Continuous Glucose Monitoring in the Intensive Care Unit During the COVID-19 Pandemic

OBJECTIVEReal-time continuous glucose monitoring (rtCGM) in critically ill hospitalized patients holds promise; however, real-world data are needed.RESEARCH DESIGN AND METHODSWe placed Dexcom G6 CGM on intensive care unit (ICU) patients at Montefiore Medical Center with confirmed coronavirus disease 2019 (COVID-19) infection and glycemic variability. We analyzed inpatient CGM accuracy using point-of-care (POC) glucose–CGM matched pairs and included patients for analysis regardless of clinical status.RESULTSWe included 11 patients with CGM: 8 on continuous insulin infusion (CII), 8 on vasopressors, 8 intubated, 4 on high-dose glucocorticoids, 6 on renal replacement therapy, and 2 with anasarca. Accuracy was 12.58% for mean and 6.3% for median absolute relative difference. CGM reduced POC testing by ∼60% for patients on CII.CONCLUSIONSIn this real-world preliminary analysis of rtCGM during critical illness, we demonstrate early feasibility, considerable accuracy, and meaningful reduction in the frequency of POC glucose testing.     

Accuracy of Dexcom G6 Continuous Glucose Monitoring in Non–Critically Ill Hospitalized Patients With Diabetes

OBJECTIVEAdvances in continuous glucose monitoring (CGM) have transformed ambulatory diabetes management. Until recently, inpatient use of CGM has remained investigational, with limited data on its accuracy in the hospital setting.RESEARCH DESIGN AND METHODSTo analyze the accuracy of Dexcom G6, we compared retrospective matched-pair CGM and capillary point-of-care (POC) glucose data from three inpatient CGM studies (two interventional and one observational) in general medicine and surgery patients with diabetes treated with insulin. Analysis of accuracy metrics included mean absolute relative difference (MARD), median absolute relative difference (ARD), and proportion of CGM values within 15, 20, and 30% or 15, 20, and 30 mg/dL of POC reference values for blood glucose >100 mg/dL or ≤100 mg/dL, respectively (% 15/15, % 20/20, % 30/30). Clinical reliability was assessed with Clarke error grid (CEG) analyses.RESULTSA total of 218 patients were included (96% with type 2 diabetes) with a mean age of 60.6 ± 12 years. The overall MARD (n = 4,067 matched glucose pairs) was 12.8%, and median ARD was 10.1% (interquartile range 4.6, 17.6]. The proportions of readings meeting % 15/15, % 20/20, and % 30/30 criteria were 68.7, 81.7, and 93.8%, respectively. CEG analysis showed 98.7% of all values in zones A and B. MARD and median ARD were higher in the case of hypoglycemia (<70 mg/dL) and severe anemia (hemoglobin <7 g/dL).CONCLUSIONSOur results indicate that CGM technology is a reliable tool for hospital use and may help improve glucose monitoring in non–critically ill hospitalized patients with diabetes.     

Comparison of the FreeStyle Libre Pro Flash Continuous Glucose Monitoring (CGM) System and Point-of-Care Capillary Glucose Testing in Hospitalized Patients With Type 2 Diabetes Treated With Basal-Bolus Insulin Regimen

OBJECTIVEWe compared the performance of the FreeStyle Libre Pro continuous glucose monitoring (CGM) and point-of-care capillary glucose testing (POC) among insulin-treated hospitalized patients with type 2 diabetes (T2D).RESEARCH DESIGN AND METHODSThis was a prospective study in adult patients with T2D admitted to general medicine and surgery wards. Patients were monitored with POC before meals and bedtime and with CGM during the hospital stay. Study end points included differences between POC and CGM in mean daily blood glucose (BG), hypoglycemia <70 and <54 mg/dL, and nocturnal hypoglycemia. We also calculated the mean absolute relative difference (MARD), ±15%/15 mg/dL, ±20%/20 mg/dL, and ±30%/30 mg/dL and error grid analysis between matched glucose pairs.RESULTSMean daily glucose was significantly higher by POC (188.9 ± 37.3 vs. 176.1 ± 46.9 mg/dL) with an estimated mean difference of 12.8 mg/dL (95% CI 8.3–17.2 mg/dL), and proportions of patients with glucose readings <70 mg/dL (14% vs. 56%) and <54 mg/dL (4.1% vs. 36%) detected by POC BG were significantly lower compared with CGM (all P < 0.001). Nocturnal and prolonged CGM hypoglycemia <54 mg/dL were 26% and 12%, respectively. The overall MARD was 14.8%, ranging between 11.4% and 16.7% for glucose values between 70 and 250 mg/dL and higher for 51–69 mg/dL (MARD 28.0%). The percentages of glucose readings within ±15%/15 mg/dL, ±20%/20 mg/dL, and ±30%/30 mg/dL were 62%, 76%, and 91%, respectively. Error grid analysis showed 98.8% of glucose pairs within zones A and B.CONCLUSIONSCompared with POC, FreeStyle Libre CGM showed lower mean daily glucose and higher detection of hypoglycemic events, particularly nocturnal and prolonged hypoglycemia in hospitalized patients with T2D. CGM’s accuracy was lower in the hypoglycemic range.     

The Effect of Discontinuing Continuous Glucose Monitoring in Adults With Type 2 Diabetes Treated With Basal Insulin

OBJECTIVETo explore the effect of discontinuing continuous glucose monitoring (CGM) after 8 months of CGM use in adults with type 2 diabetes treated with basal without bolus insulin.RESEARCH DESIGN AND METHODSThis multicenter trial had an initial randomization to either real-time CGM or blood glucose monitoring (BGM) for 8 months followed by 6 months in which the BGM group continued to use BGM (n = 57) and the CGM group was randomly reassigned either to continue CGM (n = 53) or discontinue CGM with resumption of BGM for glucose monitoring (n = 53).RESULTSIn the group that discontinued CGM, mean time in range (TIR) 70–180 mg/dL, which improved from 38% before initiating CGM to 62% after 8 months of CGM, decreased after discontinuing CGM to 50% at 14 months (mean change from 8 to 14 months −12% [95% CI −21% to −3%], P = 0.01). In the group that continued CGM use, little change was found in TIR from 8 to 14 months (baseline 44%, 8 months 56%, 14 months 57%, mean change from 8 to 14 months 1% [95% CI −11% to 12%], P = 0.89). Comparing the two groups at 14 months, the adjusted treatment group difference in mean TIR was −6% (95% CI −16% to 4%, P = 0.20).CONCLUSIONSIn adults with type 2 diabetes treated with basal insulin who had been using real-time CGM for 8 months, discontinuing CGM resulted in a loss of about one-half of the initial gain in TIR that had been achieved during CGM use.     

Thresholds of Glycemia and the Outcomes of COVID-19 Complicated With Diabetes: A Retrospective Exploratory Study Using Continuous Glucose Monitoring

OBJECTIVEAlthough elevated glucose levels are reported to be associated with adverse outcomes of coronavirus disease 2019 (COVID-19), the optimal range of glucose in patients with COVID-19 and diabetes remains unknown. This study aimed to investigate the threshold of glycemia and its association with the outcomes of COVID-19.RESEARCH DESIGN AND METHODSGlucose levels were assessed through intermittently scanned continuous glucose monitoring in 35 patients for an average period of 10.2 days. The percentages of time above range (TAR), time below range (TBR), time in range (TIR), and coefficient of variation (CV) were calculated. Composite adverse outcomes were defined as either the need for admission to the intensive care unit, need for mechanical ventilation, or morbidity with critical illness.RESULTSTARs using thresholds from 160 to 200 mg/dL were significantly associated with composite adverse outcomes after adjustment of covariates. Both TBR (<70 mg/dL) and TIR (70–160 mg/dL), but not mean sensor glucose level, were significantly associated with composite adverse outcomes and prolonged hospitalization. The multivariate-adjusted odds ratios of the CV of sensor glucose across tertiles for composite adverse outcomes of COVID-19 were 1.00, 1.18, and 25.2, respectively.CONCLUSIONSPatients with diabetes and COVID-19 have an increased risk of adverse outcomes with glucose levels >160 mg/dL and <70 mg/dL and a high CV. Therapies that improve these metrics of glycemic control may result in better prognoses for these patients.     

Use of continuous glucose monitoring in subjects with type 1 diabetes on multiple daily injections versus continuous subcutaneous insulin infusion therapy: a prospective 6-month study

OBJECTIVE

To compare use of continuous glucose monitoring in subjects with type 1 diabetes on multiple daily injection (MDI) therapy versus continuous subcutaneous insulin infusion (CSII) therapy for 6 months.

RESEARCH DESIGN AND METHODS

Sixty type 1 diabetic adults with similar baseline characteristics, using either MDI (n = 30) or CSII (n = 30) therapy, were enrolled in this 6-month prospective study. Subjects were instructed to wear the DexCom SevenPLUS continuous glucose monitor at all times throughout the study. All subjects were initially blinded from the continuous glucose monitoring (CGM) glucose data. After 4 weeks of blinded CGM use, the CGM was unblinded, making glucose data available to the patient. The CGM remained in the unblinded state for the remainder of the study (20 weeks). Clinic visits occurred every 4 weeks, at which time A1C values were collected and CGM data were downloaded.

RESULTS

Mean baseline (± SD) A1C was 7.61 (± 0.76) and 7.63 (± 0.68) for CSII and MDI, respectively (P > 0.05). Without any significant therapy change, A1C decrease at 12 weeks was similar in both groups (P = 0.03). When compared with the blinded phase, unblinded use of CGM was associated with similar but significant reductions in glycemic control and variability parameters. In addition, both therapy groups had similar changes in mean glucose and glucose variability indexes at 3 and 6 months (ITT analysis, P > 0.05). Predefined per protocol analysis (sensor use at least 6 days/week) showed greater improvement in time spent in target range glycemia, 3.9–10.0 mmol/L (70–180 mg/dL), in the CSII group.

CONCLUSIONS

We conclude that CGM provides similar benefits in glucose control for patients using MDI or CSII therapy.For patients with type 1 diabetes using intensive insulin therapy (IIT), there are two approaches for insulin delivery: multiple daily injection (MDI) therapy and continuous subcutaneous insulin infusion (CSII) therapy (1,2). With both therapies, insulin is dosed using basal/bolus regimens. With MDI, a long-acting (basal) insulin analog is injected subcutaneously once or twice daily, providing a relatively constant insulin level. In contrast, CSII administers basal insulin by a continuous infusion of rapid-acting insulin that can be adjusted throughout the day based on an individual’s insulin requirements. In both approaches, basal insulin is adjusted to avoid hypo- and hyperglycemia during interprandial periods. Additionally, mealtime or “bolus” insulin is dosed based on several factors including anticipated meal carbohydrate content, current blood glucose, and postprandial glucose trends.Severe hypoglycemia is a concern for all people with type 1 diabetes. Despite performing frequent self-monitoring of blood glucose (SMBG) four or more times daily, severe hypoglycemic episodes increase by threefold in IIT patients regardless of the method of insulin delivery (2). The recently available continuous glucose monitoring (CGM) is a device that provides patients with the ability to view real-time glucose values, review recent glucose trends, and receive hypo- or hyperglycemic alarms. The CGM systems provide a complete real-time glucose profile by measuring glucose levels at 1- to 5-min intervals. This allows for an accurate, large-scale representation of overall glucose trends, as compared with the isolated values offered by fingerstick SMBG (3–5). Furthermore, CGM use has been reported in both controlled (6–8) and nonrandomized trials (9–12) to improve glucose control, reduce hypo- and hyperglycemic excursions (10,11), and improve glucose variability (13,14). Despite limited data, it is commonly believed that optimal diabetes management can best be achieved when a CGM is used by IIT patients, especially when combined with insulin pump (CSII) therapy.This prospective study was conducted comparing the usefulness of CGM in adult patients with type 1 diabetes using MDI or CSII.     

Leveraging advances in diabetes technologies in primary care: a narrative review

Primary care providers (PCPs) play an important role in providing medical care for patients with type 2 diabetes. Advancements in diabetes technologies can assist PCPs in providing personalised care that addresses each patient’s individual needs. Diabetes technologies fall into two major categories: devices for glycaemic self-monitoring and insulin delivery systems. Monitoring technologies encompass self-measured blood glucose (SMBG), where blood glucose is intermittently measured by a finger prick blood sample, and continuous glucose monitoring (CGM) devices, which use an interstitial sensor and are capable of giving real-time information. Studies show people using real-time CGM have better glucose control compared to SMBG. CGM allows for new parameters including time in range (the time spent within the desired target glucose range), which is an increasingly relevant real-time metric of glycaemic control. Insulin pens have increased the ease of administration of insulin and connected pens that can calculate and capture data on dosing are becoming available. There are a number of websites, software programs, and applications that can help PCPs and patients to integrate diabetes technology into their diabetes management schedules. In this article, we summarise these technologies and provide practical information to inform PCPs about utility in their clinical practice. The guiding principle is that use of technology should be individualised based on a patient’s needs, desires, and availability of devices. Diabetes technology can help patients improve their clinical outcomes and achieve the quality of life they desire by decreasing disease burden.

KEY MESSAGES

• It is important to understand the role that diabetes technologies can play in primary care to help deliver high-quality care, taking into account patient and community resources. Diabetes technologies fall into two major categories: devices for glycaemic self-monitoring and insulin delivery systems. Modern self-measured blood glucose devices are simple to use and can help guide decision making for self-management plans to improve clinical outcomes, but cannot provide “live” data and may under- or overestimate blood glucose; patients’ monitoring technique and compliance should be reviewed regularly. Importantly, before a patient is provided with monitoring technology, they must receive suitably structured education in its use and interpretation.
• Continuous glucose monitoring (CGM) is now standard of care for people with type 1 diabetes and people with type 2 diabetes on meal-time (prandial) insulin. Real-time CGM can tell both the patient and the healthcare provider when glucose is in the normal range, and when they are experiencing hyper- or hypoglycaemia. Using CGM data, changes in lifestyle, eating habits, and medications, including insulin, can help the patient to stay in a normal glycaemic range (70–180 mg/dL). Real-time CGM allows for creation of an ambulatory glucose profile and monitoring of time in range (the time spent within target blood glucose of 70–180 mg/dL), which ideally should be at least 70%; avoiding time above range (>180 mg/dL) is associated with reduced diabetes complications and avoiding time below range (<70 mg/dL) will prevent hypoglycaemia. Insulin pens are simpler to use than syringes, and connected pens capture information on insulin dose and injection timing.
• There are a number of websites, software programs and applications that can help primary care providers and patients to integrate diabetes technology into their diabetes management schedules. The guiding principle is that use of technology should be individualised based on a patient’s needs, desires, skill level, and availability of devices.

     

Real-Time Continuous Glucose Monitoring Significantly Reduces Severe Hypoglycemia in Hypoglycemia-Unaware Patients With Type 1 Diabetes

OBJECTIVE

To evaluate the effect of continuous glucose monitoring (CGM) on the frequency of severe hypoglycemia (SH) in patients with established hypoglycemia unawareness.

RESEARCH DESIGN AND METHODS

We conducted a retrospective audit of 35 patients with type 1 diabetes and problematic hypoglycemia unawareness, despite optimized medical therapy (continuous subcutaneous insulin infusion/multiple daily insulin injections), who used CGM for >1 year.

RESULTS

Over a 1-year follow-up period, the median rates of SH were reduced from 4.0 (interquartile range [IQR] 0.75–7.25) episodes/patient-year to 0.0 (0.0–1.25) episodes/patient-year (P < 0.001), and the mean (±SD) rates were reduced from 8.1 ± 13 to 0.6 ± 1.2 episodes/year (P = 0.005). HbA_1c was reduced from 8.1 ± 1.2% to 7.6 ± 1.0% over the year (P = 0.005). The mean Gold score, measured in 19 patients, did not change: 5.1 ± 1.5 vs. 5.2 ± 1.9 (P = 0.67).

CONCLUSIONS

In a specialist experienced insulin pump center, in carefully selected patients, CGM reduced SH while improving HbA_1c but failed to restore hypoglycemia awareness.Although real-time continuous glucose monitoring (CGM) has been shown in randomized controlled trials to improve glycemic control and mild-to-moderate hypoglycemia, studies to date have not shown convincing reductions in severe hypoglycemia (SH) (1,2). Clinically, CGM may benefit patients with impaired awareness of hypoglycemia (IAH), who have an increased risk of SH (3), by alerting them to impending hypoglycemia, and thus providing them with “technological” awareness to replace the loss of their “physiological” awareness. In our clinical service, across two associated tertiary hospitals, we have obtained case-specific funding for CGM for 35 patients with type 1 diabetes, IAH, and problematic hypoglycemia limiting daily activities during intensified insulin therapy. This audit evaluates outcomes at 1 year to see whether the use of CGM can reduce SH or improve awareness.     

Real-Time Improvement of Continuous Glucose Monitoring Accuracy: The smart sensor concept

OBJECTIVE

Reliability of continuous glucose monitoring (CGM) sensors is key in several applications. In this work we demonstrate that real-time algorithms can render CGM sensors smarter by reducing their uncertainty and inaccuracy and improving their ability to alert for hypo- and hyperglycemic events.

RESEARCH DESIGN AND METHODS

The smart CGM (sCGM) sensor concept consists of a commercial CGM sensor whose output enters three software modules, able to work in real time, for denoising, enhancement, and prediction. These three software modules were recently presented in the CGM literature, and here we apply them to the Dexcom SEVEN Plus continuous glucose monitor. We assessed the performance of the sCGM on data collected in two trials, each containing 12 patients with type 1 diabetes.

RESULTS

The denoising module improves the smoothness of the CGM time series by an average of ∼57%, the enhancement module reduces the mean absolute relative difference from 15.1 to 10.3%, increases by 12.6% the pairs of values falling in the A-zone of the Clarke error grid, and finally, the prediction module forecasts hypo- and hyperglycemic events an average of 14 min ahead of time.

CONCLUSIONS

We have introduced and implemented the sCGM sensor concept. Analysis of data from 24 patients demonstrates that incorporation of suitable real-time signal processing algorithms for denoising, enhancement, and prediction can significantly improve the performance of CGM applications. This can be of great clinical impact for hypo- and hyperglycemic alert generation as well in artificial pancreas devices.Continuous glucose monitoring (CGM) technology has significantly modified the way glucose levels are monitored in patients with type 1 diabetes, allowing an increase in the number of readings from, for example, 3 to 4 spot measurements per day to a continuous glucose signal (1,2). In the beginning, CGM was used retrospectively to analyze glycemic profiles and to better understand glucose variability (3). Then, thanks to advances in technology, CGM systems turned into real-time devices, and their benefit in improved glycemic control and reduced risks of hypo- and hyperglycemia became evident (4–6).The large amount of data obtained by CGM sensors stimulated the development of several applications. The most straightforward application is to combine CGM with a system for real-time generation of alerts when the measured glucose value crosses hypoglycemic (e.g., 70 mg/dL) or hyperglycemic (e.g., 180 mg/dL) thresholds (7). Another possible use is within systems that combine a CGM and a continuous subcutaneous insulin infusion pump in a single unit, the so-called sensor-augmented pump, whose use can produce a reduction of hyperglycemia and an improvement in glycemic control (8–10). Among all, the most ambitious is probably the artificial pancreas (AP), a system for delivering insulin steered by a closed-loop control algorithm in which the uncertainty and accuracy of the CGM sensor play a crucial role because the CGM measurements feed the control algorithm (11).Even if the reliability of CGM outcome in accuracy is key, CGM performance is still suboptimal because of three main issues (12,13) that are related more to the way in which the stream of data given in output by the sensor is processed rather than on the electrochemical processes occurring within the sensor. The first issue is related to the uncertainty of CGM data, because glucose readings are corrupted by random noise that complicates their interpretation and use (14,15). For instance, noise may result in spurious spikes and oscillations that could trigger false hypo- or hyperglycemic alerts. Some denoising algorithms have recently been developed to deal with this problem (14–17).The second issue concerns accuracy. In fact, compared with gold standard blood glucose (BG) references measured by laboratory instruments, the CGM time series present delays, which are mainly due to the blood-to-interstitium glucose transport and the sensor processing time (18), and often systematic under- or overestimations due to calibration problems (19–21). For instance, when actual glucose is in the hypoglycemic range, systematic overestimation of glucose levels due to lack of calibration can expose the patient to critical situations. To compensate for the inaccuracy and to enhance CGM data, several strategies have been proposed in the last few years (20–25).Finally, because CGM sensors report glucose value with a delay with respect to BG, there is the necessity of generating hypo- and hyperglycemic prealerts by applying short-term glucose prediction strategies (26–32). Generation of prealerts can allow the patient to take prompt countermeasures before a forthcoming (e.g., hypoglycemic) event, increasing the possibility of mitigating or even avoiding, it.So far, these mentioned methods have always been tested in the literature as stand-alone applications, and a quantification of the improvement in CGM performance that can be achieved by their combination has never been assessed. Therefore, we propose the concept of a smart CGM (sCGM) sensor consisting of a cascade of a commercial CGM sensor and three software modules for denoising, enhancement, and prediction, able to work in real time.     

Relationship of fasting and hourly blood glucose levels to HbA1c values: safety, accuracy, and improvements in glucose profiles obtained using a 7-day continuous glucose sensor

OBJECTIVE: In this study, we evaluated the safety and efficacy of 7-day transcutaneous, real-time, continuous glucose monitoring (CGM) in subjects with insulin-requiring diabetes. RESEARCH DESIGN AND METHODS: Eighty-six subjects were enrolled at five U.S. centers. Subjects wore a sensor inserted under the skin of the abdomen for 7 days during each of three consecutive periods. Data were blinded during period 1 and unblinded during periods 2 and 3. RESULTS: Of the 6,811 matched self-monitoring of blood glucose to sensor values prospectively analyzed, 97.2% fell in the Clarke error grid zones A and B, and median absolute relative difference was 11.4%. After unblinding, subjects reduced time spent at <55 mg/dl by 0.3 h/day, reduced time spent at >240 mg/dl by 1.5 h/day, and increased time in the target zone (81-140 mg/dl) by 1.4 h/day (P < 0.05 for all three comparisons). Improvements were seen in both types 1 and 2 diabetes and with use of both multiple daily injections and continuous subcutaneous insulin infusion. Modal day graphs were generated in six groups of subjects based on HbA1c (A1C) (10%). Mean glucose levels from midnight to 7:00 a.m. (fasting and dawn phenomenon periods) were only normal for subjects with A1C 相似文献   

Insulin treatment guided by subcutaneous continuous glucose monitoring compared to frequent point-of-care measurement in critically ill patients: a randomized controlled trial

Introduction

Glucose measurement in intensive care medicine is performed intermittently with the risk of undetected hypoglycemia. The workload for the ICU nursing staff is substantial. Subcutaneous continuous glucose monitoring (CGM) systems are available and may be able to solve some of these issues in critically ill patients.

Methods

In a randomized controlled design in a mixed ICU in a teaching hospital we compared the use of subcutaneous CGM with frequent point of care (POC) to guide insulin treatment. Adult critically ill patients with an expected stay of more than 24 hours and in need of insulin therapy were included. All patients received subcutaneous CGM. CGM data were blinded in the control group, whereas in the intervention group these data were used to feed a computerized glucose regulation algorithm. The same algorithm was used in the control group fed by intermittent POC glucose measurements. Safety was assessed with the incidence of severe hypoglycemia (<2.2 mmol/L), efficacy with the percentage time in target range (5.0 to 9.0 mmol/L). In addition, we assessed nursing workload and costs.

Results

In this study, 87 patients were randomized to the intervention and 90 to the control group. CGM device failure resulted in 78 and 78 patients for analysis. The incidence of severe glycemia and percentage of time within target range was similar in both groups. A significant reduction in daily nursing workload for glucose control was found in the intervention group (17 versus 36 minutes; P <0.001). Mean daily costs per patient were significantly reduced with EUR 12 (95% CI −32 to −18, P = 0.02) in the intervention group.

Conclusions

Subcutaneous CGM to guide insulin treatment in critically ill patients is as safe and effective as intermittent point-of-care measurements and reduces nursing workload and daily costs. A new algorithm designed for frequent measurements may lead to improved performance and should precede clinical implementation.

Trial registration

Clinicaltrials.gov, {"type":"clinical-trial","attrs":{"text":"NCT01526044","term_id":"NCT01526044"}}NCT01526044. Registered 1 February 2012.

Electronic supplementary material

The online version of this article (doi:10.1186/s13054-014-0453-9) contains supplementary material, which is available to authorized users.     

A Randomized Clinical Trial Assessing Continuous Glucose Monitoring (CGM) Use With Standardized Education With or Without a Family Behavioral Intervention Compared With Fingerstick Blood Glucose Monitoring in Very Young Children With Type 1 Diabetes

OBJECTIVEThis study evaluated the effects of continuous glucose monitoring (CGM) combined with family behavioral intervention (CGM+FBI) and CGM alone (Standard-CGM) on glycemic outcomes and parental quality of life compared with blood glucose monitoring (BGM) in children ages 2 to <8 years with type 1 diabetes.RESEARCH DESIGN AND METHODSThis was a multicenter (N = 14), 6-month, randomized controlled trial including 143 youth 2 to <8 years of age with type 1 diabetes. Primary analysis included treatment group comparisons of percent time in range (TIR) (70–180 mg/dL) across follow-up visits.RESULTSApproximately 90% of participants in the CGM groups used CGM ≥6 days/week at 6 months. Between-group TIR comparisons showed no significant changes: CGM+FBI vs. BGM 3.2% (95% CI −0.5, 7.0), Standard-CGM vs. BGM 0.5% (−2.6 to 3.6), CGM+FBI vs. Standard-CGM 2.7% (−0.6, 6.1). Mean time with glucose level <70 mg/dL was reduced from baseline to follow-up in the CGM+FBI (from 5.2% to 2.6%) and Standard-CGM (5.8% to 2.5%) groups, compared with 5.4% to 5.8% with BGM (CGM+FBI vs. BGM, P < 0.001, and Standard-CGM vs. BGM, P < 0.001). No severe hypoglycemic events occurred in the CGM+FBI group, one occurred in the Standard-CGM group, and five occurred in the BGM group. CGM+FBI parents reported greater reductions in diabetes burden and fear of hypoglycemia compared with Standard-CGM (P = 0.008 and 0.04) and BGM (P = 0.02 and 0.002).CONCLUSIONSCGM used consistently over a 6-month period in young children with type 1 diabetes did not improve TIR but did significantly reduce time in hypoglycemia. The FBI benefited parental well-being.     

Assessment of patient-led or physician-driven continuous glucose monitoring in patients with poorly controlled type 1 diabetes using basal-bolus insulin regimens: a 1-year multicenter study

OBJECTIVE

The benefits of real-time continuous glucose monitoring (CGM) have been demonstrated in patients with type 1 diabetes. Our aim was to compare the effect of two modes of use of CGM, patient led or physician driven, for 1 year in subjects with poorly controlled type 1 diabetes.

RESEARCH DESIGN AND METHODS

Patients with type 1 diabetes aged 8–60 years with HbA_1c ≥8% were randomly assigned to three groups (1:1:1). Outcomes for glucose control were assessed at 1 year for two modes of CGM (group 1: patient led; group 2: physician driven) versus conventional self-monitoring of blood glucose (group 3: control).

RESULTS

A total of 257 subjects with type 1 diabetes underwent screening. Of these, 197 were randomized, with 178 patients completing the study (age: 36 ± 14 years; HbA_1c: 8.9 ± 0.9%). HbA_1c improved similarly in both CGM groups and was reduced compared with the control group (group 1 vs. group 3: −0.52%, P = 0.0006; group 2 vs. group 3: −0.47%, P = 0.0008; groups 1 + 2 vs. group 3: −0.50%, P < 0.0001). The incidence of hypoglycemia was similar in the three groups. Patient SF-36 questionnaire physical health score improved in both experimental CGM groups (P = 0.004). Sensor consumption was 34% lower in group 2 than in group 1 (median [Q1–Q3] consumption: group 1: 3.42/month [2.20–3.91] vs. group 2: 2.25/month [1.27–2.99], P = 0.001).

CONCLUSIONS

Both patient-led and physician-driven CGM provide similar long-term improvement in glucose control in patients with poorly controlled type 1 diabetes, but the physician-driven CGM mode used fewer sensors.Since the report of the results of the Diabetes Control and Complications Trial (1), so-called intensified insulin therapy has been considered the gold standard for patients with type 1 diabetes, together with frequent self-monitoring of blood glucose (SMBG), patient education about diabetes management, and close follow-up and support by the attending health care team. This intensified insulin therapy corresponds to insulin regimens involving multiple daily injections (MDI) or continuous subcutaneous insulin infusion (CSII). The availability of insulin analogs has enabled further improvements in the safety and efficacy of basal-bolus insulin therapy (2). However, a significant subset of patients with type 1 diabetes still fails to reach optimal HbA_1c levels and/or experiences debilitating BG variability and recurrent hypoglycemia (3,4). The availability of devices for real-time continuous glucose monitoring (CGM) recently has aroused considerable interest among patients and physicians, who anticipated that such devices might provide potential benefits in terms of BG control. Indeed, several randomized controlled studies demonstrate that use of CGM could improve HbA_1c levels in patients with type 1 diabetes with no increased risk of hypoglycemia (5–7). However, most of the reported studies were short-term (3 to 6 months) and included close monitoring by the investigators, thus precluding any conclusions about expected outcomes in common clinical practice and in the long-term. While compliance with CGM therapy was identified as a strong predictor of HbA_1c reduction, the role of specific patient education in the use of CGM data to adjust insulin therapy and the contribution of patient empowerment have not been widely examined. Hence, it is still not known whether patient-led or physician-driven CGM use and management have different outcomes. Answering such questions would be of great value, considering the additional cost of CGM use. We therefore designed this randomized multicenter controlled study to assess in poorly controlled patients with type 1 diabetes the outcomes on glucose control and quality of life (QoL) of two CGM approaches following a similar initial specific education procedure: patient self-management with CGM or physician-prescribed use of CGM.     

Real-Time Continuous Glucose Monitoring in Critically Ill Patients: A prospective randomized trial

OBJECTIVE

To evaluate the impact of real-time continuous glucose monitoring (CGM) on glycemic control and risk of hypoglycemia in critically ill patients.

RESEARCH DESIGN AND METHODS

A total 124 patients receiving mechanical ventilation were randomly assigned to the real-time CGM group (n = 63; glucose values given every 5 min) or to the control group (n = 61; selective arterial glucose measurements according to an algorithm; simultaneously blinded CGM) for 72 h. Insulin infusion rates were guided according to the same algorithm in both groups. The primary end point was percentage of time at a glucose level <110 mg/dl. Secondary end points were mean glucose levels and rate of severe hypoglycemia (<40 mg/dl).

RESULTS

Percentage of time at a glucose level <110 mg/dl (59.0 ± 20 vs. 55.0 ± 18% in the control group, P = 0.245) and the mean glucose level (106 ± 18 vs. 111 ± 10 mg/dl in the control group, P = 0.076) could not be improved using real-time CGM. The rate of severe hypoglycemia was lower in the real-time CGM group (1.6 vs. 11.5% in the control group, P = 0.031). CGM reduced the absolute risk of severe hypoglycemia by 9.9% (95% CI 1.2–18.6) with a number needed to treat of 10.1 (95% CI 5.4–83.3).

CONCLUSIONS

In critically ill patients, real-time CGM reduces hypoglycemic events but does not improve glycemic control compared with intensive insulin therapy guided by an algorithm.Hyperglycemia, a frequent finding in up to 90% of all critically ill patients, is associated with increased morbidity and mortality (1,2). In three monocentric studies, intensive insulin therapy to achieve and maintain normoglycemia resulted in decreased morbidity and mortality (3–5). However, in two subsequent multicenter studies, normoglycemia was not adequately reached, and the studies were stopped prematurely because of safety reasons with increased rates of severe hypoglycemia (6,7). However, in a recent trial, intensive insulin therapy resulted in improved short-term outcome in pediatric intensive care; another recent trial demonstrated increased mortality among adults under intensive glucose control (5,8). An updated meta-analysis of 26 randomized trials including 13,567 patients reported that intensive insulin therapy had no effect on the overall risk of death but simultaneously resulted in a sixfold increased risk of severe hypoglycemia. Currently, there is still an intense and conflicting discussion on the difficulty of obtaining near-normoglycemia and thereby avoiding the risk of severe hypoglycemia (9). In critically ill patients, accurate real-time continuous glucose monitoring (CGM) might be the best way to minimize a consistently reported increased rate of severe hypoglycemia associated with intensive insulin therapy and to increase effectiveness and safety of tight glucose control.Numerous studies in diabetic patients tested CGM devices and demonstrated high accuracy of the CGM-derived glucose values compared with blood glucose measurements (10–12). In particular, these devices were highly sensitive in detecting rapid glucose excursions (12). Recently, these CGM techniques have also been evaluated in critically ill patients and have yielded similar positive results (13–17). Mainly, subcutaneous CGM devices have been intensely investigated (13–17). Accuracy and reliability of a subcutaneous CGM device could be demonstrated both in critically ill patients with and without circulatory shock (16). Subcutaneous CGM worked equally in patients without and with norepinephrine therapy. Validity of the subcutaneous CGM under norepinephrine therapy was furthermore independent of levels of blood glucose values, severity of illness, and patients'' BMI (16). With use of this subcutaneous CGM device, ∼99% of all measured sensor glucose values were within the acceptable treatment zone according to an insulin titration grid analysis (16). Based on these underlying data, we hypothesized that subcutaneous real-time CGM improves glucose control, simultaneously reducing the risk of hypoglycemia.     

The Effect of Real-Time Continuous Glucose Monitoring in Pregnant Women With Diabetes: A randomized controlled trial

OBJECTIVE

To assess whether intermittent real-time continuous glucose monitoring (CGM) improves glycemic control and pregnancy outcome in unselected women with pregestational diabetes.

RESEARCH DESIGN AND METHODS

A total of 123 women with type 1 diabetes and 31 women with type 2 diabetes were randomized to use real-time CGM for 6 days at 8, 12, 21, 27, and 33 weeks in addition to routine care, including self-monitored plasma glucose seven times daily, or routine care only. To optimize glycemic control, real-time CGM readings were evaluated by a diabetes caregiver. HbA_1c, self-monitored plasma glucose, severe hypoglycemia, and pregnancy outcomes were recorded, with large-for-gestational-age infants as the primary outcome.

RESULTS

Women assigned to real-time CGM (n = 79) had baseline HbA_1c similar to that of women in the control arm (n = 75) (median 6.6 [range 5.3–10.0] vs. 6.8% [5.3–10.7]; P = 0.67) (49 [34–86] vs. 51 mmol/mol [34–93]). Forty-nine (64%) women used real-time CGM per protocol. At 33 weeks, HbA_1c (6.1 [5.1–7.8] vs. 6.1% [4.8–8.2]; P = 0.39) (43 [32–62] vs. 43 mmol/mol [29–66]) and self-monitored plasma glucose (6.2 [4.7–7.9] vs. 6.2 mmol/L [4.9–7.9]; P = 0.64) were comparable regardless of real-time CGM use, and a similar fraction of women had experienced severe hypoglycemia (16 vs. 16%; P = 0.91). The prevalence of large-for-gestational-age infants (45 vs. 34%; P = 0.19) and other perinatal outcomes were comparable between the arms.

CONCLUSIONS

In this randomized trial, intermittent use of real-time CGM in pregnancy, in addition to self-monitored plasma glucose seven times daily, did not improve glycemic control or pregnancy outcome in women with pregestational diabetes.Pregnancy in women with pregestational diabetes is still associated with adverse perinatal outcomes largely attributed to maternal hyperglycemia, including large-for-gestational-age infants, preterm delivery, and perinatal morbidity (1–4). Large-for-gestational-age infants to mothers with diabetes are at increased risk for birth trauma, transient tachypnea, and neonatal hypoglycemia (5), and maternal diabetes in pregnancy is associated with later-life morbidity in the offspring (6). The major barrier in the strive for strict maternal glycemic control is the risk of severe hypoglycemia (1), occurring up to five times more frequently in early pregnancy than in the period prior to pregnancy in women with type 1 diabetes (7).Real-time continuous glucose monitoring (CGM) measures interstitial glucose in an ongoing fashion and offers the possibility of hyper- and hypoglycemic alarms. Studies of nonpregnant patients with type 1 diabetes indicate that real-time CGM lowers HbA_1c (8–19) and may reduce the tendency to biochemical hypoglycemia (9). Pregnant women with diabetes may also profit from real-time CGM, but experience is still limited (20–26). A randomized controlled trial evaluating intermittent use of a previous CGM system (not real-time) on top of routine pregnancy care reported improved glycemic control and a reduced risk of large-for-gestational-age infants in the intervention arm (27). Against this background, it is tempting to suggest that women with pregestational diabetes would benefit even more from the use of real-time CGM in pregnancy.In this investigator-driven trial, we therefore aimed to assess whether intermittent real-time CGM, as part of routine pregnancy care, could improve maternal glycemic control and pregnancy outcome in an unselected cohort of women with pregestational type 1 or type 2 diabetes.     

Incremental Value of Continuous Glucose Monitoring When Starting Pump Therapy in Patients With Poorly Controlled Type 1 Diabetes: The RealTrend study*

OBJECTIVE

To compare the improvements in glycemic control associated with transitioning to insulin pump therapy in patients using continuous glucose monitoring versus standard blood glucose self-monitoring.

RESEARCH DESIGN AND METHODS

The RealTrend study was a 6-month, randomized, parallel-group, two-arm, open-label study of 132 adults and children with uncontrolled type 1 diabetes (A1C ≥8%) being treated with multiple daily injections. One group was fitted with the Medtronic MiniMed Paradigm REAL-Time system (PRT group), an insulin pump with integrated continuous subcutaneous glucose monitoring (CGM) capability, with instructions to wear CGM sensors at least 70% of the time. Conventional insulin pump therapy was initiated in the other group (continuous subcutaneous insulin infusion [CSII] group). Outcome measures included A1C and glycemic variability.

RESULTS

A total of 115 patients completed the study. Between baseline and trial end, A1C improved significantly in both groups (PRT group −0.81 ± 1.09%, P < 0.001; CSII group −0.57 ± 0.94%, P < 0.001), with no significant difference between groups. When the 91 patients who were fully protocol-compliant (including CGM sensor wear ≥70% of the time) were considered, A1C improvement was significantly greater in the PRT group (P = 0.004) (PRT group −0.96 ± 0.93%, P < 0.001; CSII group −0.55 ± 0.93%, P < 0.001). Hyperglycemia parameters decreased in line with improvements in A1C with no impact on hypoglycemia.

CONCLUSIONS

CGM-enabled insulin pump therapy improves glycemia more than conventional pump therapy during the first 6 months of pump use in patients who wear CGM sensors at least 70% of the time.The long-term clinical benefit of tight glycemic control in type 1 diabetic patients has been demonstrated in several reports by the Diabetes Control and Complications Trial (1,2). To achieve this goal, insulin analogs, basal-bolus multiple daily injections (MDI), and insulin pumps for continuous subcutaneous insulin infusion (CSII) have proved to be important tools for lowering glucose variability and improving glycemic control, leading to higher treatment satisfaction in patients with type 1 diabetes (3–5).Nevertheless, intensive treatment of type 1 diabetes often does not succeed in achieving target A1C levels ≤7.0% (6). Increased self-monitoring of blood glucose (SMBG) levels is correlated with better A1C levels (7,8), but for practical reasons most patients do not perform more than five to seven glucose measurements per day. Consequently, postprandial hyperglycemia and nocturnal hypoglycemia often remain unnoticed, even in individuals with well-controlled diabetes (9–11). Hence, detecting and treating these events might improve the patient''s glycemic control and have an impact on quality of life.Continuous glucose monitoring (CGM) provides information from a subcutaneous glucose sensor on interstitial glucose levels. A typical CGM system incorporates alarms for high and low glucose levels and displays glucose trend information graphically, allowing patients to anticipate hypo- and hyperglycemic events. Recent studies have shown that wearing such devices is associated with improved glycemic control in patients undergoing intensive therapy for type 1 diabetes (12,13) and in patients treated by CSII (14); however, no study has investigated the benefit of CGM in patients with poor metabolic control using MDI upon initiation of pump therapy. In this trial we randomly initiated pump therapy in patients with insufficient metabolic control despite optimized basal-bolus injection regimens with either the MiniMed Paradigm REAL-Time insulin pump (PRT), an insulin pump that can receive and display CGM data from a separate subcutaneous glucose sensor, or conventional CSII, and compared glycemic outcomes after 6 months.     

No evidence of increasing COVID-19 in health care workers after implementation of high flow nasal cannula: A safety evaluation

BackgroundInitial recommendations discouraged high flow nasal cannula (HFNC) in COVID-19 patients, driven by concern for healthcare worker (HCW) exposure. Noting high morbidity and mortality from early invasive mechanical ventilation, we implemented a COVID-19 respiratory protocol employing HFNC in severe COVID-19 and HCW exposed to COVID-19 patients on HFNC wore N95/KN95 masks. Utilization of HFNC increased significantly but questions remained regarding HCW infection rate.MethodsWe performed a retrospective evaluation of employee infections in our healthcare system using the Employee Health Services database and unit records of employees tested between March 15, 2020 and May 23, 2020. We assessed the incidence of infections before and after the implementation of the protocol, stratifying by clinical or non-clinical role as well as inpatient COVID-19 unit.ResultsDuring the study period, 13.9% (228/1635) of employees tested for COVID-19 were positive. Forty-six percent of infections were in non-clinical staff. After implementation of the respiratory protocol, the proportion of positive tests in clinical staff (41.5%) was not higher than that in non-clinical staff (43.8%). Of the clinicians working in the high-risk COVID-19 unit, there was no increase in infections after protocol implementation compared with clinicians working in COVID-19 units that did not use HFNC.ConclusionWe found no evidence of increased COVID-19 infections in HCW after the implementation of a respiratory protocol that increased use of HFNC in patients with COVID-19; however, these results are hypothesis generating.