bluuebunny/crossref_metadata_2025_split huggingface dataset:
67-LB: Nursing Perspectives on Hospital Use of Dexcom Continuous Glucose Monitor in Patients with Suspected or Known COVID-19 during Insulin Infusion
Authors: PATTISON JILLIAN , DUNGAN KATHLEEN M. , BUSCHUR ELIZABETH , EXLINE MATTHEW C. , JONES LAUREEN G. , MAY CASEY , MCNETT MOLLY , SMETANA KEATON , FAULDS EILEEN R. 
Published: May 2022

The coronavirus disease 2019 pandemic resulted in the need for remote glucose monitoring in intensive care unit (ICU) patients requiring insulin infusions. While rarely reported, perceptions from nursing staff are critical for successful implementation and expanded use. A hybrid continuous glucose monitoring (CGM) and intermittent point of care (POC) glucose monitoring was developed at the study institution. After 1.5 years of protocol use, an anonymous survey was distributed electronically to ICU nurses to obtain their perspectives on CGM utilization. The survey employed a 1-5 Likert scale regarding CGM sensor insertion, accuracy, acceptability, and usability as well as perceptions on reduced room entry and reduced workload. Of the 51 surveys completed, 50 (98%) nurses reported they cared for an ICU patient enrolled in the CGM protocol and 31 (62%) started or replaced a CGM sensor. A representative sample of questions and responses is shown in table 1. ICU nurses overwhelmingly reported CGM was accurate, reduced their workload, provided safer patient care, and was preferred over POC glucose testing alone. These results confirm nursing acceptance and preference for CGM use within a hybrid glucose monitoring protocol in the ICU setting.

Disclosure
J. Pattison: None. K. M. Dungan: Advisory Panel; Dexcom, Inc., Consultant; Boehringer Ingelheim International GmbH, Eli Lilly and Company, Elsevier, Nova Biomedical, Novo Nordisk, Tolerion, Inc., Other Relationship; UpToDate, Research Support; Abbott, Dexcom, Inc., Novo Nordisk, Sanofi, ViaCyte, Inc., Speaker’s Bureau; Academy for Continued Healthcare Learning, Cardiometabolic Health Congress, Medscape. E. Buschur: Advisory Panel; Beta Bionics, Inc., Research Support; Dexcom, Inc. M. C. Exline: Other Relationship; Abbott. L. G. Jones: None. C. May: None. M. Mcnett: None. K. Smetana: None. E. R. Faulds: Advisory Panel; Dexcom, Inc., Research Support; Dexcom, Inc., Speaker’s Bureau; Medscape.
671-P: Patterns of Sensor-Detected Hypoglycemia in Long-Duration CGM Data from a Diabetes Registry
Authors: BAUMANN PETRA M. , HOCHFELLNER DANIEL A. , CIGLER MONIKA , SIEGL REBECCA , POETTLER TINA , MADER JULIA K. 
Published: May 2022

Compared to time below range less is known about the frequency and temporal distribution of hypoglycemic episodes in CGM data. Pooled analysis of long-duration CGM data from a diabetes registry allows for large-scale exploration of sensor-detected hypoglycemic episodes (SDHE) .
66 patients with T1D used CGM over 365.7 (340.6-392.5) days (24.530 days total) . In addition to time below range (TBR) , we explored frequency of SDHE &gt;15 minutes at &lt;70, &lt;54 and 40 mg/dl by time of day and sensor runtime (30 days) as well as glycemic control by pooled individual days (12AM-12AM) .
TBR was 5.4% (&lt;70) , 1.9% (&lt;54) and 0.7% (=40) . 24.7% of SDHE&lt;70 occurred at nighttime (0-6am = 25% of hours/day) . SDHE&lt;54 and SDHE=40 had a nightly surplus with 34.4% and 41.5%, respectively. Number of daily SDHE&lt;70 slightly decreased over the first month and was more uniform across subjects. Numbers of SHDE&lt;54 and SHDE=40 are more varied, and the latter increased over time. Pooled CGM days showed a decrease and greater variance in glucose levels during the night, a surplus of nightly readings at 40mg/dl and a drop in glucose around noon.
Focus on SHDE and individual days showed similar patterns to TBR regarding the surplus and variability of nightly low readings. The greater variability and the increase of SHDE=40 over 30 days while SDHE&lt;70 remained stable suggest that sensor lifetime may be a confounder. Overall, SDHE is a valuable addition to TBR.

Disclosure
P.M.Baumann: None. D.A.Hochfellner: None. M.Cigler: None. R.Siegl: None. T.Poettler: None. J.K.Mader: Advisory Panel; Abbott Diabetes, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Medtronic, Merck Sharp & Dohme Corp., Novo Nordisk A/S, Roche Diabetes Care, Sanofi-Aventis Deutschland GmbH, Speaker's Bureau; Abbott Diabetes, Eli Lilly and Company, Medtronic, Merck Sharp & Dohme Corp., Novo Nordisk A/S, Roche Diabetes Care, Sanofi-Aventis Deutschland GmbH, Servier Laboratories, Stock/Shareholder; decide Clinical Software GmbH.

Funding
"This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement 777460. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA and T1D Exchange, JDRF, International Diabetes Federation (IDF) , The Leona M. and Harry B. Helmsley Charitable Trust."
68-OR: Using Machine Learning to Improve Screening for Undiagnosed Diabetes among Emergency Department Patients
Authors: BOHART ISAAC , CALDWELL J. REED , SWARTZ JORDAN , ROSEN PERRY E. , GENES NICHOLAS , KOZIATEK CHRISTIAN A. , NEILL DANIEL B. , LEE DAVID C. 
Published: May 2022

Introduction: Screening for previously undiagnosed diabetes in the emergency department (ED) can enhance early detection, which may particularly benefit patients with barriers to accessing primary care. To optimize use of limited ED resources, there is a need to identify ED patients who are at high risk.
Objectives: To improve identification of ED patients with undiagnosed diabetes using machine learning (ML) .
Methods: Diabetes screening by HbA1c was implemented in four EDs at an academic medical center. Patients aged 40 or older, with BMI &gt; 25, no history of diabetes and no HbA1c result within 6 months were eligible for screening. Patients were stratified by screening result and compared across demographic and clinical characteristics, and local geographic measures of health and socioeconomic variables. ML classifier models predicting a positive screening result were trained, and were validated on a prospectively withheld subset of the cohort.
Results: In the first weeks after implementation, 2423 eligible patients were identified, and 1518 (62.6%) were screened, with 114 (7.5% of those screened) found to have previously undiagnosed diabetes. Patients with diabetes more often identified as male, Hispanic or Latino, Black, or Asian, had higher BMIs and blood pressures at triage, resided in neighborhoods with higher diabetes prevalence and poverty rates, and had less prior healthcare utilization, especially in the outpatient setting. The best performing classifier models were logistic regression with elastic net regularization (AUC 0.81) and gradient-boosted trees (AUC 0.77) . These models identified a high-risk subgroup consisting of 30% of eligible patients who accounted for over 80% of newly detected cases of diabetes.
Conclusions: ML models can reliably identify ED patients at high risk of having undiagnosed diabetes based on demographic, clinical, and local geographic risk factors, and could form the basis of a selective screening program.

Disclosure
I.Bohart: None. J.Caldwell: None. J.Swartz: None. P.E.Rosen: None. N.Genes: None. C.A.Koziatek: None. D.B.Neill: None. D.C.Lee: None.
685-P: Will Choice of Treatment Influence TIR in T2D with Diabetic Complications?
Authors: KESAVADEV JOTHYDEV , SABOO BANSHI D. , SHANKAR ARUN , KRISHNAN GOPIKA , SANAL GEETHU , BASANTH ANJANA , JOTHYDEV SUNITHA 
Published: May 2022

Though TIR is a predictive marker of diabetic complications, there is paucity of data evaluating the association of TIR with therapeutic regimens. We investigated the association of CGM-derived metrics, with presence of complications and therapeutic regimen in T2D. We analyzed AGP of T2D who underwent 14-day p-CGM between 2015 and 2021. Clinical parameters were extracted from EMR. Data of 1218 T2D (baseline age=53.14±15.03 yrs; 69.2% male, avg. duration of diabetes: 14.44±9.48 yrs) on different therapeutic regimen (OHAs±insulin; namely basal only, biphasic, basal plus, basal bolus regimen) were analyzed. A regression model was run with % TIR as dependent variable and age, duration, insulin regimen, complications, and BMI as independent variables. Spearman’s correlation coefficient was calculated between lab A1C and eA1c. On analysis, age was found to have a statistically significant relation with TIR. A moderate correlation (0.60) between A1C and eA1C was observed. 68.4% achieved a target TIR of &gt;70% in T2D without complications. 69.5% with CAD/CKD achieved target TIR of &gt;50%. Among the different therapeutic regimens analyzed, use of analogue basal-bolus regimen was associated with statistically significant (p&lt;0.05) TIR target of &gt;50% and TBR&lt;1% in T2D with CAD/CKD (Figure 1) . In T2D with vascular complications, analogue basal bolus regimen could be a superior choice in reaching optimal TIR with insignificant time below range.

Disclosure
J.Kesavadev: None. B.D.Saboo: None. A.Shankar: None. G.Krishnan: None. G.Sanal: None. A.Basanth: None. S.Jothydev: None.
69-OR: Implementing Autonomous AI in a Federally Qualified Health Center (FQHC) for the Detection of Diabetic Retinopathy Improves Access to Care: A Pre–Post Comparison in the Southeastern U.S.
Authors: GOLDSTEIN JULI , WEITZMAN DENA , ABRÀMOFF MICHAEL D. 
Published: May 2022

Diabetic retinopathy (DR) and diabetic macular edema (DME) are debilitating complications of diabetes; however, patients in the most vulnerable populations often face barriers in accessing recommended ocular exams, leading to major health inequity. These barriers are especially true for FQHCs, as they serve the most under resourced and underserved populations. More recently, rigorously validated, FDA cleared, autonomous AI for the detection of DR and DME has been considered as an alternative to deployed remote reading networks. We examined the impact of autonomous AI compared to remote reading on patient access and clinical workflow efficiency, measured as number of steps by the operator. During 90 days in mid-2021, at two FQHC clinics in the southeastern US, autonomous AI was provided in place of a remote reading network. All steps were completed at the point-of-care: qualified patients with diabetes were identified in the electronic health record (EHR) , diagnosed by the autonomous AI output, provided results at time of the exam, and referred to specialists where appropriate. After 90 days, 1patients with diabetes underwent autonomous AI diagnostic testing, and 30/1patients with diabetes (28.6%) were identified by the AI as having signs of vision threatening DR or DME. Testing was accomplished with fewer steps as compared to exams completed with a remote reading network at the same clinics. We conclude that implementing autonomous AI point-of-care testing for the identification of DR and DME in an FQHC setting improved workflow efficiency and patient access compared to remote reading, demonstrating a potential to address health inequities in underserved and vulnerable populations.

Disclosure
J.Goldstein: Employee; Digital Diagnostics. D.Weitzman: Employee; Digital Diagnostics. M.D.Abràmoff: Advisory Panel; NovaGo Therapeutics AG, Board Member; Digital Diagnostics, Consultant; Digital Diagnostics, Speaker's Bureau; AbbVie Inc., Stock/Shareholder; Digital Diagnostics.
693-P: Significant Changes in Dietary Behaviour, Glycemic Control, and Weight after Participation in Personalized Glycemic Response–Based Diabefly Program
Authors: VERMA RITIKA , KHADER MOHAMMED A. , RANADIVE RUCHIRA M. , BUDYAL SWETA , SARAF AMIT A. , RASKAR VIVEK , SARWATE ASHISH , JOSHI MITALI V. , JOSHI SHILPA , SINGAL ARBINDER K. 
Published: May 2022

Background: Retrospective evaluation of glycemic responses correlated with lifestyle habits can help in mapping personalized glycemic responses (PGR) for individuals. PGR can aid in more specific nutritional interventions in individuals with diabetes ultimately aiding in better glycemic control and weight management.
Methods: Participants (n=108, mean age 47.24± 12.28 years, 55.5% females) were enrolled in PGR based Diabefly program and were provided with support for lifestyle modification through a combination of expert led care and mobile application. The outcomes were analyzed at the beginning and end of the program. Paired t-test was used for analysis with P&lt;0.considered as statistically significant.
Results: After the program, the daily mean calorie intake significantly reduced by 9.34 ± 30.3 % from a baseline of 1466.93 ± 408.16 cal (P&lt;0.001) . Similarly, the daily mean carbohydrate and fat intake significantly reduced by 10.44 ± 31.99 % and 7.95 ± 39.90 % respectively (P&lt;0.0for both) . The daily mean fiber intake improved by 4.74 ± 44.9 % from the baseline (P=0.26) . Further, the mean energy attributed from protein showed a significant increase by 10.36 ± 25.31 % (P&lt;0.001) . Among participants (n=66) who provided complete data of body weight, BMI, and HbA1c a significant reduction was observed by 3.41 ± 3.33 kg, 1.16± 1.kg/m2, and 2.02 ± 1.73% from a baseline of 76.42 ± 16.61 kg, 27.34 ± 4.40 kg/m2, and 8.78 ± 1.90 % respectively (P&lt;0.0for all) . Waist circumference in participants with complete data (n=53) showed a mean reduction by 4.85 ± 4.45 % from the baseline of 100.3 ± 14.1 cm (P&lt;0.001) .
Conclusion: Significant change in dietary behavior was observed after the completion of the program. Effective weight reduction, waist circumference reduction, and improved glycemic control was also observed suggesting the potential of understanding PGR in an effective manner through the Diabefly program for improved outcomes.

Disclosure
R.Verma: Employee; Fitterfly Healthtech Pvt Ltd, Wellthy Therapeutics Pvt Ltd. A.K.Singal: Other Relationship; Fitterfly Healthtech Pvt Ltd. M.A.Khader: Employee; Fitterfly HealthTech Pvt Ltd. R.M.Ranadive: Employee; Fitterfly Healthtech Pvt Ltd. S.Budyal: None. A.A.Saraf: None. V.Raskar: None. A.Sarwate: None. M.V.Joshi: None. S.Joshi: Consultant; Fitterfly Health Tech Pvt Ltd, Wellthy Therapeutics Pvt Ltd.
7-OR: Distinct Developmental Origins of Brown Adipose Tissue at Different Anatomical Locations
Authors: RUAN HAI-BIN , ZHANG ZENGDI , HUANG ZAN 
Published: May 2022

Brown adipose tissue (BAT) is a thermogenic organ that promotes metabolic fitness. In humans, there are two spatiotemporally distinct phases of BAT ontogeny: the prenatally developed dorsal-anterior-located interscapular and retroperitoneal BAT; and the postnatally emerged anterior cervical and supraclavicular BAT. It has been established in rodents that the dorsal-anterior-located BAT depots predominately arise from a Pax3+/Myf5+ lineage in the epaxial dermomyotome that also forms the trunk muscle. However, the developmental origins of the anterior cervical and supraclavicular BAT that adult humans have remain unknown. In this study, using lineage tracing mouse models, we found that these neck BAT depots are not derived from the Pax3+ or Myf5+ precursor cells. Knock out of PPARγ, the master regulator of adipogenesis, in Myf5+ cells caused severe atrophy of interscapular BAT, but did not affect the anterior cervical and supraclavicular depots. Instead, we identified that roughly half of the anterior cervical and supraclavicular brown adipocytes are progeny cells of a developmental lineage that also gives rise to neck muscle cells. PPARγ deficiency in this lineage did not perturb iBAT development, but caused paucity in the anterior cervical and supraclavicular regions, thus sensitizing mice to cold-induced decrease in body temperature.
In summary, our results suggest that there are region-specific myogenic precursors that contribute to BAT development at different anatomical locations. Future studies to understand the pathophysiological regulation of these developmentally distinct BAT depots could inspire new approaches to measure BAT mass and to activate BAT activity in humans for the treatment of obesity, diabetes and related metabolic disorders.

Disclosure
H.Ruan: None. Z.Zhang: None. Z.Huang: None.

Funding
American Diabetes Association (1-18-IBS-167)
70-LB: Lower A1C with Real-Time CGM than with Intermittently-Scanned CGM after One Year—The CORRIDA LIFE Study
Authors: RADOVNICKÁ LUCIE , HÁSKOVÁ ANETA , DO QUOC DAT , HOROVA EVA , NAVRÁTILOVÁ VENDULA , MIKES ONDREJ , CIHLÁR DAVID , PARKIN CHRISTOPHER , GRUNBERGER GEORGE , PRAZNY MARTIN , SOUPAL JAN 
Published: May 2022

We investigated the efficacy of real-time continuous glucose monitoring (rtCGM) and intermittently scanned continuous glucose monitoring (isCGM) on change in A1C. CORRIDA LIFE was a 12 month, real-world, non-randomized trial that assessed the impact of rtCGM (Dexcom G5 or G6) vs. isCGM (FreeStyle Libre 14-Day, Abbott) on A1C and other CGM metrics in adults with T1D. Patients on MDI therapy or CSII with no automatic functions were included. One hundred ninety-one adults with T1D (mean age 40±13 years, A1C 8.1±3.4% [65±14 mmol/mol]) participated in this study; 81 patients initiated rtCGM and 110 initiated isCGM. After 12-months, A1C was significantly lower with rtCGM vs. isCGM (7.1±3.1% [54.1±10.1 mmol/mol] vs. 7.7±3.3% [61.2±12.2 mmol/mol]) , p=0.0001. (Figure 1) The percentage of time in hypoglycemia (&lt;70 mg/dL [&lt;3.9 mmol/L]) was lower among rtCGM vs. isCGM participants (4.3±2.8% vs. 6.4±5.3%) , p=0.003) . Patients with rtCGM spent less time in clinically significant hypoglycemia (&lt;54 mg/dL [&lt;3.0 mmol/L]) (0.9±1.0% vs. 2.3±2.5%, p&lt;0.0001) and more time in target range (70-180 mg/dL [3.9-10 mmol/L]) than isCGM users (67.5±14.8% vs. 57.8±17.0%) , p=0.0002. Use of rtCGM was superior to isCGM in reducing A1C and hypoglycemia and improving other glycemic outcomes.

Disclosure
L. Radovnická: Consultant; Medtronic, Speaker’s Bureau; Boehringer Ingelheim International GmbH, Eli Lilly and Company, Novo Nordisk, Sanofi. M. Prazny: Advisory Panel; Bayer AG, Consultant; Boehringer Ingelheim International GmbH, Speaker’s Bureau; Abbott Diabetes, Amgen Inc., Eli Lilly and Company, Medtronic, Merck Sharp & Dohme Corp., Novartis Pharmaceuticals Corporation, Novo Nordisk, Sanofi. J. Soupal: Advisory Panel; Abbott Diabetes, Roche Diabetes Care, Speaker’s Bureau; Boehringer Ingelheim International GmbH, Dexcom, Inc., Lilly Diabetes, Medtronic, Novo Nordisk. A. Hásková: None. Q. Do: None. E. Horova: None. V. Navrátilová: None. O. Mikes: None. D. Cihlár: None. C. Parkin: Consultant; Abbott Diabetes, CeQur SA, Dexcom, Inc., LifeScan, MannKind Corporation, Novo Nordisk, Provention Bio, Inc., Roche Diabetes Care. G. Grunberger: Advisory Panel; LifeScan, Nevro Corp., Research Support; Medtronic, Speaker’s Bureau; Abbott Diabetes, Eli Lilly and Company, Novo Nordisk.

Funding
Ministry of Health, Czech Republic, General University Hospital in Prague
70-OR: Association of Diabetes Screening with Demographic, Neighborhood Level Census, and Area Gentrification Characteristics
Authors: THOMAS TAINAYAH , DYER WENDY T. , RODRIGUEZ LUIS A. , SCHMITTDIEL JULIE 
Published: May 2022

Research assessing predictors of preventive screening often focuses on individual patient factors, despite known relationships between neighborhood environment and health. Using a retrospective cohort study of 519,826 patients meeting US Preventive Service Task Force guideline recommendation for screening for prediabetes and type 2 diabetes (age 35-70 years and BMI ≥25) from Kaiser Permanente Northern California in 2018, we used logistic regression to assess the relationship between diabetes screening and census neighborhood tract-level measures, and area gentrification measures developed by the Urban Displacement Project, controlling for age, sex, race, ethnicity, and BMI. Thirty-eight percent of patients were screened for diabetes. Patients aged 60-70 years had higher odds of diabetes screening compared to patients aged 35-39 years (OR: 2.73, 95% CI: 2.67-2.78) . Men had lower odds of screening compared to women (OR: 0.91, 0.90-0.92) . Asian patients had higher odds of screening compared to White patients (OR 1.44, 1.42-1.46) . Patients with BMI ≥40 had higher odds of screening compared to patients with BMI 25-29.9 (OR: 1.34, 1.30-1.37) . Patients living in census tracts with the lowest median values of homes (OR: 0.93, 0.90-0.96) and with the lowest median household income (OR: 0.95, 0.91-0.99) had lower odds of diabetes screening compared to those in census tracts with the highest values. Patients living in census tracts classified as Ongoing Displacement/Low Income Susceptible to Displacement had lower odds of screening (OR: 0.96, 0.93-0.98) compared to those living in stable moderate mixed income tracts. We found insufficient screening rates and that gentrification and area-level socioeconomic factors were associated with lower diabetes screening, even after adjusting for individual demographic and clinical factors. Health care systems should consider addressing social needs and community resources as a path to improving preventive care.

Disclosure
T.Thomas: None. W.T.Dyer: None. L.A.Rodriguez: None. J.Schmittdiel: None.

Funding
Kaiser Permanente Northern California Community Benefit Programs; The Permanente Medical Group (TPMG) Delivery Science Fellowship Program; National Institute of Diabetes and Digestive and Kidney Diseases grant T32DK11668401. Dr. Schmittdiel received additional support from the NIDDK-funded Health Delivery Systems Center for Diabetes Translational Research (1P30 DK92924) .
71-OR: County Social Vulnerability and Disparities in Incidence of Diagnosed Diabetes
Authors: DUGANI SAGAR , LAHR BRIAN , BAILEY KENT R. , VELLA ADRIAN , MIELKE MICHELLE M. 
Published: May 2022

Individual sociodemographic factors determine risk of diabetes. However, the association of a multidimensional social vulnerability index (SVI) and incident diabetes is unknown. We used CDC data on incidence of diagnosed diabetes, 2011-2018, in adults ≥20 years and defined county social vulnerability using two CDC indices: SVI minority health (SVI-MH; includes 6 dimensions) and SVI (includes 4 dimensions) . Trends in the annual age-adjusted incident diabetes rate per 1000 adults (AADR) were derived from weighted least squares regression. Year and SVI-MH/SVI were fitted nonlinearly with spline functions to test 2018 vs. 20fold-changes in AADR for levels of SVI-MH/SVI. Expressed by SVI-MH percentiles, low (10th) , median (50th) , and high (90th) vulnerability corresponded to overall AADR of 6.8, 8.5, and 10.5, respectively. In all years, counties with higher vulnerability (ie, higher SVI-MH) had significantly higher AADR. In 2018, compared to 2011, counties with high vulnerability showed a small AADR fold-decrease (0.97 [0.95-0.98]; P&lt;.001) . The AADR fold-decrease was larger in counties with median vulnerability (0.92 [0.90-0.94]; P&lt;.001) . In counties with low vulnerability, there was no AADR fold-change (P=.35) . Results for AADR analyzed by SVI were generally similar.
In summary, US counties with high SVI-MH/SVI had high AADR, and these indices could inform multidimensional interventions for diabetes prevention.

Disclosure
K.R. Bailey: None. A. Vella: Advisory Panel; Crinetics Pharmaceuticals, Inc., Rezolute, Inc., vTv Therapeutics, Zealand Pharma A/S. Other Relationship; Novo Nordisk. M.M. Mielke: Consultant; Biogen, LabCorp.

Funding
National Institute on Minority Health and Health Disparities (NIH K23 MD016230)