High Risk Of Severe Hypoglycemia Research Articles

Advances in Nanomedicine for Precision Insulin Delivery.

Diabetes mellitus, which comprises a group of metabolic disorders affecting carbohydrate metabolism, is characterized by improper glucose utilization and excessive production, leading to hyperglycemia. The global prevalence of diabetes is rising, with projections indicating it will affect 783.2 million people by 2045. Insulin treatment is crucial, especially for type 1 diabetes, due to the lack of β-cell function. Intensive insulin therapy, involving multiple daily injections or continuous subcutaneous insulin infusion, has proven effective in reducing microvascular complications but poses a higher risk of severe hypoglycemia. Recent advancements in insulin formulations and delivery methods, such as ultra-rapid-acting analogs and inhaled insulin, offer potential benefits in terms of reducing hypoglycemia and improving glycemic control. However, the traditional subcutaneous injection method has drawbacks, including patient compliance issues and associated complications. Nanomedicine presents innovative solutions to these challenges, offering promising avenues for overcoming current drug limitations, enhancing cellular uptake, and improving pharmacokinetics and pharmacodynamics. Various nanocarriers, including liposomes, chitosan, and PLGA, provide protection against enzymatic degradation, improving drug stability and controlled release. These nanocarriers offer unique advantages, ranging from enhanced bioavailability and sustained release to specific targeting capabilities. While oral insulin delivery is being explored for better patient adherence and cost-effectiveness, other nanomedicine-based methods also show promise in improving delivery efficiency and patient outcomes. Safety concerns, including potential toxicity and immunogenicity issues, must be addressed, with the FDA providing guidance for the safe development of nanotechnology-based products. Future directions in nanomedicine will focus on creating next-generation nanocarriers with precise targeting, real-time monitoring, and stimuli-responsive features to optimize diabetes treatment outcomes and patient safety. This review delves into the current state of nanomedicine for insulin delivery, examining various types of nanocarriers and their mechanisms of action, and discussing the challenges and future directions in developing safe and effective nanomedicine-based therapies for diabetes management.

Impacts of medication adherence and home healthcare on the associations between polypharmacy and the risk of severe hypoglycemia among elderly diabetic patients in Taiwan from 2002 to 2012: A nationwide case-crossover study

AimTo assess how medication adherence and home healthcare support influence the role of polypharmacy in induced hypoglycemia events among elderly diabetic patients. MethodsThis case-crossover study retrieved records on diabetic patients >=65 years with severe hypoglycemia from 2002 to 2012 in Taiwan. Case period defined as 1–3 days before severe hypoglycemia was compared with a preceding control period of the same length, with an all-washout period of 30 days. Moreover, the modifiable effects of medication adherence and home healthcare service use were evaluated by stratified analysis. ResultsTotally 2,237 patients were identified. Polypharmacy use was associated with the risk of severe hypoglycemia. Patients receiving polypharmacy without home healthcare services (aOR: 1.34; 95 % CI: 1.16–1.54) and those with poor adherence to anti-diabetic medications (aOR: 1.48; 95 % CI: 1.24–1.77) were significantly associated with an elevated risk of severe hypoglycemia. In patients with good adherence, non-home healthcare users being prescribed with polypharmacy had a higher risk of severe hypoglycemia. In the group that received home healthcare services, patients with poor adherence using polypharmacy had a higher risk of severe hypoglycemia. ConclusionsGood adherence and receiving home healthcare services were associated with a decreased odds of severe hypoglycemic events in elderly diabetic patients, regardless of the fact whether they were prescribed with polypharmacy.

Intensive or liberal glucose control in intensive care units for septic patients? A meta-analysis of randomized controlled trials

ObjectiveTo compare the clinical outcomes of intensive glucose control and liberal glucose control for septic patients in intensive care unit. MethodsThe databases of PubMed, Cochrane Library, Embase and Web of Science were searched systematically from inception to November 27, 2023 to identify trials involving a randomized comparison between intensive and liberal glucose control for septic patients in intensive care unit. ResultsA total of 14 randomized controlled trials involving 6226 patients were finally included. There was no statistically significant difference observed between intensive glucose control and liberal glucose control in terms of all-cause mortality, the need for renal replacement, vasopressor-free and mechanical ventilation-free days, and length of hospital stay. However, it is noteworthy that intensive glucose control exhibited a statistically higher risk of severe hypoglycemia (RR 2.66; 95%CI 1.85 to 3.83), need for blood transfusion (RR 1.12; 95%CI 1.01 to 1.23), and statistically prolonged length of stay in the ICU (MD 1.67; 95%CI 0.22 to 3.12) compared to liberal glucose control. Nevertheless, sensitivity analysis revealed that the need for blood transfusion and length of stay in the intensive care unit were not robust. ConclusionsBoth intensive and liberal glucose control had comparable effects on improving patient outcomes, but intensive glucose control carried a higher risk of severe hypoglycemia.

A novel electronic health record-based, machine-learning model to predict severe hypoglycemia leading to hospitalizations in older adults with diabetes: A territory-wide cohort and modeling study.

Older adults with diabetes are at high risk of severe hypoglycemia (SH). Many machine-learning (ML) models predict short-term hypoglycemia are not specific for older adults and show poor precision-recall. We aimed to develop a multidimensional, electronic health record (EHR)-based ML model to predict one-year risk of SH requiring hospitalization in older adults with diabetes. We adopted a case-control design for a retrospective territory-wide cohort of 1,456,618 records from 364,863 unique older adults (age ≥65 years) with diabetes and at least 1 Hong Kong Hospital Authority attendance from 2013 to 2018. We used 258 predictors including demographics, admissions, diagnoses, medications, and routine laboratory tests in a one-year period to predict SH events requiring hospitalization in the following 12 months. The cohort was randomly split into training, testing, and internal validation sets in a 7:2:1 ratio. Six ML algorithms were evaluated including logistic-regression, random forest, gradient boost machine, deep neural network (DNN), XGBoost, and Rulefit. We tested our model in a temporal validation cohort in the Hong Kong Diabetes Register with predictors defined in 2018 and outcome events defined in 2019. Predictive performance was assessed using area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC) statistics, and positive predictive value (PPV). We identified 11,128 SH events requiring hospitalization during the observation periods. The XGBoost model yielded the best performance (AUROC = 0.978 [95% CI 0.972 to 0.984]; AUPRC = 0.670 [95% CI 0.652 to 0.688]; PPV = 0.721 [95% CI 0.703 to 0.739]). This was superior to an 11-variable conventional logistic-regression model comprised of age, sex, history of SH, hypertension, blood glucose, kidney function measurements, and use of oral glucose-lowering drugs (GLDs) (AUROC = 0.906; AUPRC = 0.085; PPV = 0.468). Top impactful predictors included non-use of lipid-regulating drugs, in-patient admission, urgent emergency triage, insulin use, and history of SH. External validation in the HKDR cohort yielded AUROC of 0.856 [95% CI 0.838 to 0.873]. Main limitations of this study included limited transportability of the model and lack of geographically independent validation. Our novel-ML model demonstrated good discrimination and high precision in predicting one-year risk of SH requiring hospitalization. This may be integrated into EHR decision support systems for preemptive intervention in older adults at highest risk.

Innovations in bio-engineering and cell-based approaches to address immunological challenges in islet transplantation.

Human allogeneic pancreatic islet transplantation is a life-changing treatment for patients with severe Type 1 Diabetes (T1D) who suffer from hypoglycemia unawareness and high risk of severe hypoglycemia. However, intensive immunosuppression is required to prevent immune rejection of the graft, that may in turn lead to undesirable side effects such as toxicity to the islet cells, kidney toxicity, occurrence of opportunistic infections, and malignancies. The shortage of cadaveric human islet donors further limits islet transplantation as a treatment option for widespread adoption. Alternatively, porcine islets have been considered as another source of insulin-secreting cells for transplantation in T1D patients, though xeno-transplants raise concerns over the risk of endogenous retrovirus transmission and immunological incompatibility. As a result, technological advancements have been made to protect transplanted islets from immune rejection and inflammation, ideally in the absence of chronic immunosuppression, to improve the outcomes and accessibility of allogeneic islet cell replacement therapies. These include the use of microencapsulation or macroencapsulation devices designed to provide an immunoprotective environment using a cell-impermeable layer, preventing immune cell attack of the transplanted cells. Other up and coming advancements are based on the use of stem cells as the starting source material for generating islet cells 'on-demand'. These starting stem cell sources include human induced pluripotent stem cells (hiPSCs) that have been genetically engineered to avoid the host immune response, curated HLA-selected donor hiPSCs that can be matched with recipients within a given population, and multipotent stem cells with natural immune privilege properties. These strategies are developed to provide an immune-evasive cell resource for allogeneic cell therapy. This review will summarize the immunological challenges facing islet transplantation and highlight recent bio-engineering and cell-based approaches aimed at avoiding immune rejection, to improve the accessibility of islet cell therapy and enhance treatment outcomes. Better understanding of the different approaches and their limitations can guide future research endeavors towards developing more comprehensive and targeted strategies for creating a more tolerogenic microenvironment, and improve the effectiveness and sustainability of islet transplantation to benefit more patients.

Glucagon prescribing and prevention of hospitalization for hypoglycemia in a large health system

AimsTo examine glucagon prescribing trends among patients at high risk of severe hypoglycemia and assess if a glucagon prescription is associated with lower rates of severe hypoglycemia requiring hospital care. MethodsRetrospective analysis of electronic health records from a large integrated healthcare system between May 2019 and August 2021. We included adults (≥18 years) with type 1 diabetes or with type 2 diabetes treated with short-acting insulin and/or recent history of hypoglycemia-related emergency department visit or hospitalization. We calculated rates of glucagon prescribing overall and by patient characteristics. We then matched 1:1 those who were and were not prescribed glucagon and assessed subsequent hypoglycemia-related hospitalization. ResultsOf 9,200 high risk adults, 2063 (22.4%) were prescribed glucagon. Among patients more likely to be prescribed glucagon were those younger, female, White, living in urban areas, with prior severe hypoglycemia, and with a recent endocrinology specialist visit. In the matched cohort (N = 1707 per arm), 62 prescribed glucagon and 33 not prescribed glucagon were hospitalized for hypoglycemia (adjusted incidence rate ratio 1.71, 95% CI 1.10–2.66; P = 0.018). ConclusionGlucagon prescribing was infrequent with significant racial and rural disparities. Patients with glucagon prescriptions did not have lower rates of hospitalization for hypoglycemia.

1022-P: Trends in Glucagon Fill Rates among U.S. Adults with Diabetes and End-Stage Kidney Disease on Dialysis

Patients with diabetes (DM) and end-stage kidney disease (ESKD) on dialysis have a high risk of severe hypoglycemia and more than 70% are treated with insulin therapy. While not all hypoglycemic events can be prevented, glucagon is recommended for patients at high risk for hypoglycemia to revert and prevent hospitalization with severe hypoglycemic events. Prior studies have shown low rates of glucagon utilization, but excluded patients with ESKD who are at highest risk for these events. We used the USRDS, the most comprehensive nationwide database of patients with ESKD in the US, to examine the quarterly rates of glucagon fills among U.S. adults with DM and ESKD receiving dialysis. Among 254,195 adults (≥18 years) with ESKD and type 1 DM (N=27,538) or type 2 DM (N=226,657), with 76.9% using insulin therapy, between 2013-2017. Fewer than 2% of patients with type 2 DM and fewer than 4% of patients with type 1 DM filled a glucagon prescription, with declining fill rates over time among patients with type 2 DM and stable rates among patients with type 1 DM. Clinical Characteristics and trends in glucagon prescriptions are shown in Table 1. Our findings suggest an urgent and critical need for education of clinicians, patients, and caregivers to improve the prescribing and use of glucagon: a life-saving rescue medication. Disclosure R.J.Galindo: Consultant; Novo Nordisk, Eli Lilly and Company, Sanofi, Pfizer Inc., Bayer Inc., WW (Weight Watchers), Research Support; Novo Nordisk, Eli Lilly and Company, Dexcom, Inc. S.Inselman: None. B.Moazzami: None. M.K.Ali: Advisory Panel; Bayer Inc., Eli Lilly and Company, Research Support; Merck & Co., Inc. G.Umpierrez: Research Support; Abbott, Dexcom, Inc., Baxter. R.G.Mccoy: Consultant; Emmi.

Impact of Race and Ethnicity on Severe Hypoglycemia Associated with Sulfonylurea Use for Type 2 Diabetes among Veterans.

Sulfonylureas are associated with hypoglycemia. Whether a racial/ethnic disparity in this safety outcome exists is unknown. We sought to assess the impact of race/ethnicity on severe hypoglycemia associated with sulfonylurea use for type 2 diabetes (T2D). Using Veterans Affairs and Medicare data, Veterans initially receiving metformin monotherapy for T2D between 2004 and 2006 were identified. Sulfonylurea use (either alone or via the addition of a prescription for a sulfonylurea to metformin) was captured and compared to remaining on metformin alone during the follow-up period (2007-2016). Hazard ratios (HR) and 95% confidence intervals (CI) from longitudinal competing risk Cox models were used to measure the association between sulfonylurea use and severe hypoglycemia defined as hospitalization for hypoglycemia. A total of 113,668 Veterans with T2D were included. A higher risk of severe hypoglycemia was associated with the receipt of sulfonylurea prescriptions versus remaining on metformin alone across all groups. The effect was largest among Hispanic Veterans (HR: 7.59, 95%CI:4.32-13.33), followed by Veterans in the other race/ethnicity cohort (HR: 4.57, 95%CI:2.50-8.36) and Non-Hispanic Black Veterans (HR: 3.67, 95%CI:2.78-4.85). The effect was smallest among Non-Hispanic White Veterans (HR: 3.11, 95%CI:2.77-3.48). In conclusion, a higher risk of severe hypoglycemia associated with sulfonylurea prescriptions was observed across all analyses. The relationship was most pronounced for Hispanic Veterans, who had nearly 8 times the risk of severe hypoglycemia with sulfonylureas versus remaining on metformin alone.

Glucagon Prescribing and Costs Among U.S. Adults With Diabetes, 2011-2021.

To characterize contemporary trends in glucagon fill rates and expenditures in a nationwide cohort of adults with diabetes overall and by key demographic and clinical characteristics. In this retrospective cohort study, we examined 1) glucagon fill rates per 1,000 person-years and 2) patient out-of-pocket and health plan costs per filled glucagon dose among adults with diabetes included in OptumLabs Data Warehouse between 1 January 2011 and 31 March 2021. The study population comprised 2,814,464 adults with diabetes with a mean age of 62.8 (SD 13.2) years. The overall glucagon fill rate decreased from 2.91 to 2.28 per 1,000 person-years (-22%) over the study period. In groups at high risk for severe hypoglycemia, glucagon fill rates increased from 22.46 to 36.76 per 1,000 person-years (64%) among patients with type 1 diabetes, 11.64 to 16.63 per 1,000 person-years (43%) among those treated with short-acting insulin, and 16.08 to 20.12 per 1,000 person-years (25%) among those with a history of severe hypoglycemia. White patients, women, individuals with high income, and commercially insured patients had higher glucagon fill rates compared with minority patients, males, individuals with low income, and Medicare Advantage patients, respectively. Total cost per dosing unit increased from $157.97 to $275.32 (74%) among commercial insurance beneficiaries and from $150.37 to $293.57 (95%) among Medicare Advantage beneficiaries. Glucagon fill rates are concerningly low and declined between 2011 and 2021 but increased in appropriate subgroups with type 1 diabetes, using short-acting insulin, or with a history of severe hypoglycemia. Fill rates were disproportionately low among minority patients and individuals with low income.

Higher risk of severe hypoglycemia in children and adolescents with a rapid loss of C-peptide during the first 6 years after type 1 diabetes diagnosis

IntroductionThe progression to insulin deficiency in type 1 diabetes is heterogenous. This study aimed to identify early characteristics associated with rapid or slow decline of beta-cell function and how it...

Quali target glicemici nelle donne con diabete gestazionale?

For glucose-lowering drugs inducing hypoglycemia, a marginally protective effect on the risk of MACE was observed for HbA1c 48-58 mmol/mol, whereas a significant reduction of microvascular complications was observed for HbA1c<49 mmol/mol, but with higher risk of severe hypoglycaemia. Drugs not inducing hypoglycaemia were associated with a reduction of MACE, renal adverse events, and all-cause mortality, for HbA1c<7% (no data for lower targets). Conclusions: the present paper illustrates the recommendations of the Italian guidelines for the treatment of type 2 diabetes on therapeutic targets for HbA1c. In synthesis, the improvement of glycemic control with drugs not inducing hypoglycemia is associated with a reduction in the risk of long-term chronic vascular and renal complications, and all-cause mortality suggesting an HbA1c target of 53 mmol/mol. When the reduction of HbA1c is achieved with drugs inducing hypoglycemia, a progressive reduction of complications and an increase in the risk of severe hypoglycemia is observed suggesting higher HbA1c thresholds (49-58 mmol/mol).

Dasiglucagon demonstrates reduced costs in the treatment of severe hypoglycemia in a budget impact model.

BACKGROUND: Approximately 7.3 million people with type 1 or type 2 diabetes (T1D/T2D) are treated with insulin, placing them at higher risk of severe hypoglycemia (SH). SH requires assistance of another individual and often necessitates the prompt administration of intravenous glucose, injectable glucagon, or both. Untreated, SH can progress to unconsciousness, seizures, coma, or death. Before 2018, all glucagon rescue treatments required reconstitution. The complexity of reconstitution is often a barrier to successful administration during a severe hypoglycemic event. Studies suggest successful administration of glucagon emergency kits range from 6%-56% of the time. Second-generation glucagon treatments and glucagon analogs do not require reconstitution and have caregiver administration success rates ranging from 94%-100%. Dasiglucagon is a glucagon analog administered via autoinjector or prefilled syringe and has been shown to result in rapid hypoglycemia recovery. Moreover, the autoinjector can be administered successfully 94% of the time by trained caregivers. Previous evaluation of costs in budget impact models (BIMs) demonstrated the potential for second-generation glucagon treatments to reduce the cost of SH events (SHEs). The current model expands on those findings with a treatment pathway and accompanying assumptions reflecting important aspects of real-world SHE treatment. OBJECTIVE: To evaluate the economic impact of dasiglucagon compared with available glucagon treatments for SHE management, considering direct cost of treatment and health care resource utilization. METHODS: A 1-year BIM with a hypothetical US commercial health plan of 1 million lives was developed with a target population of individuals with diabetes at risk of SHE. The treatment pathway model included initial and secondary treatment attempts, treatment administration success and failure, plasma glucose (PG) recovery within 15 minutes, emergency medical services, emergency department (ED) visits, and hospitalizations. A 1-way sensitivity analysis was conducted to assess the sensitivity of the model to changes in parameter values. RESULTS: In a 1 million-covered lives population, it was estimated that 12,006 SHEs would occur annually. The higher rate of initial treatment success and PG recovery within 15 minutes associated with dasiglucagon treatment resulted in lower total health care costs. Total SHE treatment costs with dasiglucagon were estimated at $13.4 million, compared with $16.7 million for injectable native glucagon, $20.7 million for nasal glucagon, $35.3 million for reconstituted glucagon, and $43.8 million for untreated individuals. Compared with untreated people, the number needed to treat (NNT) with dasiglucagon was 6 individuals to avoid 1 hospitalization. NNT for this same comparison was 59 for injectable native glucagon and 27 for nasal glucagon. CONCLUSIONS: Treatment of SH with dasiglucagon decreased total direct medical costs by reducing health care resource utilization (emergency calls, emergency transports, ED visits, and hospitalizations) and accompanying costs associated with the treatment of SH. DISCLOSURES: This research was funded by Zealand Pharma. Bromley, Hinahara, and Goss are employed by Boston Healthcare Associates, Inc., which received funding from Zealand Pharma for development of the health economic model and the manuscript. Kendall and Hammer are employed by Zealand Pharma. Weinzimer has received consulting fees from Zealand Pharma.

Analysis of Severe Hypoglycemia Among Adults With Type 2 Diabetes and Nonalcoholic Fatty Liver Disease

Previous studies have indicated that liver cirrhosis is associated with hypoglycemia, but there have been no studies investigating the association between nonalcoholic fatty liver disease (NAFLD) and hypoglycemia in noncirrhotic populations with type 2 diabetes. To explore the association of NAFLD with severe hypoglycemia among patients with type 2 diabetes. This nationwide population-based retrospective cohort study using the National Health Insurance System of South Korea included individuals aged 20 years or older who had undergone a medical health examination between January 1, 2009, and December 31, 2012, and were diagnosed with type 2 diabetes. Participants were followed up until December 31, 2015. Data analyses were performed between January 1, 2019, and February 2, 2021. The baseline fatty liver index (FLI) was used as a surrogate marker for NAFLD. The outcome of interest, severe hypoglycemia, was measured using hospital admission and emergency department visit records with a primary diagnosis of hypoglycemia. Among 1 946 581 individuals with type 2 diabetes, 1 125 187 (57.8%) were male. During a median (IQR) follow-up of 5.2 (4.1-6.1) years, 45 135 (2.3%) experienced 1 or more severe hypoglycemia events. Participants with severe hypoglycemia, vs those without severe hypoglycemia, were older (mean [SD] age, 67.9 [9.9] years vs 57.2 [12.3] years; P < .001) and had lower mean (SD) body mass index (24.2 [3.43] vs 25.1 [3.4]; P < .001). Patients with NAFLD tended to have less severe hypoglycemia without consideration of obesity status. However, after adjustment of multiple clinical covariates, including body mass index, there was a J-shaped association between FLI and severe hypoglycemia (5th decile: adjusted hazard ratio [aHR], 0.86; 95% CI, 0.83-0.90; 9th decile: aHR, 1.02; 95% CI, 0.96-1.08; 10th decile: aHR, 1.29; 95% CI, 1.22-1.37), and the estimated risk of hypoglycemia was higher in participants with NAFLD (aHR, 1.26; 95% CI, 1.22-1.30). The association was more prominent in female participants (aHR, 1.29; 95% CI, 1.23-1.36) and those with underweight (aHR, 1.71; 95% CI, 1.02-2.88). In this study, NAFLD was associated with a higher risk of severe hypoglycemia in patients with type 2 diabetes independent of obesity status. Presence of NAFLD should be considered when evaluating vulnerability to hypoglycemia in patients with type 2 diabetes.

A higher non-severe hypoglycaemia rate is associated with an increased risk of subsequent severe hypoglycaemia and major adverse cardiovascular events in individuals with type 2 diabetes in the LEADER study

Aims/hypothesisHypoglycaemia is a common side effect of insulin and some other antihyperglycaemic agents used to treat diabetes. Severe hypoglycaemia has been associated with adverse cardiovascular events in trials of intensive glycaemic control in type 2 diabetes. The relationship between non-severe hypoglycaemic episodes (NSHEs) and severe hypoglycaemia in type 2 diabetes has been documented. However, an association between more frequent NSHEs and cardiovascular events has not been verified. This post hoc analysis of the LEADER (Liraglutide Effect and Action in Diabetes: Evaluation of Cardiovascular Outcome Results) trial aimed to confirm whether there is an association between NSHEs and severe hypoglycaemic episodes in individuals with type 2 diabetes. In addition, the possible association between NSHEs and major adverse cardiac events (MACE), cardiovascular death and all-cause mortality was investigated.MethodsLEADER was a double-blind, multicentre, placebo-controlled trial that found that liraglutide significantly reduced the risk of MACE compared with the placebo. In this post hoc analysis, we explored, in all LEADER participants, whether the annual rate of NSHEs (defined as self-measured plasma glucose <3.1 mmol/l [56 mg/dl]) was associated with time to first severe hypoglycaemic episode (defined as an episode requiring the assistance of another person), time to first MACE, time to cardiovascular death and time to all-cause mortality. Participants with <2 NSHEs per year were used as reference for HR estimates. Cox regression with a time-varying covariate was used.ResultsWe demonstrate that there is an association between NSHEs (2–11 NSHEs per year and ≥12 NSHEs per year) and severe hypoglycaemic episodes (unadjusted HRs 1.98 [95% CI 1.43, 2.75] and 5.01 [95% CI 2.84, 8.84], respectively), which was consistent when baseline characteristics were accounted for. Additionally, while no association was found between participants with 2–11 NSHEs per year and adverse cardiovascular outcomes, higher rates of NSHEs (≥12 episodes per year) were associated with higher risk of MACE (HR 1.50 [95% CI 1.01, 2.23]), cardiovascular death (HR 2.08 [95% CI 1.17, 3.70]) and overall death (HR 1.80 [95% CI 1.11, 2.92]).Conclusions/interpretationThe analysis of data from the LEADER trial demonstrated that higher rates of NSHEs were associated with both a higher risk of severe hypoglycaemia and adverse cardiovascular outcomes in individuals with type 2 diabetes. Therefore, irrespective of the cause of this association, it is important that individuals with high rates of hypoglycaemia are identified so that the potentially increased risk of cardiovascular events can be managed and steps can be taken to reduce NSHEs.Trial registrationClinicalTrials.gov (NCT01179048).Graphical abstract

Intensification of Diabetes Medications at Hospital Discharge and Clinical Outcomes in Older Adults in the Veterans Administration Health System

Transient elevations of blood glucose levels are common in hospitalized older adults with diabetes and may lead clinicians to discharge patients with more intensive diabetes medications than they were using before hospitalization. To investigate outcomes associated with intensification of outpatient diabetes medications at discharge. This retrospective cohort study assessed patients 65 years and older with diabetes not taking insulin who were hospitalized in the Veterans Health Administration Health System between January 1, 2011, and September 28, 2016, for common medical conditions. Data analysis was performed from January 1, 2020, to March 31, 2021. Discharge with intensified diabetes medications, defined as filling a prescription at hospital discharge for a new or higher-dose medication than was being used before hospitalization. Propensity scores were used to construct a matched cohort of patients who did and did not receive diabetes medication intensifications. Coprimary outcomes of severe hypoglycemia and severe hyperglycemia were assessed at 30 and 365 days using competing risk regressions. Secondary outcomes included all-cause readmissions, mortality, change in hemoglobin A1c (HbA1c) level, and persistent use of intensified medications at 1 year after discharge. The propensity-matched cohort included 5296 older adults with diabetes (mean [SD] age, 73.7 [7.7] years; 5212 [98.4%] male; and 867 [16.4%] Black, 47 [0.9%] Hispanic, 4138 [78.1%] White), equally split between those who did and did not receive diabetes medication intensifications at hospital discharge. Within 30 days, patients who received medication intensifications had a higher risk of severe hypoglycemia (hazard ratio [HR], 2.17; 95% CI, 1.10-4.28), no difference in risk of severe hyperglycemia (HR, 1.00; 95% CI, 0.33-3.08), and a lower risk of death (HR, 0.55; 95% CI, 0.33-0.92). At 1 year, no differences were found in the risk of severe hypoglycemia events, severe hyperglycemia events, or death and no difference in change in HbA1c level was found among those who did vs did not receive intensifications (mean postdischarge HbA1c, 7.72% vs 7.70%; difference-in-differences, 0.02%; 95% CI, -0.12% to 0.16%). At 1 year, 48.0% (591 of 1231) of new oral diabetes medications and 38.5% (548 of 1423) of new insulin prescriptions filled at discharge were no longer being filled. In this national cohort study, among older adults hospitalized for common medical conditions, discharge with intensified diabetes medications was associated with an increased short-term risk of severe hypoglycemia events but was not associated with reduced severe hyperglycemia events or improve HbA1c control. These findings indicate that short-term hospitalization may not be an effective time to intervene in long-term diabetes management.

354-P: Interstitial Fluid Glucose as a Measure of Hypoglycemia Burden in Elderly Subjects with Type 2 Diabetes Mellitus

Background: Elderly patients with type 2 diabetes mellitus (T2DM) are at high risk of severe hypoglycemia, but evidence addressing this problem is lacking. Aims: To assess and compare the burden of exposure to low interstitial fluid glucose (IFG) in elderly subjects with tightly controlled T2DM on sulphonylurea (SU) or insulin therapy. Methods: Outpatient T2DM subjects ≥65 years of age with HbA1c ≤7.5% were recruited to wear a Freestyle Libre-Pro sensor for 2 weeks. Collected data was analyzed for low IFG burden. Results: A total of 65 subjects (35 male, 30 female), 72.0 ± 5.0 years old with diabetes duration of 21.4 ± 11.1 years and HbA1c of 6.8 ± 0.5% were recruited; 27 were insulin treated and 38 were SU treated. Patients on insulin had longer diabetes duration (25.4 ± 10.9 vs. 18.0 ± 10.4 years, p&amp;lt;0.05) and poorer kidney function (eGFR 58.6 ± 24.7 vs. 87.3 ± 48.0, p&amp;lt;0.05). Greater proportion of the insulin group performed regular capillary blood glucose (CBG) monitoring (74.1% vs. 29.7%). This group also had more monthly self reported hypoglycemic events (0.85 ± 1.70 vs. 0.14 ± 0.35, p&amp;lt;0.05) and a lower GOLD score (2.42 ± 1.72 vs. 3.73 ± 1.75, p&amp;lt;0.05). While there was no difference in mean number of low IFG events with glucose &amp;lt;72.0mg/dL between the 2 groups (0.92 ± 0.79 vs. 0.59 ± 0.48 events, p value &amp;gt;0.05), very low IFG events with glucose &amp;lt;50.0mg/dL occurred in higher proportions (52% vs. 23%, p&amp;lt;0.05), higher frequency (0.48 ± 0.70 vs. 0.16 ± 0.19 events/person, p &amp;lt;0.05) and for longer durations (1.56 ± 2.94 vs. 0.27 ± 0.43 hours/day, p&amp;lt;0.05) in the insulin group. 62.2% of the SU group and 63.0% of insulin group had at least 1 episode of very low IFG, but none of the SU group and only 2 in the insulin group reported hypoglycemic symptoms. Conclusion: Low IFG was common among the studied population, with a greater burden in insulin treated patients. Majority were unable to detect events despite regular CBG monitoring suggesting the potential use of IFG in evaluating hypoglycemia burden. Disclosure K. Tian: None. L. C. Ang: None. Y. Bee: None. S. Goh: None. M. M. Teh: None.

352-P: Assessment of a Deep Learning Model Based on Three-Year Longitudinal Electronic Health Record Data for Predicting Severe Hypoglycemia in Patients with Type 2 Diabetes

Background: This study aimed to investigate the ability of a deep learning model to predict the outcome of severe hypoglycemia (SH) using longitudinal electronic medical records data. Methods: Structured data were extracted from the clinical data warehouse, including demographics, laboratory, medications, and diagnostic information. Between January 2010 and December 2012, 17,670 adult patients were enrolled; participants had type 2 diabetes, were aged over 30 years, and visited the diabetes centers of two hospitals. The primary outcome was SH, defined as diagnostic codes for hypoglycemia (E16.x, E11.63, E13.63, E14.63) in hospitalization or emergency room records. The performance of the longitudinal deep learning model (gated recurrent units) in predicting SH was quantified using the area under the receiver operating characteristic curve (AUROC) and evaluated in terms of net reclassification improvement (NRI) versus a traditional logistic regression model. Based on predicted risk of SH, we stratified patients into four risk groups: low (&amp;lt;1.0%), intermediate (1.0% to &amp;lt;2.0%), high (2.0% to &amp;lt;5.0%), and very high (≥5.0%). Results: Of 17,670 patients, 528 patients developed SH events during the follow-up period (mean of 6.4 years). The performance of the longitudinal deep learning model was superior to the logistic model for predicting SH (AUROC of logistic model, 0.788 [95% CI 0.781-0.793] vs. longitudinal deep learning model, 0.858 [0.853-0.863]). The very high-risk group of SH categorized using the deep learning model had 73 times higher risk of SH than the low-risk group. The deep learning model improved case reclassification (NRI, 13.4%). Conclusion: We found that the longitudinal deep learning model showed better performance and classification ability for predicting than the conventional logistic model. Disclosure G. Lee: None. S. Ko: None. Y. Ahn: None. E. Lee: None. S. Cha: None. D. Kim: None. J. Yun: None. Funding Korean Diabetes Association (2018-F to J-S.Y.)

Improvement of glycemic control in type 2 diabetes: A systematic review and meta-analysis of randomized controlled trials

AimDifferent guidelines provide similar, but not identical, therapeutic targets for HbA1c in type 2 diabetes. These targets can also depend from the different pharmacological strategies adopted for intensifying glycemic control. Data synthesisThis meta-analysis includes randomized trials adopting any pharmacological regimen for intensifying glycemic control in T2DM (versus standard of care/placebo), with a trial duration ≥2 years and a between-group HbA1c difference≥0.5%. The primary outcome was to assess the effects of the improvement of glycemic control on major cardiovascular events (MACE), ocular and renal complications, and severe hypoglycemia. Mantel-Haenszel odds ratios (MH–OR) with 95% Confidence Intervals were calculated for all the outcomes considered.We included 13 trials fulfilling the inclusion criteria. The improvement of glycemic control was associated with a lower risk of MACE (MH–OR:0.89 [95%CI 0.85–0.94]) and renal adverse events (MH–OR 0.73 [0.65–0.82]), but not all-cause mortality (MH–OR 0.95 [0.88–1.01]) and ocular adverse complications (MH–OR 0.94 [0.72–1.22]). For glucose-lowering drugs inducing hypoglycemia, a protective effect on the risk of microvascular complications, but not of MACE and all-cause mortality, was observed only for HbA1c ≤ 48 mmol/mol, but with higher risk of severe hypoglycaemia (MH–OR 2.72 [1.79–4.13]). Drugs not inducing hypoglycaemia were associated with a reduction of MACE, renal adverse events, and all-cause mortality, for HbA1c< 7% (no data for lower targets). ConclusionsThe present meta-analysis show that the improvement of glycemic control with drugs not inducing hypoglycemia is associated with a reduction in the risk of long-term chronic vascular and renal complications, and all-cause mortality.

Abstract 16062: Hypoglycemia and Risk of Atherosclerotic Cardiovascular Disease (ascvd) in Patients With Diabetes

Introduction: Studies evaluating the relationship of severe hypoglycemia (SH) with the risk of atherosclerotic cardiovascular disease (ASCVD) events are limited by size, confounding and inconsistent outcome definitions. The aim of this study was to assess the association of SH, as defined by hypoglycemia-related hospital utilization, with risk of ASCVD. Methods: We conducted an observational cohort study of adults with diabetes with or without established ASCVD (as of 1/1/2014). All subjects were members of Kaiser Permanente Northern California, an integrated health care delivery system. Baseline was determined by the date of the first hypoglycemia-related utilization (primary diagnosis of hypoglycemia in the emergency department or principal diagnosis in the hospital) during 1/1/2013 - 12/31/2013. Baseline for those without SH, was a randomly assigned date in 2013. Multivariate Cox proportional hazard models were specified to estimate hazard ratios (HRs) for SH in adjusted models of time to incident ASCVD events. This outcome was defined as a composite of nonfatal myocardial infarction, ischemic stroke, or coronary heart disease death through 12/31/2017. Results: Among 233,696 eligible individuals, mean age was 63.6 years, 47.6% were women, and mean follow-up was 3.8 years. There were 2,179 episodes of SH. Age-sex-race adjusted risk of ASCVD among those with SH versus those without was HR 2.59 (95% CI 2.3-2.9). When additionally adjusted for prior ASCVD, the HR was still more than double, HR 2.32 (95% CI 2.1-2.6). In a fully saturated model with additional adjustments for diabetes type, diabetes duration, HbA1c, GFR, Charlson score, insulin use, sulfonylureas, and quartiles of neighborhood deprivation, SH was associated with a 30% increased risk (HR 1.31, 95% CI 1.16-1.5). The substantial attenuation in risk after more comprehensive adjustment suggests that patients at higher risk of SH are likely at higher risk of ASCVD. Conclusions: In a large, diverse and contemporary cohort of patients with diabetes, SH as defined by hypoglycemia-related hospital utilization is associated with greater risk of ASCVD. Increased awareness in recognizing this risk and vigilance in care for this higher risk population is warranted.

Development of a hypoglycaemia risk score to identify high-risk individuals with advanced type 2 diabetes in DEVOTE.

AimsThe ability to differentiate patient populations with type 2 diabetes at high risk of severe hypoglycaemia could impact clinical decision making. The aim of this study was to develop a risk score, using patient characteristics, that could differentiate between populations with higher and lower 2‐year risk of severe hypoglycaemia among individuals at increased risk of cardiovascular disease.Materials and methodsTwo models were developed for the risk score based on data from the DEVOTE cardiovascular outcomes trials. The first, a data‐driven machine‐learning model, used stepwise regression with bidirectional elimination to identify risk factors for severe hypoglycaemia. The second, a risk score based on known clinical risk factors accessible in clinical practice identified from the data‐driven model, included: insulin treatment regimen; diabetes duration; sex; age; and glycated haemoglobin, all at baseline. Both the data‐driven model and simple risk score were evaluated for discrimination, calibration and generalizability using data from DEVOTE, and were validated against the external LEADER cardiovascular outcomes trial dataset.ResultsBoth the data‐driven model and the simple risk score discriminated between patients at higher and lower hypoglycaemia risk, and performed similarly well based on the time‐dependent area under the curve index (0.63 and 0.66, respectively) over a 2‐year time horizon.ConclusionsBoth the data‐driven model and the simple hypoglycaemia risk score were able to discriminate between patients at higher and lower risk of severe hypoglycaemia, the latter doing so using easily accessible clinical data. The implementation of such a tool (http://www.hyporiskscore.com/) may facilitate improved recognition of, and education about, severe hypoglycaemia risk, potentially improving patient care.