Journal of Diabetes Science and Technology

Continuous glucose monitoring (CGM) technology has become more prevalent in the intensive care unit (ICU), offering potential benefits of increased safety and reduced workload in glycemic control (GC). The drift and higher point accuracy errors of CGM devices over traditional intermittent blood glucose (BG) measures have so far limited their application in the ICU. This study delineates the trade-offs of performance, safety and workload that CGM sensors provide in GC protocols.

Clinical data from 236 patients were used for clinically validated virtual trials. A CGM-enabled version of the STAR GC protocol was used to evaluate the use of guard rails and rolling windows. Safety was assessed through percentage of patients who had a severe hypoglycemic episode (BG < 40 mg/dl) as well as percentage of resampled BG < 72 mg/dl. Performance was assessed as percentage of resampled measurements in the 80-126 mg/dl and the 80-144 mg/dl target bands. Workload was measured by number of manual BG measures per day.

CGM-enabled versions of STAR decreased the number of required blood draws by up to 74%, while maintaining performance (76.6% BG measurements in the 80-126 mg/dl range vs 62.8% clinically, 87.9% in the 80-144 mg/dl range vs 83.7% clinically) and maintaining patient safety (1.13% of patients experienced a severe hypoglycemic event vs 0.85% clinically, 1.37% of BG measurements were less than 72 mg/dl vs 0.51% clinically).

CGM sensor traces were reproduced in virtual trials to guide GC. Existing GC protocols such as STAR may need to be adjusted only slightly to gain the benefits of the increased temporal measurements of CGM sensors, through which workload may be significantly decreased while maintaining GC performance and safety.

Few studies have examined the use of continuous glucose monitoring (CGM) devices in the general wards. The aim of this pilot study was to examine whether CGM readings can be successfully transmitted from the bedside to a central monitoring device in the nursing station, and whether a glucose telemetry system can prevent hypoglycemic events.

We present pilot data on 5 consecutive insulin treated general medicine patients with type 2 diabetes (T2DM) whose glucose values were observed with CGM (DEXCOM) and the results were transmitted to a central nursing station monitoring system using DEXCOM Follow and Share 2 software. CGM alarms were set-up at glucose <85 mg/dl.

Duration of CGM observation was 4.0 ± 1.6 days (mean ± SD). During CGM, the overall time spent within blood glucose (BG) target of 70-179 mg/dl was 64.68 ± 15% (mean ± SD), on hypoglycemia (<70 mg/dl) was 0.30% ± 0.39, and time spent on hyperglycemia (≥180 mg/dl) was 35.02% ± 15.5. Two patients had 3 actions of prevention of potential hypoglycemia (CGM BG <70 mg/dl for >20 minutes) captured by alarm. No patients had CGM glucose value <54 mg/dl.

This pilot study indicates that the use of CGM values in hospitalized patients can be successfully transmitted to a monitoring device in the nursing station, improving patient surveillance in insulin treated patients with diabetes.

Hypoglycemia and hyperglycemia affect outcomes in hospitalized patient. Patients with diabetes and end stage renal disease are prone to hypoglycemia and few studies have evaluated glucometrics to identify the incidence and risk factors for hypoglycemia in this population.

We designed an observational retrospective review of 150 insulin requiring inpatients with diabetes receiving hemodialysis. We collected demographics, baseline characteristics, and glucometric data focusing on episodes of hypoglycemia with glucose cutoffs <70, <54, and <40 mg/dl. Detailed glucose and insulin data for 24 hours before and after hemodialysis was analyzed for each patient in context of a hypoglycemic episode. T-tests, one-way ANOVA, and chi-square tests were used for statistical analysis.

At least one glucose value less than 70 mg/dl was observed in 51% of hemodialysis patients, less than 54 mg/dl in 28%, and less than 40 mg/dl in 11%. Patients with hypoglycemia had a higher HbA1c, standard deviation of glucose (P = .0009) and higher total daily dose (TDD) of insulin by weight (0.34 units/kg vs 0.23 units/kg, P = .003). We observed a linear increasing risk for hypoglycemia with increasing TDD, with nearly 65% of hypoglycemic episodes occurring with TDD >0.20 units/kg. A majority (61%) of all hypoglycemic episodes occurred in the 24 hours prior to a hemodialysis session. Type 1 diabetes was independently associated with hypoglycemia.

Hospitalized diabetes patients undergoing hemodialysis were found to have high rates of hypoglycemia. Our results support using a lower TDD of insulin in this population (<0.23 units/kg/day) and recommend special caution in those with type 1 diabetes.

This study was performed to investigate the efficacy of Glucommander (GM) (Glytec®), a computer-based algorithm versus standard (paper form-based) continuous insulin infusion (CII) in the treatment of patients with diabetic ketoacidosis (DKA).

This was a retrospective multicenter study involving 2665 patients with DKA treated with either GM (n = 1750) or standard protocols (n = 915) across 34 institutions in the United States. GM estimates the rate of CII using an insulin sensitivity factor referred to as a “multiplier” that ranges between 0.01 and 0.03. Outcomes of interest were differences in time to resolve DKA (blood glucose [BG] <200 mg/dL and bicarbonate < 18 mmol/L) and number of hypoglycemic events defined as a BG <70 mg/dl.

Treatment with GM was associated with lower rates of hypoglycemia during the time of the insulin drip (12.9% vs 35%, P = .001), faster time to normalization of blood glucose (9.7 ± 8.9 vs 10.97 ± 10.2 hours, P = .0001) and resolution of metabolic acidosis (13.6 ± 11.8 vs 17.3 ± 19.6 hours, P = .0001), and shorter hospital length of stay (3.2 ± 2.9 vs 4.5 ± 4.8 days, P = .01) compared to standard care. Best treatment outcomes were achieved with an initial multiplier of 0.01 and a glucose target range between 120 and 180 mg/dl.

The GM algorithm in DKA treatment resulted in lower rates of hypoglycemia and faster DKA resolution over standard paper-based algorithms. Prospective randomized clinical trials comparing the efficacy and cost of computer-based algorithms versus standard CII regimens are warranted.

Insulin infusions are commonly utilized to control hyperglycemia in critically ill patients and decrease hyperglycemia associated complications. Safety concerns have been raised in trials evaluating methods of glycemic control regarding the incidence of hypoglycemia and its relationship to increased mortality. Electronic glycemic management systems (eGMS) may result in less variable blood glucose (BG) control and less hypoglycemia. This study aimed to compare BG control, time in target BG range, and the rate of hypoglycemia when critically ill patients were managed with an insulin infusion guided by paper-based protocol (PBP) versus eGMS.

This retrospective review compared critically ill patients ≥ 18 years old that received insulin infusion from March to May 2015 (PBP group) and October to January 2017 (eGMS group). The primary outcome was the incidence of hypoglycemia. Secondary outcomes included frequency and severity of hypoglycemia, duration in glycemic target, length of insulin therapy, as well as ICU and hospital length of stay.

Fifty-four patients were evaluated, 27 in each group. Percentage of days with BG <70 mg/dL was significantly reduced after eGMS implementation (21.5% v 1.3%, P < .0001) including the frequency of severe hypoglycemia (BG < 40 mg/dL) (5.4% v 0.01%, P < .0001). Patients in the eGMS group spent a greater amount of time in target BG range (31.5% v 63.7%, P < .0001).

An eGMS has the potential to address many of the unmet needs of an optimal glycemic control strategy, minimizing hypoglycemia, and glycemic variability in a heterogeneous critically ill population.

Hyperglycemia is common in the inpatient setting and providers frequently rely on sliding scale insulin. This case study reviews the experience of one hospital moving from high utilization of sliding scale to basal bolus insulin therapy.

This Retrospective Quality Improvement Study describes the journey of clinicians at a 580-bed hospital to convert from high usage of SSI to BBI. Hyperglycemic adult patients prescribed insulin, with/without a diagnosis of diabetes, were included.

Data over the first year showed that patients treated with Glucommander (GM) spent more time in the target range of 70-180 mg/dL than patients treated with non-Glucommander (non-GM), with 2,434 fewer hypoglycemic events and 40,589 fewer hyperglycemic events. Prior to implementation of GM, SSI was close to 95%, BBI at 5%. Within the first month of use, 96% usage of BBI was achieved. Reduction of hypoglycemic events (% of BG < 70 mg/dL) by 21% with 2.16% non-GM compared to GM at 1.74% and severe Hypoglycemia (% of BG < 50 mg/dL) by 50% in the ICU 3% non-GM compared to GM at 1.5%. In addition, patients treated with GM had a shorter LOS than patients treated with non-GM by 3.18 days and used 47.4% less point of care tests per patient.

Glycemic management improved with use of eGMS. The conversion from SSI to BBI enhanced overall patient safety, eliminated the time and effort otherwise required when manually titrating insulin and reduced overall cost of care for patients on insulin therapy.

The objective was to identify root causes of hypoglycemia on medicine inpatient units using an automated tool. Data collected will guide educational interventions aimed at improving patient care and safety by decreasing rates of hypoglycemia.

A survey was conducted among RNs to identify risk factors for hypoglycemia. Survey data were used to create a hypoglycemia root cause survey tool in the EMR. RNs were prompted to utilize the tool when blood glucose (BG) < 70 mg/dL. Once the most common modifiable cause of hypoglycemia was identified, an educational intervention for safe and effective use of insulin was launched. This strategy was designed to empower the care team to reduce the insulin dose when appropriate to prevent future hypoglycemic episodes.

BG data were compared from March and April in 2016 and 2017. Rates of hypoglycemia (BG < 70 mg/dL) decreased from 2.3% to 1.5%; BG values in target range (70-180 mg/dL) increased from 59.4% to 65.7%; hyperglycemia (BG > 180 mg/dL) decreased from 38.3% to 32.8% (all P values < .001). The number of patients with recurrent hypoglycemia (3 or more episodes) decreased from 5.7% to 2.2% (P = .044).

The two most frequent modifiable causes of hypoglycemia (insulin and nutrition) were identified by an RN survey and confirmed by chart review. A targeted educational intervention addressing safe and effective insulin dosing resulted in a significant decrease in both hypoglycemia and recurrent hypoglycemia. This was associated with an improvement in overall glycemic control. Ongoing clinician education regarding insulin and nutrition accompanied by discussions between RNs and prescribers to address hypoglycemic events in real-time could continue to lower the rate of occurrence.

Quantifying hypoglycemia has traditionally been limited to using the frequency of hypoglycemic events during a given time interval using data from blood glucose (BG) testing. However, continuous glucose monitoring (CGM) captures three parameters—a Hypo-Triad—unavailable with BG monitoring that can be used to better characterize hypoglycemia: area under the curve (AUC), time (duration of hypoglycemia), and frequency of daily episodes below a specified threshold.

We developed two new analytic metrics to enhance the traditional Hypo-Triad of CGM-derived data to more effectively capture the intensity of hypoglycemia (IntHypo) and overall hypoglycemic environment called the “hypoglycemia risk volume” (HypoRV). We reanalyzed the CGM data from the ASPIRE In-Home study, a randomized, controlled trial of a sensor-integrated pump system with a low glucose threshold suspend feature (SIP+TS), using these new metrics and compared them to standard metrics of hypoglycemia.

IntHypo and HypoRV provide additional insights into the benefit of a SIP+TS system on glycemic exposure when compared to the standard reporting methods. In addition, the visual display of these parameters provides a unique and intuitive way to understand the impact of a diabetes intervention on a cohort of subjects as well as on individual patients.

The IntHypo and HypoRV are new and enhanced ways of analyzing CGM-derived data in diabetes intervention studies which could lead to new insights in diabetes management. They require validation using existing, ongoing, or planned studies to determine whether they are superior to existing metrics.

Patients with type 1 diabetes routinely utilize a single premeal fingerstick glucose to determine premeal insulin doses. Continuous glucose monitoring (CGM) provides much richer glycemic trend information, including glycemic slope (GS). How to incorporate this information into dosing decisions remains an open question.

We examined the relationship between premeal GS and postmeal glycemic excursions in 240 individuals with type 1 diabetes receiving CGM augmented insulin pump therapy. Over 23.5 million CGM values were synchronized with 264 500 meals. CGM values were integrated 2 hours premeal to compute GS and 2 hours postmeal to compute glycemic excursion outcomes. Postmeal hyperglycemia (integrated CGM glucose >180 mg/dL*hr) and postmeal hypoglycemic events (any CGM glucose < 70 mg/dL) were tabulated according to positive/negative premeal GS and according to GS bins commonly displayed as rate-of-change arrows on CGM devices.

Positive versus negative premeal GS was associated with a 2.28-fold (95% CI 2.25-2.32) risk of postmeal hyperglycemia. Negative versus positive premeal GS was associated with a 2.36-fold (95% CI 2.25-2.43) increase in one or more postprandial hypoglycemic events. Premeal GS in the bin currently displayed as “no change” on existing CGM devices (–1 to 1 mg/dL/min), conferred a 1.82-fold (95% CI 1.79-1.86) risk of postprandial hyperglycemia when positive and a 2.06-fold (95% CI 1.99-2.15) increased risk of postprandial hypoglycemia when negative.

Premeal GS predicts postmeal glycemic excursions and may help inform insulin dosing decisions. Rate-of-change arrows on existing devices obscure clinically actionable glycemic trend information from CGM users.

Strict glycemic control is known to be a vital key in the management of diabetes mellitus (DM). However, traditional methods face limitations in their efficacy due to the pain and invasiveness of needle pricking which often discourages DM patients from performing the required number of tests per day. Saliva glucose (SG) sensing has long been considered a noninvasive alternative to blood glucose monitoring for diabetes management, however the sample preparation and sensor detection limit have been deemed as challenges to overcome. Herein, we describe a preliminary clinical validation of a disposable SG sensor without any requirement for sample preparation.

The sensor utilizes glucose dehydrogenase flavine-adenine dinucleotide (GDH-FAD) in conjunction with disposable screen printed electrodes to measure glucose levels in saliva collected directly from 9 healthy subjects. Cyclic voltammetry and amperometric-time (Amp-it) assays were used to develop calibration curves and test subjects. Sensor calibration was performed using simulated saliva at 6.5 pH and 37ºC.

The lower limit of detection was determined to be 0.11 mg/dL. A lag time of 15 minutes with a positive correlation between SG and BG levels was found, which agrees with literature results. The detected SG ranges from 2.38 to 3.40 mg/dL over a BG range of 90 to 143 mg/dL.

This is the first reported use of measuring SG with GDH-FAD without prior sample preparation. Upon optimization, the sensor has the potential to serve as a supplement to blood glucose monitoring.

Continuous glucose monitoring (CGM) devices have been effective in managing diabetes and offer potential benefits for use in the intensive care unit (ICU). Use of CGM devices in the ICU has been limited, primarily due to the higher point accuracy errors over currently used traditional intermittent blood glucose (BG) measures. General models of CGM errors, including drift and random errors, are lacking, but would enable better design of protocols to utilize these devices. This article presents an autoregressive (AR) based modeling method that separately characterizes the drift and random noise of the GlySure CGM sensor (GlySure Limited, Oxfordshire, UK).

Clinical sensor data (n = 33) and reference measurements were used to generate 2 AR models to describe sensor drift and noise. These models were used to generate 100 Monte Carlo simulations based on reference blood glucose measurements. These were then compared to the original CGM clinical data using mean absolute relative difference (MARD) and a Trend Compass.

The point accuracy MARD was very similar between simulated and clinical data (9.6% vs 9.9%). A Trend Compass was used to assess trend accuracy, and found simulated and clinical sensor profiles were similar (simulated trend index 11.4° vs clinical trend index 10.9°).

The model and method accurately represents cohort sensor behavior over patients, providing a general modeling approach to any such sensor by separately characterizing each type of error that can arise in the data. Overall, it enables better protocol design based on accurate expected CGM sensor behavior, as well as enabling the analysis of what level of each type of sensor error would be necessary to obtain desired glycemic control safety and performance with a given protocol.

Tens of glycemic variability (GV) indices are available in the literature to characterize the dynamic properties of glucose concentration profiles from continuous glucose monitoring (CGM) sensors. However, how to exploit the plethora of GV indices for classifying subjects is still controversial. For instance, the basic problem of using GV indices to automatically determine if the subject is healthy rather than affected by impaired glucose tolerance (IGT) or type 2 diabetes (T2D), is still unaddressed. Here, we analyzed the feasibility of using CGM-based GV indices to distinguish healthy from IGT&T2D and IGT from T2D subjects by means of a machine-learning approach.

The data set consists of 102 subjects belonging to three different classes: 34 healthy, 39 IGT, and 29 T2D subjects. Each subject was monitored for a few days by a CGM sensor that produced a glucose profile from which we extracted 25 GV indices. We used a two-step binary logistic regression model to classify subjects. The first step distinguishes healthy subjects from IGT&T2D, the second step classifies subjects into either IGT or T2D.

Healthy subjects are distinguished from subjects with diabetes (IGT&T2D) with 91.4% accuracy. Subjects are further subdivided into IGT or T2D classes with 79.5% accuracy. Globally, the classification into the three classes shows 86.6% accuracy.

Even with a basic classification strategy, CGM-based GV indices show good accuracy in classifying healthy and subjects with diabetes. The classification into IGT or T2D seems, not surprisingly, more critical, but results encourage further investigation of the present research.

Composite metrics have the potential to provide more complete and clinically useful information about glycemic control than traditional individual metrics such as hemoglobin A1C, %/time/area under curve of hypoglycemia and hyperglycemia.

Using five key metrics that are derived from continuous glucose monitoring, we developed a new, multicomponent composite metric, the Comprehensive Glucose Pentagon (CGP) that demonstrates glycemic control both numerically and visually. Two of its axes are composite metrics—the intensity of hypoglycemia and intensity of hyperglycemia. This approach eliminates the use of the surrogate marker, hemoglobin A1C (A1C), and replaces it with glucose-centric metrics.

We reanalyzed the data from two randomized control trials, the STAR 3 and ASPIRE In-Home studies using the CGP. It provided new insights into the effect of sensor-augmented pumping (SAP) in the STAR 3 trial and sensor-integrated pumping with low-glucose threshold suspend (SIP+TS) in the ASPIRE In-Home trial.

The CGP has the potential to enable health care providers, investigators and patients to better understand the components of glycemic control and the effect of various interventions on the individual elements of that control. This can be done on a daily, weekly, or monthly basis. It also allows direct comparison of the effects on different interventions among clinical trials which is not possible using A1C alone. This new composite metric approach requires validation to determine if it provides a better predictor of long-term outcomes than A1C and/or better predictor of severe hypoglycemia than the low blood glucose index (LBGI).

The purpose was to improve the quality of care of at-risk patients through the addition of connected BG meters and CDSS to improve workflow and thus provide more efficient titration of patient’s insulin regimens remotely between office visits in an attempt to treat them to their glucose targets faster and efficiently, and maintain that improvement over time.

Hardware and software included a real-time cellular-enabled blood glucose (BG) meter and Glytec’s Glucommander™ clinical decision support software (CDSS). A quality improvement (QI) project with retrospective before-and-after comparison was conducted. The training period was 90 days and then the project ran for another 11 months. A protocol comprised Glytec CDSS software, which recommends titration intervals from 3 to 28 days as a function of glycemic control, specifically, longer intervals for better control. There were 46 clinic patients.

A1C decreased from a baseline average of 10.2% to 7.8% at 3 months, 7.8% at 6 months, 7.8% at 9 months, and 7.2% at 12 months. The baseline-to-final A1C decrease shows a P < .00001 by paired t-test. Out of 36 315 BGs, the average number of BG tests per day was 3.03 during the first 3 months and 2.47 during the final 3 months. The percentage of BGs < 54 mg/dL was 0.33% and the percentage of BGs < 40 mg/dL was 0.05%.

This QI project demonstrated the use of CDSS including its built-in feature of titration interval recommendation can safely and effectively lower A1C for at-risk patients, treat patients to target safely, and maintain those improvements over 12 months of follow-up.

Clinical interventional studies in diabetes mellitus usually exclude patients undergoing peritoneal dialysis (PD). This study evaluates the impact of an educational program and a basal-bolus insulin regimen on the blood glucose level control and risk of hypoglycemia in this population.

A before-and-after study was conducted in type 1 and type 2 DM patients undergoing PD at the Renal Therapy Services (RTS) clinic network, Bogota, Colombia. An intervention was instituted consisting of a three-month educational program and a basal-bolus detemir (Levemir, NovoNordisk) and aspart (Novorapid, NovoNordisk) insulin regimen. Prior to the intervention and at the end of treatment were conducted measures of HbA1c levels and continuous glucose monitoring (CGM).

Forty-seven patients were recruited. Mean HbA1c level decreased from 8.41% ± 0.83 to 7.68% ± 1.32 (mean difference −0.739, 95% CI −0.419, −1.059; P < .0001). Of subjects, 52% achieved HbA1c levels <7.5% at the end of study. Mean blood glucose level reduced from 194.0 ± 42.5 to 172.9 ± 31.8 mg/dl (P = .0015) measured by CGM. Significant differences were not observed in incidence of overall (P = .7739), diurnal (P = .3701), or nocturnal (P = .5724) hypoglycemia episodes nor in area under the curve (AUC) <54 mg/dl (P = .9528), but a reduction in AUC >180 (P < .01) and AUC >250 (P = .01) was evidenced for total, diurnal, and nocturnal episodes.

An intervention consisting of an educational program and a basal-bolus insulin regimen in type 1 and type 2 diabetes mellitus patients undergoing PD caused a decrease in HbA1c levels, and mean blood glucose levels as measured from CGM with no significant increases in hypoglycemia episodes.

The aim of this study was to analyze prescription patterns and disease control in patients with type 2 diabetes mellitus (T2DM) in nursing home and home care settings in Germany.

The present study is based on data from the Disease Analyzer database (QuintilesIMS). Patients with an initial diagnosis of T2DM and documented HbA1c values between January 2011 and December 2015 were included in the analysis. The index date corresponded to the last documented HbA1c value. Patients in nursing homes were matched (1:1) with patients living at home based on age, gender, and dementia diagnosis. The first outcome of the study was the share of use of several antidiabetic drugs in the two different settings. The second outcome was the mean HbA1c value and the proportion of patients with HbA1c values lower than 7% in the two different groups.

In this study, 4925 individuals lived in nursing homes and 4925 individuals lived at home. The mean age was 80.7 years (SD = 7.7). Prescription patterns differed significantly between nursing home and home care settings: insulin (57.9% vs 41.1%), metformin (46.6% vs 60.5%), sulfonylurea (24.9% vs 34.2%), DPP4 inhibitors (13.4% vs 19.8%), and other antihyperglycemic drugs (7.8% vs 12.1%). In contrast, mean HbA1c values (nursing home: 7.2%; home: 7.2%) and the share of patients with Hb1Ac values lower than 7% (nursing home: 49.1%; home: 50.9%) did not differ significantly between the two groups.

Overall, the differences in prescription patterns between nursing homes and home care were not associated with significant differences in the management of T2DM.

The aim of this study was to analyze the persistence with oral antihyperglycemic drugs in type 2 diabetes mellitus (T2DM) patients with dementia in Germany.

This study included adults with T2DM who were treated with oral antihyperglycemic drugs and were diagnosed with dementia in 1262 German general practices between January 2013 and December 2015 (index date). Dementia cases were matched (1:1) to controls without dementia by age, gender, type of practice, type of residence, physician, and initial antihyperglycemic therapy, using a propensity score method. The primary outcome of the study was the rate of persistence with oral antihyperglycemic drugs in dementia cases and controls without dementia in the year following the index date. Persistence was estimated as therapy time without treatment discontinuation, which was defined as at least 90 days without oral antihyperglycemic therapy. Cox regressions were used to determine the impact of dementia on persistence with oral antihyperglycemic treatment in the entire population and in different subgroups.

This study included 848 T2DM patients with dementia and 848 T2DM patients without dementia. In the dementia group, 39.2% of patients were men, while the control group included 40.8% men. After 12 months of follow-up, 72.7% of dementia cases and 71.4% of controls without dementia were persistent (P = .291). No significant association was found between dementia and persistence with oral antihyperglycemic drugs in the entire population and in different subgroups (HR ranging from 0.84 to 1.25).

Dementia did not have a significant impact on persistence with oral antihyperglycemic drugs in T2DM patients in Germany.

Ultra-fast-acting insulins, such as fast-acting insulin aspart (faster aspart), have pharmacokinetic properties that may be advantageous for patients using continuous subcutaneous insulin infusion (CSII), provided that they are compatible with and safe to use in CSII.

Randomized, double-blind, parallel-group, actively controlled trial evaluating compatibility, efficacy, and safety of faster aspart in adults with type 1 diabetes using their own MiniMed Paradigm pump with Quick-Set or Silhouette infusion sets. Following run-in, subjects were randomized (2:1) to faster aspart (n = 25) or insulin aspart (n = 12) for 6 weeks. Primary endpoint was the number of microscopically confirmed episodes of infusion-set occlusions.

No microscopically confirmed episodes of infusion-set occlusions were observed in either arm. Seven possible infusion-set occlusions were reported by five subjects (all faster aspart); none were prompted by a plug observed by the subject (prompted by unexplained hyperglycemia [n = 6] or leakage [n = 1]) and none were confirmed. Macroscopic and microscopic evaluation showed no color change or particle/crystal formation in the infusion sets. Premature infusion-set changes were reported in 44% and 16.7% of subjects in the faster aspart and insulin aspart groups, respectively. A nonsignificant trend toward better efficacy was observed with faster aspart (estimated treatment difference [ETD] [95% CI] in HbA1c change: –0.14% [–0.40, 0.11]). No new safety issues were found in either treatment group.

Over 6 weeks of treatment, no microscopically confirmed infusion-set occlusions were observed for faster aspart or insulin aspart, indicating similar compatibility with CSII use.

For new insulin analogs with properties that vary from human insulin, defining activity in units of human insulin based on glycemic lowering efficacy may be challenging. Here we present a new method that can be used to quantify a unit dose of an experimental insulin when the traditional euglycemic clamp method is not adequate.

Joint modeling of insulin dose and the glycemic outcome variable hemoglobin A1c (HbA1c), where both were response variables, was used to evaluate insulin unit potency for basal insulin peglispro (BIL). The data were from the Phase 3 program for BIL, which included greater than 5500 patients with type 1 or type 2 diabetes who were treated for 26 or 52 weeks with BIL or a comparator insulin. Both basal-bolus and basal insulin only studies were included, and some type 2 diabetes patients were insulin-naïve.

The analysis showed that 1 unit of BIL, composed of 9 nmol of active ingredient, had similar or slightly greater potency compared to 1 unit insulin glargine or NPH insulin for all populations.

Despite some limitations, the joint modeling of HbA1c and insulin dose provides a reasonable approach to estimate the relative potency of a new basal insulin versus an established basal insulin.

Painful subcutaneous insulin injections may decrease treatment compliance. Improving injection comfort therefore represents a particular area of technological research in which steady progress has been made since the introduction of the insulin pen in 1985. Injection pain can be influenced by many variables, but relatively little is known about their impact. This study investigated the impact of injection volume (range 0-2250 µL), speed (range 0-800 µL/sec), and site (abdomen vs thigh) on pain sensation.

In random order, patients (n = 80) with type 1 or type 2 diabetes received 24 saline injections subcutaneously through a 27G ultra-thin-wall needle. Injections were performed in the abdomen (n = 19) and thigh (n = 5) with predefined speed-volume combinations. For each injected speed-volume combination, patients scored their pain sensation on a 100 mm visual analog scale (VAS).

The mean pain scores for speed-volume combinations were all in the lower part (<20 mm) of the VAS, indicating zero to mild pain. Pain sensation was statistically higher (P < .05) with the 2250 µL volume compared to other injection volumes (range 4.3-5.1 mm) and with thigh compared to abdomen injections (2.1 mm). Pain sensation did not change with increasing injection speed. Patient acceptance of the injection pain was high for all injections (range 93.7-98.7%).

In summary, large volume and thigh injections are rated more painful, but the clinical impact of these findings is likely marginal considering the low absolute pain levels and high patient acceptance rates. Injection speed does not influence pain sensation.

We describe the development of a new mobile app called “FootSnap,” to standardize photographs of diabetic feet and test its reliability on different occasions and between different operators.

FootSnap was developed by a multidisciplinary team for use with the iPad. The plantar surface of 30 diabetic feet and 30 nondiabetic control feet were imaged using FootSnap on two separate occasions by two different operators. Reproducibility of foot images was determined using the Jaccard similarity index (JSI).

High intra- and interoperator reliability was demonstrated with JSI values of 0.89-0.91 for diabetic feet and 0.93-0.94 for control feet.

Similarly high reliability between groups indicates FootSnap is appropriate for longitudinal follow-ups in diabetic feet, with potential for monitoring pathology.

The use of self-monitoring of blood glucose (SMBG) in patients with non-insulin-treated type 2 diabetes is debated. Meta-analyses of randomized clinical trials (RCTs) suggest a small reduction of HbA1c in patients using SMBG, without considering potential confounders, such as SMBG regimen and use of SMBG data to adjust diabetes medications.

A meta-analysis was performed including RCTs in patients with non-insulin-treated type 2 diabetes, with an intervention of ≥24 weeks and HbA1c as the primary endpoint, to verify the effect of SMBG (vs no monitoring), structured SMBG (vs unstructured), and of SMBG-driven therapy adjustments.

In RCTs (n = 8) comparing SMBG with no SMBG (1277 and 1072 patients, respectively), SMBG reduced HbA1c by −0.17% (95% CI −0.25 to −0.09%, P < .003). The reduction in HbA1c was greater in RCTs (n = 3) in which SMBG data were used to adjust diabetes medications (HbA1c decrease: −0.3% [95% CI −0.49 to −0.1%]) than in RCTs (n = 6) where SMBG data were not used for this purpose (HbA1c decrease: −0.1% [95% CI −0.2 to 0.0%]) (P < .005). In the RCTs comparing structured and unstructured SMBG (757 and 750 patients, respectively), in which structured SMBG data were also used to adjust diabetes medications, the HbA1c difference between groups at study end was −0.27% (95% CI −0.49 to −0.04%, P < .018).

In RCTs performed in non-insulin-treated patients with type 2 diabetes, SMBG is associated with a significant, although small, reduction in HbA1c. HbA1c reduction was greater with structured SMBG and when structured SMBG data were used to adjust diabetes therapy.