Continuous Glucose Monitoring Study Research Articles

Updated Software for Automated Assessment of Glucose Variability and Quality of Glycemic Control in Diabetes.

Background: Glycemic variability is an important factor to consider in diabetes management. It can be assessed with multiple glycemic variability metrics and quality of control indices based on continuous glucose monitoring (CGM) recordings. For this, a robust repeatable calculation is important. A widely used tool for automated assessment is the EasyGV software. The aim of this work is to implement new methods of glycemic variability assessment in EasyGV and to validate implementation of each glucose metric in EasyGV against a reference implementation of the calculations. Methods: Validation data used came from the JDRF CGM study. Validation of the implementation of metrics that are available in EasyGV software v9 was carried out and the following new methods were added and validated: personal glycemic state, index of glycemic control, times in ranges, and glycemic variability percentage. Reference values considered gold standard calculations were derived from MATLAB implementation of each metric. Results: The Pearson correlation coefficient was above 0.98 for all metrics, except for mean amplitude of glycemic excursion (r = 0.87) as EasyGV implements a fuzzy logic approach to assessment of variability. Bland-Altman plots demonstrated validation of the new software. Conclusions: The new freely available EasyGV software v10 (www.phc.ox.ac.uk/research/technology-outputs/easygv) is a validated robust tool for analyzing different glycemic variabilities and control metrics.

Assessment of Glucose Control Metrics by Discriminant Ratio

Objective: Increasing use of continuous glucose monitoring (CGM) data has created an array of glucose metrics for glucose variability, temporal patterns, and times in ranges. However, a gold standard metric has not been defined. We assess the performance of multiple glucose metrics to determine their ability to detect intra- and interperson variability to determine a set of recommended metrics. Methods: The Juvenile Diabetes Research Foundation data set, a randomized controlled study of CGM and self-monitored blood glucose conducted in children and adults with type 1 diabetes (T1D), was used. To determine the ability of the evaluated glycemic metrics to discriminate between different subjects and attenuate the effect of within-subject variation, the discriminant ratio was calculated and compared for each metric. Then, the findings were confirmed using data from two other recent randomized clinical trials. Results: Mean absolute glucose (MAG) has the highest discriminant ratio value (2.98 [95% confidence interval {CI} 1.64-3.67]). In addition, low blood glucose index and index of glycemic control performed well (1.93 [95% CI 1.15-3.44] and 1.92 [95% CI 1.27-2.93], respectively). For percentage times in glucose target ranges, the optimal discriminator was percentage time in glucose target 70-180 mg/dL. Conclusions: MAG is the optimal index to differentiate glucose variability in people with T1D, and may be a complementary therapeutic monitoring tool in addition to glycated hemoglobin and a measure of hypoglycemia. Percentage time in glucose target 70-180 mg/dL is the optimal percentage time in range to report.

InRange: Comparison of the Second-Generation Basal Insulin Analogues Glargine 300 U/mL and Degludec 100 U/mL in Persons with Type 1 Diabetes Using Continuous Glucose Monitoring-Study Design.

IntroductionSuboptimal glycaemic control among people with type 1 diabetes (T1D) is known to lead to long-term micro- and macrovascular complications and, unfortunately, it is still prevalent even in the most affluent societies. Although glycated haemoglobin monitoring is considered to be the gold standard for assessing glycaemic control, such monitoring is unable to reliably measure acute glycaemic excursions. Continuous glucose monitoring (CGM) has been shown to improve glucose control and reduce the incidence of hypoglycaemia, and also allow a more complete assessment of overall glycaemic control and hyper- and hypoglycaemic excursions. The use of CGM has led to time-in-range, which is the time that a patient is within the glycaemic range of 70 to 180 mg/dL, to be adopted as a treatment target. To date, only limited data comparing the second-generation insulins glargine 300 U/mL (Gla-300) and degludec 100 U/mL (IDeg-100) in people with T1D are available, and there is no CGM literature on comparisons of the use of CGM results to assess primary, secondary and tertiary endpoints. The aim of the InRange study was to address this unmet need.MethodsInRange is a multicentre, randomised, active-controlled, parallel-group, 12-week, open-label, phase 4, comparative study. Adults with T1D will be randomised to receive once-daily Gla-300 or IDeg-100 by subcutaneous injection in the morning. Following an 8-week titration period, CGM data will be collected over 20 consecutive days.Planned outcomesThe primary objective is to demonstrate that Gla-300 is noninferior to IDeg-100 in terms of glycaemic control [time-in-range ≥ 70 to ≤ 180 mg/dL (≥ 3.9 to ≤ 10 mmol/L)] and variability, as assessed using CGM, in adults with T1D. The results are expected to help confirm the utility of CGM in clinical practice in this population and provide insight into its application as an outcome measure in clinical practice.Trial registrationNCT04075513. Video Electronic Supplementary MaterialThe online version of this article (10.1007/s13300-020-00781-6) contains supplementary material, which is available to authorized users.

On the Possibility of Predicting Glycaemia 'On the Fly' with Constrained IoT Devices in Type 1 Diabetes Mellitus Patients.

Type 1 Diabetes Mellitus (DM1) patients are used to checking their blood glucose levels several times per day through finger sticks and, by subjectively handling this information, to try to predict their future glycaemia in order to choose a proper strategy to keep their glucose levels under control, in terms of insulin dosages and other factors. However, recent Internet of Things (IoT) devices and novel biosensors have allowed the continuous collection of the value of the glucose level by means of Continuous Glucose Monitoring (CGM) so that, with the proper Machine Learning (ML) algorithms, glucose evolution can be modeled, thus permitting a forecast of this variable. On the other hand, glycaemia dynamics require that such a model be user-centric and should be recalculated continuously in order to reflect the exact status of the patient, i.e., an ‘on-the-fly’ approach. In order to avoid, for example, the risk of being disconnected from the Internet, it would be ideal if this task could be performed locally in constrained devices like smartphones, but this would only be feasible if the execution times were fast enough. Therefore, in order to analyze if such a possibility is viable or not, an extensive, passive, CGM study has been carried out with 25 DM1 patients in order to build a solid dataset. Then, some well-known univariate algorithms have been executed in a desktop computer (as a reference) and two constrained devices: a smartphone and a Raspberry Pi, taking into account only past glycaemia data to forecast glucose levels. The results indicate that it is possible to forecast, in a smartphone, a 15-min horizon with a Root Mean Squared Error (RMSE) of 11.65 mg/dL in just 16.15 s, employing a 10-min sampling of the past 6 h of data and the Random Forest algorithm. With the Raspberry Pi, the computational effort increases to 56.49 s assuming the previously mentioned parameters, but this can be improved to 34.89 s if Support Vector Machines are applied, achieving in this case an RMSE of 19.90 mg/dL. Thus, this paper concludes that local on-the-fly forecasting of glycaemia would be affordable with constrained devices.

Risk of Hyperglycemia and Hypoglycemia in Patients with Acute Ischemic Stroke Based on Continuous Glucose Monitoring

BackgroundIn patients with acute ischemic stroke, current guidelines recommend maintaining blood glucose levels in a range of 140-180 mg/dL and closely monitoring to prevent hypoglycemia (<60 mg/dL). We aimed to assess glucose variability by continuous glucose monitoring (CGM) and to demonstrate the risk of acute ischemic stroke patients with glucose levels outside of the glucose management recommendations. MethodsPatients with ischemic stroke admitted within 7 days after onset were prospectively enrolled, and their blood glucose levels were monitored every 15 minutes for 72-hour period using the FreeStyle Libre Pro. Multivariate logistic regression analyses were used to analyze potential predictors for hyperglycemic (>180 mg/dL) and hypoglycemic (<60 mg/dL) events. ResultsA total of 39 acute ischemic stroke patients (mean age 75.9 ± 11.5 years) were enrolled, and CGM was started from 58.6 ± 41.9 hours after stroke onset. CGM showed hypoglycemic events in 19 patients and hyperglycemic events in 21 patients, and the frequencies of hypo- and hyperglycemic events during CGM were 10.1 ± 15.7% and 11.9 ± 22.5%, respectively. Hypoglycemic events were mainly observed in the night-time in patients with normoglycemia at admission. Logistic regression analyses demonstrated significant associations between the blood glucose level at admission and hypo- and hyperglycemic events on CGM. ConclusionsThis study of CGM found that many stroke patients have blood glucose levels outside the recommended guideline range in the acute phase. Blood glucose level on admission may be used as a predictor for hypo- and hyperglycemic events after admission.

Daily improvement of blood glucose profile by continuous glucose monitoring (CGM)

Background: The problem of glucose variability has been in focus for type 1 diabetes mellitus (T1DM) on insulin treatment. Daily profile of blood glucose was studied on Continuous Glucose Monitoring (CGM) using Free Style Libre. Case presentation and results: Patient is 54 year-old T1DM female, with HbA1c 7.8%. The blood glucose variability was measured by Free Style Libre. Insulin therapy included multiple daily insulin injection (MDI) of Insulin Glargin and Aspart. The result revealed unstable blood glucose profile in day 1 and 2. After that, the level and fluctuation of blood glucose gradually decreased from day 3 to 14. Average blood glucose in a day was 174mg/dL, 159mg/dL, 138mg/dL, 125mg/dL and 110mg/dL, in day 2, 4, 7, 9, 11, respectively. There was a discrepancy of HbA1c between 7.8% by laboratory measurement and 6.3% presumed value by Free Style Libre. Discussion and Conclusion: Free Style Libre showed satisfactory results as CGM. There was lower HbA1c value by presumed calculation, which would be possibly due to every 15minutes measurement and difficulty in checking abrupt glucose surges. CGM application would probably bring diabetic subjects behavioral change of life style, leading to better diabetic control. These results would become reference data in CGM study for future research.

Normalized glucose variability by Low Carbohydrate Diet (LCD) in CGM study

In diabetic practice, continuous glucose monitoring (CGM) has been more used for glucose variability. Authors have reported and developed the clinical study of Low Carbohydrate Diet (LCD) and Calorie Restriction (CR) through Japan LCD Promotion Association (JLCDPA). A diabetic case is presented with normalizing glucose variability by LCD meal checked by CGM. Case is 51 years female with type 2 diabetes mellitus (T2DM). At first visit, HbA1c was 10.0%, glucose was 288mg/dL, BMI 39.1, GOT 16IU/mL, HDL-C 33mg/dL, Triglyceride 152mg/dL. CR meal was provided on day-1 to day-5 and LCD meal with 12% carbohydrate on day-6 to day-13. Average glucose value from CGM was 292mg/dL, 235mg/dL, 160mg/dL, 140mg/dL, 124mg/dL in day 3,5,7,9,11 respectively. On day-13, total Ketone bodies (T-KB) 540μmol/L (-131), Acetoacetic acid (AcAc) 86μmol/L (-55) and 3-hydroxybutyric acid (3-OHBA) 454μmol/L (-85). Weight reduction was 5kg on day-14 and 11.2kg on day-70 with BMI 34.6. Thus, LCD showed a remarkable glucose-lowering effect in short term. FreeStyle Libre (Abbott, USA) seems to be a useful apparatus for monitoring the detailed fluctuation of blood glucose. These results would become fundamental and reference data and contribute to the LCD research development in the future.

Glucose sensor-augmented continuous subcutaneous insulin infusion in patients with diabetic gastroparesis: An open-label pilot prospective study

Erratic blood glucose levels can be a cause and consequence of delayed gastric emptying in patients with diabetes. It is unknown if better glycemic control increases risks of hypoglycemia or improves hemoglobin A1c levels and gastrointestinal symptoms in diabetic gastroparesis. This study investigated the safety and potential efficacy of continuous subcutaneous insulin infusion (CSII) and continuous glucose monitoring (CGM) in poorly controlled diabetes with gastroparesis. Forty-five type 1 or 2 patients with diabetes and gastroparesis and hemoglobin A1c >8% from the NIDDK Gastroparesis Consortium enrolled in a 24 week open-label pilot prospective study of CSII plus CGM. The primary safety outcome was combined numbers of mild, moderate, and severe hypoglycemic events at screening and 24 weeks treatment. Secondary outcomes included glycemic excursions on CGM, hemoglobin A1c, gastroparesis symptoms, quality-of-life, and liquid meal tolerance. Combined mild, moderate, and severe hypoglycemic events occurred similarly during the screening/run-in (1.9/week) versus treatment (2.2/week) phases with a relative risk of 1.18 (95% CI 0.85–1.64, P = 0.33). CGM time in hypoglycemia (<70 mg/dL) decreased from 3.9% to 1.8% (P<0.0001), time in euglycemia (70–180 mg/dL) increased from 44.0% to 52.0% (P = 0.02), time in severe hyperglycemia (>300 mg/dL) decreased from 14.2% to 7.0% (P = 0.005), and hemoglobin A1c decreased from 9.4±1.4% to 8.3±1.3% (P = 0.001) on CSII plus CGM. Symptom scores decreased from 29.3±7.1 to 21.9±10.2 with lower nausea/vomiting, fullness/early satiety, and bloating/distention scores (P≤0.001). Quality-of-life scores improved from 2.4±1.1 to 3.1±1.1 (P<0.0001) and volumes of liquid nutrient meals tolerated increased from 420±258 to 487±312 mL (P = 0.05) at 24 weeks. In conclusion, CSII plus CGM appeared to be safe with minimal risks of hypoglycemic events and associated improvements in glycemic control, gastroparesis symptoms, quality-of-life, and meal tolerance in patients with poorly controlled diabetes and gastroparesis. This study supports the safety, feasibility, and potential benefits of improving glycemic control in diabetic gastroparesis.

CGManalyzer: an R package for analyzing continuous glucose monitoring studies.

Summary The R package CGManalyzer contains functions for analyzing data from a continuous glucose monitoring (CGM) study. It covers a wide and comprehensive range of data analysis methods including reading a series of datasets, obtaining summary statistics of glucose levels, plotting data, transforming the time stamp format, fixing missing values, evaluating the mean of daily difference and continuous overlapping net glycemic action, calculating multiscale sample entropy, conducting pairwise comparison, displaying results using various plots including a new type of plot called an antenna plot, etc. This package has been developed from our work in directly analyzing data from various CGM devices such as the FreeStyle Libre, Glutalor, Dexcom and Medtronic CGM. Thus, this package should greatly facilitate the analysis of various CGM studies. Availability and implementation The package for Windows is available from CRAN: http://cran.r-project.org/mirrors.html. The source file CGManalyzer_1.0.tar.gz is available in the Supplementary Material and at the website of Zhang's lab https://quantitativelab.fhs.umac.mo/analytic-tool/. Contact douglaszhang@umac.mo. Supplementary information Supplementary data are available at Bioinformatics online.

CGManalyzer: an R package for analyzing continuous glucose monitoring studies.

The R package CGManalyzer contains functions for analyzing data from a continuous glucose monitoring (CGM) study. It covers a wide and comprehensive range of data analysis methods including reading a series of datasets, obtaining summary statistics of glucose levels, plotting data, transforming the time stamp format, fixing missing values, evaluating the mean of daily difference and continuous overlapping net glycemic action, calculating multiscale sample entropy, conducting pairwise comparison, displaying results using various plots including a new type of plot called an antenna plot, etc. This package has been developed from our work in directly analyzing data from various CGM devices such as the FreeStyle Libre, Glutalor, Dexcom and Medtronic CGM. Thus, this package should greatly facilitate the analysis of various CGM studies. The package for Windows is available from CRAN: http://cran.r-project.org/mirrors.html. The source file CGManalyzer_1.0.tar.gz is available in the Supplementary Material and at the website of Zhang's lab https://quantitativelab.fhs.umac.mo/analytic-tool/. douglaszhang@umac.mo. Supplementary data are available at Bioinformatics online.

Hypoglycaemia Remains the Key Obstacle to Optimal Glycaemic Control - Continuous Glucose Monitoring is the Solution.

We queried PubMed and other internet databases to identify studies, meta-analyses, review articles and other data sources regarding hypoglycaemia incidence/costs/impacts and continuous glucose monitoring (CGM) use. Our analysis of the evidence showed that hypoglycaemia remains a significant health concern and a primary obstacle to optimal adherence to prescribed diabetes treatment. In addition to its adverse clinical consequences, hypoglycaemia negatively impacts quality of life and places additional financial burdens on patients, patient families, employers and healthcare payers. Clinical trials have shown that the use of CGM can reduce the incidence and duration of hypoglycaemic episodes. This article reviews relevant CGM studies, discusses the prevalence and clinical/financial implications of hypoglycaemia, and explores the strengths and limitations of current CGM systems in minimising the burden of hypoglycaemia.

A randomized controlled pilot study of continuous glucose monitoring and flash glucose monitoring in people with Type 1 diabetes and impaired awareness of hypoglycaemia

AimHypoglycaemia in Type 1 diabetes is associated with mortality and morbidity, especially where awareness of hypoglycaemia is impaired. Clinical pathways for access to continuous glucose monitoring (CGM) and flash glucose monitoring technologies are unclear. We assessed the impact of CGM and flash glucose monitoring in a high‐risk group of people with Type 1 diabetes.MethodsA randomized, non‐masked parallel group study was undertaken. Adults with Type 1 diabetes using a multiple‐dose insulin‐injection regimen with a Gold score of ≥ 4 or recent severe hypoglycaemia were recruited. Following 2 weeks of blinded CGM, they were randomly assigned to CGM (Dexcom G5) or flash glucose monitoring (Abbott Freestyle Libre) for 8 weeks. The primary outcome was the difference in time spent in hypoglycaemia (below 3.3 mmol/l) from baseline to endpoint with CGM versus flash glucose monitoring.ResultsSome 40 participants were randomized to CGM (n = 20) or flash glucose monitoring (n = 20). The participants (24 men, 16 women) had a median (IQR) age of 49.6 (37.5–63.5) years, duration of diabetes of 30.0 (21.0–36.5) years and HbA1c of 56 (48–63) mmol/mol [7.3 (6.5–7.8)%]. The baseline median percentage time < 3.3 mmol/l was 4.5% in the CGM group and 6.7% in the flash glucose monitoring. At the end‐point the percentage time < 3.3 mmol/l was 2.4%, and 6.8% respectively (median between group difference −4.3%, P = 0.006). Time spent in hypoglycaemia at all thresholds, and hypoglycaemia fear, were different between groups, favouring CGM.Conclusion CGM more effectively reduces time spent in hypoglycaemia in people with Type 1 diabetes and impaired awareness of hypoglycaemia compared with flash glucose monitoring. (Clinical Trial Registry No: NCT03028220)

Impact of Ramadan fasting on glucose levels in women with gestational diabetes mellitus treated with diet alone or diet plus metformin: a continuous glucose monitoring study

ObjectiveWomen with gestational diabetes mellitus (GDM) are categorized as at high risk for adverse events during Ramadan fasting. However, this is largely based on clinical opinion. In this study, we...

A Simple Method to Model a Continuous Glucose Monitoring Signal

Before continuous glucose monitoring (CGM) can be safely used to guide glycaemic control (GC) protocols the impact of suboptimal accuracy resulting from error or delay in calibration measurement, sensor drift, and delayed glucose diffusion must first be characterised. Characterising this error allows models to be formed so in-silico simulations can test the performance and safety of CGM driven glycaemic control protocols and examine best and worst scenarios. Existing models of CGM dynamics are now 10 years old and significant advances in sensor technology mean the level of error produced by these models no longer characterises the dynamics of more recent CGM devices. Therefore, this paper presents and validates a simple CGM error model based on the latest available CGM devices, as well as a generalisable sensor modeling approach.The model was created using 28 data sets from an observational pilot study of CGM in patients admitted to the Christchurch Hospital ICU during 2014-15. The model was characterised by empirical models of drift and noise. Autocorrelation was then used to validate the modelled data with the measured data. The median absolute difference between modelled and measured SG autocorrelation values was 0.007 with a range of 0 - 0.13. Hence, the model is judged to be suitable for use in simulation to provide better insight into using CGM to guide GC will effect control and its safety and performance. The overall modelling process is data driven and readily generalised to any other device.

Clinical Use of Continuous Glucose Monitoring in Adults with Type 2 Diabetes.

Hemoglobin A1c is an excellent population health measure for the risk of vascular complications in diabetes, while continuous glucose monitoring (CGM) is a tool to help personalize a diabetes treatment plan. The value of CGM in individuals with type 1 diabetes (T1D) has been well demonstrated when compared with utilizing self-monitoring of blood glucose (SMBG) to guide treatment decisions. CGM is a tool for patients and clinicians to visualize the important role that diet, exercise, stress management, and the appropriate selection of diabetes medications can have in managing type 2 diabetes (T2D). Several diabetes organizations have recently reviewed the literature on the appropriate use of CGM in diabetes management and concluded CGM may be a useful educational and management tool particularly for patients on insulin therapy. The indications for using CGM either as a clinic-based loaner distribution model for intermittent use (professional CGM) or a CGM system owned by the patient and used at home with real-time glucose reading (personal CGM) are only beginning to be addressed in T2D. Most summaries of CGM studies conclude that having a standardized glucose pattern report, such as the ambulatory glucose profile (AGP) report, should help facilitate effective shared decision-making sessions. The future of CGM indications for the use of CGM is evolving rapidly. In some instances, CGM is now approved for making medication adjustments without SMBG confirmation and it appears that some forms of CGM will be approved for use in the Medicare population in the United States in the near future. Many individuals with T1D and T2D and their care teams will come to depend on CGM as a key tool for diabetes management.

Inpatient Continuous Glucose Monitoring and Glycemic Outcomes.

Continuous glucose monitoring (CGM) is commonly used in the outpatient setting to improve diabetes management. CGM can provide real-time glucose trends, detecting hyperglycemia and hypoglycemia before the onset of clinical symptoms. In 2011, at the time the Endocrine Society CGM guidelines were published, the society did not recommend inpatient CGM as its efficacy and safety were unknown. While many studies have subsequently evaluated inpatient CGM accuracy and reliability, glycemic outcome studies have not been widely published. In the non-ICU setting, investigational CGM studies have commonly blinded providers and patients to glucose data. Retrospective review of the glucose data reflects increased hypoglycemia detection with CGM. In the ICU setting, data are inconsistent whether CGM can improve glycemic outcomes. Studies have not focused on hospitalized patients with type 1 diabetes mellitus, the population most likely to benefit from inpatient CGM. This article reviews inpatient CGM glycemic outcomes in the non-ICU and ICU setting.

Measures of Glycemic Variability in Type 1 Diabetes and the Effect of Real-Time Continuous Glucose Monitoring.

To report the impact of continuous glucose monitoring (CGM) on glycemic variability (GV) indices, factors predictive of change, and to correlate variability with conventional markers of glycemia. Data from the JDRF study of CGM in participants with type 1 diabetes were used. Participants were randomized to CGM or self-monitored blood glucose (SMBG). GV indices at baseline, at 26 weeks in both groups, and at 52 weeks in the control group were analyzed. The associations of demographic and clinical factors with change in GV indices from baseline to 26 weeks were evaluated. Baseline data were available for 448 subjects. GV indices were all outside normative ranges (P < 0.001). Intercorrelation between GV indices was common and, apart from coefficient of variation (CV), low blood glucose index (LBGI), and percentage of glycemic risk assessment diabetes equation score attributable to hypoglycemia (%GRADEhypoglycemia), all indices correlate positively with HbA1c. There was strong correlation between time spent in hypoglycemia, and CV, LBGI, and %GRADEhypoglycemia, but not with HbA1c. A significant reduction in all GV indices, except lability index and mean absolute glucose change per unit time (MAG), was demonstrated in the intervention group at 26 weeks compared with the control group. Baseline factors predicting a change in GV with CGM include baseline HbA1c, baseline GV, frequency of daily SMBG, and insulin pump use. CGM reduces most GV indices compared with SMBG in people with type 1 diabetes. The strong correlation between time spent in hypoglycemia and CV, LBGI, and %GRADEhypoglycemia highlights the value of these metrics in assessing hypoglycemia as an adjunct to HbA1c in the overall assessment of glycemia.

Evaluation of a Novel Tool to Adjust Insulin Boluses Based on Continuous Glucose Monitoring Trend Arrows and Insulin Sensitivity in Children and Youth with Type 1 Diabetes

Background: The continuous glucose monitoring (CGM) trend arrows show glucose direction and rate of change. The JDRF CGM Study Group recommended 10 to 20% increase/decrease in insulin dose. This bolus dose is dependent on planned amount of food and current glucose and requires user to perform mathematical calculations each time, limiting its uptake. We developed a tool based on patient's insulin sensitivity factor.

Recruitment Into a Pediatric Continuous Glucose Monitoring RCT.

The purpose was to identify patient/family characteristics and recruitment process characteristics associated with the decision to participate in a 2-year continuous glucose monitoring (CGM) RCT for youth with type 1 diabetes and their families. Study staff approached patients who were conditionally eligible according to medical record review or referred by a provider. We categorized families according to participation decision (agree vs decline) and timing of decision (day of approach vs later ["thinkers"]). Over 18 months, we approached 456 eligible patients; 19% agreed on the day of approach, 10% agreed later, 42% declined on the day of approach, and 30% declined later. Agreers were younger ( P = .002), had shorter diabetes duration ( P = .0003), had a lower insulin dose ( P = .02), checked blood glucose levels more often ( P = .002), and were more likely to use pump therapy ( P = .009) than decliners. Patients/families were more likely to agree in fall/winter (41%) than spring/summer (19%, P < .0001). Of decliners, 50% cited no interest in CGM as the reason for nonparticipation. Among thinkers, 49% of patients who made a decision within 2 weeks of being approached agreed; only 15% of thinkers who made a decision >2 weeks after being approached agreed to participate ( P < .0001). Recruitment is a critical and often challenging phase of clinical trials. Recruitment to pediatric CGM studies may be especially challenging due to youths' reluctance to use CGM. These data provide an opportunity to better understand and possibly optimize recruitment into future pediatric CGM studies and other studies of advanced diabetes technologies.

Management of Type 1 Diabetes in Pregnancy.

Women with type 1 diabetes (T1DM) have unique needs during the preconception, pregnancy, and postpartum periods. Preconception counseling is essential for women with T1DM to minimize pregnancy risks. The goals of preconception care should be tight glycemic control with a hemoglobin A1c (A1C) < 7 % and as close to 6 % as possible, without significant hypoglycemia. This will lower risks of congenital malformations, preeclampsia, and perinatal mortality. The safety of medications should be assessed prior to conception. Optimal control of retinopathy, hypertension, and nephropathy should be achieved. During pregnancy, the goal A1C is near-normal at <6 %, without excessive hypoglycemia. There is no clear evidence that continuous subcutaneous insulin infusion (CSII) versus multiple daily injections (MDI) is superior in achieving the desired tight glycemic control of T1DM during pregnancy. Data regarding continuous glucose monitoring (CGM) in pregnant women with T1DM is conflicting regarding improved glycemic control. However, a recent CGM study does provide some distinct patterns of glucose levels associated with large for gestational age infants. Frequent eye exams during pregnancy are essential due to risk of progression of retinopathy during pregnancy. Chronic hypertension treatment goals are systolic blood pressure 110–129 mmHg and diastolic blood pressure 65–79 mmHg. Labor and delivery target plasma glucose levels are 80–110 mg/dl, and an insulin drip is recommended to achieve these targets during active labor. Postpartum, insulin doses must be reduced and glucoses closely monitored in women with T1DM because of the enhanced insulin sensitivity after delivery. Breastfeeding is recommended and should be highly encouraged due to maternal benefits including increased insulin sensitivity and weight loss and infant and childhood benefits including reduced prevalence of overweight. In this article, we discuss the care of pregnant patients with T1DM.