Diabetes Specialist Research Articles

Examining the impact of Diabetes Inpatient Specialist Nursing in acute trusts in London.

To examine if the introduction of Diabetes Inpatient Specialist Nurses impacted on length of stay and rates of readmission. Knowledge discovery through data mining as part of a larger realist evaluation of the role. Data from January 2017 to January 2019 was extracted and examined. A subset of performance data from July 2017-November 2018 was analysed. This consisted of 7320 records for Hospital Episode Statistics and 272 incident reports (Datix). The data were analysed via Generalised Linear Model regression routines in R. Analysis of readmission rates utilized binary logistic regression, while for the Length of Stay a count regression method was employed. Four trusts were found to have complete and rich data sets. All Trusts that returned complete data were found to have varying decreased length of stay and reduced readmission rates. In two trusts there were significant decreases in patient readmissions and length of stay after the introduction of the Diabetes Inpatient Specialist Nurses. A marked decrease (approximately half) in patient length of stay was found in one London trust after the introduction of the post. Issues with data quality were noted. Reduced patient length of stay and rate of readmission were found since introduction of Diabetes Specialist Nurses. Patient safety data was incomplete and varied significantly between trusts. The project sought to understand the impact of employing Diabetes Inpatient Specialist Nurses in hospitals in London. Overall, the specialist nurses helped reduce length of stay and the rate of readmissions. The research will have an impact on the workforce in diabetes and also people with diabetes who need hospital care.

Glycemic control in non-critically hospitalized patients in tertiary hospitals in northeast Brazil

Objective: Inpatient glycemic control is considered an important component of hospital care and patient safety. Nevertheless, its importance in the hospital setting is often underestimated. We aimed to evaluate glycemic control in non-critically hospitalized patients in the state of Ceará, in Northeast Brazil. Methods: A cross-sectional study was conducted in three tertiary hospitals in the state of Ceará. We evaluated non-critically ill inpatients, aged 18 years or older, who had hyperglycemia during hospitalization (random plasma glucose > 140 mg/dL or self-reported diabetes mellitus [DM]). Results: We evaluated 136 patients (66.2% male, mean age 57.9 ± 16.6 years), 64% of whom had a prior DM diagnosis. Specific nutritional support for DM or hyperglycemia was prescribed for 59.6% of patients. In total, 69.9% of the patients were using insulin. Among them, 47.4% used sliding-scale regular insulin, 18.9% basal-bolus (neutral protamine Hagedorn [NPH] insulin), 28.4% basal-plus (NPH) and 3.1% basal insulin (NPH). The prescriptions given were considered adequate for 26.4% of patients. Of the patients, 69.9% presented blood glucose levels > 180 mg/dL and 29.4% presented levels > 300 mg/dL. Hypoglycemia was observed in 25.7% of patients; protocols for hypoglycemia management were prescribed for 76.5% of them. Education on diabetes during hospitalization was reported by 30.1%. Conclusion: The glycemic control regime followed neither national nor international guidelines. These data suggest a need for teams of diabetes specialists in public hospitals in Ceará, working to improve care and following protocols to guide the safety of hospitalized patients.

1088-P: Can Machine Learning Support Decision for Insulin Initiation in Patients with Type 2 Diabetes?

We examined the ability of machine learning models to predict insulin initiation by specialists and examined whether the machine learning approach could support decision-making by general physicians for insulin initiation in patients with type 2 diabetes. Prescription data from diabetes specialists’ patient registries on antidiabetic agents from 2009 to 2015 were used for 4,860 patients who received initial monotherapy with either insulin (293 patients) or non-insulin (4,567 patients) and had laboratory data. Neural network output was insulin initiation ranging from 0 to 1 with a cutoff >0.5 for the dichotomous classification. Accuracy, recall and area under the receiver operation characteristic curves (AUCs) were calculated to compare the ability of machine-learning decisions on insulin initiation to decisions by logistic regression and 22 general physicians. Then, in comparing the ability of machine-learning and logistic regression decisions to decisions made by general physicians, the gold standard was defined as 80% agreement among 9 diabetes specialists based on patients’ information. AUCs and accuracy according to logistic regression were higher than those of machine learning (AUC of logistic regression, 0.89-0.90; AUC of machine learning, 0.67-0.74). Recall (accuracy of the cases receiving insulin) in machine learning was similar to that of logistic regression analysis (recall of logistic regression, 0.05-0.68; recall of machine learning, 0.11-0.52). The accuracy of logistic regression and the machine learning model was 1.00 and 0.86, respectively, for down sampling ratios of 1:8, which was higher than that of general physicians (0.43) for 7 gold standard cases. In conclusion, machine learning had higher accuracy in prediction of insulin initiation compared with that of general physicians defined by diabetes specialists’ choice of the gold standard. Assistance by machine learning may be beneficial to any general physician in deciding upon insulin initiation. Disclosure K. Fujihara: None. H. Sone: Research Support; Self; Astellas Pharma Inc., Eisai Co., Ltd., Kyowa Kirin Co., Ltd., Novo Nordisk, Ono Pharmaceutical Co., Ltd., Taisho Pharmaceutical Co., Ltd., Takeda Pharmaceutical Co. Funding Japan Society for the Promotion of Science (19H04028)

951-P: Diabetes Management Delivery and Pregnancy Outcomes in Women with Gestational Diabetes Mellitus during the First Wave of the 2020 COVID-19 Pandemic: A Single Reference Centre Report

The COVID-19 pandemic has forced a rapid adaptation of healthcare services to secure medical care for many patients’ groups. This includes women with Gestational diabetes mellitus (GDM). We evaluated the impacts of the first COVID-19 wave on parameters such as the GDM treatment, glycemic control and pregnancy outcomes. In this retrospective study from a reference diabetes centre (Krakow, Poland), we compared patient data from two different time periods: the first wave of COVID-19 pandemic (March 2020 - June 2020) and the preceding five months (October 2019 - February 2020). Data was collected from the medical records and telephone surveys. No patient was diagnosed with concomitant COVID-19. We included 155 women - Group 1 N=73 and Group 2 N= 82 from the COVID-19 pandemic period and non-COVID-19 period, respectively. During the COVID-19 pandemic, almost half of all GDM women (N1=36, 49.3%) used telemedicine as a method of contacting their diabetic specialist while this tool was not utilized in the earlier period. Moreover, these patients reported difficulties in performing blood glucose self-control more often (N1=20, 27.4% vs. N2=7, 8.5% p=0.002) and spent less time on diabetes education and training than the control group on average (N1=39, 53.4% vs. N2=9, 9.8% below 2 hours of training; p≤0.001). Glycemic control parameters were very similar and most analysed pregnancy outcomes occurred with comparable frequencies. Differences were found with respect to the incidence of prolonged labour which was more frequent in the COVID-19 period Group 1 (N1=12, 16.4% vs. N2=3, 3.7% p=0.007) whereas no episodes of pre-eclampsia were observed in this group (N1=0 vs. N2=7, 8.5% p=0.01) during this same time period. We report that the first wave of the COVID-19 pandemic did not seem to have a negative impact on glycemic control and pregnancy outcomes in GDM women, in spite of difficulties in diabetes management delivery. Disclosure M. Wilk: None. P. Surowiec: None. B. Matejko: Other Relationship; Self; Ascensia Diabetes Care, Roche Diabetes Care. K. Cyganek: Advisory Panel; Self; Medtronic, Speaker’s Bureau; Self; Lilly Diabetes, Novo Nordisk. H. Huras: None. M. Malecki: Advisory Panel; Self; Abbott Diabetes, Medtronic, Speaker’s Bureau; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Lilly Diabetes, Merck & Co., Inc., Mundipharma International, Novo Nordisk, Sanofi-Aventis, Servier Laboratories.

595-P: CGM Use in Ambulatory Insulin-Treated Patients following Hospital Discharge

Hospitalization is an opportunity for initiation and reevaluation of diabetes management. Many inpatients are either found to have new hyperglycemia or suboptimal glucose control, necessitating the initiation or titration of insulin. Blood glucose monitoring has traditionally been used with insulin therapy. Continuous glucose monitoring (CGM) of interstitial fluid has gained favor as an effective means to achieve this end. We studied the impact of early institution of CGM in the hospital and continuing post-discharge in insulin-treated patients over a 6-month time period. The initiative was shepherded by a newly-established academic endocrinology practice consisting of a diabetes specialist and a nurse clinician. Focused inpatient diabetes teaching, followed by formal outpatient education delivered by certified educators, was employed. 47 patients (31 males, 16 females; average age 63.3 years) were either begun or progressed to intensive insulin therapy (2-4 daily injections), coupled with CGM use (Freestyle Libre or Dexcom G6) within 2 weeks of discharge. Patients used blood glucose meter readings as needed. Downloaded CGM data with ambulatory glucose profile was reviewed for each patient proactively at least once a month. Patients were advised of changes in insulin/diabetes therapy by phone or electronic portal, and during telehealth and in-person office visits. The average time-in-range (TIR) increased from 59.6% to 60.9% (p= 0.5), while the average hemoglobin A1c decreased from an inpatient value of 9.8% to 7.4% (p<0.05) over a mean period of 6 months. The CGM continuation rate was close to 100%. We conclude that with availability of expertise and clinical support, CGM and its interactive technology offers a unique tool to improve glucose control and empower insulin-treated outpatients in the critical post-hospitalization time. It would be expected to result in better outcomes with regard to complications, readmission rates, and cost of care. Disclosure A. A. Rizvi: None. C. Eberlein: None.

1093-P: Incidence of Primary Nonadherence to Sodium-Glucose Cotransporter 2 Inhibitors (SGLT2i) and Glucagon-Like Peptide-1 Agonists (GLP-1A) in an Integrated Health Care System

Background: The incidence and predictors of primary nonadherence (when a medication is prescribed but not dispensed) for newer glucose lowering medications including SGLT2i and GLP-1a is not known. Methods: Using a database that links electronic health records with commercial claims from an integrated health system in PA (UPMC), we identified patients with T2D newly prescribed an SGLT2i or GLP-1a from 2011-2019. We measured the incidence of 30-day primary nonadherence to both overall and by individual drug. We used multivariable logistic regression to examine patient and provider factors associated with primary nonadherence. Results: Our cohort included 5,505 commercially insured adults (mean age 50[SD11]) with T2D newly prescribed an SGLT2i (n=2,844) or GLP-1a (n=2,661). Overall, 1,867(34%) patients did not fill their first prescription. Among the most commonly prescribed drugs, the incidence of primary nonadherence was lowest for dulaglutide(30%) and highest for canagliflozin(44%). In a multivariable model, age>65 (OR 1.3, 95%CI:1.06-1.61), hypertension (OR 1.18, 95%CI:1.02-1.35), and number of hospitalizations (OR 1.39, 95%CI:1.16-1.65) was associated with an increased odds of primary nonadherence. Female sex (OR 0.88, 95%CI:0.77-0.99), PAD (OR 0.73, 95%CI:0.57-0.93), having a HbA1c value (OR 0.76, 95%CI:0.64-0.89), having a creatinine value (OR 0.82, 95%CI:0.71-0.94), and having an endocrinologist vs. PCP prescribe the index drug (OR 0.77, 95% CI:0.65-0.91) were associated with decreased odds of primary nonadherence. Conclusions: In this integrated health system, 1 out of 3 patients prescribed a SGLT2i and GLP-1 agonist did not fill their prescription within 30 days. Older age and increasing morbidity predicted higher odds while more active engagement with providers, including diabetes specialists, was associated with reduced odds of primary nonadherence. Disclosure J. Luo: Consultant; Self; Alosa Health, Research Support; Self; Health Action International. R. Feldman: None. S. D. Rothenberger: None. M. Fischer: None. M. T. Korytkowski: None. W. F. Gellad: None. Funding National Center for Advancing Translational Sciences (KL2TR001856)

59-OR: The Diabetes Garage: Using Automotive Maintenance Analogies to Engage Mexican-American Men to Participate in a Diabetes Self-Management and Education Program

Mexican-American (MA) men are three times more likely to have diabetes compared to non-Hispanic whites. Despite the availability of diabetes management programs, MA men are underrepresented. The CDC reports that woman (75%) disproportionately participate in diabetes programs compared to men (25%). MA men in particular, have less diabetes knowledge, are less likely to visit a diabetes specialist or adhere to treatments, and are more likely to have complications. Purpose: The Diabetes Garage (DG) is designed to engage MA men in self-management behaviors, improve glycemic and physical health outcomes. Methods: The DG was implemented 2 hours weekly/4 weeks and co-taught by CDCES and automotive technicians. Pre-posttests of self-management behaviors and physical indicators were collected at workshop 1 and at the end of workshop 4. Wilcoxon matched-pairs signed-rank tests were conducted to assess pre-post test differences in behavior (food portion/carbohydrate intake, physical activity) and paired t-tests were conducted to examine differences in health outcomes (i.e., A1c, BP, weight, waist). Results: Data from 2018-2020 (N=74) indicate 77% retention rate. Statistically significant improvements were found in self-reported food portion (Z= -2.030, p <.05) and carbohydrate intake (Z= -2.787, p <.01). Statistically significant improvements were found in systolic BP (t=2.91, mean diff=9.47, p=.01) and improvements in diastolic BP (t=1.41, mean diff=8.18, p =.176). Satisfaction surveys show that men enjoyed the workshops: “The information, the presenters, the diabetes education was totally empowering ⋯ I am looking forward to better managing my diabetes and driving my human-mobile for a long-time...with good quality of life.” Conclusion: Use of automotive maintenance analogies is a feasible approach for engaging men in diabetes management programs, self-management behaviors, and improving health indicators. Disclosure J. B. Concha: None. G. Schober: None. D. N. Portillo: None. R. F. Ruiz: None. R. Ramos: None. R. L. Romero: None. A. Lopez: None. P. Winkler: None. Funding Texas Department of State Health Services

MO651DIABETES CARE IN HAEMODIALYSIS TREATMENT : ADVANCES IN 2020

Abstract Background and Aims Management of diabetes and follow-up of haemodialysis patients are still poorly standardized. The objective of this study is to establish a descriptive inventory of all routine parameters for monitoring diabetes and glycemic control by Continuous Glucose Monitoring (CGM). Method The study recruited 32 diabetic patients in a medical dialysis unit. Glycemic control was assessed using CGM (FreeStyle Free Pro®) over at least 11 days with measurement of: mean blood glucose level, percentage of blood glucose level: < 70 mg/dl (TBR), between 70 and 180 mg/dl (TIR), and > 180 mg/dl (TAR). The results are expressed in average or median [25-75 percentiles]. SPSS® statistical analyses compared extra and per dialytic periods. Results Patients’ mean age was 71±8 years, diabetes duration was 21±6.5 years (87.5% T2DM), mean weight was 77±13kg, HbA1c was 7±0.98%, 98% had a high risk of foot injury (Grade 2 and 3) . Only 46.8% of the patients were followed by a diabetes specialist and 84.3% were treated with insulin and 18.75% received oral medication while only 50% performed self blood glucose-monitoring. The average blood glucose level was 133±13 mg/dl (∼1000 measurements). CGM results for 32 patients over the total period were: TIR at 70% [58-76], TAR at 15% [4.6-29], and TBR at 9.6% [3.7-20].Per-dialytic TIR (84%; [76-93]) was significantly higher (p=0.02) than extra-dialytic TIR (68%; [56-75]). Per-dialytic TAR (5.5%; [0.0-10.7]) was significantly lower (p<0.01) than extra-dialytic TAR (14%; [3.6-29]). TBR did not vary significantly. Conclusion The majority of patients were treated with insulin but only 50% performed self- of blood glucose. Hypoglycaemia was lower during dialysis period. 98% of patients had a high risk of foot injury. CGM could be a usefull tool for the evaluation of glycemic profile of haemodialysis patients, and allows a better adjustment of their treatment.

The Rowan Hillson Inpatient Safety Award 2019 for the best perioperative pathway for people with diabetes

Introduction: The annual National Diabetes Inpatient Audit (NaDIA) in the UK continues to show a high incidence of insulin errors in patients admitted to hospital with diabetes. New initiatives are urgently required to mitigate this risk.Method: The Joint British Diabetes Societies for Inpatient Care (JBDS-IP) organised the sixth national Rowan Hillson Inpatient Safety Award on the theme of the best perioperative pathway for people with diabetes.Result: The winner was the team from Aneurin Bevan University Health Board led by Dr David Burckett-St Laurent for their innovative re-design of the perioperative pathway by a cross-specialty working group. The main elements of the new system were standardised patient assessment, optimisation of perioperative diabetes control, personalised diabetes management plans generated from a drop-down menu with information on usual drug treatment, pre-op/day of surgery diabetes medication modification, advice on hypoglycaemia management and sick-day rules, anaesthetic review for people with HbA1c >69 mmol/mol, secondary care diabetes review for people with suboptimal diabetes control, new inpatient charts with guidance on diabetes drug management, streamlining of time and place of admission and greater engagement and education of people with diabetes and staff looking after them. This resulted in significant improvement in outcomes and reduction in risks.The Newcastle upon Tyne NHS Foundation Trust team led by Dr Nicola Leech and colleagues received the runners-up award for their 3-year quality improvement project involving multiple specialties. The project included development of trust-wide policies and protocols, educational initiatives, targeted diabetes specialist nurse in-reach, innovative electronic whiteboard alerts for glycaemic control and electronic hypo alerts. The result was a reduction in insulin errors and hypoglycaemia on surgical wards by over 50%, a reduction in Datix incidents and fewer patients suffering harm events.Summary and conclusion: These and similar schemes need to be developed, promoted and shared to reduce insulin errors in hospitalised patients with diabetes.

Patient-Tailored Decision Support System Improves Short- and Long-Term Glycemic Control in Type 2 Diabetes.

The increasing prevalence of type 2 diabetes mellitus (T2D) and specialist shortage has caused a healthcare gap that can be bridged by a decision support system (DSS). We investigated whether a diabetes DSS can improve long- and/or short-term glycemic control. This is a retrospective observational cohort study of the Diabetiva program, which offered a patient-tailored DSS using Karlsburger Diabetes-Management System (KADIS) once a year. Glycemic control was analyzed at baseline and after 12 months in 452 individuals with T2D. Time in range (TIR; glucose 3.9-10 mmol/L) and Q-Score, a composite metric developed for analysis of continuous glucose profiles, were short-term and HbA1c long-term measures of glycemic control. Glucose variability (GV) was also measured. At baseline, one-third of patients had good short- and long-term glycemic control. Q-Score identified insufficient short-term glycemic control in 17.9% of patients with HbA1c <6.5%, mainly due to hypoglycemia. GV and hyperglycemia were responsible in patients with HbA1c >7.5% and >8%, respectively. Application of DSS at baseline improved short- and long-term glycemic control, as shown by the reduced Q-Score, GV, and HbA1c after 12 months. Multiple regression demonstrated that the total effect on GV resulted from the single effects of all influential parameters. DSS can improve short- and long-term glycemic control in individuals with T2D without increasing hypoglycemia. The Q-Score allows identification of individuals with insufficient glycemic control. An effective strategy for therapy optimization could be the selection of individuals with T2D most at need using the Q-Score, followed by offering patient-tailored DSS.

Impact of diabetes specialist nurses on inpatient care: A systematic review.

People with diabetes have longer hospital stays and poorer clinical outcomes. Diabetes inpatient specialist nurses have been introduced to improve care. To assess the evidence for the benefit of diabetes specialist nurses in the inpatient setting. A systematic search of MEDLINE (ovid), Embase (ovid), CINAHL (EBSCO) and Web of Science core collection from January 1998 to September 2019 was performed using key terms for diabetes specialist nurses and hospital setting. Studies measuring patient care using any standardised or validated outcome measures after introduction of a dedicated diabetes specialist nurse or nursing team were eligible for inclusion and findings reported by narrative synthesis. There were 10 studies which met the inclusion criteria. One was a randomised controlled study and the remaining nine studies were before and after studies with three of them using a time series analysis methodology. The majority reported length of stay (LOS) and showed a reduction in median LOS by between 0.5 and 3days. Reductions in bed occupancy ranged from 39% to 47%. There was a paucity of evidence for outcomes related to patient care with some measures limited to single studies. These included a 52% reduction in total drug errors, improved patient knowledge, higher patient satisfaction and improved glycaemic control post-discharge. There was no reduction of mortality observed. These studies suggest a reduction in LOS and improved clinical care for patients with diabetes after the introduction of diabetes inpatient specialist nurses. Future research should examine a range of benefits associated with diabetes inpatient specialist nurse delivered services, including reduction of inpatient complications such as infections and cardiovascular events.

Comparative effectiveness of team-based care with a clinical decision support system versus team-based care alone on cardiovascular risk reduction among patients with diabetes: Rationale and design of the D4C trial

Diabetes has become a major public health challenge worldwide, especially in low- and middle-income countries (LMICs). Uncontrolled hyperglycemia, hypertension, and dyslipidemia major risk factors for all-cause mortality and cardiovascular disease (CVD) are common in patients with diabetes in China. We propose to compare the effectiveness of team-based care plus a clinical decision support system (CDSS) with team-based care alone on glycemic, blood pressure (BP), and lipid control, and clinical CVD reduction among patients with type-2 diabetes and at high risk for CVD. The Diabetes Complication Control in Community Clinics (D4C) study is a cluster-randomized trial conducted among 38 community health centers in Xiamen City, China. Nineteen clinics have been randomly assigned to team-based care plus CDSS and 19 to team-based care alone. Team-based care includes primary care providers, health coaches, and diabetes specialists working collaboratively with patients to achieve shared treatment goals for CVD risk factor reduction. The CDSS integrates guideline-based treatment algorithms for glycemic, BP, and lipid control, along with a patient's medical history and insurance policy, to recommend treatment and follow-up plans. In phase 1, the co-primary outcomes are mean reduction in glycated hemoglobin (HbA1c), systolic BP (SBP), and low-density lipoprotein (LDL)-cholesterol over 18 months, and the proportion of patients with controlled HbA1c, SBP, and LDL-cholesterol at 18 months' between the 2 comparison groups. In phase 2, the primary outcome is the difference in major CVD incidence (non-fatal stroke, non-fatal myocardial infarction, hospitalized heart failure, and CVD mortality) between the 2 comparison groups. Mean reduction in HbA1c, SBP, and LDL-cholesterol levels will be simultaneously modeled for a single overall treatment effect. The D4C trial will generate evidence on whether a CDSS will further reduce the CVD burden among patients with diabetes beyond team-based care at community clinics. If proven effective, this implementation strategy could be scaled up within primary care settings in China and other LMICs to reduce CVD incidence and mortality among patients with diabetes.

The Queensland Inpatient Diabetes Survey (QuIDS) 2019: the bedside audit of practice

To assess the quality of care for patients with diabetes in Queensland hospitals, including blood glucose control, rates of hospital-acquired harm, the incidence of insulin prescription and management errors, and appropriate foot and peri-operative care. Cross-sectional audit of 27 public hospitals in Queensland: four of five tertiary/quaternary referral centres, four of seven large regional or outer metropolitan hospitals, seven of 13 smaller outer metropolitan or small regional hospitals, and 12 of 88 hospitals in rural or remote locations. 850 adult inpatients with diabetes mellitus in medical, surgical, mental health, high dependency, or intensive care wards. Twenty-seven of 115 public hospitals that admit acute inpatients participated in the audit, including 4175 of 6652 eligible acute hospital beds in Queensland. A total of 1003 patients had diabetes (24%), and data were collected for 850 (85%). Their mean age was 65.9 years (SD, 15.1 years), 357 were women (42%), and their mean HbA1c level was 66mmol/mol (SD, 26mmol/mol). Rates of good diabetes days (appropriate monitoring, no more than one blood glucose measurement greater than 10mmol/L, and none below 5mmol/L) were low in patients with type 1 diabetes (22.1 per 100 patient-days) or type 2 diabetes treated with insulin (40.1 per 100 patient-days); hypoglycaemia rates were high for patients with type 1 diabetes mellitus (24.1 episodes per 100 patient-days). One or more medication errors were identified for 201 patients (32%), including insulin prescribing errors for 127 patients (39%). Four patients with type 1 diabetes experienced diabetic ketoacidosis in hospital (8%); 121 patients (14%) met the criteria for review by a specialist diabetes team but were not reviewed by any diabetes specialist (medical, nursing, allied health). We identified several deficits in inpatient diabetes management in Queensland, including high rates of medication error and hospital-acquired harm and low rates of appropriate glycaemic control, particularly for patients treated with insulin. These deficits require attention, and ongoing evaluation of outcomes is necessary.

Association of British Clinical Diabetologists (ABCD) and Diabetes UK joint position statement and recommendations for non-diabetes specialists on the use of sodium glucose co-transporter 2 inhibitors in people with type 2 diabetes (January 2021)

Sodium glucose co-transporter 2 (SGLT2) inhibitors are now an established class of medications for the treatment of type 2 diabetes (T2D), no longer reserved for use by specialists in diabetes. They are being used increasingly for their cardiac and renal benefits by primary care, cardiology and renal teams for indications in parallel with diabetes care as part of holistic management. This guidance provides essential information on SGLT therapy, including the main advantages and the important risks of which healthcare professionals should be aware.

Progression to unscheduled hospital admissions in people with diabetes: a qualitative interview study.

BackgroundPeople with diabetes often have difficulty maintaining optimal blood glucose levels, risking progressive complications that can lead to unscheduled care. Unscheduled care can include attending emergency departments, ambulance callouts, out-of-hours care, and non-elective hospital admissions. A large proportion of non-elective hospital admissions involve people with diabetes, with significant health and economic burden.AimTo identify precipitating factors influencing diabetes-related unscheduled hospital admissions, exploring potential preventive strategies to reduce admissions.Design & settingThirty-six people with type 1 (n = 11) or type 2 (n = 25) diabetes were interviewed. They were admitted to hospital for unscheduled diabetes-related care across three hospitals in Scotland, Northern Ireland, and the Republic of Ireland. Participants were admitted for peripheral limb complications (n = 17), hypoglycaemia (n = 5), hyperglycaemia (n = 6), or for comorbidities presenting with erratic blood glucose levels (n = 8).MethodFactors precipitating admissions were examined using framework analysis.ResultsThree aspects of care influenced unscheduled admissions: perceived inadequate knowledge of diabetes complications; restricted provision of care; and complexities in engagement with self-care and help-seeking. Limited specialist professional knowledge of diabetes by staff in primary and community care, alongside inadequate patient self-management knowledge, led to inappropriate treatment and significant delays. This was compounded by restricted provision of care, characterised by poor access to services — in time and proximity — and poor continuity of care. Complexities in patient engagement, help-seeking, and illness beliefs further complicated the progression to unscheduled admissions.ConclusionDedicated investment in primary care is needed to enhance provision of and access to services. There should be increased promotion and earlier diabetes specialist team involvement, alongside training and use of technology and telemedicine, to enhance existing care.

Impact of deprivation on glycaemic control in youth with type 1 diabetes in the southwestern region of France.

The objective of this multicenter cross-sectional study was to determine predictors of poor glycaemic control in children with type 1 diabetes mellitus (T1DM), particularly with respect to socioeconomic status (SES). Our study population consisted of 1154 children who attended T1DM follow-up consultation with a pediatric diabetes specialist. Clinical and demographic data were retrieved retrospectively from patients' records. Individual deprivation was defined by an EPICES (Evaluation of the Deprivation and Inequalities of Health in Healthcare Centers) score ≥ 30. Patients were assigned to quintiles of the European Deprivation Index (EDI) based on their area deprivation scores. We used multivariable linear regression models to detect potential associations between glycaemic control and indicators of low SES. In total, 33% (n=376) of patients had an EPICES score ≥ 30 and 23% (n=268) were in the 5th EDI quintile. Multivariable linear regression analysis showed that poor glycaemic control was associated with both individual (β 0.38; 95%CI 0.26-0.5; p < 0.001) and area deprivation (β 0.26; 95%CI 0.08-0.43; p=0.004). Demographic factors, body mass index (BMI) and insulin regimen were also independently associated with poor glycaemic control (p < 0.001). Interestingly, access to diabetes technologies was not related to SES or either glycaemic control. Low SES is associated with a higher risk of poor glycaemic control, independently of insulin regimen. BMI, age at the time of consultation, duration of diabetes, and insulin regimen. Also have an impact on HbA1c. These parameters need to be considered when developing novel treatment strategies for children with T1DM to better target at-risk patients.

Practical Considerations for Initiating and Utilizing Flash Continuous Glucose Monitoring in Clinical Practice.

Use of continuous glucose monitoring (CGM) has been shown to improve clinical outcomes in patients with type 1 diabetes (T1D) and type 2 diabetes (T2D), including improved glycemic control, better treatment adherence, and an increased understanding of their treatment regimens. Retrospective analysis of CGM data allows clinicians and patients to identify glycemic patterns that support and facilitate informed therapy adjustments. There are currently 2 types of CGM systems: real-time CGM (rtCGM) and flash CGM. The FreeStyle Libre 2 (FSL2) is the newest flash CGM system commercially available. Because the FSL2 system was only recently cleared for use in the US, many endocrinologists and diabetes specialists may be unfamiliar with the strengths, limitations, and potential of the FSL2 system. This article focuses on practical approaches and strategies for initiating and using flash CGM in endocrinology and diabetes specialty practices.

POS-353 DIABETES CARE IN HEMODIALYSIS PATIENTS: ADVANCES MADE IN 2020

Management of diabetes and follow-up of haemodialysis patients are still poorly standardized. The objective of this study is to establish a descriptive inventory of all routine parameters for monitoring diabetes and glycemic control by Continuous Glucose Monitoring (CGM). The study recruited 60 diabetic patients in a medical dialysis unit. Glycemic control was assessed using the MCG (FreeStyle Free Pro®) over at least 11 days with measurement of: mean blood glucose level, percentage of blood glucose level: < 0.70g/dl TBR, between 0.70 and 1.80g/dl TIR, and > 1.80 g/dl TAR. The results are expressed in average or median [25-75 percentiles]. SPSS® statistical analyses compared extra and per dialytic periods. Patients’ mean age was 70±11 years, history of diabetes was 22±9 years (93.5% T2DM), mean weight was 78±16kg, HbA1c is 6.9±1.2%, 98% had a high risk of foot injury. Only 43% of the patients were followed by a diabetes specialist and 50% performed self-monitoring of blood glucose levels. 73% were insulin-treated and 19.5% received oral medication. The average blood glucose level was 145.8±12.8 mg/l (~1000 measurements). CGM results for the first 21 patients over the total period were: TIR at 66% [52-73], TAR at 23.2% [13-38], and TBR at 5.4% [0.3-17]. Per-dialytic TIR (80.2%; [75-89]) was significantly higher (p=0.02) than extra-dialytic TIR (65.2%; [52-72]). Per-dialytic TAR (4%; [0-14]) was significantly lower (p<.001) than extra-dialytic TAR (23.8%; [10-37]). TBR did not vary significantly (p=.781). The majority of patients were insulin-treated but only 50% performed self-monitoring of blood glucose levels. Hypoglycaemia was low in dialysis. 98% of patients had a high risk of foot injury. CGM could be a valuable tool in the evaluation of the glycemic profile of haemodialysis patients, the detection of asymptomatic hypoglycemia and thus allow for better adjustment of their treatment.

Clinical care and other categories posters: Diabetes specialist nurse

Clinical care and other categories posters: Diabetes specialist nurse

The "Early Treatment" Approach Reducing Cardiovascular Risk in Patients with Type2 Diabetes: A Consensus From an Expert Panel Using the Delphi Technique.

IntroductionThere is no consensus on the optimal therapeutic approach to adopt in patients with newly diagnosed type 2 diabetes mellitus (T2DM) to prevent cardiovascular disease (CVD). The study aimed to gather an expert consensus on the hypoglycemic treatment and CV risk management in patients with newly diagnosed T2DM through the Delphi methodology.MethodsTo address this issue, a list of 30 statements concerning the definition of “early T2DM patient”, early treatment, CV risk in T2DM, treat-to-benefit approach, and indications for treatment with glucagon-like peptide 1 receptor agonists (GLP-1RAs) and sodium-glucose co-transporter 2 (SGLT2) inhibitors was developed. Using a two-round Delphi methodology, the survey was distributed to 80 Italian diabetes specialists who rated their level of agreement with each statement on a 5-point Likert scale. Consensus was predefined as more than 66% of the panel agreeing/disagreeing with any given statement.ResultsA total of 27/30 statements achieved consensus. A patient was defined as “early” according to pathophysiological or clinical interpretation, and/or the timing of the diagnosis. There was agreement on the importance to reach the lowest possible HbA1c level, since diagnosis, also using combination therapy with hypoglycemic drugs with a proven CV benefit. There was a consensus that a treat-to-benefit approach involves the addition of a glucose-lowering agent with proven CV benefits to metformin since diagnosis. The use of GLP-1RAs and SGLT2 inhibitors was considered a key strategy in this approach and the benefits were recognized also for patients with T2DM without established CVD. GLP-1RAs should be used at an earlier stage than SGLT2 inhibitors to prevent CVD, especially in patients with evidence of subclinical atherosclerotic disease.ConclusionThis Delphi consensus recognized the importance to adopt a tailored hypoglycemic treatment of patients with T2DM according to their CVD risk and the key role of glucose-lowering agents with proven CV efficacy, GLP-1RAs and SGLT2 inhibitors, in the context of an early treat-to-benefit approach.Supplementary InformationThe online version contains supplementary material available at 10.1007/s13300-021-01045-7.