bluuebunny/crossref_metadata_2025_split huggingface dataset:
922-P: Diabits—An AI-Powered Smartphone Application for Blood Glucose Monitoring and Predictions
Authors: HAYERI AMIR 
Published: June 2019

This paper presents DiaBits, an iPhone application that allows patients with diabetes to efficiently monitor and manage their blood glucose levels in real time. Using current CGM data, smartphone sensor data, and user inputs, DiaBits takes advantage of modern machine learning techniques to predict future blood glucose fluctuations up to 60 minutes in advance. These predictions, which are over 95% clinically acceptable according to standard Error Grid Analysis methods, give the patients an opportunity to take corrective action in advance in order to keep their blood glucose values within normal range. Additionally, the presence of predictive alarms and statistical tools for analysis of past data makes it easier for the users to manage their condition and have better blood glucose control. The paper discusses some of the details of the predictive approach, provides accuracy results for 1.63 million predictions made for free-living users of the application, gives a detailed overview of the available features and the interface of DiaBits, and describes some challenges related to using machine learning for real-time predictions of blood glucose behavior.

Disclosure
A. Hayeri: Other Relationship; Self; Bio Conscious Technologies INC.
925-P: Performance of Sensors for Continuous Glucose Monitoring (CGM) under Extreme Sports Conditions in People with Diabetes
Authors: DELGADO ELIAS , FOKKERT MARION M.J. , VAN DIJK P.R. , EDENS MIREILLE A. , DIEZ ALBERTO , SLINGERLAND R. J. , BILO HENK 
Published: June 2019

Everyone with insulin-dependent diabetes wants to know as reliably and easily as possible the course of glucose levels during exercise. Several CGM sensors are on the market, recently the Free Style Libre Flash Monitor (FLM) has been introduced.
Objective: what is the effect of use during extreme exercise on the accuracy of both the CGM and the FLM.
Methods: Both CGM (Medtronic) and FLM (Abbott) were compared to the finger prick/strip (cBGMstrip) during a Bas van de Goor Foundation mountainbike challenge in the Mont-Blanc massif (218km, 7900hm).
Results: CGM and FLM are clearly less accurate during a period of intensive efforts compared to normal daily activities. In addition, glucose values in the interstitial fluid frequently were higher than found with capillary measurements, also in the lower glucose concentrations. This makes the signaling of hypoglycemia through CGM use less reliable during extreme exercise. Clinically relevant differences were also seen when comparing CGM and FLM (see Table).
Conclusions: During extreme exercise, CGM and FLM often showed higher interstitial glucose concentrations than actually present, also at the hypoglycemic range. This is in contrast to measurements taken under normal conditions. In addition, results of the continuous measurement technics differed considerably. This makes the use of CGM and FLM less reliable during extreme exercise.

Disclosure
E. Delgado: Advisory Panel; Self; Abbott, Almirall, S.A., AstraZeneca, Esteve, Novo Nordisk A/S, Sanofi. Research Support; Self; AstraZeneca, Menarini Group. Speaker's Bureau; Self; Boehringer Ingelheim International GmbH, Lilly Diabetes. M.M. Fokkert: None. P. van Dijk: None. M.A. Edens: None. A. Diez: None. R.J. Slingerland: None. H. Bilo: None.
929-P: Clinical Factors Associated with the Glucagon Stimulation Test and Daily and Day-to-Day Glucose Variability Determined by Continuous Glucose Monitoring in Type 2 Diabetic Patients
Authors: OHARA MAKOTO , NAGAIKE HIROE , GOTO SATOSHI , KUSHIMA HIDEKI , HIROMURA MUNENORI , YAMAMOTO TAKESHI , MORI YUSAKU , FUKUI TOMOYASU , HIRANO TSUTOMU 
Published: June 2019

Aims: We studied clinical factors in association with the glucagon stimulation test and daily and day-to-day glucose variability as determined by continuous glucose monitoring (CGM).
Methods: We performed a cross-sectional analysis of type 2 diabetes (T2DM; insulin-treated, n=83; not insulin-treated, n=126) patients who underwent the glucagon stimulation test and over 72 h of continuous glucose monitoring. Correlations of clinical factors with the mean amplitude of glycemic excursions (MAGE), percentage coefficient of variation for glucose (%CV), and mean of daily differences (MODD) in CGM were analyzed.
Results: In T2DM with insulin therapy, age and HbA1c were correlated with MAGE and %CV; FPG was correlated with MAGE and MODD; and the increment of CPR (ΔCPR) was inversely correlated with MAGE, %CV, and MODD. In T2DM without insulin therapy, age, diastolic blood pressure, and triglycerides were correlated with MODD; ΔCPR was inversely correlated with %CV; HbA1c was correlated with MAGE and MODD; the use of α-glucosidase inhibitors was inversely correlated with %CV; and the use of sulfonylurea (SU) was correlated with MAGE and %CV.
Conclusions: Insulin secretion is related to glucose variability in insulin-treated T2DM. Poor glycemic control greatly contributes to glucose variability in T2DM. SU administration increases the daily glucose variability in T2DM without insulin therapy.

Disclosure
M. Ohara: None. H. Nagaike: None. S. Goto: Research Support; Spouse/Partner; DENKA SEIKEN CO., LTD. H. Kushima: None. M. Hiromura: None. T. Yamamoto: None. Y. Mori: None. T. Fukui: None. T. Hirano: None.
930-P: Blood Glucose Levels Prediction Accuracy for T1DM Patients Using Neural Networks to Combine Insulin Doses, Food Nutrients, and Heart Rate
Authors: FOSS-FREITAS MARIA CRISTINA , MOREIRA GABRIEL S. , ANTLOGA VITOR P. , NETO CAMILO R. , RODRIGUES EDUARDO MATOS , DA COSTA MARIANA FERRACINI , DOS SANTOS ANDRÉ PEREIRA , MATSUMOTO YURI K. 
Published: June 2019

This study analyzed the accuracy of a BGL predictive model (BGL-PM) for type 1 diabetes mellitus patients (T1DM) in a real-world environment. The study population consisted of 10 individuals with T1DM, half of them were female, age 33 (SD:11.2), BMI of 26.1 (4.2) and 60% were under carbohydrate-count treatment. After consent, patients underwent a medical evaluation and registered their daily activities using a smartphone application (GlucoTrends) for 28 days, with BGL and heart rate continuously monitored. BGL-PM was developed using a Deep Learning architecture, based on Recurrent Neural Networks. Models were trained for each patient using different training sets sizes (7, 14, 21 days). Prediction accuracy was evaluated by Mean Absolute Percentage Error (MAPE) on the last 5 days for different Prediction Horizons (PH): 30, 60, 120, 180 and 360 minutes, comparing full day and nocturnal period. The model predicted BGL with relevant accuracy for the dataset with 21 training days up to 60 minutes in both periods: full day (median MAPE 22.5%) and nocturnal (14.3%) (Figure). The BGL-PM was able to provide useful BGL predictions, especially during the night period, which can be improved by increasing the training period. Consequently, this BGL-PM poses as a complementary tool for the prevention of acute complications such as hypoglycemia and hyperglycemia in the management of DM.

Disclosure
M. Foss-Freitas: None. G.S. Moreira: Stock/Shareholder; Self; GlucoGear Tecnologia. V.P. Antloga: Stock/Shareholder; Self; GlucoGear Tecnologia. C.R. Neto: Research Support; Self; University of Sao Paulo. E.M. Rodrigues: Consultant; Self; GlucoGear Tecnologia. M.F. da Costa: Research Support; Self; GlucoGear. A.P. dos Santos: None. Y.K. Matsumoto: Board Member; Self; GlucoGear. Stock/Shareholder; Self; GlucoGear. Other Relationship; Self; GlucoGear.
932-P: Impacts of Glucose Variability on the Correlation between Estimated Hemoglobin A1c and Measured Glycated Hemoglobin A1c in Patients with Type 2 Diabetes
Authors: YANG DAIZHI , LIN BEISI , XU WEN , YANG XUBIN , YAO BIN , WEI XUEYING 
Published: June 2019

Objectives: With the mean blood glucose obtained from a continuous glucose monitoring system (iPro2), an estimated glycated hemoglobin A1c (eA1c) could be calculated by the equation. If the eA1c is highly matched the measured A1c, it can be widely used in day-to-day clinical practice. Therefore, this study aimed to compare the eA1c from iPro2 and the measured A1c in non-critically ill inpatients with type 2 diabetes (T2D) to determine whether eA1c from iPro2 is applicable to this population.
Methods: Data were collected from the patients withT2D between July 2017 and December 2018 in our department. Patients with abnormal hemoglobin levels and acute diabetic complications were excluded. After a 48-hour to 72-hour continuous glucose monitoring with iPro2, values of eA1c obtained from iPro2 were recorded. The corresponding A1c values of the patients were measured by high performance liquid chromatography. Mean amplitude of glycemic excursions (MAGE), an index representing glucose variability, was assessed with data from iPro2. Patients were categorized into four groups based on the quartile categories of MAGE. Spearman correlation was used to analyze the correlation between eA1c and the measured A1c within the quartile categories of MAGE.
Results: A total of 42 reports from 42 patients with T2D were studied. The patients were 54.48(13.47) [mean (SD)] years old, with the diabetes duration of 8.71(6.34) years, measured A1c of 6.66 (1.06) % and eA1c of 6.20(0.66) %. There was a positive correlation between eA1c and measured A1c (r= 0.57, p=0.0001). Correlation coefficients of eA1c and measured A1c was 0.70 (p=0.017), 0.63 (p=0.049), 0.56 (p=0.096) and 0.58 (p=0.06) for the first, second, third and fourth MAGE quartile, respectively.
Conclusions: The correlation between eA1c and measured A1c was altered by MAGE. Value of eA1c obtained from iPro2 should not be used in T2D patients with high glucose variability.

Disclosure
D. Yang: None. B. Lin: None. W. Xu: None. X. Yang: None. B. Yao: None.
943-P: Improvement in Glycemic Variability with Dulaglutide Therapy Evaluated by Continuous Glucose Monitoring System: A Real-World Experience of Indian Patients with Type 2 DM
Authors: PATANGE SONALI A. 
Published: June 2019

Dulaglutide-based therapy may potentiate improved glycemic variability (GV) in patients with type 2 diabetes (T2D) owing to their several pancreatic and extra-pancreatic effects. This study assessed the effects of dulaglutide on GV using continuous glucose monitoring system (CGMS) in patients with T2D. Patients (N = 27) from a CGMS specialty center in India received dulaglutide as add-on to oral antidiabetics (OADs) with or without insulin and were subsequently monitored for continuous glucose data using CGMS (FreeStyle Libre Pro) for ≤15 days considering 70-140 mg/dL as target. CGMS data revealed a significant reduction (P&lt;.05) in overall within-patient mean CGM glucose on Day 5 relative to Day 1 which further reduced at Day 15 (Figure), warranting titration of doses of OADs and/or insulin. Interestingly, the coefficient of variation for overall within-patient mean glucose on Day 15 (21.99%) was less compared to Day 1 (31.56%). Through Day 1 to Day 15, overall within-patient mean % time of glucose in target significantly (P&lt;.05) increased (25.19% to 66.42%) along with reduced mean % time above target (68.96% to 17.42%). A non-significant increase in overall mean % time below target (5.86% to 16.16%) was observed with no events of severe hypoglycemia. Dulaglutide improved GV and is well tolerated in patients with T2D.

Disclosure
S.A. Patange: None.
944-P: Blood Glucose Prediction for Type 2 Diabetic Patients Using Machine Learning
Authors: KIM DAEYEON , LEE HAN-BEOM , KIM YEOJOO , KIM SANG JIN , LEE SANG-JEONG , CHUN SUNG WAN 
Published: June 2019

The blood glucose prediction algorithms were widely studied for the patients with type 1 diabetes.
We analyzed the data of 16 type 2 diabetic patients underwent continuous glucose monitoring (CGM) through deep-learning models. Feed Forward Neural Network (FFNN) and Long Short-Term Memory (LSTM) were used to model and predict the blood glucose. Blood glucose levels after 5, 15, 30, and 45 min were predicted for each patient. To evaluate the predictive power of model, the Root Mean Square Error (RMSE) was calculated by comparing the actual blood glucose and predictive blood glucose. The value of RMSE by FFNN was lower than that by LSTM. The predictive success rates for the glycemic excursion at 5 minutes by FFNN, LSTM were 96% and 95%, the predictive power of two models gradually decreased with time (Figure 1).
These results suggest that the well trained deep-learning model would be possible to predict the harmful glycemic events among type 2 diabetic patient.

Disclosure
D. Kim: None. H. Lee: None. Y. Kim: None. S. Kim: None. S. Lee: None. S. Chun: None.
954-P: Continuous Glucose Monitoring in Hospitalized Patients: A Systematic Meta-analysis and Review
Authors: HASHEMI NASSEH , HOULIND KIM , EJSKJAER NIELS 
Published: June 2019

Background: Hyperglycemia and hypoglycemia is associated with negative outcomes in hospitalized patients with diabetes mellitus (DM). Novel technologies that continuously measure glucose have emerged, but are only scarcely implemented. Continuous glucose monitoring (CGM) is still not deemed safe and reliable by clinicians for in-hospital use.
Aim: The aim of this systematic meta-analysis and review was to assess safety and accuracy of the use of CGM in hospital settings.
Methodology: A systematic search was conducted in PubMed, Embase, and the Cochrane Library for full-text studies evaluating safety and accuracy of CGM devices in hospitalized adult patients. Furthermore, a statistical analysis and meta-regression was conducted on continuous data (mean glucose levels) and dichotomous data (hypoglycemic events) extracted from the included studies.
Results: 49 studies were included evaluating subcutaneous CGM (SC-CGM), microdialysis CGM (MD-CGM), intravascular CGM (IV-CGM), transdermal CGM (TD-CGM), and closed-loop systems in hospitalized patients. 18 studies presented adequate data for a statistical meta-analysis. SC-CGM (Chi² = 4.04, (P = 0.77); I² = 0%) and closed-loop CGM (Chi² = 1.64, (P = 0.20); I² = 39%) showed no significant heterogeneity and significant effect (P&lt;0.00001) and (P=0.02), respectively, compared to conventional glucose measurements. When comparing dichotomous data, the overall risk of detecting hypoglycemic events was increased in the intervention group RR: 1.86 [0.95, 3.65] P=0.07.
Conclusions: SC-CGM and MD-CGM appear less safe and accurate compared to conventional glucose measurement, while the opposite is true for TD-CGM, IV-CGM and closed-loop CGM systems. CGM has the potential to decrease glycemic fluctuations and prevent episodes of hypoglycemia, but more studies on safety and accuracy are needed.

Disclosure
N. Hashemi: None. K. Houlind: None. N. Ejskjaer: None.
956-P: Patient-Reported Outcomes and Improved Glycemic Control with CGM in Well-Controlled Elderly Diabetes Patients
Authors: VOLCANSEK SPELA , LUNDER MOJCA , JANEZ ANDREJ 
Published: June 2019

Objective: Integrative diabetes care encompasses Patient Reported Outcome Measures (PROMs); instruments to measure patients’ perceptions of treatment options. In T1D and T2D patients with well controlled diabetes on MDI, introduction of new means of glucose monitoring (e.g., CGM) should not impose additional psychological burden regardless supposed added benefit in glucose control. The aim of present study was to investigate CGM as a tool for optimizing and personalizing treatment goals in elderly.
Methods: Elderly patients (n=25, age 67±6 years, HbA1c=7.1±0.7%) treated with MDI were instructed to use a CGM device. Blinded CGM was followed by real-time CGM use. Glycemic control metrics (time in range (TIR), glycemic variability expressed by Coefficient of Variation (CV)) were compared by paired samples T-test. PROMs were measured by questionnaires.
Results: Significant improvements in TIR (3.9 - 10.0 mmol/L) (66,3±12,4% vs. 76,9±14,1%; p&lt;0.05), as well as reduced glycemic variability (%CV) (37.3±11.1 vs. 32.9±6.3; p&lt;0.05) were observed. Satisfaction with CGM use was high; every patient (100%) reported that CGM use was overall positive and helpful, with perceived advantages as very common (average grading 4.22 out of 5) and annoyances as modest (1.82 out of 5). Majority (95%) of participants expressed improved sense of security with CGM use, 68% reported of improved sleep quality and 82% were willing to use a CGM device after having finished the study protocol. CGM introduction did not impose additional diabetes related distress (no significant difference in PAID Questionnaire score after intervention).
Conclusion: Introduction of CGM use in well controlled diabetes patients on MDI results in more stable glycemia and more time spent in target range; without additional diabetes treatment related emotional burden detected. Despite confronted with novel diabetes technology, the elderly reported of positive subjective outcomes and were keen to adopt CGM.

Disclosure
S. Volcansek: None. M. Lunder: None. A. Janez: None.
96-LB: Effectiveness of FreeStyle Libre Flash Glucose Monitoring System Observed in Real-World, Chart Review Study in Austria, in Adults with Type 2 Diabetes
Authors: FASCHING PETER , BRATH HELMUT , EBENBICHLER CHRISTOPH , MADER JULIA K. , SCHELKSHORN CHRISTIAN , WEITGASSER RAIMUND 
Published: June 2019

This retrospective chart review study evaluated the effectiveness of the FreeStyle Libre Flash Glucose Monitoring SystemTM when used by adults with type 2 diabetes (T2DM) as their standard of care in a real-world setting. This study aimed to determine the effect of FreeStyle Libre when used for between 3 months to 6 months on HbA1c. The study population included adults on a basal-bolus insulin regimen for at least 1 year who had been using FreeStyle Libre regularly for at least 3 months (either self-funded or prescribed) and with HbA1c between 8.0 and 12.0% (64 to 108 mmol/mol). Pregnant patients were excluded, as were patients on dialysis. A total of 92 records from insulin using patients with T2DM from 6 hospital sites in Austria were included in this chart review. On average, HbA1c was 8.8±0.8% (72.2±8.9 mmol/mol), prior to FreeStyle Libre use, age was 64.4±9.7 years, BMI was 30.8±5.5 kg/m2 and average duration of insulin use 7.6±5.6 years (mean±SD), 79.3% of patients were on oral antidiabetic medications (metformin, TZD, DPP-4 or SGLT) and 54.3% were male. CVD and renal complications were reported by 34.8% and 27.2% of patients, respectively. HbA1c results were recorded between 91 to 194 days from the start of use of FreeStyle Libre, between September 2014 and July 2018. To minimise selection bias, all patients that had used FreeStyle Libre for at least 3 months from each study site were included in the analysis. After at least 3 months of using FreeStyle Libre, HbA1c (primary outcome) was significantly reduced by 0.9±0.8% (mean±SD); p&lt;0.0001.This real-world, chart review study concluded that people with T2DM on insulin therapy, using FreeStyle Libre for between 3 to 6 months significantly reduced HbA1c.

Disclosure
P. Fasching: None. H. Brath: Advisory Panel; Self; Boehringer Ingelheim International GmbH, Novo Nordisk Inc., Servier. Speaker’s Bureau; Self; Amgen Inc., Ascensia Diabetes Care, AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Merck Sharp & Dohme Corp., Novartis AG, Novo Nordisk Inc., Pfizer Inc., Sanofi. C. Ebenbichler: None. J.K. Mader: Advisory Panel; Self; Boehringer Ingelheim International GmbH, Eli Lilly and Company, Prediktor Medical, Roche Diabetes Care, Sanofi. Speaker’s Bureau; Self; Abbott, AstraZeneca, Dexcom, Inc., Novo Nordisk Inc. Stock/Shareholder; Self; decide Clinical Software GmbH. C. Schelkshorn: Advisory Panel; Self; AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Merck Sharp & Dohme Corp. Speaker’s Bureau; Self; Abbott, Novo Nordisk Inc., Sanofi-Aventis Deutschland GmbH. Stock/Shareholder; Self; Lilly Diabetes. R. Weitgasser: Research Support; Self; Abbott.
966-P: The Long-Term Impact of a Flash Glucose Monitoring System on Glycemic Control
Authors: FUJIYA ATSUSHI , KIYOSE TOSHIKI , SHIBATA TAIGA , SOBAJIMA HIROSHI 
Published: June 2019

Background: Tight control of blood glucose with lower frequency of hypoglycemia is required to prevent diabetic complications. However, HbA1c doesn’t reflect intraday- and interday glycemic excursions. The aim of this study is to characterize fluctuations in blood glucose levels with the flash glucose monitoring (FGM) and to observe the course of patients in whom this information was used to direct their daily medical treatment.
Methods: This 52-week, retrospective observational study involved diabetic patients who wore the FGM device (FreeStyle Libre Pro®) for 2 weeks. Changes in HbA1c were observed by clinical phenotypes of diabetes and manifestations of glucose variability. The frequency of hypoglycemia was analyzed in patients who had performed SMBG. We divided patients into two groups (those who received a check-up after 1 week or 2 weeks after starting to wear the device) and compared their change in of HbA1c. The differences of HbA1c change from baseline were analyzed by the t-test and P-values &lt; 0.05 were considered significant.
Results: Of 114 patients who received a FGM device, 79 were enrolled in the study (type 1: n=14, type 2: n=62, other: n=3). Overall, the mean HbA1c (%) was significantly decreased at week 12 compared with baseline (8.35 vs. 7.61, P&lt;0.001) and this reduction was continued at week 24 (7.78, P&lt;0.001) and week 52 (7.62, P&lt;0.001). Only nutritional guidance with FGM-use was associated with a similar reduction in HbA1c at week 52 compared with medication regimen changes (0.77 vs. 0.68, P=0.37). The frequency of hypoglycemia was lower at week 12, 24, and 52 compared with baseline. A trend towards a greater reduction in HbA1c at week 12 was observed in patients who received a check-up 1 week after initiating to wear FGM device comparted with those with a check-up 2 weeks afterwards (0.94 vs. 0.72, p=0.14).
Conclusion: Visualization of glucose variability with a FGM device was safe and has the potential to improve glucose profiles for a long term even though this method was only a temporary measure.

Disclosure
A. Fujiya: None. T. Kiyose: None. T. Shibata: None. H. Sobajima: None.
968-P: Volatile Organic Compounds in Breath Predict Hypoglycemia Well Before Plasma Glucose Levels Fall
Authors: SIEGEL AMANDA P. , DANESHKHAH ALI , MATHER KIEREN J. , AGARWAL MANGILAL 
Published: June 2019

Volatile organic compounds in breath are produced during hypoglycemia (hypo VOCs) are detectable by canines and by GC/MS analysis of human breath. However, it is not known whether hypo VOCs fall or recover at the same time or before or after plasma glucose (PG) levels. Breath samples were collected from 11 volunteers with type 1 diabetes during an insulin clamp study at different Checkpoints, including: Initial (fasting; median 150 mg/dL (range 107-333), Euglycemia (90 and 80 mg/dL), Hypoglycemia (Hypo; 70, 60, 50 mg/dL) and after PG returned to 90 mg/dL (Recovery; 35 (23-62 min from last Hypo sampling)). In total, 94 breath samples were analyzed by GC/MS. We compared VOC levels between Initial and Hypo Checkpoints using univariate and multivariate tests to identify hypo VOCs. Next, we analyzed the data from the Intermediate and Recovery Checkpoints. Samples collected at Euglycemia (90, 80 mg/dL) and Recovery (90 mg/dL) Checkpoints demonstrated VOC concentrations similar to those seen at Hypo Checkpoints. Fold change analysis for twelve exemplar hypo VOCs are shown in Figure 1.
In summary, breath contains VOCs that may predict hypoglycemia with falling PG well before hypoglycemia is achieved, and these VOCs may recover more slowly than plasma glucose after a hypoglycemic event. Tests for detecting hypo VOCs may provide clinically useful tools to aid in control of diabetes.

Disclosure
A.P. Siegel: None. A. Daneshkhah: None. K.J. Mather: Advisory Panel; Self; Roche Diabetes Care. Research Support; Self; Abbott, Merck & Co., Inc., Novo Nordisk A/S, Sanofi. M. Agarwal: None.

Funding
National Natural Science Foundation of China