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IMPORTANCE Understanding cardiovascular outcomes of initiating second-line antidiabetic medications (ADMs) may help inform treatment decisions after metformin alone is not sufficient or not tolerated. To date, no studies have compared the cardiovascular effects of all major second-line ADMs during this early decision point in the pharmacologic management of type 2 diabetes. OBJECTIVE To examine the association of second-line ADM classes with major adverse cardiovascular events. DESIGN, SETTING, AND PARTICIPANTS Retrospective cohort study among 132 737 insured adults with type 2 diabetes who started therapy with a second-line ADM after taking either metformin alone or no prior ADM. This study used 2011-2015 US nationwide administrative claims data. Data analysis was performed from January 2017 to October 2018. EXPOSURES Dipeptidyl peptidase 4 (DPP-4) inhibitors, glucagon-like peptide-1 (GLP-1) receptor agonists, sodium-glucose cotransporter 2 (SGLT-2) inhibitors, thiazolidinediones (TZDs), basal insulin, and sulfonylureas or meglitinides (both referred to as sulfonylureas hereafter). The DPP-4 inhibitors served as the comparison group in all analyses. MAIN OUTCOMES AND MEASURES The primary outcome was time to first cardiovascular event after starting the second-line ADM. This composite outcome was based on hospitalization for the following cardiovascular conditions: congestive heart failure, stroke, ischemic heart disease, or peripheral artery disease. RESULTS Among 132 737 insured adult patients with type 2 diabetes (men, 55%; aged 45-64 years, 58%; white, 63%), there were 3480 incident cardiovascular events during 169 384 person-years of follow-up. Patients were censored after the first cardiovascular event, discontinuation of insurance coverage, transition from International Classification of Diseases, Ninth Revision (ICD-9) to end of ICD-9 coding, or 2 years of follow-up. After adjusting for patient, prescriber, and health plan characteristics, the risk of composite cardiovascular events after starting GLP-1 receptor agonists was lower than DPP-4 inhibitors (hazard ratio [HR], 0.78; 95% CI, 0.63-0.96), but this finding was not significant in all sensitivity analyses. Cardiovascular event rates after starting treatment with SGLT-2 inhibitors (HR, 0.81; 95% CI, 0.57-1.53) and TZDs (HR, 0.92; 95% CI, 0.76-1.11) were not statistically different from DPP-4 inhibitors. The comparative risk of cardiovascular events was higher after starting treatment with sulfonylureas (HR, 1.36; 95% CI, 1.23-1.49) or basal insulin (HR, 2.03; 95% CI, 1.81-2.27) than DPP-4 inhibitors.

In June 2016, Diabetes Technology Society convened a panel of US experts in inpatient diabetes management to discuss the current and potential role of continuous glucose monitoring (CGM) in the hospital. This discussion combined with a literature review was a follow-up to a meeting, which took place in May 2015. The panel reviewed evidence on use of CGM in 3 potential inpatient scenarios: (1) the intensive care unit (ICU), (2) non-ICU, and (3) transitioning outpatient CGM use into the hospital setting. Panel members agreed that data from limited studies and theoretical considerations suggested that use of CGM in the hospital had the potential to improve patient clinical outcomes, and in particular reduction of hypoglycemia. Panel members discussed barriers to widespread adoption of CGM, which patients would benefit most from use of this technology, and what type of outcome studies are needed to guide use of CGM in the inpatient setting.

This article is the work product of the Continuous Glucose Monitor and Automated Insulin Dosing Systems in the Hospital Consensus Guideline Panel, which was organized by Diabetes Technology Society and met virtually on April 23, 2020. The guideline panel consisted of 24 international experts in the use of continuous glucose monitors (CGMs) and automated insulin dosing (AID) systems representing adult endocrinology, pediatric endocrinology, obstetrics and gynecology, advanced practice nursing, diabetes care and education, clinical chemistry, bioengineering, and product liability law. The panelists reviewed the medical literature pertaining to five topics: (1) continuation of home CGMs after hospitalization, (2) initiation of CGMs in the hospital, (3) continuation of AID systems in the hospital, (4) logistics and hands-on care of hospitalized patients using CGMs and AID systems, and (5) data management of CGMs and AID systems in the hospital. The panelists then developed three types of recommendations for each topic, including clinical practice (to use the technology optimally), research (to improve the safety and effectiveness of the technology), and hospital policies (to build an environment for facilitating use of these devices) for each of the five topics. The panelists voted on 78 proposed recommendations. Based on the panel vote, 77 recommendations were classified as either strong or mild. One recommendation failed to reach consensus. Additional research is needed on CGMs and AID systems in the hospital setting regarding device accuracy, practices for deployment, data management, and achievable outcomes. This guideline is intended to support these technologies for the management of hospitalized patients with diabetes.

Background: A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data. Methods: We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low–glucose and low-glucose hypoglycemia; very high–glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation. Results: The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals. Conclusion: The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.

Insulin can help achieve ideal hemoglobin A1c goals for patients with type 2 diabetes. Barriers such as adherence, patient preferences, clinician preferences, and resource allocation must be addressed.

The Diabetes Control and Complications Trial (DCCT) demonstrated that intensive therapy reduced the development and progression of retinopathy in type 1 diabetes (T1D) compared with conventional therapy. The Epidemiology of Diabetes Interventions and Complications (EDIC) study observational follow-up showed persistent benefits. In addition to glycemia, we now examine other potential retinopathy risk factors (modifiable and nonmodifiable) over more than 30 years of follow-up in DCCT/EDIC. RESEARCH DESIGN AND METHODS The retinopathy outcomes were proliferative diabetic retinopathy (PDR), clinically significant macular edema (CSME), and ocular surgery. The survival (event-free) probability was estimated using the Kaplan-Meier method. Cox proportional hazards models assessed the association between risk factors and subsequent risk of retinopathy. Both forward-and backward-selection approaches determined the multivariable models. RESULTS Rate of ocular events per 1,000 person-years was 12 for PDR, 14.5 for CSME, and 7.6 for ocular surgeries. Approximately 65%, 60%, and 70% of participants remained free of PDR, CSME, and ocular surgery, respectively. The greatest risk factors for PDR in descending order were higher mean HbA 1c , longer duration of T1D, elevated albumin excretion rate (AER), and higher mean diastolic blood pressure (DBP). For CSME, risk factors, in descending order, were higher mean HbA 1c , longer duration of T1D, and greater age and DBP and, for ocular surgeries, were higher mean HbA 1c , older age, and longer duration of T1D. CONCLUSIONS Mean HbA 1c was the strongest risk factor for the progression of retinopathy. Although glycemic control is important, elevated AER and DBP were other modifiable risk factors associated with the progression of retinopathy. Retinopathy is a major complication of type 1 diabetes. The Diabetes Control and Complications Trial (DCCT) demonstrated that a mean of 6.5 years of intensive diabetes therapy with a mean HbA 1c of ;7% substantially reduced microvascular complications, including retinopathy, compared with conventional therapy with a mean HbA 1c of ;9% (1). Thereafter, observational follow-up of DCCT participants continued in the Epidemiology of Diabetes Interventions and Complications (EDIC) study (1994 to present) (2). Over the first 4 years of EDIC, the former DCCT intensive

Background Identifying factors associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection among health care workers (HCWs) may help health systems optimize SARS-CoV-2 infection control strategies. Methods We conducted a cross-sectional analysis of baseline data from the Northwestern HCW SARS-CoV-2 Serology Cohort Study. We used the Abbott Architect Nucleocapsid IgG assay to determine seropositivity. Logistic regression models (adjusted for demographics and self-reported community exposure to coronavirus disease 2019 [COVID-19]) were fit to quantify the associations between occupation group, health care delivery tasks, and community exposure and seropositive status. Results A total of 6510 HCWs, including 1794 nurses and 904 non-patient-facing administrators, participated. The majority were women (79.6%), 74.9% were White, 9.7% were Asian, 7.3% were Hispanic, and 3.1% were non-Hispanic Black. The crude prevalence of seropositivity was 4.8% (95% CI, 4.6%–5.2%). Seropositivity varied by race/ethnicity as well as age, ranging from 4.2% to 9.6%. Out-of-hospital exposure to COVID-19 occurred in 9.3% of HCWs, 15.0% (95% CI, 12.2%–18.1%) of whom were seropositive; those with family members diagnosed with COVID-19 had a seropositivity rate of 54% (95% CI, 44.2%–65.2%). Support service workers (10.4%; 95% CI, 4.6%–19.4%), medical assistants (10.1%; 95% CI, 5.5%–16.6%), and nurses (7.6%; 95% CI, 6.4%–9.0%) had significantly higher seropositivity rates than administrators (referent; 3.3%; 95% CI, 2.3%–4.4%). However, after adjustment, nursing was the only occupation group with a significantly higher odds (odds ratio, 1.9; 95% CI, 1.3–2.9) of seropositivity. Exposure to patients receiving high-flow oxygen therapy and hemodialysis was significantly associated with 45% and 57% higher odds for seropositive status, respectively. Conclusions HCWs are at risk for SARS-CoV-2 infection from longer-duration exposures to people infected with SARS-CoV-2 within health care settings and their communities of residence.