bluuebunny/crossref_metadata_2025 huggingface dataset:
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 <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 (<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.
1249-P: Development and Validation of a Measure for Appropriateness of Glucose-Lowering Therapy
Authors: MCCOY ROZALINA G. , LIPSKA KASIA J. , HOUTEN HOLLY VAN , SHAH NILAY
Published: June 2019

Current diabetes quality indicators do not reflect considerations of clinical complexity, hypoglycemia risk, and treatment burden in identifying optimal therapeutic regimens and targets.

Based on existing evidence and input from patients and clinician, payer, and quality measurement experts, we developed a metric that incorporates HbA1c level, comorbidity burden, and number/type of glucose lowering medications (Table) to classify patients as potentially overtreated, undertreated, or appropriately treated. Measure was tested in a national cohort of commercial and Medicare Advantage enrollees with diabetes using the OptumLabs® Data Warehouse.

Among 215,429 adults ≥18 years (mean 65 years, 49.6% female, 59.4% white, 4.2% type 1 diabetes, 12.1% clinically complex, 52.7% with HbA1c <7%), 71.0% were treated appropriately, 21.3% undertreated, and 7.7% overtreated in 2015. Compared with current measures (HbA1c <8% for all), 4.4% low complexity and 12.6% high complexity patients were reclassified as no longer appropriately treated. Rates of hypoglycemia-related ED/hospitalization visits in 2016 were 7.0, 12.4, and 22.9 per 1000 persons/year for those appropriately, under-, and over-treated in 2015. Rates of hyperglycemia-related visits were 2.5, 12.2, and 4.6.

Implementation of this measure to minimize over- and under-treatment through a patient-centered approach to glucose lowering therapy is planned.

Disclosure

R.G. McCoy: None. K.J. Lipska: Consultant; Self; Health Services Advisory Group (HSAG). Research Support; Self; National Institute on Aging. Other Relationship; Self; Centers for Medicare and Medicaid Services. H. Van Houten: None. N. Shah: None.

Funding

American Association of Retired Persons; OptumLabs; National Institute of Diabetes and Digestive and Kidney Diseases (K23DK114497)
1250-P: Developing an “Appropriateness of Glucose-Lowering Therapy” Measure: Qualitative Analysis of Delphi Panel Deliberations
Authors: LAVECCHIA CHRISTINA M. , MONTORI VICTOR M. , SHAH NILAY , MCCOY ROZALINA G.
Published: June 2019

Despite the increasing focus on individualized diabetes care, current diabetes quality measures are still based on meeting generic HbA1c targets. We proposed an alternative measure to assess the appropriateness (vs. over- or under-treatment) of glucose-lowering therapy that accounts for patient complexity and treatment burden. We worked with OptumLabs® to convene a 12-member modified Delphi panel, selected from among its partner institutions and the NQF Measure Incubator® network, to refine the measure. Members included clinicians (endocrinology, primary care, geriatrics), pharmacists, nurses, researchers, and representatives of public and private health plans. In this study, we examined the factors the panel valued in an ideal appropriateness measure.

Two researchers independently coded panelists’ written responses to a range of questions about the appropriateness measure. Conducting an inductive analysis, codes and overarching themes emerged by consensus. Participants conveyed that an ideal measure must promote individualized care (evidence-based treatments that are consistent with patient psychosocial and biological context, their values and preferences), allow for clinical decision making, and have optimal accountability properties (be easy to implement with reasonable accuracy and reliability, without unintended effects on utilization). Panelists prioritized protecting patient and clinician autonomy, reducing patient treatment burden and clinician punitive potential, and allowing for reasonable clinical exceptions.

Our analysis identified factors that stakeholders value in formulating an appropriateness measure for glucose-lowering therapy. Addressing these factors, and those identified by people with diabetes in subsequent research, may produce a quality measure that strikes a patient-centered balance between glucose control targets and the everyday complexities of being, and treating, people with diabetes.

Disclosure

C.M. LaVecchia: None. V.M. Montori: None. N. Shah: None. R.G. McCoy: None.

Funding

American Association of Retired Persons; OptumLabs; National Institute of Diabetes and Digestive and Kidney Diseases (K23DK114497)
1251-P: The Diabetes Transition of Hospital Care (DiaTOHC) Pilot Study: A Randomized Controlled Trial of an Intervention Designed to Reduce Readmission Risk of Patients with Diabetes
Authors: RUBIN DANIEL J. , GOLDEN SHERITA , FOSTER GARY , FISHER SUSAN , VAZ CHERIE LISA , ZHAO HUAQING , TANNER SAMUEL , RECCO DOMINIC , TIVON MADELINE , DILLARD FELICIA R. , WATTS SAMANTHA , JOYCE KATHERINE E. , KARUNAKARAN ABHIJANA , REZNICK TANYA , IWAMAYE AMY , MILLER ELI , MATHAI CHRISTINE , ALBURY BONNIE S.
Published: June 2019

Hospital readmission within 30 days of discharge (30dRe) is a high-priority quality measure and target for cost reduction. Patients with diabetes are at higher risk of 30dRe than patients without diabetes. There have been no published studies of interventions designed to reduce 30dRe risk specifically among diabetes patients.

We conducted a pilot randomized controlled trial (RCT) of the DiaTOHC intervention in adult patients with diabetes admitted to any medical-surgical unit at Temple University Hospital between 10/2017 and 12/2018. Patients predicted to be high risk (>=27%) for 30dRe based on a validated tool (DERRITM) were randomized 1:1 to the intervention (INT) or usual care (UC). The intervention consisted of novel, brief inpatient diabetes education, coordination of care, and post-discharge support by a nurse practitioner and an A1C-based algorithm to adjust diabetes therapy. Patients received weekly calls for 30 days after discharge. The primary outcome was unplanned 30dRe.

Follow-up data was available for 26 INT and 30 UC patients. Mean age was 57.5 years, duration of diabetes 17 years, and median admission A1C 7.9%. The cohort was 75% black, 20% white, 13% Hispanic, 55% female, mostly low-income, and mostly insured by Medicare and/or Medicaid. Most patients (95%) had type 2 diabetes. There were few baseline differences between groups, including years of school (13.5 INT, 11.9 UC, p=0.03) and preadmission insulin use (59.6% INT, 40.4% UC, p<0.01). Six INT and 10 UC patients had a 30dRe (23.1%, 33.3%, P=0.40), yielding a non-significant relative risk reduction of 30.6%, absolute risk reduction of 10.2% and number needed to treat of 10. Median A1C at 3 months among the 18 patients with data was 6.7% INT and 8.4% UC, p=0.19.

This small pilot trial shows the DiaTOHC intervention is feasible. The non-significant but measurable reductions in readmission risk and A1C merit further investigation in a larger RCT.

Disclosure

D.J. Rubin: Research Support; Self; AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc. S. Golden: Research Support; Self; Merck & Co., Inc. G. Foster: Employee; Self; Weight Watchers International, Inc. S. Fisher: None. C. Vaz: None. H. Zhao: None. S. Tanner: None. D. Recco: None. M. Tivon: None. F.R. Dillard: None. S. Watts: None. K.E. Joyce: None. A. Karunakaran: None. T. Reznick: None. A. Iwamaye: None. E. Miller: None. C. Mathai: None. B.S. Albury: None.

Funding

National Institute of Diabetes and Digestive and Kidney Diseases (K23DK102963 to D.J.R.)
1253-P: High Health-Care Utilization (HCU) and Costs Associated with Diabetes Mellitus (DM) and Nonalcoholic Fatty Liver Disease/Nonalcoholic Steatohepatitis (NAFLD/NASH) in Medicare Patients
Authors: GORDON STUART , FRAYSSE JEREMY , LI SUYING , OZBAY A. BURAK , WONG ROBERT
Published: June 2019

Background: One of the common comorbidities in NAFLD/NASH patients is DM. This study evaluated HCU and costs in NAFLD/NASH patients with varying liver disease severity by DM status.

Methods: This was a retrospective study from 2007-2015 of a 20% sample of U.S. Medicare beneficiaries and included NAFLD/NASH patients (ICD codes) with and without DM (ICD codes/medications). Mean annual healthcare costs were reported in 2015 USD and stratified by NAFLD/NASH only, compensated cirrhosis (CC), decompensated cirrhosis (DCC), liver transplant (LT), and hepatocellular carcinoma (HCC) and presence of DM.

Results: This study identified 260,950 NAFLD/NASH patients, of which 144,862 (55.5%) also had comorbid DM. NAFLD/NASH CC patients with DM had high rates of additional metabolic comorbidities: 77.5% with cardiovascular disease and 87.5% with both hypertension and hyperlipidemia. Mean annual number of hospitalizations in NAFLD/NASH only with DM was 0.6, which significantly increased in CC (0.8) and DCC (2.2) DM patients. Mean annual healthcare costs were significantly higher in those with comorbid DM (except HCC) (Figure).

Conclusions: Comorbid DM among NAFLD/NASH patients is associated with high metabolic comorbidity burden and significantly higher HCU and healthcare costs in U.S. Medicare patients.

Disclosure

S. Gordon: Consultant; Self; Gilead Sciences, Inc., Intercept Pharmaceuticals, Inc. J. Fraysse: Employee; Self; Gilead Sciences, Inc. S. Li: Consultant; Self; Gilead Sciences, Inc. A. Ozbay: Employee; Self; Gilead Sciences, Inc. Stock/Shareholder; Self; Gilead Sciences, Inc. R. Wong: Advisory Panel; Self; Gilead Sciences, Inc. Consultant; Self; Gilead Sciences, Inc. Research Support; Self; Gilead Sciences, Inc. Speaker's Bureau; Self; Gilead Sciences, Inc.
1258-P: Physician Survey on Discontinuing Dipeptidyl Peptidase-4 Inhibitors (DPP-4is) following Insulin Initiation in Patients with Type 2 Diabetes (T2D) in the U.S.
Authors: FERNANDES GAIL , JAFFE DENA H. , BEYER GRACE DIBONAVENTURA , MATOS JOANA E. , IGLAY KRISTY , GANTZ IRA , RAJPATHAK SWAPNIL
Published: June 2019

At the time of insulin initiation, physicians often discontinue oral therapies in patients with T2D. A recent randomized clinical trial demonstrated continuing sitagliptin, a DPP-4i, results in a clinically meaningful greater reduction in HbA1c without an increase in hypoglycemia. An online U.S. physician survey (n=400) is being conducted to understand reasons for discontinuation of DPP-4i therapy. Preliminary analysis is based on completed surveys (n=180; 41 endocrinologists, 74 family practice, 63 internal medicine, 2 diabetologists) from physicians treating a monthly average of 137 patients. Table lists key physician characteristics and their responses for continuing/discontinuing DPP-4i on initiation of insulin. The top responses for continuing DPP-4i include meeting treatment goals, using a lower dose of insulin, good tolerability, reducing weight gain and hypoglycemia. The top responses for discontinuing include adverse events/tolerability, cost, treatment goals not being met, patient preference, and prior authorization requirements. In this ongoing study, further data is being collected on the remaining physician sample and a medical chart review will be conducted on a sub-sample of patients treated by these physicians to explore patient characteristics associated with DPP-4i discontinuation.

Disclosure

G. Fernandes: None. D.H. Jaffe: Consultant; Self; Merck & Co., Inc. G. DiBonaventura Beyer: None. J.E. Matos: None. K. Iglay: Employee; Self; Merck & Co., Inc. Stock/Shareholder; Self; Merck & Co., Inc. I. Gantz: Employee; Self; Merck & Co., Inc. S. Rajpathak: None.
126-OR: Fat Catch-Up Growth in Early Infancy and Cardiometabolic Outcomes at 5 Years of Age
Authors: ANDERSEN GREGERS S. , WIBAEK RASMUS , ADMASSU BITIYA , VISTISEN DORTE , JØRGENSEN MARIT E. , MICHAELSEN KIM F. , WELLS JONATHAN C. , FRIIS HENRIK
Published: June 2019

Introduction: The role of infant fat growth in childhood obesity and adult type 2 diabetes is not clear. We have previously used latent class analysis (LCA) to identify four distinct fat mass (FM) growth patterns in early infancy. Here we examine their associations with adiposity and cardiometabolic outcomes at 5 years (y) of age.

Methods: Air Displacement Plethysmography (ADP) was used to repeatedly measure FM from 0-6 months of age in a cohort of 314 healthy urban-living Ethiopian infants and LCA was used to identify FM growth patterns (catch-up, accelerated, average and delayed) (Figure 1). At 5y of age, cardiometabolic outcomes were obtained from ADP and blood samples. Multiple linear regression was used to examine associations between infant FM growth patterns and cardiometabolic outcomes at 5y, adjusted for gender, birthweight, breastfeeding, maternal age and education and SES.

Results: Overall mean (SD) BMI at 5y was 14.98 (1.19) kg/m2. Compared to children with an “average fat” growth pattern, children with a "catch-up fat" pattern had significantly higher BMI (0.79 kg/m2 [95% CI, 0.04; 1.54]) and FMI (0.69 kg/m2 [95% CI, 0.06; 1.37]), but not FFMI (0.05 kg/m2 [-0.51; 0.62]) at 5y of age (Figure 1). There were no differences in any other cardiometabolic outcomes.

Conclusions: Fat catch-up growth in early infancy was associated with a higher BMI in childhood, which was almost entirely fueled by increased adipose tissue.

Disclosure

G.S. Andersen: Stock/Shareholder; Self; Novo Nordisk Inc. R. Wibaek: None. B. Admassu: None. D. Vistisen: Stock/Shareholder; Self; Novo Nordisk A/S. M.E. Jørgensen: Research Support; Self; Amgen Inc., AstraZeneca, Danish Diabetes Association, Sanofi-Aventis. Stock/Shareholder; Self; Novo Nordisk A/S. K.F. Michaelsen: None. J.C. Wells: None. H. Friis: Research Support; Self; Arla Food for Health.

Funding

Danish Council for Strategic Research; Danida (104.Dan.8-1207); Danish Diabetes Academy; Innovation Fund, Denmark
1260-P: A Clinical Care Improvement Pilot Program: Individualized Health Coaching and Use of Incentives for Youth with Type 1 Diabetes and Their Caregivers
Authors: CLARY LAUREN , DAUBER ANDREW , ARBOINE SUE-ANN , DILLON MEREDITH S. ,   BEAKAL (BK) ESHETE, HATCHELL SHAINA , JONES SHARI , VAIDYANATHAN PRIYA , COGEN FRAN R.
Published: June 2019

In pediatric type 1 diabetes (T1D) care, collaboration among youth, caregivers, and medical providers is important for optimal glycemic control; however, many youth are still unable to achieve A1c goals (A1c < 7.5%). This study examined the effect of a 10-week clinical care pilot program on A1c in youth with suboptimal diabetes control. The program consisted of two primary components: 1) weekly individualized health coaching and insulin dose adjustments and 2) incentives for program completion and decreases in A1c. The program was offered to 179 youth with T1D receiving routine diabetes care (A1c 8.6%-10%). Twenty-five caregivers/youth (youth ages 4-18; Mage=11.6 years, Mean A1c=9.4%) completed the 10-week coaching sessions and follow-up A1c measure. Coaching, conducted at no cost via phone call and email (with caregiver and/or youth), was individualized based on family needs and conducted by diabetes team members. Each youth/family who participated and returned for an A1c measure received a gift card to a local grocery store to further dietary goals. Youth were entered into a drawing to win an iPad upon completion of A1c measure and for reductions in A1c. Of the 25 participants, 20 (80%) exhibited a decrease in A1c level at the end of the 10 weeks (mean decreased A1c of 0.5% across 25). Seventeen youth (68%) had a decrease in A1c greater than 0.5% and 7 youth (28%) had a decrease greater than 1.0%. Results from qualitative interview with health coaches suggest increased engagement and empathy from diabetes team members. Communication via phone/email coupled with outcome based incentives was an effective strategy for decreasing A1c in the short-term. This data suggests that moving from an office centric model of care to a sustained health coaching model delivered via phone/email may be a more effective strategy to improve diabetes outcomes. Patient satisfaction and sustained effect of the intervention will be monitored over time.

Disclosure

L. Clary: None. A. Dauber: Consultant; Self; Novo Nordisk Inc. Research Support; Self; Novo Nordisk Inc. S. Arboine: None. M.S. Dillon: None. B. Eshete: None. S. Hatchell: None. S. Jones: None. P. Vaidyanathan: None. F.R. Cogen: None.
1262-P: Physicians’ Perceptions and Attitudes towards Digitalization and New Technologies in Diabetes Care
Authors: KULZER BERNHARD , ROOS TIMM , HERMANNS NORBERT , EHRMANN DOMINIC , HEINEMANN LUTZ
Published: June 2019

Digitalization is an emerging field. However, little is known about the perceptions and expectations of physicians towards digitalization and new technologies in diabetes care. We conducted a survey in secondary care practices throughout Germany. A total of 422 physicians completed the online survey (46% female; mean age: 53 years; mean work experience: 25 years). Per practice, 160 people with diabetes used Flash sensor-based glucose monitoring, 41 used real-time continuous glucose monitoring, and 60 used insulin pump therapy. 79.6% of physicians reported a positive or very positive attitude towards digitalization in diabetes care which was somewhat higher than for general digitalization (63.7% reported a positive or very positive attitude). Major advantages of digitalization referred to patient safety (63.4%), better quality of care (62.4%), and decision support (61.9%). The three major barriers were costs (54.6%), legal issues (49.2%), and concerns about data security (40.3%). Current and expected use in 5 years of new technologies are shown in the Figure. The potential for digitalization in diabetes is perceived as very high by physicians. However, costs and safety issues need to be addressed to overcome barriers towards implementation of digital solutions. Currently, the use of digital solutions in routine practice is not common and is expected to grow only marginally in the next 5 years.

Disclosure

B. Kulzer: Advisory Panel; Self; Ascensia Diabetes Care, Berlin-Chemie AG, Medtronic, Novo Nordisk Inc., Roche Diabetes Care. Research Support; Self; Abbott, Berlin-Chemie AG, Roche Diabetes Care. Speaker's Bureau; Self; Abbott, Ascensia Diabetes Care, Berlin-Chemie AG, Eli Lilly and Company, Novo Nordisk Inc., Roche Diabetes Care. T. Roos: None. N. Hermanns: Advisory Panel; Self; Abbott. Board Member; Self; Eli Lilly and Company. Research Support; Self; Abbott, Berlin-Chemie AG, Dexcom, Inc., Roche Diabetes Care. Speaker's Bureau; Self; Berlin-Chemie AG, Dexcom, Inc., Novo Nordisk A/S. D. Ehrmann: Advisory Panel; Self; Medtronic. Speaker's Bureau; Self; Abbott, Berlin-Chemie AG, Dexcom, Inc. L. Heinemann: Consultant; Self; Roche Diabetes Care. Research Support; Self; Dexcom, Inc. Stock/Shareholder; Self; Profil Institute for Metabolic Research, ProSciento.

Funding

Berlin-Chemie AG
1264-P: Predictors of Success in Usual and Intensive Specialty Diabetes Programs
Authors: DEL VALLE KAYLA L. , GRIZALES ANA MARIA , DONAHUE MARINA , TURCHIN ALEXANDER , MCDONNELL MARIE E.
Published: June 2019

Intensive management (IM) is recognized as an important approach in treatment of poorly controlled diabetes. However, it is unknown what factors determine response to usual diabetes specialty care (UC) vs. IM and understanding these will improve allocation of costly resources. We sought to identify predictors of response in UC and IM, a high-resource 6-month service designed for those who fail UC that includes telemedicine, remote data sharing, and in-person visits. A response metric was defined as an A1c reduction of >/=0.5 within 12 months of the initial visit. We analyzed two groups with baseline A1c >8: 1) UC patients referred to a diabetes specialty clinic between 12/2016 and 8/2017 (N= 163) and 2) IM patients, who were referred after failing metric between 10/2015 and 6/2018 (N=62). The majority met metric (UC-Met: 74%, IM-Met: 61%). In UC and IM, there were no differences between those who met metric and those who did not in age, gender, race or ethnicity. The UC-Met vs. UC-Notmet were similar across quality parameters (SBP, statin prescription, LDL), prior A1c pattern (peak and nadir A1c), and the presence of cognitive impairment. However, IM-Notmet had more cognitive dysfunction (20% vs. 2.6%, p=0.04) and substance abuse (16% vs. 5%, p=0.35) than IM-Met. Those in IM-Met had a lower A1c nadir in the last decade (6.87 vs. 7.26, p=0.04). Prevalence of psychiatric disease was high in both IM groups (60% IM-Notmet and 53% IM-Met).

In conclusion, specialty diabetes services delivered via UC was effective in 74% of patients. The majority of those who fail to achieve clinically significant A1c lowering benefit from resource-intensive programs, especially those who achieved lower A1c levels in the past. However, cognitive impairment and active substance abuse appear to be risk factors for lack of response to high intensity programs, suggesting that resources designed to manage these conditions should be prioritized in affected individuals with diabetes.

Disclosure

K.L. Del Valle: None. A. Grizales: None. M. Donahue: None. A. Turchin: Advisory Panel; Self; Monarch Medical Technologies. Research Support; Self; Eli Lilly and Company. Stock/Shareholder; Self; Brio Systems. M.E. McDonnell: None.
1265-P: The Paradox of Care Delivery for Type 1 Diabetes in Primary Care Settings: A Call for Interventions to Reduce Health Disparities
Authors: WALKER ASHBY F. , CUTTRISS NICOLAS , HALLER MICHAEL J. , YABUT KATARINA , ANEZ-ZABALA CLAUDIA , LAL RAYHAN , SHEEHAN ELENI , BASINA MARINA , HOOD KOREY K. , BERNIER ANGELINA , BAER LINDA G. , FILIPP STEPHANIE L. , WANG C. JASON , TOWN MARISSA , GURKA MATTHEW J. , MAAHS DAVID M.
Published: June 2019

Adults and children with type 1 diabetes (T1D) in the U.S. do not meet targets for HbA1c. These deficits are even more pronounced based on race/ethnicity and socioeconomic status (SES). Most studies on T1D health outcomes focus on data from endocrinology clinics, yet, primary care clinics play a central role in the management of patients, especially those affected by health disparities. To better understand barriers for individuals with T1D in primary care settings, survey research was conducted with 110 Primary Care Providers (PCPs) in Florida (n=52) and California (n=58) using publicly available state-wide provider directories. Surveys were completed by PCPs in a range of settings including solo (20%), multi-specialty group (35%), single-specialty group (21%), hospital/hospital-owned systems (12%) and ‘other’ (12%). For pediatric T1D populations, 58% of PCPs reported referring children to endocrinologists for diabetes care and only 45% checked HbA1c for children with T1D during clinic visits. For adult T1D populations, 30% of PCPs referred patients to endocrinologists and 65% checked for HbA1c. Overall, providers expressed a lack of confidence in caring for T1D patients, especially when it came to technologies like CGMs (only 28% said they were “moderately” or “extremely” confident herein) and insulin pumps (only 17% said they were “moderately” or “extremely” confident). The lack of confidence surrounding diabetes-technologies was especially evident for PCPs in practice 1-10 years or 31+ years (p<.05 for CGMs and p<.01 for pumps). Despite the lack of confidence in caring for patients with T1D, 73% of PCPs reported filling an insulin prescription for a T1D patient in the last year. Surveys with PCPs from a range of clinical settings demonstrate gaps in delivering standard of care and concurrently reveal the critical role PCPs play in T1D care. Efforts to improve outcomes in T1D must include targeted interventions in primary care settings.

Disclosure

A.F. Walker: None. N. Cuttriss: None. M.J. Haller: Advisory Panel; Self; Pancreum, SAB Biotherapeutics. K. Yabut: None. C. Anez-Zabala: None. R. Lal: Consultant; Self; Abbott. E. Sheehan: None. M. Basina: None. K.K. Hood: Consultant; Self; Lilly Diabetes. Research Support; Self; Dexcom, Inc. Speaker's Bureau; Self; Johnson & Johnson Diabetes Institute. A. Bernier: None. L.G. Baer: None. S.L. Filipp: None. C. Wang: None. M. Town: None. M.J. Gurka: None. D.M. Maahs: Advisory Panel; Self; Novo Nordisk Inc. Consultant; Self; Abbott, Sanofi. Research Support; Self; Dexcom, Inc., Tandem Diabetes Care.

Funding

The Leona M. and Harry B. Helmsley Charitable Trust
127-OR: Changes in Adipocyte-Derived Exosomal MicroRNAs May Play a Role in the Progression from Obese Normoglycemia to Hyperglycemia/Diabetes
Authors: MAGGE SHEELA N. , FREISHTAT ROBERT J.
Published: June 2019

We previously showed that visceral adipocyte-derived exosomal microRNA content is pathologically altered in obesity to impair insulin receptor signaling. However, why some obese youth (± insulin resistance) remain normoglycemic, while others progress to hyperglycemia, is not fully understood.

We hypothesize that compared to obese normoglycemic youth (ON), circulating adipocyte-derived exosomes from obese hyperglycemic youth (OH) are enriched for microRNAs targeting carbohydrate metabolism and this association is modified by the amount of visceral fat (VFAT).

We conducted a cross-sectional analysis of 55 obese pubertal adolescents (12-17 years), who underwent OGTT, whole body DXA, and serum adipocyte-derived exosomal microRNA assays. Univariate and regression analyses compared ON and OH groups. MicroRNA profiles were evaluated via ANCOVA and ANOVA.

ON (n=32) and OH (n=23) groups were similar for sex, age, race, pubertal stage, BMI, and fat mass (FM). However, OH had increased VFAT compared to ON (88.1±24.6cm2 vs. 71.4±21.6, p=0.01). Between OH and ON, 33 known mature human microRNAs were differentially expressed (p<0.01) in circulating adipocyte-derived exosomes. Once filtered for microRNAs with experimentally confirmed or highly conserved predicted targets, 14 microRNAs remained, which collectively putatively target 2331 mRNAs. These mRNAs were further filtered for association with VFAT (p<0.05), leaving 1304 mRNAs. These mRNAs were enriched for 179 canonical pathways (p<0.05). Notable carbohydrate metabolism targets include activation of PI3K/AKT (z=+1.4; 19/127 elements) and STAT3 signaling (z=+3.3; 21/132 elements).

Although similar to ON for BMI and FM, OH show changes in adipocyte-derived exosomal microRNAs targeting carbohydrate metabolism that are associated with increased VFAT. Circulating visceral adipocyte-derived exosomal microRNAs may help drive the transition from obese normoglycemia to hyperglycemia.

Disclosure

S.N. Magge: None. R.J. Freishtat: Stock/Shareholder; Self; Adipomics, Inc.

Funding

National Institutes of Health
1274-P: Impact of Type 1 Known Diabetes (T1D) Risk on Health-Care Utilization at Diagnosis
Authors: FROHNERT BRIGITTE I. , MANSEAU KATHERINE , ALONSO GUY T.
Published: June 2019

Despite increasing incidence of T1D worldwide, most newly-diagnosed individuals have no family history. Many experience delay in diagnosis, resulting in >30% of children with new-onset T1D presenting in diabetic ketoacidosis (DKA). Screening for T1D is currently restricted to the research setting. We sought to describe differences in health care utilization at T1D diagnosis in known at-risk patients identified through research studies compared to matched controls. Cases (n=39) were known islet autoantibody positive children (0-19 y) diagnosed with T1D between 2010-2017 at the Barbara Davis Center. Controls were matched 3:1 by dates of birth and diagnosis from community-diagnosed patients (n=117). The Colorado All Payer’s Claims Database was used to count diagnoses, procedures, and healthcare claims in the year before and following diagnosis. Continuous variables were compared using t-test or Wilcoxon rank sum test, categorical by chi-square. There was no difference between cases and controls for age of onset (9.6 +/- 3.6 vs. 9.6 +/- 3.5 y p=1.0) or gender (54 vs. 46% male, p=1.0); however, cases had higher rate of private insurance (72 vs. 49%, p=.01) and higher percentage of non-Hispanic whites (87 vs. 65%, p=.008). At diagnosis with T1D, cases had lower HbA1c (6.7 [6.1, 8.1]% vs. 12.1 [10.5, 14.1]%, p<.0001) and rate of DKA (5 vs. 57%, p<.0001). In the year prior to diagnosis, there were no differences in the number of claims, procedures, diagnoses, or medications. In contrast, in the year of diagnosis, cases had fewer emergency room claims (0 [0, 2] vs. 2 [0, 4], p<.0001), total claims (20 [9, 30] vs. 33 [19, 48], p<.0001), procedures (31 [16, 43] vs. 52 [28, 77] p<.0001) and unique diagnoses (7 [4, 10] vs. 12 [7, 19] p=.0003), while there was no difference in number of medications (8 [4, 10] vs10 [7, 13] p=.07).

In conclusion, antibody-screened patients had lower healthcare utilization in year of diagnosis. Further study, including cost estimation, may lend insight into benefits of T1D screening programs.

Disclosure

B.I. Frohnert: None. K. Manseau: None. G.T. Alonso: None.

Funding

JDRF (5-ECR-2017-388-A-N)
1278-P: Innovative Quality Improvement Medical Education Program for General Practitioners in Japan
Authors: MITRI JOANNA , GABBAY ROBERT A. , EL SAYED NUHA ALI , HSU WILLIAM C. , HEINE ROBERT J.
Published: June 2019

Only around 30% of patients with diabetes meet targets for A1c (A), blood pressure (B) or lipid (C) and 14% meet targets for ABC and non-smoking. Following a survey among general practitioners (GPs) on educational needs, an education program was designed to improve the performance of the GP and the quality of diabetes care in Japan. GPs and nurses learned at a live workshop how to create a plan-do-study-act cycle and established an improvement plan to implement. In a 2nd workshop, held 3 months later, GPs examined whether the improvement plan was carried out and established strategies for further improvement. A, B, C and smoking status were provided by each participant on up to 25 patients pre and post intervention. Goals were: A1c <7%, BP <130/80 mmHg, LDL-C< 120mg/dl (based on Japan Diabetes Society). The proportions of patients in each of the groups at goal pre and post program were compared using chi-square.

Results: 2 cohorts of GPs provided data on 648 patients. The intervention was associated with an increased proportion of patients at goal in the data provided at the post-intervention workshop (Table 1) and an increased odds ratio to achieve goals (Table 2). This innovative program focused on collaboration and quality improvement strategies, has the potential to enhance the quality of diabetes care in Japan. These preliminary data could serve as a platform for the design of broader programs to implement in the GP community.

Disclosure

J. Mitri: Research Support; Spouse/Partner; AbbVie Inc., Janssen Pharmaceuticals, Inc. Research Support; Self; Kowa Pharmaceutical Europe Co. Ltd., National Dairy Council. Research Support; Spouse/Partner; Takeda Pharmaceutical Company Limited. Other Relationship; Self; National Dairy Council. R.A. Gabbay: Advisory Panel; Self; HealthReveal, Hygieia, Lark, Onduo LLC. Consultant; Self; Sanofi. N. El Sayed: None. W.C. Hsu: Consultant; Self; Onduo LLC. R.J. Heine: Employee; Self; Eli Lilly and Company.
128-LB: Insulin Glargine 300 U/mL (Gla-300) vs. First-Generation Basal Insulins (BI) in Insulin-Naïve Type 2 Diabetes (T2D) Patients (Pts) With and Without Cardiovascular Disease (CVD): Outcomes of the Randomized Pragmatic Real-Life Clinical Trial ACHIEVE Control
Authors: FRIAS JUAN P. , EVENOU PIERRE , DALTON DAVID , DUNGAN KATHLEEN M.
Published: June 2019

ACHIEVE Control demonstrated superiority of Gla-300 vs. standard of care (SOC) BIs (glargine 100 U/mL and detemir), with a significantly greater proportion of insulin-naive T2D pts treated with Gla-300 achieving the composite primary endpoint of individualized HEDIS A1C targets at 6 months without severe and/or symptomatic documented hypoglycemia (blood glucose [BG] ≤70 mg/dL). Here we report outcomes in pts with and without CVD at baseline (BL). Characteristics at BL were generally well balanced between treatments, with a potential difference in GLP-1 receptor agonist use in CVD pts. In pts without CVD, outcomes consistently favored Gla-300 (Table). In CVD pts, composite endpoint attainment at 12 months was similar with Gla-300 and SOC-BI, while avoidance of hypoglycemia, especially serious hypoglycemia (BG <54 mg/dL), showed a trend favoring Gla-300 (Table); changes in A1C from BL to 12 months with Gla-300 and SOC-BI were similar (LS mean difference [95% CI] 0.12% [-0.12 to 0.35]; p=0.34). The results of this analysis are promising and warrant further exploration of the possibility that the more stable action profile of Gla-300 translates to T2D patients with CVD experiencing serious hypoglycemia less often with Gla-300 than first-generation BIs.

Disclosure

J.P. Frias: Advisory Panel; Self; Becton, Dickinson and Company, Eli Lilly and Company, Gilead Sciences, Inc., Sanofi. Consultant; Self; Echosens, Genentech, Inc., Johnson & Johnson Diabetes Institute, Novo Nordisk Inc., Zafgen, Inc. Research Support; Self; AbbVie Inc., Akcea Therapeutics, Allergan, Amgen Inc., AstraZeneca, Bayer US, Boehringer Ingelheim Pharmaceuticals, Inc., Bristol-Myers Squibb Company, Cirius Therapeutics, Elcelyx Therapeutics, Inc., Eli Lilly and Company, Enanta Pharmaceuticals, Inc., GENFIT, Intarcia Therapeutics, Inc., Intercept Pharmaceuticals, Inc., Ionis Pharmaceuticals, Inc., Janssen Pharmaceuticals, Inc., Merck & Co., Inc., NGM Biopharmaceuticals, Novartis Pharmaceuticals Corporation, Novo Nordisk A/S, Oramed Pharmaceuticals, Pfizer Inc., Sanofi, TaiwanJ Pharmaceuticals Co., Ltd., Theracos, Inc. Speaker’s Bureau; Self; Merck & Co., Inc., Sanofi. P. Evenou: Employee; Self; Sanofi US. D. Dalton: Employee; Self; Sanofi US. K.M. Dungan: Advisory Panel; Self; Eli Lilly and Company, Elsevier, Sanofi-Aventis. Research Support; Self; Eli Lilly and Company, GlaxoSmithKline plc., Novo Nordisk Inc., Sanofi-Aventis, Viacyte. Other Relationship; Self; DKBmed, UpToDate.

Funding

Sanofi US
128-OR: Clustering of Metabolomics, Clinical Biomarkers, and Ectopic Liver Fat in Children/Adolescents: An Integrated Network Analysis
Authors: CIOFFI CATHERINE E. , VENKAT NARAYAN K.M. , UPPAL KARAN , PIERPONT BRIDGET , CAPRIO SONIA , SANTORO NICOLA , VOS MIRIAM B.
Published: June 2019

Hepatic fat accumulation is often associated with cardiometabolic disease (CMD) risk factors in children, but the mechanism is unclear. We measured untargeted metabolomics in plasma from 219 obese children/adolescents with (37%) and without (63%) fatty liver assessed by magnetic resonance imaging. An integrative network analysis, which involved several steps including pairwise association analyses and a multilevel community detection algorithm, was performed using xMWAS to explore clustering patterns of metabolomics, clinical biomarkers, and abdominal and hepatic fat. This identified 3 communities consisting of significantly correlated variables (|r| > 0.4, p < 0.05) as depicted in Figure 1. Notably, the community featuring hepatic fat clustered with several CMD risk factors, including insulin resistance and dyslipidemia. Pathway analyses of the metabolites in this cluster using mummichog showed enrichment in carnitine shuttle and glycerophospholipid metabolism (p<0.05), including several acyl-carnitine species and linoleic acid. Other metabolites in the cluster were the aromatic and branched chain amino acids tyrosine and isoleucine, respectively. These results suggest that hepatic fat is more closely correlated with CMD risk factors compared to other abdominal fat depots and may offer insights into biological mechanisms that underlie this finding.

Disclosure

C.E. Cioffi: None. K. Narayan: None. K. Uppal: None. B. Pierpont: None. S. Caprio: None. N. Santoro: None. M.B. Vos: Advisory Panel; Self; Intercept Pharmaceuticals, Inc. Board Member; Self; TARGET PharmaSolutions. Consultant; Self; Boehringer Ingelheim International GmbH, Bristol-Myers Squibb Company, Shire. Research Support; Self; Gemphire Therapeutics Inc., Immuron, TARGET PharmaSolutions.

Funding

National Institute of Diabetes and Digestive and Kidney Diseases (R01DK111038, T32DK007734, P30DK111024); Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD028016, R21HD089056)
1283-P: PTS Diagnostics A1CNow+: A Valuable Tool in a Value-Based World
Authors: SZABLOWSKI CARRIE J. , DAVIS KADDY , ANDERSON JAMES H.
Published: June 2019

Background: Value-based medicine is a new healthcare model to shift physician focus from the quantity of services and revenue generated to quality and value of healthcare provided. The basis for this model is the idea of the right care for the right patient in the right environment at the right time, also known as Patient Centered Medical Home (PCMH). Point-of-care A1C testing can maximize patient and provider engagement by obtaining the results in real-time which allows for effective communication, healthy discussions, and improved healthcare. This study evaluated the accuracy of A1CNow+ as a tool to assist with PCMH for diabetes management.

Method: Blood was collected from PTS employees. A1C values ranged from 4.4 - 12.4% A1C for a total of 90 data points. Capillary blood was tested on the PTS Diagnostics A1CNow+ system while venous blood was tested at PTS Diagnostics on the Tosoh G8 and Roche Cobas® Integra® 400 Plus as clinical laboratory (CL) comparators. For reference, venous blood was tested at LabCorp on the Roche Cobas c513. Deming regression analysis was used to evaluate accuracy and paired differences to measure bias. Clinical risk was assessed using A1C clinical cut points of < 5.7, 5.7 - 6.4, and ≥ 6.5% A1C. Fisher’s exact test was used to assess differences amongst methods.

Results: Average paired percent bias was -0.83% for the A1CNow+ and +0.17% for the CL analyzers. Slopes were 0.92 and 0.93 (p=0.35) and intercepts were 0.42 and 0.44 (p=0.098) for the A1CNow+ and CL, respectively. Clinical risk agreement was 89% for A1CNow+ and 97% for CL analyzers (p=0.08).

Conclusion: The A1CNow+ was shown to be as accurate as the clinical lab in measuring A1C. Risk analysis showed no statistical difference between the CL and A1CNow+ in classifying A1C clinical cut points. The A1CNow+ is a valuable tool to help meet the goals of the Quadruple Aim which seeks to improve the health of the population, improve the patient experience of care, improve the provider experience of giving care, and reduce the cost of healthcare in a value-based world.

Disclosure

C.J. Szablowski: Employee; Self; PTS Diagnostics. K. Davis: None. J.H. Anderson: Board Member; Self; Generex Biotechnology Corp., VoCare. Consultant; Self; Know Labs. Employee; Self; PTS Diagnostics. Other Relationship; Self; Lilly Diabetes.
1289-P: The Impact of Medication Interventions in a Clinical Pharmacy Specialist Medication Management Clinic on A1C Reduction and Glycemic Goals
Authors: DESKO LAUREN
Published: June 2019

Background: To identify the effects of medication interventions made in diabetic patients in a clinical pharmacy specialist (CPS) medication management clinic on A1C reduction and achievement of glycemic goals in a Department of Veterans Affairs (VA) clinic setting.

Methods: A total of 61 new patients referred to the CPS medication management clinic between October 1, 2017, and March 31, 2018, were evaluated. The number of medication interventions at the initial CPS visit, and all visits in the first 6 months of clinic enrollment, were assessed. The patient’s baseline A1C was assessed prior to enrollment in clinic, followed by re-assessment 3 and 6 months later. The number of patient visits during the 6-month period was individualized.

Results: A total of 53 patients were included. The average baseline A1C was 9.19% (range of 5.2% to 14%). The average A1C at 3 months was 7.96%, and the average A1C at 6 months was 7.29%. The average change in A1C was -1.3% in 3 months and -2.27% in 6 months. The CPS made a median of 2 medication interventions at the initial visit (range of 0 to 7), including a median of 2 diabetes medication interventions (range of 0 to 5). The CPS made a total of 259 medication interventions during the 6-month period, with a median of 5 medication interventions per patient (range of 1 to 11). The CPS had 243 total patient visits during the 6-month period, with a median of 4 visits per patient (range of 1 to 12). Seventeen patients (32%) had achieved A1C goal at 3 months, and twenty-five patients (47%) had achieved A1C goal at 6 months. Forty-two patients (79%) had achieved appropriate statin use at 3 months.

Conclusions: This project demonstrates how the CPS medication management clinic achieved substantial A1C reduction within the first 6 months of patient referral, as well as positive impacts on glycemic goals. The CPS in the VA is uniquely poised to positively impact patient outcomes, and this practice model has potential for future adaptation to other settings.

Disclosure

L. Desko: None.
1291-P: Type 1 Diabetes Patients Can Achieve Stable Glycemic Control with Routine Clinical Visits: Results of a Novel Integrated Outpatient Diabetes Management System
Authors: XIE YUTING , LI XIA , LIU FANG , ZHOU ZHIGUANG
Published: June 2019

Objective: To describe a novel integrated outpatient diabetes management system set up in China. To evaluate its effectiveness in type 1 diabetes (T1D) control.

Methods: A multiple disciplinary clinic, assembling a diabetologist, a diabetes education nurse, a dietitian, an ophthalmologist team, a psychiatrist, and a physiotherapist was set up to evaluate patient’s characteristics. The integrated outpatient care was carried out at baseline and every 3-6 months based on patient’s condition with an individualized principle tailored to meet each patient’s needs. Cross sectional data from type 1 diabetes patients attending the clinic through 2015 to 2018 were compared. Outcome measures included HbA1c levels, percentage of patients within HbA1c targets, quality of life score, and diabetes self-management score.

Results: A total of 749 T1D patients (380 children and 369 adults) attended the clinic within three years. Mean HbA1c level at baseline was 9.21±2.66%, with 93 (30.2%) children and 87 (26.4%) adults achieving HbA1c targets. 285 (44.7%) patients had an HbA1c level over 9%. Mean HbA1c declined by 1.44% - 1.85% within routine follow-up visits and remained stable at 7.36%-7.77% for up to three years (p<0.01). Percentage of children achieving HbA1c targets (<7.5%) increased to 67.3%. Percentage of adults achieving HbA1c targets (<7.0%) increased to 63.6%. Mean scores for self-management skills and quality of life increased significantly (p<0.05).

Conclusions: This new outpatient management system can significantly improve the overall diabetes control of T1D patients with sustainable influences. It is a dramatic improvement on T1D management over conventional clinics in China, and provides insights into a more rounded diabetes management model routinely involving six specialists.

Disclosure

Y. Xie: None. X. Li: None. F. Liu: None. Z. Zhou: None.

Funding

National Key Research and Development Program of China (2016YFC1305000, 2016YFC1305002, 2017YFC1309604)