bluuebunny/crossref_metadata_2025_split huggingface dataset:
1228-P: Sodium-Glucose Cotransporter 2 Inhibitors and Risk of Genital Mycotic and Urinary Tract Infections: A Population-Based Study of Older Women and Men with Diabetes
Authors: LEGA ILIANA C. , BRONSKILL SUSAN , CAMPITELLI MICHAEL A. , GUAN JUN , STALL NATHAN M. , LAM KENNETH , MCCARTHY LISA M. , GRUNEIR ANDREA , ROCHON PAULA A. 
Published: June 2019

Sodium-glucose cotransporter 2 inhibitors (SGLT2i) are the newest class of oral diabetes medications. There are concerns of an increased risk of genitourinary infections based on trial data, however this risk at a population level is understudied. We conducted a population-based study using linked administrative databases in Ontario of women and men with diabetes over the age of 66 who were incident users of SGLT2i from Jan-Dec 2016. We quantified the association between SGLT2i and each of genital mycotic infection and urinary tract infection within 30 days of drug initiation using a retrospective cohort design. The comparator group were new users of a DPP-4i. We identified 21,444 incident SGLT2i users and 22,463 DPP-4i users. Among SGLT2i users, 41% were women and the mean age at index was 71.8 ± 5 years. After adjusting for propensity score, age, sex, and recent UTI, there was a 2.47 fold increased risk of a genital mycotic infection with SGLT2i (adjusted hazard ratio (HR), 2.47; 95% CI 2.08-2.92, p &lt; 0.001) within 30 days. For UTI, the adjusted HR was 0.89 (95% CI 0.78-1.00, p=0.05) overall. Incident SGLT2i use among older women and men is associated with an increased risk of genital mycotic infections within 30 days; there is no associated increased risk of UTI. These findings from a real-world setting provide evidence of the potential harms of SGLT2i.

Disclosure
I.C. Lega: None. S. Bronskill: None. M.A. Campitelli: None. J. Guan: None. N.M. Stall: None. K. Lam: None. L.M. McCarthy: None. A. Gruneir: None. P.A. Rochon: None.

Funding
Canadian Institutes of Health Research (PJT153060)
123-LB: Continuous Subcutaneous Insulin Infusion from Diagnosis of Type 1 Diabetes Improves Intermediate Glycaemic Variability in Children
Authors: SCOTT EMMA S. , JANUSZEWSKI ANDRZEJ S. , FULCHER GREG , JONES TIMOTHY , DAVIS ELIZABETH A. , JENKINS ALICIA J. 
Published: June 2019

Background: Glucose variability (GV) may be reflected by variation in either the short-term glucose levels (assessed by continuous glucose monitoring (CGM)) or the long-term by HbA1c or intermediate by 1,5-anhydroglucitol (1,5-AG). In patients with T1DM management by continuous subcutaneous insulin infusion (CSII) is usually associated with improved glycaemia compared to multiple daily insulin injections (MDI). Serum 1,5-anhydroglucitol (1,5-AG) correlates with GV on CGM in adult and pediatric T1D groups. It is not known whether CSII therapy without CGM improves intermediate GV.
Methods: Blood was obtained from children (mean age 14.1 +/- 1.3 years) within 3-months of T1D diagnosis. Children were treated with CSII (n=12) or MDI (n=15) and followed for a further 1 to 3 time points (median, LQ-UQ 535, 519-563 days follow-up). GV was assessed measuring 1,5-anhydroglucitol (1,5-AG) (GlycoMark, Inc., Winston-Salem, NC). The mean difference, standard deviation (SD) and coefficient of variation (CV) of 1,5-AG and HbA1c was compared between treatment arms.
Results: There was no difference between treatment modalities in baseline HbA1c (median, LQ-UQ 11.8, 10.2-13.5 % (105, 87-124 mmol/mol) vs. 12.6, 10.3-14.0 % (114, 89-130 mmol/mol) CSII vs. MDI) nor baseline 1,5-AG (median, LQ-UQ 2.8, 1.8-4.0 μg/mL vs. 3.6, 2.1-12.1 μg/mL CSII vs. MDI). 1,5-AG improved more (increased) in the CSII vs. MDI group (3.1 +/- 4.1 vs. -2.2 +/- 7.0 μg/ml respectively, P=0.029). This difference persisted following adjustment for the duration of follow-up. There was no significant difference in the HbA1c improvement between the CSII and MDI groups. There was no significant difference in the SD or CV of 1,5-AG or HbA1c between groups.
Conclusions: Early use of CSII post-T1D-diagnosis in a pediatric population is associated with a higher 1,5-AG than with MDI therapy implying reduced glycaemic variability.

Disclosure
E.S. Scott: None. A.S. Januszewski: None. G. Fulcher: Advisory Panel; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Janssen Research & Development, Merck Sharp & Dohme Corp., Novo Nordisk Inc. Consultant; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Janssen Research & Development, Merck Sharp & Dohme Corp., Novo Nordisk Inc. Research Support; Self; Novo Nordisk Inc. T. Jones: Research Support; Self; Dexcom, Inc., Medtronic MiniMed, Inc. Speaker's Bureau; Self; Eli Lilly and Company, Roche Diabetes Care. E.A. Davis: None. A.J. Jenkins: Advisory Panel; Self; Abbott, Australian Diabetes Society, Medtronic. Research Support; Self; Abbott, GlySens Incorporated, Medtronic, Mylan. Speaker’s Bureau; Self; Eli Lilly and Company, Novo Nordisk Inc.
1232-P: Predictors of ß-Cell Function Changes: Pooled Analysis from Phase 3 Ertugliflozin Studies
Authors: GALLO SILVINA , RAJI ANNASWAMY , CALLE ROBERTO A. , ELLISON MISOO C. , MEYER CHRISTIAN 
Published: June 2019

Improving glycemic control generally improves β-cell function in T2DM but the predictors for this effect are unclear. SGLT2 inhibitors improve glycemia and have no known direct effects on β-cell function. Therefore, we aimed to identify predictors of β-cell function changes by treatment with the SGLT2 inhibitor ertugliflozin (ERTU). Data of patients (pts) randomized to ERTU (5 or 15 mg) only or placebo (PBO) only from four phase 3 ERTU studies of the VERTIS clinical program were pooled and analyzed. Change from baseline in β-cell function (fasting homeostatic model assessment beta [HOMA-β] and C-peptide Insulinogenic Index [IGI] at 30 min of a mixed meal tolerance test) at Week 26 was assessed, followed by linear regression analyses to examine the relationship between change in β-cell function versus metabolic and demographic characteristics. ERTU compared with PBO improved β-cell function when assessed by the changes from baseline (95% CIs) in HOMA-β (14.7 [12.3,17.1] versus −0.4 [−3.4, 2.5]) but not IGI. Change in HOMA-β correlated negatively and similarly with change in A1C in both groups (ERTU: r=−0.29; PBO: r=−0.36, both P&lt;0.001). In the ERTU group, change in HOMA-β correlated positively with baseline fasting plasma glucose (FPG; r=0.23, P&lt;0.001) and baseline A1C (r=0.14, P&lt;0.001) but negatively with baseline HOMA-β (r=−0.32, P&lt;0.001) in simple linear regression analyses; no significant correlations were found with age, sex, ethnicity, diabetes duration, baseline serum triglycerides, creatinine, weight or systolic blood pressure. Correlations between change in HOMA-β and baseline FPG, A1C and HOMA-β remained significant in multiple regression analyses. ERTU improves fasting HOMA-β but not the postprandial IGI in pts with T2DM. Pts with high baseline FPG and A1C values but low baseline HOMA-β may derive greater benefit than those with low baseline FPG, A1C and high HOMA-β with ertugliflozin treatment in terms of fasting HOMA-β.

Disclosure
S. Gallo: Employee; Self; Pfizer Inc. Stock/Shareholder; Self; Pfizer Inc. A. Raji: Employee; Self; Merck & Co., Inc. Stock/Shareholder; Self; Merck & Co., Inc. R.A. Calle: Employee; Self; Pfizer Inc. Stock/Shareholder; Self; Pfizer Inc. M.C. Ellison: Employee; Self; Merck & Co., Inc. C. Meyer: Employee; Self; Merck & Co., Inc.

Funding
Merck Sharp & Dohme Corp; Pfizer Inc.
1237-P: Factors Associated with Health-Care Avoidance among Medicare Beneficiaries Aged 65 Years and Over with Reported Type 2 Diabetes
Authors: NG BOON PENG , PEACH BRIAN C. , PARK CHANHYUN , TOWNE SAMUEL D. , LAMANNA JACQUELINE B. 
Published: June 2019

Healthcare avoidance by Medicare beneficiaries (MBs) with type 2 diabetes (T2D) can result in significant health and financial burdens. Timely detection/treatment of T2D-related complications can mitigate these burdens. There is limited research using the Anderson Model of Healthcare Utilization to examine healthcare avoidance among MBs with T2D. Therefore, we examined factors associated with healthcare avoidance among MBs with reported T2D. We analyzed two healthcare avoidance measures (“do anything to avoid going to the doctor (DATAGTTD)” and “try to keep sickness to themselves (TTKSTT)”) using the nationally representative 2015 Medicare Current Beneficiary Survey (n=12,311) from CMS. MBs were stratified into two groups, those with and without T2D. We performed four survey-weighted logit models, with two healthcare avoidance variables on those with and without T2D, adjusting for covariates. Approximately 22.4% of MBs reported having T2D. Among those with and without T2D, 28.5% and 26.1% respectively reported they would DATAGTTD. Also, 37.8% of MBs with T2D and 39% of MBs without T2D reported they would TTKSTT. Two key factors were positively associated (P&lt;.01) with DATAGTTD for both groups: 1) worrying about health more than others, 2) past experiences with doctors missing health issues. Also, reporting past experiences with doctors missing health issues was a key factor positively associated (P&lt;.01) with TTKSTT for both groups. Other factors such as socio-economic status, physical limitations, depression, and cost were associated (P&lt;.05) with TTKSTT and DATAGTTD. However, factors differed between those with and without T2D. Although over 90% of MBs with T2D have a regular doctor, one-third of them reported TTKSTT and one-fourth of them reported DATAGTTD. There is a need to address and improve doctor-patient communication for MBs with T2D to avoid unnecessary and costly healthcare avoidance for this particular at-risk group.

Disclosure
B. Ng: None. B.C. Peach: None. C. Park: None. S.D. Towne: None. J.B. LaManna: None.
1238-P: Utilization of Comprehensive Pharmacy Services in Diabetes Care Coordination for Hospitalized Patients
Authors: WELCH ANDREW A. , BOESKEN TIMMI ANNE , WOOLF BRITTANY , BERGER KIMBERLY , CORBIN ADELE , BACHMAN NICHOLE E. , SCHAUER DANIEL , FALCIGLIA MERCEDES 
Published: June 2019

A major barrier to effective diabetes care is difficulty with affording and knowing how to use medications. At a 750-bed urban hospital, comprehensive pharmacy services (PS) are offered to support patients in accessing and understanding their medications. Specifically, Medication Access Services (MAS) collaborates with providers and patients to utilize available resources that assist patients in obtaining needed medications. We examined use of these services in a cohort of inpatients with poorly controlled diabetes who were enrolled in a diabetes care-coordination program after discharge, Sweet Transitions (ST). Because participation in ST had been associated with significant reductions in HbA1c (12.0 to 9.0%) and 30-day readmissions, both sustained at one year, the role of PS utilization was explored. We hypothesized that there was greater use of PS among ST participants compared to matched controls and that this may have contributed to the improved outcomes observed. We reviewed data from 544 patients; 197 ST participants with poorly controlled diabetes (A1C ≥ 9) and 347 controls matched for poor diabetes control and cardiovascular disease. Utilization of PS and MAS within 90 days after discharge was compared. Cost savings, defined as expenditures for medications covered by sources outside the patient or healthcare system, were calculated. Compared to 49% of controls, 77% of ST patients used PS, p&lt;0.001. Specifically, 2.6% of the controls vs. 9.1% of the ST cohort utilized MAS, p=0.002. The total cost savings in the ST group using MAS was $61,180 or $3399 per patient. Patients enrolled in the ST program utilized comprehensive pharmacy services more often than controls, suggesting that such services may have contributed to the significant and sustained improved outcomes observed. Approaches that assist patients with the affordability, access, and understanding of their diabetes medications may contribute to sustainably improved outcomes and should be examined further.

Disclosure
A.A. Welch: None. T.A. Boesken: None. B. Woolf: None. K. Berger: None. A. Corbin: None. N.E. Bachman: None. D. Schauer: None. M. Falciglia: None.
124-OR: Comparison of OGTT Model-Derived Measures of ß-Cell Function between Youth and Adults
Authors: UTZSCHNEIDER KRISTINA , MARI ANDREA , TRIPPUTI MARK , MATHER KIEREN J. , NADEAU KRISTEN J. , EDELSTEIN SHARON , HANNON TAMARA S. , ARSLANIAN SILVA A. , CREE-GREEN MELANIE , BUCHANAN THOMAS A. , CAPRIO SONIA , KAHN STEVEN E. , CONSORTIUM RISE 
Published: June 2019

Results from hyperglycemic clamps in the Restoring Insulin Secretion (RISE) Study found that youth (10-19 y) with impaired glucose tolerance (IGT) or early type 2 diabetes (T2D) are more insulin resistant and secrete more insulin than adults (20-65 y), despite similar BMI. Here we use Mari modeling of RISE 3-h OGTT data to explore differences in β-cell function between youth and adults. The Mari model describes the relationship between insulin secretion rate (ISR) and glucose concentration. The slope of the relationship is β-cell glucose sensitivity (GS); ISR at a fixed glucose is also calculated. Derived terms include the 3-h to baseline potentiation factor ratio (PFR; a relative insulin secretion increase during the OGTT) and rate sensitivity (RS; the dependence of ISR on the glucose rate of change). Analysis of covariance was used to compare youth vs. adults, with secretion measures adjusted for insulin sensitivity (M/I) from hyperglycemic clamps. Adjusted GS was significantly higher in youth vs. adults with IGT but not in those with T2D. Adjusted RS and ISR at plasma glucose of 5 and 7 mmol/L were higher in youth vs. adults for both IGT and T2D. The PFR was lower in youth than adults, suggesting possible differences in either incretin effects or glucose potentiation. Our OGTT findings provide additional evidence that β-cells in youth are more responsive to glucose than adults, with reduced secretion parameters in those with T2D.

Disclosure
K. Utzschneider: Consultant; Self; Novo Nordisk Inc. A. Mari: Consultant; Self; Eli Lilly and Company. Research Support; Self; Boehringer Ingelheim International GmbH. M. Tripputi: None. K.J. Mather: Advisory Panel; Self; Roche Diabetes Care. Research Support; Self; Abbott, Merck & Co., Inc., Novo Nordisk A/S, Sanofi. K.J. Nadeau: None. S. Edelstein: None. T.S. Hannon: Advisory Panel; Self; Eli Lilly and Company. S.A. Arslanian: None. M. Cree-Green: None. T.A. Buchanan: Research Support; Self; Allergan, Apollo EndoSurgery. S. Caprio: None. S.E. Kahn: Advisory Panel; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Eli Lilly and Company, Intarcia Therapeutics, Inc., Janssen Pharmaceuticals, Inc., Merck & Co., Inc., Novo Nordisk A/S. Consultant; Self; Neurimmune. Other Relationship; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Merck & Co., Inc., Novo Nordisk A/S. R. Consortium: None.

Funding
American Diabetes Association (1-14-RISE-01 to S.E.K.); National Institute of Diabetes and Digestive and Kidney Diseases
1243-P: Novel Use of Natural Language Processing to Identify Reasons for Insulin Discontinuation in Patients with T2DM: A Real-World Evidence Study
Authors: WU JIANMIN , MORRISON FRITHA J. , ZHAO ZHENXIANG , HE XUANYAO , SHUBINA MARIA , MALMASI SHERVIN , GE WENDONG , PENG XIAOMEI , TURCHIN ALEXANDER 
Published: June 2019

Insulin is an important part of treatment of type 2 diabetes mellitus (T2DM). Nevertheless, reasons for discontinuation of insulin therapy are poorly understood.
We conducted a real-world-evidence analysis of EMR data at Partners HealthCare to study adults with T2DM who were prescribed insulin between 2010 and 2016. Reasons for insulin discontinuation were identified from a combination of structured EMR data and natural language processing analysis of EMR notes using a validated tool (sensitivity 76.9% and positive predictive value 89.7%).
The mean age of study patients was 60.2 years; 52.7% were male. Among 7,009 study patients, 2,957 (42.2%) discontinued insulin within 12 months after study start. In multivariable logistic regression analysis adjusted for demographics and comorbidities, patients were more likely to discontinue either basal or bolus insulin if they were on a basal-bolus regimen compared to basal only (OR 1.56, 95% CI 1.33 to 1.83; p &lt; 0.001) or if they were being seen by an endocrinologist (OR 2.58; 95% CI 2.22 to 2.99; p &lt; 0.001). Most patients (2,121/71.7%) had one or more reasons for discontinuation documented. The most common reasons were improving blood glucose (BG) control (33.2%), weight loss (likely ultimately also leading to lower BG) (18.5%) and initiation of non-insulin diabetes medications (16.7%). In multivariable analysis, patients were more likely to stop insulin due to achievement of good BG control if they were treated by an endocrinologist (OR 2.52; 95% CI 2.11 to 3.00; p &lt; 0.001) or by a female physician (OR 1.43; 95% CI 1.26 to 1.63; p &lt; 0.001).
This study represents a successful attempt to use natural language processing to understand reasons for insulin discontinuation. Insulin discontinuation is common, and particularly so for patients on a basal-bolus regimen. Achievement of glycemic control, weight loss and initiation of other diabetes medications were the most common reasons for insulin discontinuation.

Disclosure
J. Wu: None. F.J. Morrison: None. Z. Zhao: Employee; Self; Lilly Diabetes. X. He: Employee; Self; Eli Lilly and Company. M. Shubina: None. S. Malmasi: None. W. Ge: None. X. Peng: None. A. Turchin: Advisory Panel; Self; Monarch Medical Technologies. Research Support; Self; Eli Lilly and Company. Stock/Shareholder; Self; Brio Systems.

Funding
Eli Lilly and Company
1246-P: Reducing Hypoglycemia in the Noncritical Care Setting Using CGM Devices: The Glucose Telemetry System
Authors: SPANAKIS ELIAS , SATYARENGGA MEDHA , SINGH LAKSHMI G. , PINAULT LILLIAN , SORKIN JOHN D. , ZHAN MIN , UMPIERREZ GUILLERMO E. 
Published: June 2019

Objective: In a pilot study we have previously demonstrated successful transmission of glucometric values from continuous glucose monitoring (CGM) devices to a monitoring system at the nursing station (Glucose Telemetry System-GTS). In an ongoing study, we are evaluating whether use of GTS can reduce inpatient hypoglycemia compared to point of care (POC) capillary glucose testing, without increasing hyperglycemia.
Design: Twenty-four patients with DM2 at high risk for inpatient hypoglycemia were randomized to GTS (N=12 intervention group) or POC (N=12 standard of care). Nursing staff were asked to proceed with hypoglycemia preventative actions for GTS low glucose alarms set at &lt;85 mg/dL. Subjects randomized to standard of care used “blinded” CGM. Mean percent time spent in hypoglycemia, hyperglycemia, and hypoglycemia event rates were evaluated.
Results: Twenty-one subjects were included in the analysis, 2 subjects withdrew due to minor bleeding at CGM insertion site and 1 subject had insufficient CGM data due to sensor failure. 30 GTS hypoglycemia alarms occurred in 7 different subjects. Overall percent time spent in hypoglycemia (≤70 mg/dL) was 0.18%±0.21 versus 1.64%±1.59 (p=0.056), and clinically significant hypoglycemia (&lt;54mg/dL) was 0.01%±0.01 versus 0.48%±1.07 (p=0.135), between intervention and control groups, respectively. Rate of hypoglycemic episodes per day/per patient was 0.12±0.17 vs. 0.27±0.33 (p=0.19) for GTS and POC, respectively. Percent time spent in normoglycemia (70-179 mg/dL) was 52.04%±23.65 versus 52.92%±27.37 (p=0.94), while percent time in hyperglycemia (≥180 mg/dL) was 47.80%±23.65 versus 45.43%±28.05 (p=0.84), respectively.
Conclusion: In this preliminary analysis, subjects monitored by GTS had a non-statistically significant decrease in inpatient hypoglycemia compared to standard of care.

Disclosure
E. Spanakis: None. M. Satyarengga: None. L.G. Singh: None. L. Pinault: None. J.D. Sorkin: None. M. Zhan: None. G.E. Umpierrez: Advisory Panel; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Janssen Pharmaceuticals, Inc. Research Support; Self; AstraZeneca, Merck & Co., Inc., Novo Nordisk Inc., Sanofi US.

Funding
U.S. Department of Veterans Affairs (1I01CX001825-01)
1248-P: Estimating Diabetes Duration from Electronic Medical Records (EMRs) with an Intrinsic Diabetes Risk Prediction Model: From Clinical Trial to Real-World Clinical Practice
Authors: SHAO HUI , YANG SHUANG , STOECKER CHARLES , FONSECA VIVIAN , CHENG XIANG , SHI LIZHENG 
Published: June 2019

We have previously described the utility and high predictive value of the Building, Relating, Assessing, and Validating Outcomes (BRAVO) model, which was developed from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial data. However, it uses diabetes duration as a key factor for modelling diabetes progression, and diabetes duration is not routinely collected in EMRs. This study use machine learning to estimate diabetes duration with EMR information. Among individuals (N=8,026) with complete records from the ACCORD trial, diabetes duration was estimated with an ordinary least squares (OLS) model, a least absolute shrinkage and selection operator (LASSO) model, a pruned decision tree model, and a random forest model using 37 baseline predictors such as biomarkers, demographics, disease history, and medications. Model performance was compared using root-mean-square Error (RMSE), mean-absolute error (MAS), and R-squared. All three indicators were calculated through 10-fold cross-validation with 1000 iterations in order to address over-fitting. Age, current HbA1c value, retinopathy, number of oral antidiabetic drugs, number of Insulin types, and use of sulfonylureas were identified as key predictors for patient-reported diabetes duration. The random forest model achieved the best performance on all three indicators (RMSE=6.16, MAE= 4.71, R2=0.30), significantly higher than the pruned decision tree model (RMSE=6.57, MAE= 5.03, R2=0.21) and LASSO model (RMSE=6.60, MAE= 5.08, R2=0.20). Diabetes duration can be predicted using fields routinely included in EMRs. Integration of the results of this prediction equation into predictive diabetes models (such as BRAVO) will allow patient-specific estimates of diabetes duration based solely on EMRs. This will increase precision when inferring risk level and using models to aid clinical decision making among patients with type 2 diabetes.

Disclosure
H. Shao: None. S. Yang: None. C. Stoecker: None. V. Fonseca: Board Member; Self; American Association of Clinical Endocrinologists. Consultant; Self; Abbott, Asahi Kasei Corporation, Novo Nordisk Inc., Sanofi US, Takeda Pharmaceutical Company Limited. Research Support; Self; Bayer US. Stock/Shareholder; Self; Amgen Inc., BRAVO4HEALTH, Mellitus Health. X. Cheng: None. L. Shi: None.