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Background: Elective surgery in patients with insulin-treated type 2 diabetes mellitus (T2D) and the admission period in the hospital, comprise a distinctive and challenging situation for physicians, nurses, as well as for the patients themselves. There is a lack of widely accepted evidence-based and standardized approach of care in regard to perioperative management of patients with insulin-treated T2D. Methods: The main purpose of this proof-of-concept study was to investigate whether a standardized insulin and meal regimen on the day of surgery leads to a better management of diabetes in terms of blood glucose (BG) levels. Two different insulin and meal regimens—group A with half of insulin dose given with a standardized postoperative meal and group B with a custom preoperative breakfast and full insulin dose—were compared with Group C with routine care (no meal and no insulin injection on the day of surgery). Each group consisted of 12 to 15 patients. BG measurements were performed pre- and immediately postoperatively, before meals and at bedtime. Results: Both standardized and well-defined insulin and meal regimens resulted in better average BG levels in the perioperative period, especially in the morning after the surgery. Conclusions: In this study, we observed that a standardized perioperative insulin regimen efficiently lowered postoperative BG levels. Providing a custom breakfast and a full insulin dose resulted in lower postoperative BG levels. These approaches were not associated with an increase in hypoglycemic events. Physicians and nursing staff gave positive feedback to the structured and well-defined approaches.

ZusammenfassungDieses Positionspapier beinhaltet die Empfehlungen der Österreichischen Diabetes Gesellschaft zum Management von erwachsenen Patient:innen mit Diabetes mellitus während stationärer Aufenthalte und basiert auf aktueller Evidenz zu Blutglukosezielbereichen, Insulintherapie und Therapie mit oralen/injizierbaren Antidiabetika während stationärer Aufenthalte. Zusätzlich werden Spezialsituationen wie intravenöse Insulintherapie, begleitende Steroidtherapie sowie die Anwendung von Diabetestechnologie im stationären Bereich diskutiert.

ZusammenfassungDie Hyperglykämie ist wesentlich an der Entstehung der Spätkomplikationen bei an Diabetes mellitus Typ 2 erkrankten Patienten/Patientinnen beteiligt. Während Lebensstilmaßnahmen die Eckpfeiler jeder Diabetestherapie bleiben, benötigen im Verlauf die meisten Patienten/Patientinnen mit Typ 2 Diabetes eine medikamentöse Therapie. Bei der Definition individueller Behandlungsziele stellen die Therapiesicherheit, die Effektivität sowie substanzspezifische, kardiovaskuläre Effekte der Therapie die wichtigsten Faktoren dar. In dieser Leitlinie haben wir die rezenten evidenzbasierten Daten für die klinische Praxis zusammengestellt.

ZusammenfassungDiese Leitlinie repräsentiert die Empfehlungen der Österreichischen Diabetes Gesellschaft (ÖDG) zur Nutzung von Diabetes-Technologie (Insulinpumpentherapie; kontinuierliche Glukosemesssysteme, CGM; Hybrid Closed Loop Systeme, HCL; Automated Insulin Delivery Systeme, AID, Diabetes-Apps) und den Zugang zu diesen technologischen Innovationen für Menschen mit Diabetes mellitus. Die Leitlinie wurde basierend auf aktueller wissenschaftlicher Evidenz erstellt.

Zusammenfassung„Continuous glucose monitoring systems“ (CGM-Systeme) und CGM-basierte Metrik gewannen in den letzten 10 Jahren massiv an Bedeutung. Dennoch ist der HbA1c nach wie vor der meistverwendete und international anerkannte Marker zur Beurteilung der glykämischen Kontrolle. Ebenso stellt er in klinischen Studien immer noch den wichtigsten Surrogatparameter zur Beurteilung klinischer Outcomes dar. Die Verwendung der Zeit im Zielbereich („time in range“ [TIR]) hat im Vergleich zum HbA1c den Vorteil, dass Hypoglykämien und Glukosevariabilität besser dargestellt werden. Durch Nutzung der TIR kann man auch individuelle Zielbereiche definieren, beispielsweise bei Schwangeren oder multimorbiden Personen. Auch gibt es erste Hinweise, dass klinische Studienergebnisse anhand von TIR und anderen CGM-basierten Metriken bewertet werden können, auch wenn hierzu noch Langzeit- und Endpunktstudien fehlen. Einen wesentlichen Vorteil zeigt die TIR bei der Prädiktion diabetesassoziierter Komplikationen. So kann, basierend auf Änderungen beim erreichten Zielbereich, nicht nur das Auftreten neuropathischer, mikro- oder makrovaskulärer Komplikationen vorhergesagt werden, sondern auch das relative Risiko deren Manifestation. Die Nutzung von CGM im Allgemeinen und das Erreichen der TIR-Ziele spielen auch für Menschen mit Diabetes mellitus und deren Einschätzung ihrer Lebensqualität eine immer größere Rolle.

To investigate the seroconversion following first and second COVID-19 vaccination in people with type 1 and type 2 diabetes in relation to glycaemic control prior to vaccination and to analyse the response in comparison to individuals without diabetes.This prospective, multicentre cohort study analysed people with type 1 and type 2 diabetes and a glycated haemoglobin level ≤58 mmol/mol (7.5%) or58 mmol/mol (7.5%), respectively, and healthy controls. Roche's Elecsys anti-SARS-CoV-2 S immunoassay targeting the receptor-binding domain was used to quantify anti-spike protein antibodies 7 to 14 days after the first and 14 to 21 days after the second vaccination.A total of 86 healthy controls were enrolled in the study, as well as 161 participants with diabetes, of whom 150 (75 with type 1 diabetes and 75 with type 2 diabetes) were eligible for the analysis. After the first vaccination, only 52.7% of participants in the type 1 diabetes group and 48.0% of those in the type 2 diabetes group showed antibody levels above the cut-off for positivity. Antibody levels after the second vaccination were similar in participants with type 1 diabetes, participants with type 2 diabetes and healthy controls after adjusting for age, sex and multiple testing (P 0.05). Age (r = -0.45, P 0.001) and glomerular filtration rate (r = 0.28, P = 0.001) were significantly associated with antibody response.Anti-SARS-CoV-2 S receptor-binding domain antibody levels after the second vaccination were comparable in healthy controls and in participants with type 1 and type 2 diabetes, irrespective of glycaemic control. Age and renal function correlated significantly with the extent of antibody levels.

BACKGROUND: The possible advantage of hybrid closed-loop therapy (i.e., artificial pancreas) over sensor-augmented pump therapy in very young children with type 1 diabetes is unclear. METHODS: In this multicenter, randomized, crossover trial, we recruited children 1 to 7 years of age with type 1 diabetes who were receiving insulin-pump therapy at seven centers across Austria, Germany, Luxembourg, and the United Kingdom. Participants received treatment in two 16-week periods, in random order, in which the closed-loop system was compared with sensor-augmented pump therapy (control). The primary end point was the between-treatment difference in the percentage of time that the sensor glucose measurement was in the target range (70 to 180 mg per deciliter) during each 16-week period. The analysis was conducted according to the intention-to-treat principle. Key secondary end points included the percentage of time spent in a hyperglycemic state (glucose level, >180 mg per deciliter), the glycated hemoglobin level, the mean sensor glucose level, and the percentage of time spent in a hypoglycemic state (glucose level

Aims: To conduct a pooled analysis to assess the performance of intermittently scanned continuous glucose monitoring (isCGM) in association with the rate of change in sensor glucose in a cohort of children, adolescents, and adults with type 1 diabetes. Material and methods: In this pooled analysis, isCGM system accuracy was assessed depending on the rate of change in sensor glucose. Clinical studies that have been investigating isCGM accuracy against blood glucose, accompanied with collection time points were included in this analysis. isCGM performance was assessed by means of median absolute relative difference (MedARD), Parkes error grid (PEG) and Bland-Altman plot analyses. Results: Twelve studies comprising 311 participants were included, with a total of 15 837 paired measurements. The overall MedARD (interquartile range) was 12.7% (5.9-23.5) and MedARD differed significantly based on the rate of change in glucose (P < 0.001). An absolute difference of -22 mg/dL (-1.2 mmol/L) (95% limits of agreement [LoA] 60 mg/dL (3.3 mmol/L), -103 mg/dL (-5.7 mmol/L)) was found when glucose was rapidly increasing (isCGM glucose minus reference blood glucose), while a -32 mg/dL (1.8 mmol/L) (95% LoA 116 mg/dL (6.4 mmol/L), -51 mg/dL (-2.8 mmol/L)) absolute difference was observed in periods of rapidly decreasing glucose. Conclusions: The performance of isCGM was good when compared to reference blood glucose measurements. The rate of change in glucose for both increasing and decreasing glucose levels diminished isCGM performance, showing lower accuracy during high rates of glucose change. Keywords: continuous glucose monitoring (CGM); type 1 diabetes.

Innovations in syringe and pen needle (PN) technology over the last 100 years have led to important advances in insulin delivery for people with diabetes, paralleling the strides made in developing recombinant DNA human insulin and insulin analogs with varying onset and duration of action. In this review, the history of advances in insulin delivery is described, focusing on progress in syringe, needle, and PN technologies. The early glass and metal syringes that required sterilization by boiling have been replaced by disposable, single-use syringes or pens with clear labeling for precise insulin dosing. The early needles ranging in length from 19 to 26 mm that required manual sharpening against a whetstone have been replaced by syringe needles of 6 mm and PNs of 4 mm in length as slender as 34 gauge. Imaging studies using ultrasound and computed tomography measured the thickness of skin and subcutaneous tissue layers to show feasibility of targeted insulin administration with shorter needles. These developments, coupled with innovations in needle/PN wall and tip structure, have led to improved injection experience for people with diabetes. It is also important to acknowledge the role of injection technique education, together with these advances in injection technology, for improving clinical outcomes and patient satisfaction. With continued projected growth of diabetes prevalence, particularly in developing countries where expensive and complex insulin delivery systems may not be practical, insulin syringes and pens will continue to serve as reliable and cost-effective means of insulin delivery for people with diabetes.

Background: Is insulin initiation linked to increasing body mass index (BMI) in all patients with type-2-diabetes (T2D)? To determine distinct longitudinal patterns of BMI change over time. Materials and Methods: 5057 patients with T2D (55% males, median BMI [IQR]: 30.0 [26.9-33.3] kg/m2) aged ≥40 years at diabetes diagnosis and with ≥2 years of follow-up after insulin initiation irrespective of previous or concurrent use of metformin/dipeptidyl peptidase-4-inhibitor from the multicenter prospective diabetes registry DPV were studied. To identify subgroups following a similar pattern of BMI change after insulin initiation, longitudinal group-based trajectory modeling was applied. Multinomial logistic regression was then used to analyze covariates associated with group membership. Results: Three heterogeneous groups with either relevant BMI increase (delta-BMI: +4.0 kg/m2 after 2 years; 12% of patients); slight BMI increase (+0.4 kg/m2; 80%); or BMI decrease (-3.2 kg/m2; 8%) were identified. Patients with older age [OR (95% CI): 1.37 (1.11-1.69)] and obesity [2.05 (1.65-2.55)] before insulin start were more often in the BMI decreasing group, and less often in the BMI increasing class [0.80 (0.67-0.95); 0.82 (0.69-0.98)]. A worse HbA1c both at insulin start and during follow-up [1.90 (1.60-2.26); 1.17 (1.07-1.27)], a higher insulin dose [1.67 (1.33-2.10)], and severe hypoglycemic events [2.38 (1.60-3.53)] after insulin initiation were all linked with higher odds of belonging to the BMI increasing trajectory. Conclusions: Patient heterogeneity with respect to weight gain after initiation of insulin therapy in adult T2D was detected by an objective computer algorithm. Older people with obesity should not defer from insulin use due to fear of weight gain.

Objective: Many hybrid closed-loop (HCL) systems struggle to manage unusually high glucose levels as experienced with intercurrent illness or pre-menstrually. Manual correction boluses may be needed, increasing hypoglycemia risk with overcorrection. The Cambridge HCL system includes a user-initiated algorithm intensification mode (“Boost”), activation of which increases automated insulin delivery by approximately 35%, while remaining glucose-responsive. In this analysis, we assessed the safety of “Boost” mode. Methods: We retrospectively analyzed data from closed-loop studies involving young children (1-7 years, n = 24), children and adolescents (10-17 years, n = 19), adults (≥24 years, n = 13), and older adults (≥60 years, n = 20) with type 1 diabetes. Outcomes were calculated per participant for days with ≥30 minutes of “Boost” use versus days with no “Boost” use. Participants with Results: Eight weeks of data for 76 participants were analyzed. There was no difference in time spent 300 mg/dL was 1.39 percentage points (1.01 to 1.77; P < .001) higher on “Boost” days, with higher mean glucose and lower time in target range ( P < .001). Conclusions: Use of an algorithm intensification mode in HCL therapy is safe across all age groups with type 1 diabetes. The higher time in hyperglycemia observed on “Boost” days suggests that users are more likely to use algorithm intensification on days with extreme hyperglycemic excursions.

Achieving healthy sleep supports general health and well-being1,2. Sleep disturbance in type 1 diabetes (T1DM) can be caused by factors including fear of hypoglycaemia and suboptimal glucose control3. For example, hypoglycaemia or hyperglycaemia can disturb sleep, through mechanisms including sympathetic activation and neuropathic pain, respectively4. Several observational studies have suggested that hybrid closed-loop (HCL) system initiation is associated with improved sleep,5,6 but these data could be limited by confounding and other biases. Moreover, different HCL devices could have differing effects on sleep; outcomes could be influenced by user age and the measures by which sleep is assessed. Our objectives, in older adults with T1DM, were to: a) assess the impact of an adaptive HCL system (CamAPS FX) on subjectively and objectively assessed sleep traits in a post hoc analysis of randomised controlled trial (RCT) data; and b) assess the relationship between glucose control and sleep traits in the trial cohort., This work was funded by the National Institute of Diabetes and Digestive and Kidney Diseases under the grant number 1DP3DK112176-01 and the UK MRC Clinical Academic Research Partnership (MR/T023899/1). Additional support for the artificial pancreas work from National Institute for Health Research Cambridge Biomedical Research Centre, and JDRF. Dexcom supplied discounted continuous glucose monitoring devices

OBJECTIVE To evaluate the impact of CamAPS FX hybrid closed-loop (HCL) automated insulin delivery in very young children with type 1 diabetes (T1D) on caregivers’ well-being, fear of hypoglycemia, and sleepiness. RESEARCH DESIGN AND METHODS We conducted a multinational, open-label, randomized crossover study. Children (age 1–7 years) with T1D received treatment for two 4-month periods in random order, comparing HCL with sensor augmented pump (control). At baseline and after each treatment period, caregivers were invited to complete World Health Organization–Five Well-Being Index, Hypoglycemia Fear Survey, and Epworth Sleepiness Scale questionnaires. RESULTS Caregivers of 74 children (mean ± SD age 5 ± 2 years and baseline HbA1c 7.3 ± 0.7%; 42% female) participated. Results revealed significantly lower scores for hypoglycemia fear (P < 0.001) and higher scores for well-being (P < 0.001) after HCL treatment. A trend toward a reduction in sleepiness score was observed (P = 0.09). CONCLUSIONS Our results suggest better well-being and less hypoglycemia fear in caregivers of very young children with T1D on CamAPS FX HCL.

GlucoTab@MobileCare, a digital workflow and decision support system with integrated basal and basal-plus insulin algorithm was investigated for user acceptance, safety and efficacy in persons with type 2 diabetes receiving home health care by nurses. During a three months study nine participants (five female, age 77 ± 10 years, HbA1c 60 ± 13 mmol/mol (study start) vs. 57 ± 12 mmol/mol (study end) received basal or basal-plus insulin therapy as suggested by the digital system. In total 95% of all suggested tasks (blood glucose (BG) measurements, insulin dose calculations, insulin injections) were performed according to the digital system. Mean morning BG was 171 ± 68 mg/dL in the first study month vs. 145 ± 35 mg/dL in the last study month, indicating a reduced glycemic variability of 33 mg/dL (standard deviation). No hypoglycemic episode < 54 mg/dL occurred. User’s adherence was high and the digital system supported a safe and effective treatment. Larger scale studies are needed to confirm findings under routine care.German Clinical Trials Register IDDRKS00015059

AIMS: Fasting periods represent a significant challenge for glycaemic control in hospitalised patients. The study aims to evaluate the efficacy and safety of basal-bolus insulin therapy in managing glycaemia during fasting periods in hospitalised patients with type 2 diabetes. MATERIALS AND METHODS: We performed a post-hoc analysis of two prospective, uncontrolled interventional trials that applied electronic decision support system guided basal-bolus (meal-related and correction) insulin therapy. We searched for fasting periods (invasive or diagnostic procedures, medical condition) during inpatient stays. In a mixed-model analysis, patients' glucose levels and insulin doses on days with regular food intake were compared to days with fasting periods. RESULTS: Out of 249 patients, 115 patients (33.9% female, age 68.3±10.3 years, diabetes duration 15.1±10.9 years, BMI 30.1±5.4 kg/m2 , HbA1c 69±20 mmol/mol) had 194 days with fasting periods. Mean daily blood glucose was lower (Modelled difference (ModDiff): -0.5±0.2 mmol/L, p=0.006), and the proportion of glucose values within the target range (3.9-10.0 mmol/L) increased on days with fasting periods compared to days with regular food intake (ModDiff: +0.06±0.02, p=0.005). Glycaemic control on fasting days was driven by a reduction in daily bolus insulin doses (ModDiff: -11.0±0.9 IU, p

AIAPS is a novel smartphone-based closed-loop algorithm in development that can be combined with any insulin pump and continuous glucose monitor. AIAPS strives to reduce burden of diabetes by requiring only limited input and by handling unannounced meals. Prior to launching the clinical research program in-silico modelling of the most recent features was performed. The objective of this study was to evaluate the AIAPS performance using the UVA/PADOVA Type 1 Diabetes (T1D) Simulator in people with T1D. AIAPS was used in the in-silico population of 33 subjects (children, adolescents, and adults) of the UVA/PADOVA T1D Simulator. Upon initialisation, 5 min CGM data, body weight, average daily insulin dose and sex for each person was available. To model a scenario with limited information for each individual upon initialization, no data on basal insulin profiles (BIP) , carbohydrate-to-insulin ratio (CR) and insulin sensitivity factor (ISF) were put into AIAPS. Additionally, no information on meals (time or carbohydrate content) were put into AIAPS. The following scenario was modelled: 84 days of closed-loop therapy without user interaction and with variable routine conditions representing the duration of the planned clinical study. Data on glycemic control for the three age groups over the 84 days are shown in table 1. Modelling included 2-5 unannounced meals with a random schedule and carbohydrate content of 130-250 g per day. In an in-silico model the AIAPS closed-loop system exceeded current glycemic targets with a TIR >70% and a TBR Table 1 - Glycemic control for the three age groups. Data are mean standard deviation. Disclosure A.J.Dedeshko: Stock/Shareholder; AI APS France. V.Fedotov: Stock/Shareholder; AIAPS France. A.Simic: None. C.Thivolet: Board Member; Insulet Corporation, Medtronic, Research Support; Abbott Diabetes, Lilly Diabetes, Novo Nordisk A/S, Sanofi. J.K.Mader: Advisory Panel; Abbott Diabetes, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Medtronic, Merck Sharp & Dohme Corp., Novo Nordisk A/S, Roche Diabetes Care, Sanofi-Aventis Deutschland GmbH, Speaker's Bureau; Abbott Diabetes, Eli Lilly and Company, Medtronic, Merck Sharp & Dohme Corp., Novo Nordisk A/S, Roche Diabetes Care, Sanofi-Aventis Deutschland GmbH, Servier Laboratories, Stock/Shareholder; decide Clinical Software GmbH.

Background: Hypoglycemia and its impact on people with diabetes is typically reported retrospectively and is subject to recall bias. Ecological momentary assessment (EMA) uses repeated sampling to collect real time, granular data about patient experiences, typically over 3-5 days. We developed the Hypo-METRICS app to determine the physical, psychological, and economic impact in people with type 1 (T1D) and type 2 diabetes (T2D) over weeks using EMA. We tested user acceptability of the Hypo-METRICS app. Methods: We recruited 323 adults (T1D=186, T2D=137, 55% men, mean age 54±16 years, diabetes duration 22±13 years) using insulin with ≥1 episode of hypoglycemia in the last month. Participants were prompted to complete three daily EMA and two weekly questionnaires (the Work Productivity and Activity Impairment Questionnaire and PROMIS Sleep Disturbance scale) for weeks. EMA included 29 items over morning, afternoon and evening time points. EMA questions spanned seven categories including experience of hypoglycemia, sleep, and daily functioning. Participants recorded hypoglycemia episodes using a symptom tracker in real time. Results: The overall mean completion rate was 89±10% (T1D=87±11%, T2D=91±8%) , with 86,487 completed items. EMAs had a completion rate of 88±11% (T1D=87±11% T2D=90±9%) . Weekly questionnaires had a completion rate of 92±11% (T1D=91±12 T2D=95±8) . A total of 5,585 episodes of participant-reported hypoglycemia (T1D=4,118, T2D=1,467) were recorded in real time with a mean of 1.7±1.4 episodes reported per participant/week (T1D=2.2±1.4 T2D=1.1±1.0) . Conclusion: The Hypo-METRICS app is an acceptable tool to collect prospective detailed data on the frequency of hypoglycemia episodes and their impact on daily functioning over a prolonged period. Disclosure N.Zaremba: None. R.J.Mccrimmon: Advisory Panel; Novo Nordisk, Sanofi, Research Support; Diabetes UK, European Union, MedImmune. E.Renard: Consultant; Abbott, AstraZeneca, Boehringer Ingelheim International GmbH, Dexcom, Inc., Insulet Corporation, LifeScan, Lilly, Novo Nordisk, Roche Diabetes Care, Sanofi, Tandem Diabetes Care, Inc. S.R.Heller: Advisory Panel; Eli Lilly and Company, Medtronic, Mylan N.V., Zealand Pharma A/S, Zucara Therapeutics, Other Relationship; Eli Lilly and Company, Research Support; Dexcom, Inc., Speaker's Bureau; Novo Nordisk A/S. M.Evans: Advisory Panel; Pila Pharma, Zucara Therapeutics, Other Relationship; Abbott Diabetes, Dexcom, Inc., Medtronic, Novo Nordisk, Research Support; AstraZeneca, Sanofi, Speaker's Bureau; Lilly Diabetes. J.K.Mader: Advisory Panel; Abbott Diabetes, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Medtronic, Merck Sharp & Dohme Corp., Novo Nordisk A/S, Roche Diabetes Care, Sanofi-Aventis Deutschland GmbH, Speaker's Bureau; Abbott Diabetes, Eli Lilly and Company, Medtronic, Merck Sharp & Dohme Corp., Novo Nordisk A/S, Roche Diabetes Care, Sanofi-Aventis Deutschland GmbH, Servier Laboratories, Stock/Shareholder; decide Clinical Software GmbH. A.Seibold: Employee; Abbott Diabetes, Stock/Shareholder; Abbott. S.A.Amiel: Advisory Panel; Medtronic, Novo Nordisk, Other Relationship; Sanofi. P.Choudhary: Advisory Panel; Abbott Diabetes, Lilly Diabetes, Medtronic, Research Support; Novo Nordisk, Speaker's Bureau; Dexcom, Inc., Glooko, Inc., Insulet Corporation, Sanofi. Hypo-resolve consortium: n/a. G.Martine-edith: Other Relationship; Novo Nordisk. P.Divilly: None. Z.Mahmoudi: Employee; Novo Nordisk A/S. U.Soeholm: Research Support; Novo Nordisk A/S. M.M.Broadley: None. F.Pouwer: Research Support; Eli Lilly and Company, Novo Nordisk, Sanofi. B.E.De galan: Research Support; Novo Nordisk. U.Pedersen-bjergaard: Advisory Panel; Novo Nordisk A/S, Sanofi. Funding Innovative Medicines Initiative (777460)

Aim: To examine how hypoglycemia while asleep impacts self-reported daily functioning among adults with type 1 diabetes (T1D) . Methods: On 70 consecutive mornings, 250 people with T1D wearing a continuous glucose monitor reported sleep quality, mood, and alertness via the Hypo-METRICS smartphone app. Each night was categorized based on presence (+) or absence (-) of person-reported hypoglycemia (PRH) and sensor-detected hypoglycemia (SDH) into one of four types (see Figure 1, Type A-D) . Multilevel regression assessed associations between hypoglycemia and daily functioning (unstandardized coefficients (β) on original 0-scale) , adjusted for baseline demographic, clinical and psychological factors. Results: In the first 54 adults (52% women, age[M±SD]: 45±15 years) , nighttime PRH (Type C & D, Figure 1) was associated with reduced subjective sleep quality (C: β=-0.67; D: β=-0.58) , mood (C: β=-0.43; D: β=-0.58) and alertness (C: β=-0.34, p=0.015; D: β=-0.46) (all p0.05) . Conclusion: These novel data show the potentially pivotal role of the subjective experience of hypoglycemia for sleep quality, mood and alertness. Disclosure U.Soeholm: Research Support; Novo Nordisk A/S. E.Renard: Consultant; Abbott, AstraZeneca, Boehringer Ingelheim International GmbH, Dexcom, Inc., Insulet Corporation, LifeScan, Lilly, Novo Nordisk, Roche Diabetes Care, Sanofi, Tandem Diabetes Care, Inc. E.J.Abbink: None. P.M.Baumann: None. S.R.Heller: Advisory Panel; Eli Lilly and Company, Medtronic, Mylan N.V., Zealand Pharma A/S, Zucara Therapeutics, Other Relationship; Eli Lilly and Company, Research Support; Dexcom, Inc., Speaker's Bureau; Novo Nordisk A/S. B.E.De galan: Research Support; Novo Nordisk. U.Pedersen-bjergaard: Advisory Panel; Novo Nordisk A/S, Sanofi. M.Evans: Advisory Panel; Pila Pharma, Zucara Therapeutics, Other Relationship; Abbott Diabetes, Dexcom, Inc., Medtronic, Novo Nordisk, Research Support; AstraZeneca, Sanofi, Speaker's Bureau; Lilly Diabetes. D.Pollard: Other Relationship; Novo Nordisk. R.J.Mccrimmon: Advisory Panel; Novo Nordisk, Sanofi, Research Support; Diabetes UK, European Union, MedImmune. S.A.Amiel: Advisory Panel; Medtronic, Novo Nordisk, Other Relationship; Sanofi. M.M.Broadley: None. C.Hendrieckx: None. J.Speight: Advisory Panel; Insulet Corporation, Research Support; Novo Nordisk, Sanofi. P.Choudhary: Advisory Panel; Abbott Diabetes, Lilly Diabetes, Medtronic, Research Support; Novo Nordisk, Speaker's Bureau; Dexcom, Inc., Glooko, Inc., Insulet Corporation, Sanofi. F.Pouwer: Research Support; Eli Lilly and Company, Novo Nordisk, Sanofi. N.Zaremba: None. P.Divilly: None. G.Martine-edith: Other Relationship; Novo Nordisk. G.Nefs: None. Z.Mahmoudi: Employee; Novo Nordisk A/S. J.K.Mader: Advisory Panel; Abbott Diabetes, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Medtronic, Merck Sharp & Dohme Corp., Novo Nordisk A/S, Roche Diabetes Care, Sanofi-Aventis Deutschland GmbH, Speaker's Bureau; Abbott Diabetes, Eli Lilly and Company, Medtronic, Merck Sharp & Dohme Corp., Novo Nordisk A/S, Roche Diabetes Care, Sanofi-Aventis Deutschland GmbH, Servier Laboratories, Stock/Shareholder; decide Clinical Software GmbH. M.Cigler: None. Funding This work was supported by the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement 777460

Compared to time below range less is known about the frequency and temporal distribution of hypoglycemic episodes in CGM data. Pooled analysis of long-duration CGM data from a diabetes registry allows for large-scale exploration of sensor-detected hypoglycemic episodes (SDHE) . 66 patients with T1D used CGM over 365.7 (340.6-392.5) days (24.530 days total) . In addition to time below range (TBR) , we explored frequency of SDHE >15 minutes at TBR was 5.4% ( Focus on SHDE and individual days showed similar patterns to TBR regarding the surplus and variability of nightly low readings. The greater variability and the increase of SHDE=40 over 30 days while SDHE Disclosure P.M.Baumann: None. D.A.Hochfellner: None. M.Cigler: None. R.Siegl: None. T.Poettler: None. J.K.Mader: Advisory Panel; Abbott Diabetes, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Medtronic, Merck Sharp & Dohme Corp., Novo Nordisk A/S, Roche Diabetes Care, Sanofi-Aventis Deutschland GmbH, Speaker's Bureau; Abbott Diabetes, Eli Lilly and Company, Medtronic, Merck Sharp & Dohme Corp., Novo Nordisk A/S, Roche Diabetes Care, Sanofi-Aventis Deutschland GmbH, Servier Laboratories, Stock/Shareholder; decide Clinical Software GmbH. Funding "This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement 777460. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA and T1D Exchange, JDRF, International Diabetes Federation (IDF) , The Leona M. and Harry B. Helmsley Charitable Trust."

Impaired awareness of hypoglycemia (IAH) is attributed to hypoglycemia exposure. Continuous glucose monitoring (CGM) detects more hypoglycemia particularly during sleep. We compared rates and duration of sensor detected hypoglycemia (SDH) and patient reported hypoglycemia (PRH) in people with type 1 diabetes (T1D) using CGM or flash monitoring.We recruited 92 participants with T1D using CGM/Flash reporting >1 hypoglycemic episode in the last month. Participants were defined as normal awareness of hypoglycemia (NAH) (Gold score 4; n=22) . All wore an additional blinded CGM and recorded episodes of PRH (symptomatic episodes that resolved on carbohydrate ingestion, or a measured glucose Disclosure P.Divilly: None. S.R.Heller: Advisory Panel; Eli Lilly and Company, Medtronic, Mylan N.V., Zealand Pharma A/S, Zucara Therapeutics, Other Relationship; Eli Lilly and Company, Research Support; Dexcom, Inc., Speaker’s Bureau; Novo Nordisk A/S. M.Evans: Advisory Panel; Pila Pharma, Zucara Therapeutics, Other Relationship; Abbott Diabetes, Dexcom, Inc., Medtronic, Novo Nordisk, Research Support; AstraZeneca, Sanofi, Speaker’s Bureau; Lilly Diabetes. J.K.Mader: Advisory Panel; Abbott Diabetes, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Medtronic, Merck Sharp & Dohme Corp., Novo Nordisk A/S, Roche Diabetes Care, Sanofi-Aventis Deutschland GmbH, Speaker’s Bureau; Abbott Diabetes, Eli Lilly and Company, Medtronic, Merck Sharp & Dohme Corp., Novo Nordisk A/S, Roche Diabetes Care, Sanofi-Aventis Deutschland GmbH, Servier Laboratories, Stock/Shareholder; decide Clinical Software GmbH. S.A.Amiel: Advisory Panel; Medtronic, Novo Nordisk, Other Relationship; Sanofi. P.Choudhary: Advisory Panel; Abbott Diabetes, Lilly Diabetes, Medtronic, Research Support; Novo Nordisk, Speaker’s Bureau; Dexcom, Inc., Glooko, Inc., Insulet Corporation, Sanofi. A.Seibold: Employee; Abbott Diabetes, Stock/Shareholder; Abbott. G.Martine-edith: Other Relationship; Novo Nordisk. Z.Mahmoudi: Employee; Novo Nordisk A/S. N.Zaremba: None. U.Soeholm: Research Support; Novo Nordisk A/S. U.Pedersen-bjergaard: Advisory Panel; Novo Nordisk A/S, Sanofi. R.J.Mccrimmon: Advisory Panel; Novo Nordisk, Sanofi, Research Support; Diabetes UK, European Union, MedImmune. B.E.De galan: Research Support; Novo Nordisk. E.Renard: Consultant; Abbott, AstraZeneca, Boehringer Ingelheim International GmbH, Dexcom, Inc., Insulet Corporation, LifeScan, Lilly, Novo Nordisk, Roche Diabetes Care, Sanofi, Tandem Diabetes Care, Inc. Funding Innovative Medicines Initiative (777460)

Continuous glucose monitoring (CGM) provides a more holistic picture of glycemic control than capillary blood glucose (BG) measurements. We simulated CGM use with a clinical decision support system (cDSS) with a basal-bolus insulin algorithm using CGM values recorded at the same time (CGM0) vs. 15 minutes after the BG measurement (CGM+15) to simulate typical CGM trend information. Data of 30 inpatients with T2D (age: 74.1±10.9 years, female: 60 %, Caucasian: 100 %, BMI: 28.6±5.6 kg/m2, diabetes duration: 13.2±11.6 years, HbA1c: 72±22 mmol/mol, stay duration: 8.2±3.7 days) were used. Simulation results were analysed for insulin doses, overall glycemic control, and potentially avoidable hypoglycemic events (PAHE) : those occurring within 3 hours of a bolus insulin injection when the suggested dose based on CGM was lower. Glucose levels and insulin doses were lower when using CGM. Glucose differences were mainly due to nocturnal hypoglycemia undetected by BG (Table 1) . 36 PAHEs occurred in 17 patients with CGM0 vs. 38 PAHEs in patients with CGM+15. In the simulation, lower CGM readings and undetected nocturnal hypoglycemia resulted in lower insulin doses (single and total daily doses) . Consequently, there were hypoglycemic events that could have been avoided had CGM been used. Combining cDSS and CGM is feasible and can facilitate glycemic control whilst avoiding hypoglycemia. Disclosure K.Bytyqi: None. P.M.Baumann: None. D.A.Hochfellner: None. T.Poettler: None. A.Simic: None. P.Beck: Employee; decide Clinical Software GmbH, Stock/Shareholder; decide Clinical Software GmbH. J.K.Mader: Advisory Panel; Abbott Diabetes, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Medtronic, Merck Sharp & Dohme Corp., Novo Nordisk A/S, Roche Diabetes Care, Sanofi-Aventis Deutschland GmbH, Speaker’s Bureau; Abbott Diabetes, Eli Lilly and Company, Medtronic, Merck Sharp & Dohme Corp., Novo Nordisk A/S, Roche Diabetes Care, Sanofi-Aventis Deutschland GmbH, Servier Laboratories, Stock/Shareholder; decide Clinical Software GmbH.

Background and Aims: Lumee Glucose is a small soft glucose-sensitive hydrogel (5.0 x 0.75 x 0.65mm hydrated) currently in clinical development and intended for permanent subcutaneous placement by injection to continuously monitor glucose. The sensor is comprised of a hydrogel scaffold containing fluorescent molecules that produce near infrared light proportional to glucose concentration when interrogated by an optical skin reader placed over the hydrogel. The reader utilizes a set of LEDs to excite the fluorophores within the hydrogel and communicates wirelessly with a tablet. Machine learning and quality data processing algorithms are also being assessed to establish monitoring accuracy using MARD. Methods: Glucose monitoring including a carbohydrate-rich meal was conducted during 8-hour in-clinic visits in 28 subjects with insulin-treated diabetes (female, 48 yrs [20-82], BMI 26.2kg/m2 [19.7-40.3]) over a 3-month period. Frequent venous blood glucose measurements were obtained as reference (Super GL analyzer) . Sensor signals from a total of 188 monitoring traces and 3030 paired values were subjected to retrospective data processing with 3-point glucose reference calibration using machine learning and quality filters to remove potentially compromised optical signals. Results: The average MARD for all 3030 paired values during the 6 to 91-day monitoring period was 12.3%. The MARD was 11.8% when applying additional signal quality filters. Conclusions: Following injection, the hydrogel is suitable for long-term non-invasive glucose monitoring for a minimum period of three months. Projected performance parameters are within the range of currently available glucose monitoring devices. Quality signal processing algorithms revealed marginal performance improvements and should be further optimized with respect to compensating for signal perturbations resulting from motion, temperature, and ambient light changes. Disclosure J.K.Mader: Advisory Panel; Abbott Diabetes, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Medtronic, Merck Sharp & Dohme Corp., Novo Nordisk A/S, Roche Diabetes Care, Sanofi-Aventis Deutschland GmbH, Speaker’s Bureau; Abbott Diabetes, Eli Lilly and Company, Medtronic, Merck Sharp & Dohme Corp., Novo Nordisk A/S, Roche Diabetes Care, Sanofi-Aventis Deutschland GmbH, Servier Laboratories, Stock/Shareholder; decide Clinical Software GmbH. K.Rebrin: Stock/Shareholder; Profusa, Inc. R.Siegl: None. A.K.Reinhard: None. J.Kopanz: None. A.Simic: None. M.Hartmann: None. S.J.Gamsey: None. S.Banerjee: None. U.Kamecke: Consultant; Profusa, Inc.

Maintaining good glycemic control with the same infusion set for longer than three days may improve quality of life of insulin pump users. The aim of the present study was to assess the efficacy and safety of the novel, extended-wear infusion set over seven days of wear in adults with type 1 diabetes. Sixteen participants completed three identical 8-hour euglycemic clamp experiments on days 1, 4, and 7 of infusion set wear. Between the experiments, the participants were discharged home for routine diabetes management while wearing the same extended-wear infusion set throughout the study. Time to reach the maximum glucose infusion rate (TGIRmax ) on day 7 was reduced by 67% compared to day 1 (p

Background: Medication errors are common in hospitals. Our aim was to investigate risks of the analogue and digitally-supported medication use process and potential for solutions.Methods: A mixed-method study including a structured literature search, and questionnaires based on the Delphi method was conducted. To identify major risks, a literature search was supplemented by risks from Critical-Incident-Reporting-System cases and risks from experts’ know-how. First, all risks were structured into main and sub-risks and second, risks were grouped into risk clusters. Third, experts were asked to assess risk clusters regarding likelihood of occurrence and impact on patient safety and regarding potential for solutions to strengthen the competence of health care professionals and potential for solutions in digitalization.Results: Overall, 160 main risks and 542 sub-risks were identified. Main risks were grouped into 43 risk clusters. In total, 33 experts (56% female, 50% with >20 years professional-experience) ranked the likelihood of occurrence and the impact on patient safety to 15 top risk clusters regarding the following process steps: admission (n=4), prescribing (n=3), verifying (n=1), preparing/dispensing (n=3), administering (n=1), discharge (n=1), health care professional competence (n=1), and patient (n=1). In total 28 experts (64% female, 43% with >20 years professional-experience) mostly suggested awareness building and training, strengthening of networking, and involvement of pharmacists at point-of-care as solutions to strengthen health care professional competence. For solutions in digitalization a digital medication list, digital warning systems, barcode-technology, and digital support in integrated care were suggested.Conclusions: A multitude of different risks was identified in the medication use process. To increase patient safety and to minimize potential errors, different solutions to strengthen health care professional competence as well as digital solutions exist. As a next step, health care professional competence and digital solutions will be investigated more deeply to derive specific recommendations for intra-hospital clinical practice use.

Short-term glycemic variability is associated with the risk of hypoglycemia and hyperglycemia in people living with type 1 diabetes and can potentially affect clinical outcomes. Continuous glucose monitoring (CGM) is of increasing importance to evaluate glycemic variability in greater detail. Specific metrics for assessing glycemic variability were proposed, such as the SD of mean glucose level and associated coefficient of variation, and time in target glucose range to guide study designs, therapy and allow people with diabetes more transparency in interpreting their own CGM data. Randomized controlled trials (RCT) and real-world evidence provide complementary information about the efficacy/effectiveness and safety of interventions. Insulin glargine 300 U/mL (Gla-300) has a longer lasting and less variable action than insulin glargine U100 (Gla-100) with a lower risk of hypoglycemia. While insulin degludec U100 (iDeg-100) was associated with lower glucose values but more time below range in one randomized study compared with Gla-300, Gla-300 was associated with a higher per cent time in range, but also above the therapeutic range. However, a real-world study did not find differences during the day between Gla-300 and iDeg-100. The upcoming InRange RCT is the first head-to-head comparison of Gla-300 with iDeg-100 using CGM in an international population using CGM metrics as the primary endpoint. The non-interventional COMET-T real-world study will determine the real-world effectiveness of Gla-300 using CGM metrics and cover a broad spectrum of clinical practice decisions irrespective of the prior basal insulin.

Physical exercise is an important component in the management of type 1 diabetes across the lifespan. Yet, acute exercise increases the risk of dysglycaemia, and the direction of glycaemic excursions depends, to some extent, on the intensity and duration of the type of exercise. Understandably, fear of hypoglycaemia is one of the strongest barriers to incorporating exercise into daily life. Risk of hypoglycaemia during and after exercise can be lowered when insulin-dose adjustments are made and/or additional carbohydrates are consumed. Glycaemic management during exercise has been made easier with continuous glucose monitoring (CGM) and intermittently scanned continuous glucose monitoring (isCGM) systems; however, because of the complexity of CGM and isCGM systems, both individuals with type 1 diabetes and their healthcare professionals may struggle with the interpretation of given information to maximise the technological potential for effective use around exercise (ie, before, during and after). This position statement highlights the recent advancements in CGM and isCGM technology, with a focus on the evidence base for their efficacy to sense glucose around exercise and adaptations in the use of these emerging tools, and updates the guidance for exercise in adults, children and adolescents with type 1 diabetes.

Background: GlucoTab, an electronic diabetes management system (eDMS), supports healthcare professionals (HCPs) in inpatient blood glucose (BG) management at point-of-care and was implemented for the first time under routine conditions in a regional hospital to replace the paper insulin chart. Method: To investigate quality of the eDMS for inpatients with type 2 diabetes mellitus a monocentric retrospective before-after evaluation was conducted. We compared documentation possibilities by assessing a blank paper chart vs the eDMS user interface. Further quality aspects were compared by assessing filled-in paper charts ( n = 106) vs filled-in eDMS documentation ( n = 241). HCPs ( n = 59) were interviewed regarding eDMS satisfaction. Results: The eDMS represented an improvement of documentation possibilities by offering a more structured and comprehensive user interface compared to the blank paper chart. The number of good diabetes days averaged to a median value of four days in both groups (paper chart: 4.38 [0-7] vs eDMS: 4.38 [0-7] days). Median daily BG was 170 (117-297) mg/dL vs 168 (86-286) mg/dL and median fasting BG was 152 (95-285) mg/dL vs 145 (69-333) mg/dL, and 0.1% vs 0.4% BG values Conclusions: The eDMS completely replaced the paper chart, showed comparable glycemic control, was positively accepted by HCPs, and is suitable for inpatient diabetes management.

ZusammenfassungTyp-1-Diabetes mellitus (T1D) ist eine der häufigsten chronischen Stoffwechselerkrankungen im Kindes- und Jugendalter, immer häufiger sind auch Kleinkinder davon betroffen.In den letzten Jahren gab es große Fortschritte im Bereich der Therapie v. a. in Bezug auf Insulinpumpen und Systeme zur kontinuierlichen Glukosemessung (CGM).Closed-Loop-Systeme bestehen aus einer am Körper getragenen Insulinpumpe und einem CGM und passen die Insulinzufuhr automatisiert anhand der ermittelten Glukosewerte an.KidsAP ist ein multinationales, von der Europäischen Union gesponsertes Projekt, koordiniert durch die Universität Cambridge, UK. Unter den internationalen Projektpartnern finden sich auch die Medizinischen Universitäten Innsbruck, Wien und Graz. Ziel ist die Testung eines durch die Universität Cambridge entwickelten Closed-Loop-Systems bei Kleinkindern im Alter von 1 bis 7 Jahren mit T1D.Die Pilotstudie wurde bereits im Sommer 2018 erfolgreich durchgeführt. Die Kinder trugen den Closed-Loop für insgesamt 6 Wochen im heimischen Umfeld. Jeder Teilnehmer und jede Teilnehmerin absolvierte zwei 21-tägige Perioden unter Verwendung des Closed-Loop-Systems mit verdünntem Insulin (U20) bzw. mit Standardinsulin (U100) in zufällig zugeteilter Reihenfolge.In beiden Studienarmen zeigte sich ein hoher Zeitanteil mit Sensorwerten im Zielbereich („time in range“) zwischen 70 und 180 mg/dl.Die Zufriedenheit der Teilnehmer mit dem System war groß. Vor allem die erreichten Glukosewerte im Sinn einer hohen „time-in range“ war für die Eltern überzeugend. Hervorzuheben ist auch die Zufriedenheit hinsichtlich der Verbesserung des Nachtschlafs durch die stabilere nächtliche Glukoseeinstellung.Im Oktober 2019 startete der zweite Teil des EU-Projekts. In der nun laufenden Hauptstudie soll überprüft werden, ob das Tragen des Closed-Loop- versus des Open-Loop-Systems im Cross-over-Design für jeweils 4 Monate die „time in range“ der Glukosespiegel verbessert. Zusätzlich werden auch Interviews bezüglich Lebensqualität durchgeführt.

Vaccination and potentially related side effects might impact glucose management in people with diabetes. In this study, we investigated effects of COVID-19 vaccination on glycemia assessed by continuous glucose monitoring (CGM) in people with type 1 and type 2 diabetes. 74 participants of the ongoing multicenter prospective COVAC-DM-study, investigating the immune response to COVID-19 vaccines in people with diabetes, were willing to participate in this CGM sub-study. Time spent in glycemic ranges (time in range [TIR] 70-180 mg/dL; time below range [TBR] 180 mg/dL) was assessed daily from two days prior to three days after the first COVID-19 vaccination. Participants were asked to document side effects in response to vaccination, insulin injections, and carbohydrate intake. 58 participants with type 1 (27 female, mean age 39.5 years, HbA1c 57 ± 12 mmol/mol) and 16 with type 2 diabetes (9 females, mean age 60.6 years, HbA1c 63 ± 11mmol/mol) were included in this study. The mean TIR did not change on the day of the vaccination and the following 3 days (p>0.05). In people with type 1 diabetes, the TIR (p=0.033) and the TAR (p= 0.043) deteriorated on days with side effects as compared to those without. Side effects occurring after COVID-19 vaccination significantly reduce the TIR and increase the TAR in people with type 1 diabetes, while no impact was observed in people with type 2 diabetes.

The aim of this study was to evaluate the accuracy and usability of a novel continuous glucose monitoring (CGM) system designed for needle-free insertion and reduced environmental impact. We assessed the sensor performance of two GlucoMen

Background CamAPS FX is a hybrid closed-loop smartphone app used to manage type one diabetes. The closed-loop algorithm has a default target glucose of 5.8 mmol/L (104.5 mg/dL), but users can select personal glucose targets (adjustable between 4.4 mmol/L and 11.0 mmol/L [79 mg/dL and 198 mg/dL, respectively]). Method In this post-hoc analysis, we evaluated the impact of personal glucose targets on glycemic control using data from participants in five randomized controlled trials. Results Personal glucose targets were widely used, with 20.3% of all days in the data set having a target outside the default target bin (5.5-6.0 mmol/L [99-108 mg/dL]). Personal glucose targets >6.5 mmol/L (117 mg/dL) were associated with significantly less time in target range (3.9-10.0 mmol/L [70-180 mg/dL]; 6.5-7.0 mmol/L [117-126 mg/dL]: mean difference = −3.2 percentage points [95% CI: −5.3 to −1.2; P < .001]; 7.0-7.5 mmol/L [126-135 mg/dL]: −10.8 percentage points [95% CI: −14.1 to −7.6; P < .001]). Personal targets >6.5 mmol/L (117 mg/dL) were associated with significantly lower time (Conclusions Discrete study populations showed differences in glucose control when applying similar personal targets.

AimsImmune response to COVID-19 vaccination and a potential impact of glycaemia on antibody levels in people with diabetes remains unclear. We investigated the seroconversion following first and second COVID-19 vaccination in people with type 1 and type 2 diabetes in relation to glycaemic control prior to vaccination and analysed the response in comparison to individuals without diabetes.Materials and MethodsThis prospective, multicenter cohort study analysed people with type 1 and type 2 diabetes, well (HbA1cResults86 healthy controls and 161 participants with diabetes were enrolled, 150 (75 with type 1 diabetes and 75 with type 2 diabetes) were eligible for the analysis. After the first vaccination, only 52.7% in the type 1 diabetes group and 48.0% in the type 2 diabetes group showed antibody levels above the cut-off for positivity. Antibody levels after the second vaccination were similar in people with type1, type 2 diabetes and healthy controls if adjusted for age, sex and multiple testing (p>0.05). Age (r=−0.45, pConclusionsAnti-SARS-CoV-2 S antibody levels after the second vaccination were comparable in healthy controls, people with type 1 and type 2 diabetes, irrespective of glycaemic control. Age and renal function correlated significantly with the extent of antibody levels.

Aims Reliable estimation of the time spent in different glycaemic ranges (time-in-ranges) requires sufficiently long continuous glucose monitoring. In a 2019 paper (Battelino et al., Clinical targets for continuous glucose monitoring data interpretation: recommendations from the international consensus on time in range. Diabetes Care. 2019;42:1593-1603), an international panel of experts suggested using a correlation-based approach to obtain the minimum number of days for reliable time-in-ranges estimates. More recently (in Camerlingo et al., Design of clinical trials to assess diabetes treatment: minimum duration of continuous glucose monitoring data to estimate time-in-ranges with the desired precision. Diabetes Obes Metab. 2021;23:2446-2454) we presented a mathematical equation linking the number of monitoring days to the uncertainty around time-in-ranges estimates. In this work, we compare these two approaches, mainly focusing on time spent in (70-180) mg/dL range (TIR). Methods The first 100 and 150days of data were extracted from study A (148subjects, ~180days), and the first 100, 150, 200, 250 and 300days of data from study B (45subjects, ~365days). For each of these data windows, the minimum monitoring duration was computed using correlation-based and equation-based approaches. The suggestions were compared for the windows of different durations extracted from the same study, and for the windows of equal duration extracted from different studies. Results When changing the dataset duration, the correlation-based approach produces inconsistent results, ranging from 23 to 64days, for TIR. The equation-based approach was found to be robust versus this issue, as it is affected only by the characteristics of the population being monitored. Indeed, to grant a confidence interval of 5% around TIR, it suggests 18days for windows from study A, and 17days for windows from study B. Similar considerations hold for other time-in-ranges. Conclusions The equation-based approach offers advantages for the design of clinical trials having time-in-ranges as final end points, with focus on trial duration.

This case report demonstrates that using a non‐approved long‐acting GLP‐1‐RA (dulaglutide) in adolescents with T2D is possible and feasible under special circumstances when approved therapeutic options for the pediatric population fail to achieve adequate glycemic control.

BackgroundType 1 diabetes in young children is a heavy parental burden. As part of pilot phase of the KIDSAP01 study, we conducted a baseline assessment in parents to study the association between hypoglycemia fear, parental well-being and child behavior.MethodsAll parents were invited to fill in baseline questionnaires: hypoglycemia fear survey (HFS), WHO-5, Epworth Sleepiness Scale and Strength and Difficulties Questionnaire (SDQ).Results24 children (median age: 5-year, range 1-7 years, 63% male, mean diabetes duration: 3 ± 1.7 years) participated. 23/24 parents filled out the questionnaires. We found a higher score for the hypoglycemia fear behavior 33.9 ± 5.6 compared to hypoglycemia worry 34.6 ± 12.2. Median WHO-5 score was 16 (8 - 22) with poor well-being in two parents. Median daytime sleepiness score was high in five parents (>10). For six children a high total behavioral difficulty score (>16) was reported. Pro social behavior score was lower than normal in six children (r = - 0.50, p r = - 0.44, p r = - 0.45, p = .05) and positively with child age and diabetes duration (r = 0.58, p r = 0.6, p ConclusionRegular screening of parental well-being, hypoglycemia fear and child behavior should be part of routine care to target early intervention.

Introduction Drug errors pose a major health hazard to a number of patient populations. However, patients with type 2 diabetes mellitus seem especially vulnerable to this risk as diabetes mellitus is usually concomitant with various comorbidities and polypharmacy, which present significant risk factors for the occurrence of drug errors. Despite this fact, there is little data on drug errors from patients’ perspective. The present survey aimed to examine the viewpoints of patients with type 2 diabetes mellitus regarding their experiences with medication errors, the overall treatment satisfaction, and their perceptions on how a medication error was handled in daily hospital routine. Materials and methods Inpatients at the Department of Endocrinology and Diabetology of the University Hospital of Graz were included in the survey. Out of 100 patients, one-half had insulin therapy before hospitalization while the other half had no insulin therapy prior to admission. After giving informed consent, patients filled out a questionnaire with 22 items. Results Independent of their preexisting therapy, 25% of patients already suffered at least one drug error, whereby prescribing a wrong dose seemed to be the most common type of error. Furthermore, 26% of patients in the non-insulin versus 50% in the insulin group (p = 0.084) were convinced that drug errors were addressed honestly by the medical staff, while 54% in the non-insulin versus 80% in the insulin-group (p = 0.061) assumed that adequate measures were taken to prevent drug errors. Finally, 9 out of 10 patients seemed satisfied with their treatment regardless of their diabetes therapy. Discussion/conclusion The results of the survey clearly showed that patients experienced at least one medication error during hospitalization. However, these errors only rarely led to patient harm. The survey also revealed the value of an honest and respectful doctor-patient relationship regarding patient perception of medication errors and general complaints. Increasing patient awareness on the existing in-hospital error management systems could eliminate treatment-related concerns and create a climate of trust that is essential for effective treatment.

Introduction: Basal insulin is the first choice for geriatric persons with type 2 diabetes (T2D) who require insulin therapy. A digital workflow and decision support system (GlucoTab@MobileCare) with an integrated basal or basal-plus insulin algorithm has demonstrated to be an effective and safe treatment in geriatric persons with T2D receiving home health care during a three months study period. The aim of this study was to investigate the long-term effect on diabetes management following treatment by GlucoTab@MobileCare. Methods: Diabetes related aspects (glycemic control, acute hospital admissions, general practitioner (GP) consultations under routine conditions) were assessed in nine patients (5 female, age 77±10 years, BMI 27.7±4.7 kg/m2, HbA1c 60±13 mmol/mol [start of intervention] vs. 57±12 mmol/mol [end of intervention]) during the three months study intervention, six months before (n=9) and six months after (n=8, due to the death of one patient) study intervention. Data from electronic health records and diabetes documentation were analyzed. Results: Mean fasting blood glucose (BG) was 212±72 mg/dL before, 156±33 mg/dL during and 162±42 mg/dL after intervention. During the study, no acute hospital admissions due to BG derailments were observed. Eight derailments before and four derailments after intervention occurred. Causes for diabetes-related emergency department (ED) admissions were hyperglycemia (n=10), hypoglycemia (n=1) and undefined (n=1). Nine of the twelve admissions resulted in hospitalizations. GPs were consulted for diabetes management 19-times before and 16-times after GlucoTab@MobileCare treatment. Reasons for consultations were routine check-up (n=12), therapy adjustment (n=22) and HbA1c measurement (n=1). Conclusion: A sustained effect on glycemic control with reduced requirements for hospital admissions was observed six months after GlucoTab@MobileCare treatment, whereas GP consultations were not affected. Disclosure J. Kopanz: None. T. Pieber: Advisory Panel; Self; ADOCIA, Arecor, AstraZeneca, Eli Lilly and Company, Novo Nordisk A/S, Sanofi, Research Support; Self; AstraZeneca, Novo Nordisk A/S. J. K. Mader: Advisory Panel; Self; Becton, Dickinson and Company, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Medtronic, Roche Diabetes Care, Speaker’s Bureau; Self; Abbott Diabetes, Novo Nordisk A/S, Roche Diabetes Care, Sanofi, Stock/Shareholder; Self; decide Clinical Software GmbH. L. K. Scholle: None. K. M. Lichtenegger: None. G. Ambrosch: None. A. Libiseller: None. F. Aberer: None. M. Pandis: None. K. Donsa: Stock/Shareholder; Self; decide Clinical Software GmbH. T. Truskaller: None.

Continuous glucose monitors (CGMs) are increasingly becoming indispensable in everyday diabetes management. This study assessed the accuracy of the new GlucoMen Day® CGM (Waveform Cascade, launched by A. Menarini Diagnostics) by comparing it to a laboratory reference instrument and a self-monitoring blood glucose (SMBG) device. Eight individuals with type 1 diabetes (3 females, age 41.6 ± 13.3 years, BMI 28.0 ± 6.1 kg/m2, HbA1c 55.6 ± 12.2 mmol/mol, diabetes duration 13.9 ± 6.5 years) spent 14 days at home simultaneously wearing two CGMs in the abdominal subcutaneous adipose tissue while also performing a minimum of five SMBG measurements (GlucoMen Day® Meter, A. Menarini Diagnostics, IT) per day. On days four and ten of sensor wear, the participants underwent a 5-hour meal and insulin challenge at the research center. The plasma glucose concentration was determined in 20-min intervals with a laboratory reference instrument (YSI 2300 Stat Plus, Yellow Springs, OH). The CGM accuracy was assessed by calculating the mean absolute relative difference (MARD) and by performing the consensus error grid analysis (CEG). The overall MARDs calculated for YSI and SMBG were 9.7 % and 13.1 %, respectively. The CEG analysis showed that 98% of all data points were in the clinically acceptable zones A and B. The study data indicate that the CGM accuracy meets the current clinical requirements for state-of-the-art continuous glucose monitors. Disclosure A. Simic: None. M. T. Taucher: None. D. A. Hochfellner: None. T. Poettler: None. F. Aberer: None. J. K. Mader: Advisory Panel; Self; Becton, Dickinson and Company, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Medtronic, Roche Diabetes Care, Speaker9s Bureau; Self; Abbott Diabetes, Novo Nordisk A/S, Roche Diabetes Care, Sanofi, Stock/Shareholder; Self; decide Clinical Software GmbH. Funding A. Menarini Diagnostics

Guidelines recommend insulin therapy and the use of clinical decision support systems to manage hyperglycemia and diabetes mellitus in hospitalized patients. The aim of the study was to evaluate glycemic control using insulin degludec in an algorithm driven electronic decision support system in hospitalized patients with type 2 diabetes (T2D). The electronic decision support system GlucoTab® was applied for diabetes management in 30 hospitalized patients with T2D (18 women, age 74.1±10.9 years, HbA1c 71±23mmol/mol, BMI 28.6±5.6kg/m2, diabetes duration 14.9±15.1 years, creatinine 1.5±1.2mg/dl, length of hospital stay 9.1±4.0 days). Insulins degludec and aspart were used for basal-bolus insulin therapy. Blood glucose was tested four times daily using a point-of-care device integrated into the hospital information system. The overall mean blood glucose (BG) value was 144±53 mg/dl (mean pre-enrolment BG 211±84mg/dl). BG measurements were in 74.9% in the range of 70- Disclosure J. K. Mader: Advisory Panel; Self; Becton, Dickinson and Company, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Medtronic, Roche Diabetes Care, Speaker’s Bureau; Self; Abbott Diabetes, Novo Nordisk A/S, Roche Diabetes Care, Sanofi, Stock/Shareholder; Self; decide Clinical Software GmbH. T. Pieber: Advisory Panel; Self; ADOCIA, Arecor, AstraZeneca, Eli Lilly and Company, Novo Nordisk A/S, Sanofi, Research Support; Self; AstraZeneca, Novo Nordisk A/S. P. Beck: Employee; Self; decide Clinical Software GmbH, Stock/Shareholder; Self; decide Clinical Software GmbH. P. M. Baumann: None. F. Aberer: None. D. A. Hochfellner: None. A. Simic: None. L. Bytyqi: None. T. Poettler: None. B. Hoell: Employee; Self; decide Clinical Software GmbH. A. Fahrleitner-pammer: None. Funding Novo Nordisk

Insulin icodec* (icodec) is a once-weekly basal insulin in development. This post hoc analysis used data from the NCT03951805 trial to investigate CGM-derived time in, above, below range (TIR, TAR, TBR) and glycemic variability measured as coefficient of variation (CV%). Insulin-naive patients with T2D (N=205) received insulin glargine U100 (IGlar U100; pre-breakfast SMBG target 80-130 mg/dL, adjusted ±4U/day), icodec titration A (80-130 mg/dL, ±21U/week), B (80-130 mg/dL, ±28U/week; most relevant comparator to IGlar U100), or C (70-108 mg/dL, ±28U/week). Treatments were titrated weekly. TIR (70-180 mg/dL), TAR (>180 mg/dL), TBR ( 70% from weeks 7 and 8, TAR decreased to 70% TIR and TBR 70% TIR and TBR Disclosure I. Lingvay: Advisory Panel; Self; Bayer Healthcare Pharmaceuticals Inc., Lilly Diabetes, Consultant; Self; TARGET PharmaSolutions, Other Relationship; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Merck Sharp & Dohme Corp., Novo Nordisk, Pfizer Inc., Zealand Pharma A/S, Research Support; Self; Mylan N. V., Sanofi. R. Beck bang: Employee; Self; Novo Nordisk A/S. M. M. Koefoed: Employee; Self; Novo Nordisk. J. K. Mader: Advisory Panel; Self; Becton, Dickinson and Company, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Medtronic, Roche Diabetes Care, Speaker’s Bureau; Self; Abbott Diabetes, Novo Nordisk A/S, Roche Diabetes Care, Sanofi, Stock/Shareholder; Self; decide Clinical Software GmbH. J. Pettus: Advisory Panel; Self; Novo Nordisk, Sanofi, Consultant; Self; Diasome Pharmaceuticals, Inc., Insulet Corporation, Lilly Diabetes, MannKind Corporation, Tandem Diabetes Care. L. Wagner: None. C. Mathieu: Advisory Panel; Self; Novo Nordisk, Sanofi, Merck Sharp and Dohme Ltd., Eli Lilly and Company, Novartis, AstraZeneca, Boehringer Ingelheim, Roche, Medtronic, ActoBio Therapeutics, Pfizer, Insulet and Zealand Pharma, Research Support; Self; Medtronic, Novo Nordisk, Sanofi and ActoBio Therapeutics, Speaker’s Bureau; Self; Novo Nordisk, Sanofi, Eli Lilly and Company, Boehringer Ingelheim, AstraZeneca and Novartis.