bluuebunny/crossref_metadata_2025_split huggingface dataset:
An Assessment of Protocol Utilization and Efficacy when Transitioning from Infusion to Subcutaneous Insulin at an Academic Medical Center
Authors: VINCENT LAUREN , BOX KEVIN , KULASA KRISTEN 
Published: June 2018

Background: The transition from intravenous to subcutaneous insulin is a complicated process often associated with poor glycemic control. Our academic medical center has protocols for guiding providers through this transition. Our protocols have previously been shown to improve glycemic control. We sought to assess protocol utilization and whether it continues to produce successful insulin transitions.
Methods: Data was collected through retrospective chart review of all transitions from intravenous to subcutaneous insulin completed at our medical center from April 2015 through June 2015. A successful transition was defined as one in which the insulin drip was not restarted, mean blood glucose was less than 180 mg/dL for the first 48 hours after transition, and there was no hypoglycemia (blood glucose less than 70 mg/dL).
Results: In total, 267 transitions from intravenous to subcutaneous insulin occurred at our medical center over the study period, and 211 were included in the analysis. For the group of patients with diabetes and transitioned to long acting subcutaneous insulin, 25 out of 1(23.1%) were transitioned using either our paper or computer protocol, and 82 out of 1(75.9%) were not transitioned via protocol. Of the 25 patients transitioned via protocol, 15 (60%) were successfully transitioned from intravenous to subcutaneous insulin. Of the 82 patients transitioned without using the protocol, 39 (47.6%) were successful. Among patients without a history of diabetes, 62 out of 65 patients (95.4%) were successfully transitioned from intravenous to subcutaneous insulin. The majority of these patients maintained good glycemic control with correctional insulin only.
Conclusions: Our protocol is effective in producing successful transitions from intravenous to subcutaneous insulin, however it is underutilized in our academic medical center.

Disclosure
L. Vincent: None. K. Box: None. K. Kulasa: None.
Predictors of Achieving Glycemic Targets among People with Type 2 Diabetes—The CARRS Trial
Authors: MASOOD MUHAMMAD Q. , SINGH KAVITA , MAWANI MINAZ Z. , ALI MOHAMMED K. , DEVARAJAN RAJI , KONDAL DIMPLE , SHIVASHANKAR ROOPA , AJAY VAMADEVAN S. , VARTHAKAVI PREMLATA K. , DHARMALINGAM MALA , SAHAY RAKESH , MENON USHA , VISWANATHAN VIJAY , BANTWAL GANAPATHI , KHADGAWAT RAJESH , DESAI ANKUSH , SETHI BIPIN , KADIR MUHAMMAD M. , VENKAT NARAYAN K.M. , PRABHAKARAN DORAIRAJ , TANDON NIKHIL 
Published: June 2018

While poor glycemic (A1c), blood pressure (BP), and lipid control are common in patients with diabetes, the CARRS Trial among 1,146 type 2 diabetes patients showed the effectiveness of a multicomponent intervention (care coordinators and decision-support electronic health records). Compared to usual care, the intervention was associated with twice the probability of achieving glycemic and combined diabetes care goals (A1c&lt;7% and BP&lt;130/80 mmHg or LDLc&lt;100 mg/dl). In absolute terms, however, only 20% of the intervention participants achieved glycemic targets. In this analysis, we evaluated the predictors of achieving good (A1c&lt;7%) and avoiding poor glycemic control (A1c&gt;9%) among CARRS participants (mean age: 54.2 years, median diabetes duration: 7 years and mean A1c:9.9%). Using generalized estimating equations, we analyzed four regression models (demographics, disease-related, self-care and other risk factors) to identify predictors of glycemic control, adjusting for intervention. We found income, education, diabetes duration, adherence to prescribed medication/diet/exercise, and diabetic retinopathy to be significantly associated with glycemic control (Table 1). Our data highlight the importance of tailoring treatments based on patients’ socioeconomic status (SES) and disease severity and also further research into innovations to facilitate better self-care in lower SES groups.

Disclosure
M.Q. Masood: None. K. Singh: None. M.Z. Mawani: None. M.K. Ali: None. R. Devarajan: None. D. Kondal: None. R. Shivashankar: None. V.S. Ajay: None. P.K. Varthakavi: None. M. Dharmalingam: None. R. Sahay: Advisory Panel; Self; Boehringer Ingelheim, Sanofi, Eli Lilly and Company, Novo Nordisk A/S. Advisory Panel; Spouse/Partner; Intas Pharmaceuticals Ltd., Pfizer Inc.. Speaker's Bureau; Self; Sanofi, Eli Lilly and Company, Boehringer Ingelheim, Dr. Reddys Laboratories, USV Private Limited, Glenmark. Speaker's Bureau; Spouse/Partner; Boehringer Ingelheim. U. Menon: None. V. Viswanathan: None. G. Bantwal: Speaker's Bureau; Self; Abbott. Advisory Panel; Self; AstraZeneca. Speaker's Bureau; Self; Biocon, Boehringer Ingelheim Pharmaceuticals, Inc., Novo Nordisk Inc., Sanofi-Aventis. R. Khadgawat: None. A. Desai: None. B. Sethi: None. M.M. Kadir: None. K. Narayan: None. D. Prabhakaran: None. N. Tandon: None.
A Novel Economic Analysis Applied to Innovative Diabetes Digital Health Intervention Demonstrates Significant Financial Benefits
Authors: SHOMALI MANSUR , IYER ANAND K. , GRIFFIN BRIAN T. , PEEPLES MALINDA 
Published: June 2018

The healthcare industry has had limited success in translating the value of innovative diabetes interventions into true economic return on investment. Our objective was to determine the value of improving diabetes outcomes and then to apply those values to the utilization of a digital health tool, BlueStar™. The analysis was conducted using the MarketScan® Research Databases owned by Truven Health Analytics®, part of IBM Watson Health™. These research data sets fully integrate de-identified patient-level health data, productivity, laboratory results, health risk assessments, hospital discharges, electronic medical records (EMR) and morbidity data into data available for research. Data are contributed by large employers, managed care organizations, hospitals, EMR providers, and Medicare and Medicaid programs. A complex algorithm was applied to the MarketScan data, resulting in quantified total costs broken down by A1C. We examined two population segments: commercial (age 40-64, n=2,320) and Medicare (age 65+, n=821). Cost estimates could thus directly be applied from the tables derived from the database to the A1C reductions associated with the BlueStar population of users, demonstrating significant cost savings associated with the use of BlueStar™.Healthcare Costs by A1C Band for Commercial and Medicare PopulationsCommercial PopulationMedicare PopulationA1C BandPercentage in BandAnnual Cost Per PatientPercentage in BandAnnual Cost Per Patient&lt;7%30%$13,08042%$11,6537%-8%34%$14,29835%$13,7868%-9%16%$15,03614%$16,726&gt;9%20%$19,9809%$17,036

Disclosure
M. Shomali: Consultant; Self; WellDoc, Inc. A.K. Iyer: Employee; Self; WellDoc, Inc. B.T. Griffin: Other Relationship; Self; WellDoc, Inc. M. Peeples: Employee; Self; WellDoc, Inc..
Establishment of the Type 1 Diabetes Exchange QI Learning Collaborative (T1DX-LC)
Authors: SHAH AVNI , CORATHERS SARAH , ALONSO GUY T. , BUCKINGHAM DON A. , CABRERA ALYSSA B. , CLEMENTS MARK A. , DESALVO DANIEL J. , KAMBOJ MANMOHAN K. , LAMBERT KATHRYN L. , MEHTA SANJEEV N. , OHMER AMY L. , RIOLES NICOLE , SONABEND RONA , LEE JOYCE 
Published: June 2018

Background: The proportion of patients with type 1 diabetes (T1D) who meet recommended HbA1c targets remains suboptimal, with little improvement over the last two decades. A Learning Collaborative (LC) is a multi-clinic learning and sharing quality improvement (QI) process known to accelerate improvement in chronic conditions. We created the T1DX-LC to spread learning to improve health outcomes of individuals living with T1D.
Methods: In 2014, a design process engaged clinicians, researchers, patient advocates, and QI leaders to develop a roadmap for improvement, based on the Chronic Care Model. The T1DX-LC launched in 2016 with 7 pediatric and 3 adult diabetes practices, coordinated by the T1D Exchange (Boston, MA). Over 18 months, we established a LC foundation: teams representing each clinic, governance, QI training and capability, online collaboration space, standard measures, and population data systems. Teams selected clinical tests of change to pilot for depression screening, pre-visit planning, and patient-centered clinic design.
Results: Teams completed formal QI coursework and collaborated via monthly calls, emails, an online collaboration space, and 3 in-person learning sessions. Six teams increased depression screening, 5 teams adopted multiple change ideas for pre-visit-planning, and 5 teams shared patient-centered tools and practices. A QI capability assessment survey was established and showed proficiency for all teams. We established a HIPAA-compliant data repository, data formatting specifications, data extraction/transmission standards, and a prototype data visualization dashboard. Two sites demonstrated the ability to extract and transmit EHR data to the central database.
Conclusions: The T1DX-LC demonstrated broad engagement, improved QI capability, advanced 3 focus areas, and developed a data exchange infrastructure. Our next steps are to build upon these efforts to accelerate improvement of outcomes in high-risk patients with T1D defined by HbA1c &gt;9%.

Disclosure
A. Shah: None. S. Corathers: None. G.T. Alonso: None. D.A. Buckingham: None. A.B. Cabrera: None. M.A. Clements: Speaker's Bureau; Self; Medtronic. Advisory Panel; Self; Glooko, Inc. D.J. DeSalvo: Consultant; Self; Dexcom, Inc.. Speaker's Bureau; Self; Insulet Corporation. M.K. Kamboj: None. K.L. Lambert: None. S.N. Mehta: None. A.L. Ohmer: None. N. Rioles: None. R. Sonabend: None. J. Lee: Consultant; Self; Unitio.
Cost Savings Associated with Digitally Enhanced Insulin Therapy
Authors: HODISH ISRAEL , BISGAIER STANLEY G. , UNGER EMILY , AUSTIN MARY M. 
Published: June 2018

Improved glycemic control in patients with type 2 diabetes (T2D) is associated with avoidance of complications and reduced healthcare costs. Yet for decades, most patients have not achieved treatment goals and a third have A1&gt;9%. The d-Nav® Insulin Guidance Service facilitates scalable attainment and maintenance of therapy goals without increasing provider burden. d-Nav is a handheld device that automatically titrates insulin dosage based on the patient’s glucose readings obtained using the d-Nav device. Additionally, the service includes dedicated support of care specialists and pharmacological optimization if A1c improves. This 9-month study assessed the financial impact of the d-Nav service on diabetes related costs in sub-optimally control patients with T2D. We enrolled 218 insulin-treated patients, 193 of whom completed the 3-month follow-up. Thus far, 156 have completed the study. See Figure for A1c changes and patient satisfaction. Furthermore, 88% have chosen to enroll in a 12-month extension. Direct savings was estimated at $6,172 per patient per year (PPPY) for patients using branded medications and $1,736 PPPY across the entire cohort (p&lt;0.001). Given the simplicity of use for the patient and no added provider burden, expansion of such a service may lead to a sizable reduction in cost.

Disclosure
I. Hodish: Stock/Shareholder; Self; Hygieia. S.G. Bisgaier: Employee; Self; Hygieia. E. Unger: Employee; Self; Hygieia. M.M. Austin: None.
Blue Index—The Diabetes Health Score
Authors: PANCHAL DHARMENDRA , PHATAK SANJEEV , ZINZUWADIA PADMANABH , SABOO BANSHI D. , HASNANI DHRUVI , KALRA SANJAY , DAS A. K. , DWIVEDI SHIVANGI 
Published: June 2018

Aim: To evaluate and monitor quality of diabetes care in a single risk score format BLUE INDEX (BI).
Design and Method: We assessed overall health of patients with T2DM based on 10 parameters - 3 goals (A1c, BP, lipids), 4 self-care parameters (diet, exercise, follow-up, habits), and 3 health parameters (general health, infection, hypoglycemia). We categorized each parameter into six grades and formulated BI with minimum score 0 and maximum 50. BI &gt; 45 = excellent and &gt; 40 is considered as favorable. BI &lt; 35 = undesirable. We calculated BI for 100 patients with T2DM &gt; 1 year and repeated it at 2nd visit (3 months) and 3rd visit (6 months). Patients were educated for importance of each parameter and their overall health score was discussed. At the end of 3rd visit improvement in each parameter was studied.
Result: 100 patients with T2DM &gt; 1 year were studied and divided into 3 groups as per BI. Group A= BI &lt; 35, Group B = BI 35-45 and Group C = BI &gt; 45. At baseline there were 76, 17 and 7 patients in Group A, B and C respectively with mean BI =26 ±2. After 3 month - 33, 44 and 23 patients in Group A, B and C respectively with mean BI = 37±3 and after 6 months there were 21, 41 and 38 patients in Group A, B and C respectively with mean BI = 42±2. There was 61% improvement in BI over the 6 months.
Conclusion: The BI provides comprehensive, pragmatic and patient centered mean of assessing care in persons with diabetes. The main role is in clinical triage, helping identify high risk persons who need intensive clinical intervention.

Disclosure
D. Panchal: None. S. Phatak: None. P. Zinzuwadia: None. B.D. Saboo: None. D. Hasnani: None. S. Kalra: None. A.K. Das: None. S. Dwivedi: None.
Cost Savings Associated with Usage and Blood Glucose Control for Members of the Livongo for Diabetes Program
Authors: BOLLYKY JENNIFER , LU WEI , SCHNEIDER JENNIFER , WHALEY CHRISTOPHER 
Published: June 2018

Background: Novel technologies to support real-time self-management of chronic conditions are of interest to entities bearing financial risk for healthcare costs. The Livongo Diabetes Program offers patients with diabetes (1) a cellular-enabled, two-way messaging device that measures blood glucose, (2) free unlimited BG test strips; and (3) access to Certified Diabetes Educators for real-time support and goal setting. Previous work has shown decreased medical costs of $83pmpm for Livongo members vs. matched controls during first year of program. This study uses biometric and claims data to investigate the association between use of the Livongo program, BG control and medical spending among a commercially insured population of diabetes patients.
Methods: We compared medical spending and clinical outcomes for Livongo users (n =2027) with matched non-Livongo users (n=8385) for 12 months before and after the launch of the Livongo program using multivariate instrumental variable regression models that included controls for patient demographics (e.g., age, gender) and risk characteristics (e.g., comorbidity index, prior-period medical spending and program usage).
Results: Following launch of Livongo, risk-adjusted total spending for users and non-users decreased by 15.9%. Launch of Livongo led to a risk-adjusted 76.2% increase in the monthly use rate of Livongo. A 10% increase in the monthly Livongo usage rate is associated with a 2.1% decrease in medical spending associated (p&lt;0.01), which was driven by a 2.9% reduction in spending on office-based services. Mean monthly medical spending for users with mean BG&lt;154mg/dL was significantly lower than for users with mean BG&gt;154mg/dL ($505.86 vs. $681.01, p=0.02).
Conclusion: Patients with diabetes enrolled in the Livongo program for 12 months experience medical cost savings associated with improved BG control. Cost savings for Livongo program users are primarily due to decreased spending on outpatient visits.

Disclosure
J. Bollyky: Employee; Self; Livongo Health. Stock/Shareholder; Self; Livongo Health. W. Lu: None. J. Schneider: Employee; Self; Livongo Health. Stock/Shareholder; Self; Livongo Health. C. Whaley: Consultant; Self; Livongo Health.
Impact of Hyperglycemia on 30-Day Readmission Rates
Authors: GAINES MARY , PRATLEY RICHARD E. 
Published: June 2018

Background: The Centers for Medicare and Medicaid (CMS) Hospital Readmissions Reduction Program (HRRP) aims to reduce 30-day readmission rates for patients with acute myocardial infarction, heart failure, pneumonia, chronic obstructive pulmonary disease and hip/knee arthroplasty. The impact of hyperglycemia on readmission rates for these diagnoses is not known.
Methods: We analyzed 30-day readmission rates for patients discharged from Florida Hospital Orlando with CMS HRRP diagnoses from July 1, 2014 through June 30, 2017. Subjects were grouped according to eligibility (2 point-of-care glucoses within 24 hours) for our glycemic monitoring program (GMP) or not (non-GMP). The GMP group was further divided into controlled (C-GMP, mean blood glucose during hospitalization 70-180 mg/dL) and hyperglycemic (H-GMP, mean blood glucose &gt; 180 mg/dL) groups.
Results: The GMP group had significantly higher readmission rates than the non-GMP group, as did the H-GMP group relative to the C-GMP group (Table). These differences remained significant after adjusting for difference in age, sex, diagnosis, case mix index and other factors in logistic regression analyses.
Conclusion: Hyperglycemic patients discharged with a CMS HRRP diagnosis have higher 30-day readmission rates. Strategies to prevent readmission should target glycemic control during and after hospitalization.VariableNon-GMPGMPC-GMPH-GMPN1767517008111205888Female %53.9%49.8%49.7%49.9%White %78.5%71.8%71.0%73.3%Age (years)67686967Weight (kg)80.28885.592.7Case Mix Index1.492.072.311.6130-Day Readmission Rate (%)14.421.5**21.122.3* * P &lt; 0.05, ** P &lt; 0.01 in risk-adjusted logistic regression analyses.

Disclosure
M. Gaines: None. R.E. Pratley: Other Relationship; Self; AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Eisai Inc., GlaxoSmithKline plc., Janssen Pharmaceuticals, Inc., Lexicon Pharmaceuticals, Inc., Ligand Pharmaceuticals, Inc., Eli Lilly and Company, Merck & Co., Inc., Novo Nordisk Inc., Pfizer Inc., Sanofi-Aventis, Takeda Development Center Americas, Inc..
Impact of Diabetes Mellitus on Outcomes of Patients Undergoing Autologous Stem Cell Transplantation for Multiple Myeloma in the U.S.
Authors: SIWAKOTI KRISHMITA , GIRI SMITH , BHATT VIJAY 
Published: June 2018

High dose chemotherapy followed by Autologous Stem Cell Transplant (Auto-SCT) is considered standard of care for most patients with Multiple Myeloma (MM) in the U.S. However, recent evidence suggests that diabetic patients with MM are less likely to receive Auto-SCT due to concerns for higher toxicity. We sought to determine the impact of diabetes on in-hospital outcomes of MM patients undergoing autoSCT. We queried the Nationwide Inpatient Sample (NIS) from the years 2009-2011 to identify all adult hospitalizations with MM and auto-SCT. Diabetic status was ascertained using ICD-9 diagnostic codes. Primary and secondary outcomes were in-hospital mortality and length of stay (LOS) or costs of hospitalization (COH) respectively. Univariate methods included chi-square test and t-test where appropriate. Multivariate analysis was done using Generalized Linear Mixed Models (GLMM) to account for clustering in the dataset. All p values were two sided and level of significance was chosen at 0.05. A total of 2868 (14,153 estimated) hospitalizations for MM related auto-SCT was identified, out of which 13% had diabetes. The mean age was 58 ± 8.7 years, 43% females and 67% whites. The overall in-patient mortality rate was 1.78% whereas mean LOS and COH was 17 days and $160,720 respectively. Diabetes did not impact inpatient mortality (1.31% vs. 1.85%; p 0.45), LOS (18 vs. 17.6 days; p 0.33) or COH ($160,665 vs. $160,728; p 0.99). Similar results were seen on GLMM for in-hospital mortality (adjusted Odds Ratio, aOR 0.67; 95% CI 0.26-1.75; p 0.42) after adjusting for patient (age, sex, race, comorbidity, insurance status) and hospital characteristics (bedsize, location, teaching status). Our large U.S. population-based study suggests that diabetic patients with MM undergoing Auto-SCT have similar in-hospital outcomes compared to their nondiabetic counterparts. Future studies should explore the incidence of other treatment related complications in this population.

Disclosure
K. Siwakoti: None. S. Giri: None. V. Bhatt: None.
Childhood Habitual Sleep Duration Modifies the Polygenic Risk for Obesity through Leptin Pathway—A Longitudinal Study
Authors: LI MING , FU JUNLING , HAN LANWEN , LI GE , GAO SHAN , GRANT STRUAN F. ,   BCAMS GROUP
Published: June 2018

Sleep disruption is known to be an obesity risk factor, however, little is known about its interplay with genetic predisposition and pathways involved in obesity pathogenesis, especially in the longitudinal setting. Leveraging a large cohort from the Beijing Child and Adolescent Metabolic Syndrome (BCAMS) study, we aimed to examine a possible sleep-gene interaction for childhood obesity risk, including longitudinal assessment in a 10-year follow-up and further test if there is any mediation through the leptin pathway. A total of 3,211 children (6-18 years) were recruited from this cohort study. Baseline leptin levels and twelve established adult BMI loci were examined for the associations with habitual sleep duration. After adjusting for pubertal stages, diet score and activity, short sleep duration at baseline was significantly associated with increased overweight/obesity risk at both baseline and follow-up. Genetic predisposition scores, particularly consisting of the central nervous system (CNS) gene variation (GPSCNS), were robustly correlated with baseline overweight/obesity in children who slept ≤8h/day (P&lt;0.001), whereas the association was ablated in those who slept ≥10h/day (P&gt;0.05). Comparable observations were made at follow-up. Mediation analysis revealed a modest direct effect of the GPSCNS-sleep interaction on BMI at baseline, while an indirect effect of this interaction was found to be mediated principally through elevated leptin (52.6%); moreover, the mediation effect via leptin remained stable over 10 years. This study provides longitudinal evidence that short sleep duration in children has a long-term impact on the association of polygenic risk for obesity from childhood to young adulthood and leptin pathway explains a key mechanism via a modification effect. Therefore, adequate sleep duration during childhood is important for the early prevention of obesity, especially if there is a genetic predisposition to this trait.

Disclosure
M. Li: None. J. Fu: None. L. Han: None. G. Li: None. S. Gao: None. S.F. Grant: None.