The main objective of diabetes control is to correct hyperglycaemia while avoiding hypoglycaemia, especially in insulin-treated patients. Fear of hypoglycaemia is a hurdle to effective correction of hyperglycaemia because it promotes under-dosing of insulin. Strategies to minimise hypoglycaemia include education and training for improved hypoglycaemia awareness and the development of technologies to allow their early detection and thus minimise their occurrence. Patients with impaired hypoglycaemia awareness would benefit the most from these technologies. The purpose of this systematic review is to review currently available or in-development technologies that support detection of hypoglycaemia or hypoglycaemia risk, and identify gaps in the research. Nanomaterial use in sensors is a promising strategy to increase the accuracy of continuous glucose monitoring devices for low glucose values. Hypoglycaemia is associated with changes on vital signs, so electrocardiogram and encephalogram could also be used to detect hypoglycaemia. Accuracy improvements through multivariable measures can make already marketed galvanic skin response devices a good noninvasive alternative. Breath volatile organic compounds can be detected by dogs and devices and alert patients at hypoglycaemia onset, while near-infrared spectroscopy can also be used as a hypoglycaemia alarms. Finally, one of the main directions of research are deep learning algorithms to analyse continuous glucose monitoring data and provide earlier and more accurate prediction of hypoglycaemia. Current developments for early identification of hypoglycaemia risk combine improvements of available 'needle-type' enzymatic glucose sensors and noninvasive alternatives. Patient usability will be essential to demonstrate to allow their implementation for daily use in diabetes management.

To design a clinical trial it is important to know for how long CGM data should be collected to accurately assess time in different glucose ranges (time in ranges). Several studies approached this problem through the computation of the correlation coefficient (R2) between a metric computed in a month-long trial and in several shorter windows of increasing duration. The minimal duration (MD) granting R2>threshold (e.g., 0.9) is then used to estimate the long-term metric. Here, we focus on time below range (TBR), defined as time <70 mg/dl [%]. We first implemented the R2-based analysis on trials of different duration: A1, A2, A3, lasting 100, 200, 300 days (d), respectively, obtained selecting portions of the same Study A (n=45, duration=360 d, sensor=Abbott Freestyle Libre). Table 1 shows that the longer the trial duration, the larger the resulting MD. Notably, all the obtained trial duration fractions are similar. Then, the analysis was repeated for other two trials of equal duration: Study B (n=85, duration=240 d, sensor=Dexcom G5) and A4, obtained selecting 240 d from Study A. Although B and A4 refer to different T1D populations and different sensors, the resulting trial duration fraction is the same. In conclusion, the R2-based analysis yields different results based on the duration of the considered dataset, and seems to identify only the fraction of data needed to match the reference TBR. Disclosure N. Camerlingo: None. M. Vettoretti: None. M. Cigler: None. A. Facchinetti: None. G. Sparacino: None. J.K. Mader: Advisory Panel; Self; Becton, Dickinson and Company, Eli Lilly and Company, Medtronic, Prediktor Medical, Sanofi-Aventis. Speaker’s Bureau; Self; Abbott, Eli Lilly and Company, Medtronic, Novo Nordisk A/S, Roche Diabetes Care, Sanofi-Aventis. P. Choudhary: Advisory Panel; Self; Abbott, Eli Lilly and Company, Insulet Corporation, Medtronic. Research Support; Self; European Union, JDRF. Speaker’s Bureau; Self; Dexcom, Inc., Novartis AG, Novo Nordisk A/S, Sanofi-Aventis. S. Del Favero: Research Support; Self; Dexcom, Inc. Funding Innovative Medicines Initiative 2 Joint Undertaking (777460); European Union; European Federation of Pharmaceutical Industries and Associations; T1D Exchange; JDRF; International Diabetes Federation; The Leona M. and Harry B. Helmsley Charitable Trust

Attainment of good glycemic control in hospitalized patients with type 2 diabetes (T2D) is often hampered by fear of hypoglycemia and insufficient knowledge in insulin therapy. An electronic algorithm-based decision support system (GlucoTab®, decide Clinical Software GmbH, Graz, Austria) for inpatient management was developed and has proven efficacy in clinical trials. After implementing GlucoTab® in routine care, a retrospective analysis of T2D patients requiring subcutaneous insulin therapy on an Endocrinology ward was performed. Data from days when patients received algorithm-steered basal-bolus insulin therapy (BBI) was compared with days when insulin therapy was performed according to local standard care (SC) provided by an endocrinologist. As the treating physician was free to start/stop decision support during inpatient stay, patients switched between BBI and SC. During the first six months following GlucoTab® implementation, a total of 71 T2D patients required subcutaneous insulin therapy and subsequently were included in this analysis. Incomplete treatment days (e.g., admission and discharge days; days, where both therapy schemes applied) were excluded, resulting in 261 BBI and 274 SC days. Baseline characteristics were as follows: 39 % women, 99 % Caucasian, age 73.5 ± 12.5 years, diabetes duration 15.4 ± 10.6 years, HbA1c 70.4 ± 24.2 mmol/mol, BMI 29.0 ± 5.5 kg/m² and creatinine 1.6 ± 1.2 mg/dL. Mean daily glucose was lower for BBI vs. SC: 156.9 ± 35.7 mg/dL vs. 172.1 ± 47.3 mg/dL. Total daily insulin dose was higher for BBI vs. SC: 42.2 ± 25.2 U vs. 33.7 ± 20.9 U. Blood glucose (BG) in target (70-180 mg/dL) was higher for BBI vs. SC: 70 % vs. 61.2 %. BG <70 mg/dL was 1.6 % vs. 1.9 % and BG <54 mg/dL was 0.2 % vs. 0.6 % (BBI vs. SC, respectively). Continuous use of BBI resulted in a BG decline from 182 (day 1) to 147 mg/dL (day 10) while it remained elevated in SC (188 to 177 mg/dL). BBI showed beneficial glycemic control compared to SC in routine use during inpatient diabetes management. Disclosure D.A. Hochfellner: None. H. Ziko: None. M.H. Sagmeister: None. H. Elsayed: None. M. Cigler: None. T. Poettler: None. F. Aberer: None. G. Sendlhofer: None. P. Beck: Employee; Self; decide Clinical Software. Stock/Shareholder; Self; decide Clinical Software. J.K. Mader: Advisory Panel; Self; Becton, Dickinson and Company, Eli Lilly and Company, Medtronic, Prediktor Medical, Sanofi-Aventis. Speaker’s Bureau; Self; Abbott, Eli Lilly and Company, Medtronic, Novo Nordisk A/S, Roche Diabetes Care, Sanofi-Aventis.

Studies on CGM assume equal glycemic control between MDI and CSII. However, observation periods are often short and the impact of hypoglycemia on glycemic control and variability is unclear. In this study, we analyzed 12846 days (35 years; CSII 2232 days [6 years]; MDI 10614 days [29 years]) of CGM readings obtained from 99 patients with T1D. In the overall analysis mean glucose was 169.6 ± 18.9mg/dl for CSII and 175.0 ± 29.5 mg/dl for MDI. Estimated A1c was 7.54 ± 0.66% (CSII) and 7.7 ± 1.0% (MDI). Percentage of readings in target range (70-180mg/dL) was 54.0 ± 15.5% for CSII and 57.4 ± 11.4% for MDI. In total, 3.1/2.2% of readings were < 54 mg/dL and 14.9/12.4% were > 250mg/dL for CSII and MDI, respectively. In total, 464 hypoglycemic events (< 54mg/dL) occurred in CSII (day: 305 [64%]; night: 170 [36%]), corresponding to 0.21 hypoglycemic events daily. In MDI, 2297 hypoglycemic events occurred (day: 1507/66%; night: 783/34%), corresponding to 0.22 hypoglycemic events daily. Mean duration of hypoglycemia was 146.5 ± 65.9 minutes (CSII) and 132.8 ± 42.8 minutes (MDI); 233 (CSII) vs. 982 (MDI) events were prolonged (>120 minutes). CV was 41.1 ± 6.7% for CSII and 40.8 ± 3.8% for MDI. In the 24 hours prior to hypoglycemia, mean glucose was higher (182.9 ± 30.5 vs. 166.9 ± 26.6mg/dl; p<0.001 for CSII and 167.2 ± 14.7 vs. 152.7 ± 19.1mg/dl; p=0.013 for MDI) and CV was lower (p<0.001 for both treatments) compared to the subsequent 24 hours. Age correlated to CV (r=-.254; p=0.028 for MDI), HbA1c to eA1c (r=.611; p=0.009 for CSII; r=.482; p=0.028 for MDI), and C-peptide to CGM-readings in target range (r=.246; p=0.042 for MDI), which negatively correlated to HbA1c (r=-.484; p<0.001 for MDI). Glycemic control was comparable between MDI and CSII in T1D. Glycemic variability was lower prior to a hypoglycemic event and in older patients, while remaining insulin secretion was associated with longer time in target range. Disclosure A. Melmer: None. T. Züger: None. T. Pöttler: None. H. Kojzar: None. M. Cigler: None. F. Aberer: None. M. Laimer: 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.