRQ