Threshold-based insulin-pump interruption for reduction of hypoglycemia

*Q1Artículo original224-232Background The threshold-suspend feature of sensor-augmented insulin pumps is designed to minimize the risk of hypoglycemia by interrupting insulin delivery at a preset sensor glucose value. We evaluated sensor-augmented insulin-pump therapy with and without the threshold-suspend feature in patients with nocturnal hypoglycemia. Methods We randomly assigned patients with type 1 diabetes and documented nocturnal hypoglycemia to receive sensor-augmented insulin-pump therapy with or without the threshold-suspend feature for 3 months. The primary safety outcome was the change in the glycated hemoglobin level. The primary efficacy outcome was the area under the curve (AUC) for nocturnal hypoglycemic events. Two-hour threshold-suspend events were analyzed with respect to subsequent sensor glucose values. Results A total of 247 patients were randomly assigned to receive sensor-augmented insulinpump therapy with the threshold-suspend feature (threshold-suspend group, 121 patients) or standard sensor-augmented insulin-pump therapy (control group, 126 patients). The changes in glycated hemoglobin values were similar in the two groups. The mean AUC for nocturnal hypoglycemic events was 37.5% lower in the thresholdsuspend group than in the control group (980±1200 mg per deciliter [54.4±66.6 mmol per liter]×minutes vs. 1568±1995 mg per deciliter [87.0±110.7 mmol per liter]×minutes, P<0.001). Nocturnal hypoglycemic events occurred 31.8% less frequently in the threshold-suspend group than in the control group (1.5±1.0 vs. 2.2±1.3 per patientweek, P<0.001). The percentages of nocturnal sensor glucose values of less than 50 mg per deciliter (2.8 mmol per liter), 50 to less than 60 mg per deciliter (3.3 mmol per liter), and 60 to less than 70 mg per deciliter (3.9 mmol per liter) were significantly reduced in the threshold-suspend group (P<0.001 for each range). After 1438 instances at night in which the pump was stopped for 2 hours, the mean sensor glucose value was 92.6±40.7 mg per deciliter (5.1±2.3 mmol per liter). Four patients (all in the control group) had a severe hypoglycemic event; no patients had diabetic ketoacidosis. Conclusions This study showed that over a 3-month period the use of sensor-augmented insulinpump therapy with the threshold-suspend feature reduced nocturnal hypoglycemia, without increasing glycated hemoglobin values. (Funded by Medtronic MiniMed; ASPIRE ClinicalTrials.gov number, NCT01497938.

Continuous Glucose Monitors and Automated Insulin Dosing Systems in the Hospital Consensus Guideline.

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

Consensus Recommendations for the Use of Automated Insulin Delivery (AID) Technologies in Clinical Practice

International audienceThe significant and growing global prevalence of diabetes continues to challenge people with diabetes (PwD), healthcare providers and payers. While maintaining near-normal glucose levels has been shown to prevent or delay the progression of the long-term complications of diabetes, a significant proportion of PwD are not attaining their glycemic goals. During the past six years, we have seen tremendous advances in automated insulin delivery (AID) technologies. Numerous randomized controlled trials and real-world studies have shown that the use of AID systems is safe and effective in helping PwD achieve their long-term glycemic goals while reducing hypoglycemia risk. Thus, AID systems have recently become an integral part of diabetes management. However, recommendations for using AID systems in clinical settings have been lacking. Such guided recommendations are critical for AID success and acceptance. All clinicians working with PwD need to become familiar with the available systems in order to eliminate disparities in diabetes quality of care. This report provides much-needed guidance for clinicians who are interested in utilizing AIDs and presents a comprehensive listing of the evidence payers should consider when determining eligibility criteria for AID insurance coverage

A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings

BackgroundA 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.MethodsWe 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.ResultsThe 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.ConclusionThe 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

Computing the Surveillance Error Grid Analysis

The surveillance error grid (SEG) analysis is a tool for analysis and visualization of blood glucose monitoring (BGM) errors, based on the opinions of 206 diabetes clinicians who rated 4 distinct treatment scenarios. Resulting from this large-scale inquiry is a matrix of 337 561 risk ratings, 1 for each pair of (reference, BGM) readings ranging from 20 to 580 mg/dl. The computation of the SEG is therefore complex and in need of automation. The SEG software introduced in this article automates the task of assigning a degree of risk to each data point for a set of measured and reference blood glucose values so that the data can be distributed into 8 risk zones. The software's 2 main purposes are to (1) distribute a set of BG Monitor data into 8 risk zones ranging from none to extreme and (2) present the data in a color coded display to promote visualization. Besides aggregating the data into 8 zones corresponding to levels of risk, the SEG computes the number and percentage of data pairs in each zone and the number/percentage of data pairs above/below the diagonal line in each zone, which are associated with BGM errors creating risks for hypo- or hyperglycemia, respectively. To illustrate the action of the SEG software we first present computer-simulated data stratified along error levels defined by ISO 15197:2013. This allows the SEG to be linked to this established standard. Further illustration of the SEG procedure is done with a series of previously published data, which reflect the performance of BGM devices and test strips under various environmental conditions. We conclude that the SEG software is a useful addition to the SEG analysis presented in this journal, developed to assess the magnitude of clinical risk from analytically inaccurate data in a variety of high-impact situations such as intensive care and disaster settings

The Benefit of Insulin Degludec/Liraglutide (IDegLira) Compared With Basal-Bolus Insulin Therapy is Consistent Across Participant Subgroups With Type 2 Diabetes in the DUAL VII Randomized Trial

Insulin degludec/liraglutide (IDegLira) results in glycated hemoglobin (HbA1c) levels comparable with basal-bolus (BB) therapy. Here, we assessed the effect of once-daily IDegLira compared with BB (once-daily insulin glargine 100 U/mL and insulin aspart ≤4 times/day) across subgroups with varying characteristics. Materials and methods: DUAL VII trial participants (type 2 diabetes [T2D], HbA1c 53-86 mmol/mol [7.0%-10.0%]) were subgrouped post hoc based on the following baseline characteristics: HbA1c (≤58.5, >58.5 to ≤69.4, and >69.4 mmol/mol; ≤7.5%, >7.5 to ≤8.5%, and >8.5%), body mass index (<30, ≥30 to <35, and ≥35 kg/m2), age (18 to <65 and ≥65 years), duration of diabetes (≥0 to 10 and ≥10 years), total pretrial daily basal insulin dose (20 to <30, ≥30 to <40, and ≥40 to ≤50 U), and fasting plasma glucose (<7.2 mmol/L/<130 mg/dL and ≥7.2 mmol/L/≥130 mg/dL). Results: Compared with BB, and in all subgroups, IDegLira treatment consistently gave similar HbA1c reductions, less severe or blood glucose-confirmed hypoglycemia, lower end-of-trial (EOT) total daily insulin dose, and weight loss. In all subgroups, mean EOT HbA1c was ≤53 mmol/mol (≤7.0%). The greatest HbA1c reduction occurred in the highest baseline HbA1c subgroup. Overall, mean EOT daily insulin dose was 0.43 to 0.52 U/kg with IDegLira and 0.74 to 1.07 U/kg with BB. More participants achieved the triple composite endpoint (HbA1c 58.5 to ≤69.4 mmol/mol [41.1% vs 8.3%], and >69.4 mmol/mol [23.8% vs 3.4%]). Conclusion: These results support initiating IDegLira in patients with varying baseline characteristics and uncontrolled T2D on basal insulin.Sin financiaciónNo data JCR (2021)1.142 SJR (2021) Q1, 30/153 BioengineeringNo data IDR 2020UE