Real-world outcomes of Omnipod DASH system use in people with type 1 diabetes: Evidence from the Association of British Clinical Diabetologists (ABCD) study.

AIMS: To evaluate real-world outcomes in people with Type 1 Diabetes (PwT1D) initiated on Omnipod DASH® Insulin Management System. METHODS: Anonymized clinical data were submitted to a secure web-based tool within the National Health Service network. Hemoglobin A1c (HbA1c), sensor-derived glucometrics, total daily dose of insulin (TDD), and patient-reported outcome changes between baseline and follow-up were assessed. Individuals were classified to "new-to-pump" (switched from multiple daily injections) and "established-on-pump" (switched from a tethered insulin pump) groups. RESULTS: 276 individuals from 11 centers [66.7 % female; 92 % White British; median age 41 years (IQR 20-50); diabetes duration 20 years (IQR 11-31); 49.3 % within "new-to-pump" group] were included. Baseline HbA1c was 8.0 ± 1.3 % (64 ± 14 mmol/mol). At follow-up [3 years (IQR 1.5-3.2)], HbA1c reduced by 0.3 % [(3 mmol/mol); p = 0.002] across the total population, 0.4 % [(5 mmol/mol); p = 0.001] in those "new-to-pump" and remained unchanged in those "established-on-pump". TDD decreased in the "new-to-pump" cohort (baseline:44.9 ± 21.0units vs follow-up:38.1 ± 15.4units, p = 0.002). Of those asked, 141/143 (98.6 %) stated Omnipod DASH had a positive impact on quality of life. CONCLUSIONS: Omnipod DASH was associated with improvements in HbA1c in PwT1D "new-to-pump" and maintained previous HbA1c levels in those "established-on-pump". User satisfaction in all groups and TDD reduction in those "new-to-pump" were reported.

Developing and exploring the validity of a patient reported experience measure for adult inpatient diabetes care.

AIM: To develop and explore the validity of a Patient Reported Experience Measure (PREM) for adult inpatient diabetes care. METHOD: 27 in-depth interviews were conducted to inform the development of the 42-item PREM which was cognitively tested with 10 people. A refined 38-item PREM was piloted with 228 respondents completing a paper (n = 198) or online (n = 30) version. The performance of the PREM was evaluated by exploring (i) uptake/number of responses and (ii) survey validity by investigating whether the PREM data were of adequate quality and delivered useful information. RESULTS: The PREM had low drop-out or missing data rates suggesting it was appropriately constructed. Analysis of item frequencies and variances, and problem score calculations concluded that questions provided sufficient score differentiation. CONCLUSIONS: This new PREM allows for experiences of inpatient diabetes care to be measured, understood and reported on to help identify priority areas for improving care quality.

Hybrid Closed-Loop Therapy in Adults With Type 1 Diabetes and Above-Target HbA1c: A Real-world Observational Study.

OBJECTIVE: We explored longitudinal changes associated with switching to hybrid closed-loop (HCL) insulin delivery systems in adults with type 1 diabetes and elevated HbA1c levels despite the use of intermittently scanned continuous glucose monitoring (isCGM) and insulin pump therapy. RESEARCH DESIGN AND METHODS: We undertook a pragmatic, preplanned observational study of participants included in the National Health Service England closed-loop pilot. Adults using isCGM and insulin pump across 31 diabetes centers in England with an HbA1c ≥8.5% who were willing to commence HCL therapy were included. Outcomes included change in HbA1c, sensor glucometrics, diabetes distress score, Gold score (hypoglycemia awareness), acute event rates, and user opinion of HCL. RESULTS: In total, 570 HCL users were included (median age 40 [IQR 29-50] years, 67% female, and 85% White). Mean baseline HbA1c was 9.4 ± 0.9% (78.9 ± 9.1 mmol/mol) with a median follow-up of 5.1 (IQR 3.9-6.6) months. Of 520 users continuing HCL at follow-up, mean adjusted HbA1c reduced by 1.7% (95% CI 1.5, 1.8; P < 0.0001) (18.1 mmol/mol [95% CI 16.6, 19.6]; P < 0.0001). Time in range (70-180 mg/dL) increased from 34.2 to 61.9% (P < 0.001). Individuals with HbA1c of ≤58 mmol/mol rose from 0 to 39.4% (P < 0.0001), and those achieving ≥70% glucose time in range and <4% time below range increased from 0.8 to 28.2% (P < 0.0001). Almost all participants rated HCL therapy as having a positive impact on quality of life (94.7% [540 of 570]). CONCLUSIONS: Use of HCL is associated with improvements in HbA1c, time in range, hypoglycemia, and diabetes-related distress and quality of life in people with type 1 diabetes in the real world.

UK's Association of British Clinical Diabetologist's Diabetes Technology Network (ABCD-DTN): Best practice guide for hybrid closed-loop therapy.

This best practice guide is written with the aim of providing an overview of current hybrid closed-loop (HCL) systems in use within the United Kingdom's (UK) National Health Service (NHS) and to provide education and advice for their management on both an individual and clinical service level. The environment of diabetes technology, and particularly HCL systems, is rapidly evolving. The past decade has seen unprecedented advances in the development of HCL systems. These systems improve glycaemic outcomes and reduce the burden of treatment for people with type 1 diabetes (pwT1D). It is anticipated that access to these systems will increase in England as a result of updates in National Institute of Health and Care Excellence (NICE) guidance providing broader support for the use of real-time continuous glucose monitoring (CGM) for pwT1D. NICE is currently undertaking multiple-technology appraisal into HCL systems. Based on experience from centres involved in supporting advanced technologies as well as from the recent NHS England HCL pilot, this guide is intended to provide healthcare professionals with UK expert consensus on the best practice for initiation, optimisation and ongoing management of HCL therapy.

Variation in the Current Use of Technology to Support Diabetes Management in UK Hospitals: Results of a Survey of Health Care Professionals.

BACKGROUND: There has been a significant increase in the use of wearable diabetes technologies in the outpatient setting over recent years, but this has not consistently translated into inpatient use. METHODS: An online survey was undertaken to understand the current use of technology to support inpatient diabetes care in the United Kingdom. RESULTS: Responses were received from 42 different organizations representing 104 hospitals across the United Kingdom. Significant variation was found between organizations in the use of technology to support safe, effective inpatient diabetes care. Benefits of the use of technology were reported, and areas of good practice identified. CONCLUSION: Technology supports good inpatient diabetes care, but there is currently variation in its use. Guidance has been developed which should drive improvements in the use of technology and hence improvements in the safety and effectiveness of inpatient diabetes care. Key recommendations include implementation of this guidance (especially for continuous glucose monitoring), ensuring specialist support is available for the use of wearable diabetes technology in hospital, optimizing information sharing across the health care system, and making full use of data from networked glucose and ketone meters.

How Can Point-of-Care Technologies Support In-Hospital Diabetes Care?

People with diabetes admitted to hospital are at risk of diabetes related complications including hypoglycaemia and diabetic ketoacidosis. Point-of-care (POC) tests undertaken at the patient bedside, for glucose, ketones, and other analytes, are a key component of monitoring people with diabetes, to ensure safety. POC tests implemented with a quality framework are critical to ensuring accuracy and veracity of results and preventing erroneous clinical decision making. POC results can be used for self-management of glucose levels in those well-enough and/or by healthcare professionals to identify unsafe levels. Connectivity of POC results to electronic health records further offers the possibility of utilising these results proactively to identify patients 'at risk' in real-time and for audit purposes. In this article, the key considerations when implementing POC tests for diabetes in-patient management are reviewed and potential to drive improvements using networked glucose and ketone measurements are discussed. In summary, new advances in POC technology should allow people with diabetes and the teams looking after them whilst in hospital to integrate to provide safe and effective care.

Continuous Glucose Monitoring Within Hospital: A Scoping Review and Summary of Guidelines From the Joint British Diabetes Societies for Inpatient Care.

Increasing numbers of people, particularly with type 1 diabetes (T1D), are using wearable technologies. That is, continuous subcutaneous insulin infusion (CSII) pumps, continuous glucose monitoring (CGM) systems, and hybrid closed-loop systems, which combine both these elements. Given over a quarter of all people admitted to hospital have diabetes, there is a need for clinical guidelines for when people using them are admitted to hospital. The Joint British Diabetes Societies for Inpatient Care (JBDS-IP) provide a scoping review and summary of guidelines on the use of diabetes technology in people with diabetes admitted to hospital.JBDS-IP advocates enabling people who can self-manage and use their own diabetes technology to continue doing so as they would do out of hospital. Whilst people with diabetes are recommended to achieve a target of 70% time within range (3.9-10.0 mmol/L [70-180 mg/dL]), this can be very difficult to achieve whilst unwell. We therefore recommend targeting hypoglycemia prevention as a priority, keeping time below 3.9 mmol/L (70 mg/dL) at < 1%, being aware of looming hypoglycemia if glucose is between 4.0 and 5.9 mmol/L (72-106 mg/dL), and consider intervening, particularly if there is a downward CGM trend arrow.Health care organizations need clear local policies and guidance to support individuals using diabetes technologies, and ensure the relevant workforce is capable and skilled enough to ensure their safe use within the hospital setting. The current set of guidelines is divided into two parts. Part 1, which follows below, outlines the guidance for use of CGM in hospital. The second part outlines guidance for use of CSII and hybrid closed-loop in hospital.

Insulin Pumps and Hybrid Close Loop Systems Within Hospital: A Scoping Review and Practical Guidance From the Joint British Diabetes Societies for Inpatient Care.

This article is the second of a two-part series providing a scoping review and summary of the Joint British Diabetes Societies for Inpatient Care (JBDS-IP) guidelines on the use of diabetes technology in people with diabetes admitted to hospital. The first part reviewed the use of continuous glucose monitoring (CGM) in hospital. In this article, we focus on the use of continuous subcutaneous insulin infusion (CSII; insulin pumps) and hybrid closed-loop systems in hospital. JBDS-IP advocates enabling people who can self-manage and are willing and capable of using CSII to continue doing so as they would do out of hospital. CSII should be discontinued if the individual is critically ill or hemodynamically unstable. For individuals on hybrid closed-loop systems, the system should be discontinued from auto-mode, and may be used individually (as CGM only or CSII only, if criteria are met). Continuing in closed-loop mode may only be done so under specialist guidance from the Diabetes Team, where the diabetes teams are comfortable and knowledgeable about the specific devices used. Health care organizations need to have clear local policies and guidance to support individuals using these wearable technologies, and ensure the relevant workforce is capable and skilled enough to ensure their safe use within the hospital setting.

Using Technology to Improve Diabetes Care in Hospital: The Challenge and the Opportunity.

The past 10 years have seen a revolution in technology improving the lives of people with diabetes. This has implications for diabetes care in hospitalized inpatients. These technological developments have the potential to significantly improve the care of people with diabetes in hospital. Combining point of care glucose monitoring, electronic prescribing, electronic observations with electronic referral, and electronic health records allow teams to daily oversee the whole hospital population. To make the most of these tools as well as developing the use of pumps and glucose sensors in hospital, the diabetes team needs to work in new ways. To date, very little work has described how these should be combined. We describe how this technology can be combined to improve diabetes care in hospital.

Time in range following flash glucose monitoring: Relationship with glycaemic control, diabetes-related distress and resource utilisation in the Association of British Clinical Diabetologists national audit.

AIMS: The aim of this study was to understand the relationship between time in range (TIR) achieved using the isCGM with changes in glycaemic control, diabetes-related distress (DRD) and resource utilisation in people living with diabetes. METHODS: Clinicians from 106 National Health System (NHS) UK hospitals submitted isCGM user baseline and follow-up data in a web-based tool held within the UK NHS network. Linear regression analysis was used to identify the relationship between follow-up glucose TIR (3.9-10 mmol/L) categories (TIR% 50-70 and TIR% >70) with change in haemoglobin A1c (HbA1c), DRD and Gold score (measure of hypoglycaemia unawareness, where a score ≥4 suggests impaired awareness of hypoglycaemia). RESULTS: Of 16,427 participants, 1241 had TIR follow-up data available. In this cohort, the mean TIR was 44.8% (±22.5). With the use of isCGM, at 7.9 months mean follow-up, improvements were observed in HbA1c (-6.9 [13.5] mmol/mol, p < 0.001), Gold score (-0.35 [1.5], p < 0.001) and Diabetes Distress Screening (-0.73 [1.23], p < 0.001). In the regression analysis restricted to people living with type 1 diabetes, TIR% 50-70 was associated with a -8.9 mmol/mol (±0.6, p < 0.001) reduction in HbA1c; TIR% >70 with a -14 mmol/mol (±0.8, p < 0.001) reduction in HbA1c. Incremental improvement in TIR% was also associated with significant improvements in Gold score and DRD. TIR% >70 was associated with no hospital admissions due to hypoglycaemia, hyperglycaemia/diabetic ketoacidosis, and a 60% reduction in the paramedic callouts and 77% reduction in the incidence of severe hypoglycaemia. CONCLUSION: In a large cohort of UK isCGM users, we demonstrate a significant association of higher TIR% with improvement in HbA1c, hypoglycaemia awareness, DRD and resource utilisation.

Association of British Clinical Diabetologists, Diabetes Technology Network UK and Association of Children's Diabetes Clinicians Survey of UK Healthcare Professional Attitudes Towards Open-Source Automated Insulin Delivery Systems.

INTRODUCTION: Automated insulin delivery (AID) systems can enable improved glycaemic outcomes with reduced mental burden. Open-source AID (OS-AID) systems overcome some of the developmental and access barriers enabling a wider use of these systems. Limited data are available on healthcare professional (HCP) opinions and current practice regarding these systems. The aim of this survey was to gain insight into HCP perceptions and practices around OS-AID. METHODS: This survey was developed collaboratively with OS-AID users and distributed to adult and children's teams, using an online survey tool. Results were received between February and April 2019. Responses were assessed using simple descriptive statistics with analyses stratified by respondent characteristics. RESULTS: 317 responses were obtained from a range of HCPs in both adult and paediatric services. Key results include: HCP perception of OS-AID as "risky in the wrong hands" (43%); 91% felt uncomfortable initiating discussions around OS-AID because of lack of regulation (67%) and/or their own lack of knowledge (63%). Half of HCPs (47%) reported that they would choose OS-AID if they themselves had type 1 diabetes. CONCLUSIONS: HCPs are generally supportive of OS-AID users but many feel uncomfortable with the technicalities of the systems given the lack of approval. Knowledge around the use of these systems was limited. Re-assessment of HCP perceptions should be performed in the future given the evolving landscape of diabetes technology, recent consensus statements and emerging ethical and legal perspectives.

Open-source automated insulin delivery systems are an increasingly encountered diabetes technology. These involve a small glucose sensor and an insulin delivery device called an insulin pump. These two devices interact to allow adjustment of insulin delivery to maintain glucose levels in a desirable range. The computer codes which drive these systems are developed by people with diabetes or their families rather than by device companies; as such, they have not been through formal approval processes and therefore there is limited formal evidence concerning whether they are safe or beneficial to use. Users report high satisfaction with these devices and improvements in their diabetes management. This survey was performed to assess the opinions of UK healthcare professionals and their usual practice. Key results include: UK healthcare professionals would not routinely recommend the use of these devices and there was concern about the medicolegal implications of use. However, UK healthcare professionals were generally supportive of those who chose to use the devices. Interestingly, almost half of the healthcare professionals would use the systems if they had diabetes.

Current provision and HCP experiences of remote care delivery and diabetes technology training for people with type 1 diabetes in the UK during the COVID-19 pandemic.

BACKGROUND: The COVID-19 pandemic has led to the rapid implementation of remote care delivery in type 1 diabetes. We studied current modes of care delivery, healthcare professional experiences and impact on insulin pump training in type 1 diabetes care in the United Kingdom (UK). METHODS: The UK Diabetes Technology Network designed a 48-question survey aimed at healthcare professionals providing care in type 1 diabetes. RESULTS: One hundred and forty-three healthcare professionals (48% diabetes physicians, 52% diabetes educators and 88% working in adult services) from approximately 75 UK centres (52% university hospitals, 46% general and community hospitals), responded to the survey. Telephone consultations were the main modality of care delivery. There was a higher reported time taken for video consultations versus telephone (p < 0.001). Common barriers to remote consultations were patient familiarity with technology (72%) and access to patient device data (67%). We assessed the impact on insulin pump training. A reduction in total new pump starts (73%) and renewals (61%) was highlighted. Common barriers included patient digital literacy (61%), limited healthcare professional experience (46%) and time required per patient (44%). When grouped according to size of insulin pump service, pump starts and renewals in larger services were less impacted by the pandemic compared to smaller services. CONCLUSION: This survey highlights UK healthcare professional experiences of remote care delivery. While supportive of virtual care models, a number of factors highlighted, especially patient digital literacy, need to be addressed to improve virtual care delivery and device training.

The impact of islet mass, number of transplants, and time between transplants on graft function in a national islet transplant program.

The UK islet allotransplant program is nationally funded to deliver one or two transplants over 12 months to individuals with type 1 diabetes and recurrent severe hypoglycemia. Analyses were undertaken 10 years after program inception to evaluate associations between transplanted mass; single versus two transplants; time between two transplants and graft survival (stimulated C-peptide >50 pmol/L) and function. In total, 84 islet transplant recipients were studied. Uninterrupted graft survival over 12 months was attained in 23 (68%) single and 47 (94%) (p = .002) two transplant recipients (separated by [median (IQR)] 6 (3-8) months). 64% recipients of one or two transplants with uninterrupted function at 12 months sustained graft function at 6 years. Total transplanted mass was associated with Mixed Meal Tolerance Test stimulated C-peptide at 12 months (p < .01). Despite 1.9-fold greater transplanted mass in recipients of two versus one islet infusion (12 218 [9291-15 417] vs. 6442 [5156-7639] IEQ/kg; p < .0001), stimulated C-peptide was not significantly higher. Shorter time between transplants was associated with greater insulin dose reduction at 12 months (beta -0.35; p = .02). Graft survival over the first 12 months was greater in recipients of two versus one islet transplant in the UK program, although function at 1 and 6 years was comparable. Minimizing the interval between 2 islet infusions may maximize cumulative impact on graft function.

Inpatient hypoglycaemia in older people is associated with a doubling in the increased length of stay compared with the younger population.

BACKGROUND: Hypoglycaemia during hospital admission is associated with poor outcomes including increased length of stay. In this study, we compared the incidence of inpatient hypoglycaemia and length of stays among people of three age groups: ≤65 years, 65-80 years and >80 years old. METHODS: The study was conducted using a 4-year electronic patient record dataset from Oxford University Hospitals NHS Foundation Trust. The dataset contains hospital admission data for people with diabetes. We analysed the blood glucose (BG) measurements and identified all level 1 (BG <4 mmol/l) and level 2 (BG <3 mmol/l) hypoglycaemic episodes. We compared the length of stays between different age groups and with different levels of hypoglycaemia. RESULTS: We analysed data obtained from 17,658 inpatients with diabetes who underwent 32,758 hospital admissions. The length of stays for admissions with no hypoglycaemia were 3[1,6], 3[1,8] and 4[2,11] (median[interquartile range]) days for age groups ≤65 years, 65-80 years and >80 years, respectively. These were statistically significantly lower (P < 0.01 for all pairwise comparisons) than the length of stays for admissions with level 1 hypoglycaemia, which were 6[3,13], 10[5,20] and 12[6,22] days, and level 2 hypoglycaemia, which were 7[3,14], 11[5,24] and 13[6,24] days. CONCLUSIONS: In all age groups, admissions with either level 1 or level 2 hypoglycaemia were associated with an increased length of stay. However, in both the older groups, the length of stay increments were much higher (double) than the younger counterparts. The clinical consequences of hypoglycaemia were more severe in older people compared with the younger population.

Hybrid closed-loop therapy: Where are we in 2021?

Hybrid closed-loop systems are characterized by the coexistence of algorithm-driven automated insulin delivery combined with manual mealtime boluses. Used correctly, these insulin delivery systems offer better glucose control and reduced risk of hypoglycaemia and represent the most advanced form of insulin delivery available for people with type 1 diabetes. The aim of this paper was to compare the currently available commercial hybrid closed-loop systems in the UK: the Medtronic 670G/780G, Tandem t:slim X2 Control IQ and CamAPS FX systems. The Medtronic 670G/780G systems use Guardian 3 sensor (7-day use, two to four calibrations per day), while Tandem and CamAPS systems use the calibration-free Dexcom G6 sensor (10 days). The CamAPS system is available as an android app, whereas the other two systems have the algorithm embedded in the insulin pump. During pivotal studies, depending on the study population and baseline glycated haemoglobin level, these systems achieve a time spent in the target range 3.9 to 10 mmol/L (70 to 180 mg/dL) of 65% to 76% with low burden of hypoglycaemia. All three systems allow a higher glucose target for announced exercise, while the Tandem system offers an additional night-time tighter target. The CamAPS system offers fully customizable glucose targets and is the only system licensed for use during pregnancy. Additional education is required for both users and healthcare professionals to harness the best performance from these systems as well as to troubleshoot when "automode exits" occur. We provide consensus recommendations to develop pragmatic pathways to guide patients, clinicians and commissioners in making informed decisions on the appropriate use of the diabetes technology.

Predicting the Risk of Inpatient Hypoglycemia With Machine Learning Using Electronic Health Records.

OBJECTIVE: We analyzed data from inpatients with diabetes admitted to a large university hospital to predict the risk of hypoglycemia through the use of machine learning algorithms. RESEARCH DESIGN AND METHODS: Four years of data were extracted from a hospital electronic health record system. This included laboratory and point-of-care blood glucose (BG) values to identify biochemical and clinically significant hypoglycemic episodes (BG ≤3.9 and ≤2.9 mmol/L, respectively). We used patient demographics, administered medications, vital signs, laboratory results, and procedures performed during the hospital stays to inform the model. Two iterations of the data set included the doses of insulin administered and the past history of inpatient hypoglycemia. Eighteen different prediction models were compared using the area under the receiver operating characteristic curve (AUROC) through a 10-fold cross validation. RESULTS: We analyzed data obtained from 17,658 inpatients with diabetes who underwent 32,758 admissions between July 2014 and August 2018. The predictive factors from the logistic regression model included people undergoing procedures, weight, type of diabetes, oxygen saturation level, use of medications (insulin, sulfonylurea, and metformin), and albumin levels. The machine learning model with the best performance was the XGBoost model (AUROC 0.96). This outperformed the logistic regression model, which had an AUROC of 0.75 for the estimation of the risk of clinically significant hypoglycemia. CONCLUSIONS: Advanced machine learning models are superior to logistic regression models in predicting the risk of hypoglycemia in inpatients with diabetes. Trials of such models should be conducted in real time to evaluate their utility to reduce inpatient hypoglycemia.

Inpatient hypoglycaemia: understanding who is at risk.

AIMS/HYPOTHESIS: We analysed data obtained from the electronic patient records of inpatients with diabetes admitted to a large university hospital to understand the prevalence and distribution of inpatient hypoglycaemia. METHODS: The study was conducted using electronic patient record data from Oxford University Hospitals NHS Foundation Trust. The dataset contains hospital admission data for patients coded for diabetes. We used the recently agreed definition for a level 1 hypoglycaemia episode as any blood glucose measurement <4 mmol/l and a level 2 hypoglycaemia episode as any blood glucose measurement <3 mmol/l. Any two or more consecutive low blood glucose measurements within a 2 h time window were considered as one single hypoglycaemic episode. RESULTS: We analysed data obtained from 17,658 inpatients with diabetes (1696 with type 1 diabetes, 14,006 with type 2 diabetes, and 1956 with other forms of diabetes; 9277 men; mean ± SD age, 66 ± 18 years) who underwent 32,758 hospital admissions between July 2014 and August 2018. The incidence of level 1 hypoglycaemia was 21.5% and the incidence of level 2 hypoglycaemia was 9.6%. Recurrent level 1 and level 2 hypoglycaemia occurred, respectively, in 51% and 39% of hospital admissions in people with type 2 diabetes with at least one hypoglycaemic episode, and in 55% and 45% in those with type 1 diabetes. The incidence of level 2 hypoglycaemia in people with type 2 diabetes, when corrected for the number of people who remained in hospital, remained constant for the first 100 h at approximately 0.15 events per h per admission. With regards to the hypoglycaemia distribution during the day, after correcting for the number of blood glucose tests per h, there were two clear spikes in the rate of hypoglycaemia approximately 3 h after lunch and after dinner. The highest rate of hypoglycaemia per glucose test was seen between 01:00 hours and 05:00 hours. Medication had a significant impact on the incidence of level 2 hypoglycaemia, ranging from 1.5% in people with type 2 diabetes on metformin alone to 33% in people treated with a combination of rapid-acting insulin analogue, long-acting insulin analogue and i.v.-administered insulin. CONCLUSIONS/INTERPRETATION: Retrospective analysis of data from electronic patient records enables clinicians to gain a greater understanding of the incidence and distribution of inpatient hypoglycaemia. This information should be used to drive evidence-based improvements in the glycaemic control of inpatients through targeted medication adjustment for specific populations at high risk of hypoglycaemia.