The surveillance error grid

Accuracy and Feasibility of Real-time Continuous Glucose Monitoring in Critically Ill Patients After Abdominal Surgery and Solid Organ Transplantation

OBJECTIVE

Glycemia management in critical care is posing a challenge in frequent measuring and adequate insulin dose adjustment. In recent years, continuous glucose measurement has gained accuracy and reliability in outpatient and inpatient settings. The aim of this study was to assess the feasibility and accuracy of real-time continuous glucose monitoring (CGM) in ICU patients after major abdominal surgery.

RESEARCH DESIGN AND METHODS

We included patients undergoing pancreatic surgery and solid organ transplantation (liver, pancreas, islets of Langerhans, kidney) requiring an ICU stay after surgery. We used a Dexcom G6 sensor, placed in the infraclavicular region, for real-time CGM. Arterial blood glucose measured by the amperometric principle (ABL 800; Radiometer, Copenhagen, Denmark) served as a reference value and for calibration. Blood glucose was also routinely monitored by a StatStrip bedside glucose meter. Sensor accuracy was assessed by mean absolute relative difference (MARD), bias, modified Bland-Altman plot, and surveillance error grid for paired samples of glucose values from CGM and acid-base analyzer (ABL).

RESULTS

We analyzed data from 61 patients and obtained 1,546 paired glucose values from CGM and ABL. Active sensor use was 95.1%. MARD was 9.4%, relative bias was 1.4%, and 92.8% of values fell in zone A, 6.1% fell in zone B, and 1.2% fell in zone C of the surveillance error grid. Median time in range was 78%, with minimum (<1%) time spent in hypoglycemia. StatStrip glucose meter MARD compared with ABL was 5.8%.

CONCLUSIONS

Our study shows clinically applicable accuracy and reliability of Dexcom G6 CGM in postoperative ICU patients and a feasible alternative sensor placement site.

Performance of Subcutaneous Continuous Glucose Monitoring in Adult Critically Ill Patients Receiving Vasopressor Therapy

Background: Subcutaneous continuous glucose monitoring (CGM) may facilitate glucose control in the ICU. We aimed to assess the accuracy of CGM (Dexcom G6) against arterial blood glucose (ABG) in adult critically ill patients receiving intravenous insulin infusion and vasopressor therapy. We also aimed to assess feasibility and tolerability of CGM in this setting. Methods: We included ICU patients receiving mechanical ventilation, insulin, and vasopressor therapy. Numerical accuracy was assessed by the mean absolute relative difference (MARD), overall, across arterial glucose strata, over different noradrenaline equivalent infusion rates, and over time since CGM start. MARD <14% was considered acceptable. Clinical accuracy was assessed using Clarke Error Grid (CEG) analysis. Feasibility outcome included number and duration of interrupted sensor readings due to signal loss. Tolerability outcome included skin reactions related to sensor insertion or sensor adhesives. Results: We obtained 2946 paired samples from 40 patients (18 with type 2 diabetes) receiving a median (IQR) maximum noradrenaline equivalent infusion rate of 0.18 (0.08-0.33) µg/kg/min during CGM. Overall, MARD was 12.7% (95% CI 10.7-15.3), and 99.8% of CGM readings were within CEG zones A and B. MARD values ≥14% were observed when ABG was outside target range (6-10 mmol/L [108-180 mg/dL]) and with noradrenaline equivalent infusion rates above 0.10 µg/kg/min. Accuracy improved with time after CGM start, reaching MARD values <14% after 36 h. We observed four episodes of interrupted sensor readings due to signal loss, ranging from 5 to 20 min. We observed no skin reaction related to sensor insertion or sensor adhesives. Conclusions: In our ICU cohort of patients receiving vasopressor infusion, subcutaneous CGM demonstrated acceptable overall numerical and clinical accuracy. However, suboptimal accuracy may occur outside glucose ranges of 6-10 mmol/L (108-180 mg/dL), during higher dose vasopressor infusion, and during the first 36 h after CGM start.

Using Artificial Intelligence to Improve the Accuracy of a Wrist-Worn, Noninvasive Glucose Monitor: A Pilot Study

BACKGROUND

Self-monitoring of glucose is important to the successful management of diabetes; however, existing monitoring methods require a degree of invasive measurement which can be unpleasant for users. This study investigates the accuracy of a noninvasive glucose monitoring system that analyses spectral variations in microwave signals.

METHODS

An open-label, pilot design study was conducted with four cohorts (N = 5/cohort). In each session, a dial-resonating sensor (DRS) attached to the wrist automatically collected data every 60 seconds, with a novel artificial intelligence (AI) model converting signal resonance output to a glucose prediction. Plasma glucose was measured in venous blood samples every 5 minutes for Cohorts 1 to 3 and every 10 minutes for Cohort 4. Accuracy was evaluated by calculating the mean absolute relative difference (MARD) between the DRS and plasma glucose values.

RESULTS

Accurate plasma glucose predictions were obtained across all four cohorts using a random sampling procedure applied to the full four-cohort data set, with an average MARD of 10.3%. A statistical analysis demonstrates the quality of these predictions, with a surveillance error grid (SEG) plot indicating no data pairs falling into the high-risk zones.

CONCLUSIONS

These findings show that MARD values approaching accuracies comparable to current commercial alternatives can be obtained from a multiparticipant pilot study with the application of AI. Microwave biosensors and AI models show promise for improving the accuracy and convenience of glucose monitoring systems for people with diabetes.

Comparison of Point Accuracy Between Two Widely Used Continuous Glucose Monitoring Systems

BACKGROUND

Safe and effective self-management of glucose levels requires immediate access to accurate data. We assessed the point accuracy of the Dexcom G7 Continuous Glucose Monitoring System (Dexcom, Inc., San Diego, CA, USA) and FreeStyle Libre 3 (Abbott Diabetes Care, Alameda, CA, USA) sensors in a head-to-head comparison.

METHOD

Multicenter, single-arm, prospective, nonsignificant risk evaluation enrolled adults (≥ 18 years) with diagnosed type 1 diabetes (T1D) or type 2 diabetes (T2D). Accuracy was assessed by comparing sensor data to laboratory reference values Yellow Springs Instrument [YSI] and capillary blood glucose values. Outcome measures were differences in mean absolute relative difference (MARD), number and percentage of matched glucose pairs within ±20 mg/dL/±20 of reference values within glucose ranges: < 54, 54 to 69, 70 to 180, 181 to 250, > 250 mg/dL, and combined.

RESULTS

Data from 55 adults were included in the analysis. Analysis showed significantly lower MARD with the FreeStyle Libre 3 sensor vs the Dexcom G7 sensor (8.9% vs 13.6%, respectively, P < .0001) with a higher percentage of glucose values within ±20 mg/dL/±20 of reference (91.4% vs 78.6%). The MARD values for both continuous glucose monitoring (CGM) sensors were similar during the first 12 hours; however, the FreeStyle Libre 3 MARD was notably lower than the Dexcom G7 MARD during the next 12 hours (10.0% vs 15.1%, respectively, P < .0001) and throughout the study period.

CONCLUSIONS

The FreeStyle Libre 3 sensor was more accurate than the Dexcom G7 sensor in all metrics evaluated throughout the study period. This is the first head-to-head study to our knowledge that compares the flagship products currently in widespread use of the two largest CGM manufacturers.

Comparing Clinical Accuracy and Analytical Accuracy Between Continuous Glucose Monitors

This study compares performance between two continuous glucose monitors (CGMs). The study design contains a mix of laboratory results (CGM vs YSI) and home results (CGM vs glucose meter). Analysis is provided for both clinical accuracy and analytical accuracy of CGM glucose measurements. Both types of accuracy are important. Error grid analysis informs about clinical accuracy. Analytical error is important as most users would prefer a CGM with a smaller spread of CGM versus reference differences. The authors provide the percentage of time that no result was obtained. Study design, data analysis, and editorial support were provided by a manufacturer of one of the products studied. This study provides a template for comparisons.

Accuracy of continuous glucose monitoring during exercise-related hypoglycemia in individuals with type 1 diabetes

Background

Hypoglycemia is common in individuals with type 1 diabetes, especially during exercise. We investigated the accuracy of two different continuous glucose monitoring systems during exercise-related hypoglycemia in an experimental setting.

Materials and methods

Fifteen individuals with type 1 diabetes participated in two separate euglycemic-hypoglycemic clamp days (Clamp-exercise and Clamp-rest) including five phases: 1) baseline euglycemia, 2) plasma glucose (PG) decline ± exercise, 3) 15-minute hypoglycemia ± exercise, 4) 45-minute hypoglycemia, and 5) recovery euglycemia. Interstitial PG levels were measured every five minutes, using Dexcom G6 (DG6) and FreeStyle Libre 1 (FSL1). Yellow Springs Instruments 2900 was used as PG reference method, enabling mean absolute relative difference (MARD) assessment for each phase and Clarke error grid analysis for each day.

Results

Exercise had a negative effect on FSL1 accuracy in phase 2 and 3 compared to rest (ΔMARD = +5.3 percentage points [(95% CI): 1.6, 9.1] and +13.5 percentage points [6.4, 20.5], respectively). In contrast, exercise had a positive effect on DG6 accuracy during phase 2 and 4 compared to rest (ΔMARD = -6.2 percentage points [-11.2, -1.2] and -8.4 percentage points [-12.4, -4.3], respectively). Clarke error grid analysis showed a decrease in clinically acceptable treatment decisions during Clamp-exercise for FSL1 while a contrary increase was observed for DG6.

Conclusion

Physical exercise had clinically relevant impact on the accuracy of the investigated continuous glucose monitoring systems and their ability to accurately detect hypoglycemia.

The Myth of MARD (Mean Absolute Relative Difference): Limitations of MARD in the Clinical Assessment of Continuous Glucose Monitoring Data

The mean absolute relative difference (MARD) is a numerical metric that has been adopted by the diabetes technology community as the main indicator that describes the accuracy of a glucose sensor at a single point in time. The appropriateness of this adoption is questionable because there is limited evidence that MARD has meaningful clinical relevance in the current era of sensor technology. The calculation may be simple, but evaluation of MARD can be very complex because it is substantially impacted by the design of the data collection in an accuracy study. Factors that can influence the overall MARD include participant demographics such as type of diabetes and age, site of sensor wear, and the percentage of collected values in each glycemic range during the study that is, in turn, a function of the study design. MARD is only one of several important statistical metrics such as bias and precision that are relevant to assessing accuracy of a sensor. Furthermore, these analytic metrics convey little information about the safety and effectiveness of sensor use with an automated insulin delivery system or a standalone device. There are no clinical studies in people with diabetes (PWD) proving that MARD can accurately differentiate between a safe and unsafe sensor or between a more and less clinically effective sensor. Moreover, there are alternatives to MARD that can do this in a clinically meaningful way, which include error grid analyses and clinical studies in PWD. This review attempts to demythologize the status of MARD for the diabetes community in an effort to shift the focus from MARD to using clinically relevant assessments.

Artificial Intelligence and Machine Learning for Improving Glycemic Control in Diabetes: Best Practices, Pitfalls, and Opportunities

OBJECTIVE

Artificial intelligence and machine learning are transforming many fields including medicine. In diabetes, robust biosensing technologies and automated insulin delivery therapies have created a substantial opportunity to improve health. While the number of manuscripts addressing the topic of applying machine learning to diabetes has grown in recent years, there has been a lack of consistency in the methods, metrics, and data used to train and evaluate these algorithms. This manuscript provides consensus guidelines for machine learning practitioners in the field of diabetes, including best practice recommended approaches and warnings about pitfalls to avoid.

METHODS

Algorithmic approaches are reviewed and benefits of different algorithms are discussed including importance of clinical accuracy, explainability, interpretability, and personalization. We review the most common features used in machine learning applications in diabetes glucose control and provide an open-source library of functions for calculating features, as well as a framework for specifying data sets using data sheets. A review of current data sets available for training algorithms is provided as well as an online repository of data sources.

SIGNIFICANCE

These consensus guidelines are designed to improve performance and translatability of new machine learning algorithms developed in the field of diabetes for engineers and data scientists.

Accuracy of a Non-Invasive Home Glucose Monitor for Measurement of Blood Glucose

Introduction

Patients with diabetes mellitus monitor their blood glucose at home with monitors that require a drop of blood or use a continuous glucose monitoring device that implants a small needle in the body. However, both cause discomfort to the patients which may inhibit them for regular blood glucose checks. Photoplethysmogram (PPG) sensing technology is an approach for non-invasive blood glucose measurement and PPG sensors can be used to predict hypoglycaemic episodes. InChcek is a PPG-based non-invasive glucose monitor. However, its accuracy has not been checked yet. Hence, this study aimed to evaluate the accuracy of InCheck, a non-invasive glucose monitor for the estimation of blood glucose.

Methods

In a tertiary care hospital, patients who came for blood glucose estimation were tested for blood glucose non-invasively on the InCheck device and then by the laboratory method (glucose oxidase-peroxidase). These two readings were compared. We used International Organization for Standardization (ISO) 15197:2013 (95% of values should be within ± 15 mg/dL of reference reading if reference glucose <100 mg/dL or within ± 15% of reference reading if reference glucose ≥100 mg/dL and 99% of the values should be within zones A and B in consensus error grid), and Surveillance Error Grid for analyzing the accuracy.

Results

A total of 1223 samples were analyzed. There was a significant difference between the reference method glucose level (135 [Q1-Q3: 97 - 179] mg/dL) and monitor-measured glucose level (188.33 [Q1-Q3: 167.33-209.33] mg/dL) (P < 0.0001). A total of 18.5% of readings were following ISO 15197:2013 criteria and 67.25% of coordinates were within zone A and zone B of the consensus error grid. In the surveillance error grid analysis, about 29.4% of values were in the no-risk zone, 51.8% in slight risk, 18.6% in moderate risk, and 0.2% were in the severe risk zone.

Conclusion

The accuracy of the InCheck device for the estimation of blood glucose by PPG signal is not following the recommended guidelines. Hence, further research is necessary for programming or redesigning the hardware and software for a better result from this optical sensor-based non-invasive home glucose monitor.

Flash glucose monitoring system in gestational diabetes: a study of accuracy and usability

PURPOSE

Studies of flash glucose monitoring systems (FGMSs) in pregnancy are insufficient, especially in gestational diabetes (GD). Our aim was to evaluate Freestyle Libre's usability and accuracy (compared to self-monitoring of blood glucose [SMBG]) for GD patients in real-life conditions.

METHODS

This is a prospective study with pregnant women diagnosed with GD (n = 24 for the usability analysis; n = 19 for the accuracy analysis). The study duration was up to 28 days (lifetime of two sensors). Participants executed a minimum of four daily FGMS readings obtained immediately after capillary SMBG. Analytical accuracy was assessed with mean absolute relative difference (MARD) and mean absolute difference (MAD); clinical accuracy was assessed with Surveillance Error Grid (SEG). Usability was evaluated with a user acceptability questionnaire.

RESULTS

The mean pregestational BMI was 25.21 ± 5.15 kg/m2 (mean ± SD), the mean gestational age was 30.31 ± 2.02 weeks, and the mean glucose values were 76.63 ± 7.49 mg/dL. A total of 1339 SMBG-FGMS pairs of values were obtained. Analytical accuracy was good with an overall MARD of 14.07% and an in-target MARD of 13.79%. The number of SMBG-FMGS pairs for above-target values was low (122 of 1339) with an associated MARD of 17.95%. Clinical accuracy of the FGMS was demonstrated, with 94.4% of values in the no-risk or slight, lower risk zones of the SEG. FGMS accuracy was unaffected by pregestational BMI or gestational age. The user acceptability questionnaire showed high levels of satisfaction, with 95.8-100% preferring FGMS to SMBG. No unexpected or severe adverse effects occurred.

CONCLUSION

FGMS showed good performance in GD regarding accuracy and usability. Larger studies are needed to corroborate our results, verify the analytical accuracy of above-target values as this glucose range might lead to initiation or adjustment of pharmacological therapy, and ultimately establish definitive recommendations regarding prescription of FGMS for GD patients.

Clinical Performance Evaluation of Continuous Glucose Monitoring Systems: A Scoping Review and Recommendations for Reporting

The use of different approaches for design and results presentation of studies for the clinical performance evaluation of continuous glucose monitoring (CGM) systems has long been recognized as a major challenge in comparing their results. However, a comprehensive characterization of the variability in study designs is currently unavailable. This article presents a scoping review of clinical CGM performance evaluations published between 2002 and 2022. Specifically, this review quantifies the prevalence of numerous options associated with various aspects of study design, including subject population, comparator (reference) method selection, testing procedures, and statistical accuracy evaluation. We found that there is a large variability in nearly all of those aspects and, in particular, in the characteristics of the comparator measurements. Furthermore, these characteristics as well as other crucial aspects of study design are often not reported in sufficient detail to allow an informed interpretation of study results. We therefore provide recommendations for reporting the general study design, CGM system use, comparator measurement approach, testing procedures, and data analysis/statistical performance evaluation. Additionally, this review aims to serve as a foundation for the development of a standardized CGM performance evaluation procedure, thereby supporting the goals and objectives of the Working Group on CGM established by the Scientific Division of the International Federation of Clinical Chemistry and Laboratory Medicine.

Clinical and Analytic Accuracy of Simultaneously Acquired Hemoglobin Measurements: A Multi-Institution Cohort Study to Minimize Redundant Laboratory Usage

OBJECTIVES

Frequent diagnostic blood sampling contributes to anemia among critically ill children. Reducing duplicative hemoglobin testing while maintaining clinical accuracy can improve patient care efficacy. The objective of this study was to determine the analytical and clinical accuracy of simultaneously acquired hemoglobin measurements with different methods.

DESIGN

Retrospective cohort study.

SETTING

Two U.S. children's hospitals.

PATIENTS

Children (< 18 yr old) admitted to the PICU.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We identified hemoglobin results from complete blood count (CBC) panels paired with blood gas (BG) panels and point-of-care (POC) devices. We estimated analytic accuracy by comparing hemoglobin distributions, correlation coefficients, and Bland-Altman bias. We measured clinical accuracy with error grid analysis and defined mismatch zones as low, medium, or high risk-based on deviance from unity and risk of therapeutic error. We calculated pairwise agreement to a binary decision to transfuse based on a hemoglobin value. Our cohort includes 49,004 ICU admissions from 29,926 patients, resulting in 85,757 CBC-BG hemoglobin pairs. BG hemoglobin was significantly higher (mean bias, 0.43-0.58 g/dL) than CBC hemoglobin with similar Pearson correlation ( R2 ) (0.90-0.91). POC hemoglobin was also significantly higher, but of lower magnitude (mean bias, 0.14 g/dL). Error grid analysis revealed only 78 (< 0.1%) CBC-BG hemoglobin pairs in the high-risk zone. For CBC-BG hemoglobin pairs, at a BG hemoglobin cutoff of greater than 8.0 g/dL, the "number needed to miss" a CBC hemoglobin less than 7 g/dL was 275 and 474 at each institution, respectively.

CONCLUSIONS

In this pragmatic two-institution cohort of greater than 29,000 patients, we show similar clinical and analytic accuracy of CBC and BG hemoglobin. Although BG hemoglobin values are higher than CBC hemoglobin values, the small magnitude is unlikely to be clinically significant. Application of these findings may reduce duplicative testing and decrease anemia among critically ill children.

Blood Glucose Level Time Series Forecasting: Nested Deep Ensemble Learning Lag Fusion

Blood glucose level prediction is a critical aspect of diabetes management. It enables individuals to make informed decisions about their insulin dosing, diet, and physical activity. This, in turn, improves their quality of life and reduces the risk of chronic and acute complications. One conundrum in developing time-series forecasting models for blood glucose level prediction is to determine an appropriate length for look-back windows. On the one hand, studying short histories foists the risk of information incompletion. On the other hand, analysing long histories might induce information redundancy due to the data shift phenomenon. Additionally, optimal lag lengths are inconsistent across individuals because of the domain shift occurrence. Therefore, in bespoke analysis, either optimal lag values should be found for each individual separately or a globally suboptimal lag value should be used for all. The former approach degenerates the analysis's congruency and imposes extra perplexity. With the latter, the fine-tunned lag is not necessarily the optimum option for all individuals. To cope with this challenge, this work suggests an interconnected lag fusion framework based on nested meta-learning analysis that improves the accuracy and precision of predictions for personalised blood glucose level forecasting. The proposed framework is leveraged to generate blood glucose prediction models for patients with type 1 diabetes by scrutinising two well-established publicly available Ohio type 1 diabetes datasets. The models developed undergo vigorous evaluation and statistical analysis from mathematical and clinical perspectives. The results achieved underpin the efficacy of the proposed method in blood glucose level time-series prediction analysis.

Accuracy of Flash Glucose Monitoring in Hemodialysis Patients With and Without Diabetes Mellitus

AIMS

Glucose and insulin metabolism are altered in hemodialysis patients, and diabetes management is difficult in these patients. We aimed to validate flash glucose monitoring (FGM) in hemodialysis patients with and without diabetes mellitus as an attractive option for glucose monitoring not requiring regular self-punctures.

METHODS

We measured interstitial glucose using a FreeStyle Libre device in eight hemodialysis patients with and seven without diabetes mellitus over 14 days and compared the results to simultaneously performed self-monitoring of capillary blood glucose (SMBG).

RESULTS

In 720 paired measurements, mean flash glucose values were significantly lower than self-measured capillary values (6.17±2.52 vs. 7.15±2.41 mmol/L, p=1.3 E-86). Overall, the mean absolute relative difference was 17.4%, and the mean absolute difference was 1.20 mmol/L. The systematic error was significantly larger in patients without vs. with diabetes (- 1.17 vs. - 0.82 mmol/L) and on dialysis vs. interdialytic days (-1.09 vs. -0.90 mmol/L). Compared to venous blood glucose (72 paired measurements), the systematic error of FGM was even larger (5.89±2.44 mmol/L vs. 7.78±7.25 mmol/L, p=3.74E-22). Several strategies to reduce the systematic error were evaluated, including the addition of +1.0 mmol/L as a correction term to all FGM values, which significantly improved accuracy.

CONCLUSIONS

FGM systematically underestimates blood glucose in hemodialysis patients but, taking this systematic error into account, the system may be useful for glucose monitoring in hemodialysis patients with or without diabetes.

Causality analysis in type 1 diabetes mellitus with application to blood glucose level prediction

Effective control of blood glucose level (BGL) is the key factor in the management of type 1 diabetes mellitus (T1D). BGL prediction is an important tool to help maximise the time BGL is in the target range and thus minimise both acute and chronic diabetes-related complications. To predict future BGL, histories of variables known to affect BGL, such as carbohydrate intake, injected bolus insulin, and physical activity, are utilised. Due to these identified cause and effect relationships, T1D management can be examined via the causality context. In this respect, this work initially investigates these relations and quantifies the causality strengths of each variable with BGL using the convergent cross mapping method (CCM). Then, considering the extended CCM, the causality strengths of each variable for different lags are quantified. After that, the optimal time lag for each variable is determined according to the quantified causality effects. Subsequently, the feasibility of leveraging causality information as prior knowledge for BGL prediction is investigated by proposing two approaches. In the first approach, causality strengths are used as weights for relevant affecting variables. In the second approach, the optimal causal lags and the corresponding causality strengths are considered the shifts and weights for the variables, respectively. Overall, the evaluation criteria and statistical analysis used for comparing results show the effectiveness of using causality analysis in T1D management.

Post-Market Surveillance Assessment of the Clinical Accuracy of a Blood Glucose Monitoring System with an Improved Algorithm for Enhanced Product Performance

BACKGROUND

On-going manufacturer-led post-market surveillance (PMS), assessing the clinical accuracy of blood glucose monitoring (BGM) systems, is critical to substantiate the performance of such products for people with diabetes.

MATERIALS AND METHODS

Batches of Verio test-strip product were randomly and routinely selected over the period from launch of an improved-algorithm product to reporting date and sent to 3 clinic sites for clinician-led accuracy assessment. Accuracy is reported as per recently adopted FDA guidance for BGM systems, EN ISO 15197:2015 and MARD/MAD (Mean absolute relative difference/Mean absolute difference).

RESULTS

Thirty-three individual test-strip batches were evaluated corresponding to 506 unique donors. Accuracy performance - FDA: 98.9% of values within ±15% of comparator; ISO: 99.0% within ±15 mg/dL or ±15% at <100 mg/dL (<5.55 mmol/L) or ≥100 mg/dL (≥5.55 mmol/L) glucose, respectively. Overall MARD was 4.19% with a MARD range of 3.54%-5.73% across all test strip batches.

CONCLUSIONS

This post-market surveillance program demonstrates the new BGM system consistently meets measures of clinical accuracy specified by regulators. This program supports a growing demand by regulators for real-world evidence demonstrating consistent in-market product efficacy as opposed to the current largely passive approach that relies on assessment of reports filed by device users.

Post-Market Surveillance of a Blood Glucose Test Strip Demonstrates No Evidence of Interference on Clinical Accuracy in a Large Cohort of People with Type 1 or Type 2 Diabetes

BACKGROUND

Regulations and industry guidance relating to testing for interference in blood glucose monitoring (BGM) systems continue to focus on in vitro laboratory bench tests. Post-market surveillance (PMS) in a clinical setting allows for BGM accuracy assessments to evaluate the impact of real-world exposure to polypharmacy in people with diabetes. This study evaluated the OneTouch Select Plus® BGM test-strip accuracy with respect to polypharmacy using a clinical registry dataset.

METHODS

Medication profiles were analysed for 1023 subjects (425 with type 1 (T1D) and 598 with type 2 diabetes (T2D)) attending 3 UK hospitals. Blood samples were analysed to determine clinical accuracy of the BGM test-strip against a laboratory comparator.

RESULTS

538 different medications (48 diabetes and 490 non-diabetes) were recorded across the 1023 subjects. Patients took on average 6.9 (n = 1-36) individual medications and 4.1 (n = 1-13) unique medication classes. Clinical accuracy to EN ISO 15197:2015 criteria were met irrespective of increasing average number of individual medications, categorized from 1-3, 4-6, 7-9, 10-12 and >12 taken per subject (97.7%, 97.7%, 97.8%, 97.8%, and 98.4%, respectively). Clinical accuracy criteria were met across 15 classes of medication using the combined dataset (97.9%; 29784/30433). Surveillance Error Grid (SEG) analysis showed 98.7% (29959/30368) of readings presented no clinical risk. No individual class or combination of medication classes impacted clinical accuracy of the BGM test-strip.

CONCLUSIONS

Clinical performance for the test strip under assessment demonstrated no evidence of interference from over 500 prescription medications, with clinical accuracy maintained across a range of polypharmacy conditions in people with diabetes.

Continuous glucose monitoring and metrics for clinical trials: an international consensus statement

Randomised controlled trials and other prospective clinical studies for novel medical interventions in people with diabetes have traditionally reported HbA1c as the measure of average blood glucose levels for the 3 months preceding the HbA1c test date. The use of this measure highlights the long-established correlation between HbA1c and relative risk of diabetes complications; the change in the measure, before and after the therapeutic intervention, is used by regulators for the approval of medications for diabetes. However, with the increasing use of continuous glucose monitoring (CGM) in clinical practice, prospective clinical studies are also increasingly using CGM devices to collect data and evaluate glucose profiles among study participants, complementing HbA1c findings, and further assess the effects of therapeutic interventions on HbA1c. Data is collected by CGM devices at 1-5 min intervals, which obtains data on glycaemic excursions and periods of asymptomatic hypoglycaemia or hyperglycaemia (ie, details of glycaemic control that are not provided by HbA1c concentrations alone that are measured continuously and can be analysed in daily, weekly, or monthly timeframes). These CGM-derived metrics are the subject of standardised, internationally agreed reporting formats and should, therefore, be considered for use in all clinical studies in diabetes. The purpose of this consensus statement is to recommend the ways CGM data might be used in prospective clinical studies, either as a specified study endpoint or as supportive complementary glucose metrics, to provide clinical information that can be considered by investigators, regulators, companies, clinicians, and individuals with diabetes who are stakeholders in trial outcomes. In this consensus statement, we provide recommendations on how to optimise CGM-derived glucose data collection in clinical studies, including the specific glucose metrics and specific glucose metrics that should be evaluated. These recommendations have been endorsed by the American Association of Clinical Endocrinologists, the American Diabetes Association, the Association of Diabetes Care and Education Specialists, DiabetesIndia, the European Association for the Study of Diabetes, the International Society for Pediatric and Adolescent Diabetes, the Japanese Diabetes Society, and the Juvenile Diabetes Research Foundation. A standardised approach to CGM data collection and reporting in clinical trials will encourage the use of these metrics and enhance the interpretability of CGM data, which could provide useful information other than HbA1c for informing therapeutic and treatment decisions, particularly related to hypoglycaemia, postprandial hyperglycaemia, and glucose variability.

Experimental and probabilistic model validation of ultrasonic MEMS transceiver for blood glucose sensing

In contrast to traditional laboratory glucose monitoring, recent developments have focused on blood glucose self-monitoring and providing patients with a self-monitoring device. This paper proposes a system based on ultrasound principles for quantifying glucose levels in blood by conducting an in-vitro experiment with goat blood before human blood. The ultrasonic transceiver is powered by a frequency generator that operates at 40 kHz and 1.6 V, and variations in glucose level affect the ultrasonic transceiver readings. The RVM probabilistic model is used to determine the variation in glucose levels in a blood sample. Blood glucose levels are measured simultaneously using a commercial glucose metre for confirmation. The experimental data values proposed are highly correlated with commercial glucose metre readings. The proposed ultrasonic MEMS-based blood glucometer measures a glucose level of [Formula: see text] mg/dl. In the near future, the miniature version of the experimental model may be useful to human society.

The Devil Is in the Details: Use, Limitations, and Implementation of Continuous Glucose Monitoring in the Inpatient Setting

Until recently, continuous glucose monitoring (CGM) systems were reserved for use in the outpatient setting or for investigational purposes in hospitalized patients. However, during the coronavirus disease 2019 pandemic, use of CGM in the inpatient setting has grown rapidly. This review outlines important details related to the accuracy, limitations, and implementation of, as well as necessary staff education for, inpatient CGM use and offers a glimpse into the future of CGM in the inpatient setting.

Continuous Glucose Deviation Interval and Variability Analysis (CG-DIVA): A Novel Approach for the Statistical Accuracy Assessment of Continuous Glucose Monitoring Systems

BACKGROUND

The accuracy of continuous glucose monitoring (CGM) systems is crucial for the management of glucose levels in individuals with diabetes mellitus. However, the discussion of CGM accuracy is challenged by an abundance of parameters and assessment methods. The aim of this article is to introduce the Continuous Glucose Deviation Interval and Variability Analysis (CG-DIVA), a new approach for a comprehensive characterization of CGM point accuracy which is based on the U.S. Food and Drug Administration requirements for "integrated" CGM systems.

METHODS

The statistical concept of tolerance intervals and data from two approved CGM systems was used to illustrate the CG-DIVA.

RESULTS

The CG-DIVA characterizes the expected range of deviations of the CGM system from a comparison method in different glucose concentration ranges and the variability of accuracy within and between sensors. The results of the CG-DIVA are visualized in an intuitive and straightforward graphical presentation. Compared with conventional accuracy characterizations, the CG-DIVA infers the expected accuracy of a CGM system and highlights important differences between CGM systems. Furthermore, it provides information on the incidence of large errors which are of particular clinical relevance. A software implementation of the CG-DIVA is freely available (https://github.com/IfDTUlm/CGM_Performance_Assessment).

CONCLUSIONS

We argue that the CG-DIVA can simplify the discussion and comparison of CGM accuracy and could replace the high number of conventional approaches. Future adaptations of the approach could thus become a putative standard for the accuracy characterization of CGM systems and serve as the basis for the definition of future CGM performance requirements.

Mean Absolute Relative Difference of Blood Glucose Monitoring Systems and Relationship to ISO 15197

BACKGROUND

The analytical quality of a blood glucose monitoring system (BGMS) is often assessed according to the requirements described in the international standard ISO 15197. However, the mean absolute relative difference (MARD) is sometimes used as well. This analysis aims at providing empirical data from BGMS evaluation studies conducted according to ISO 15197 and at providing an estimation of how MARD and percentage of measurement results within ISO accuracy limits are related.

METHODS

Results of 77 system accuracy evaluations conducted according to ISO 15197 were used to calculate MARD between BGMS and a laboratory comparison method's results (glucose oxidase or hexokinase method). Additionally, bias and 95%-limits of agreement (LoA) using the Bland and Altman method were calculated.

RESULTS

MARD results ranged from 2.3% to 20.5%. The lowest MARD of a test strip lot that showed <95% of results within ISO limits was 6.1%. The distribution of MARD results shows that only 3.6% of test strip lots with a MARD equal to or below 7% showed <95% of results within ISO limits (2.2% of all test strip lots). Bias of test strip lots that showed ≥95% of results within the limits ranged from -10.3% to +7.4%. The half-width of the 95%-LoA of test strip lots that showed ≥95% of results within the limits ranged from 4.8% to 24.0%.

CONCLUSION

There is a threshold MARD that may allow an estimate whether ISO 15197 requirements are fulfilled, but this statement cannot be made with certainty.

Accuracy and Stability of a Subcutaneous Flash Glucose Monitoring System in Critically Ill Patients

BACKGROUND

Flash glucose monitoring (FGM) systems can reduce glycemic variability and facilitate blood glucose management within the target range. However, in critically ill patients, only small (n < 30) studies have examined the accuracy of FGM and none have assessed the stability of FGM accuracy. We evaluated the accuracy and stability of FGM in critically ill patients.

METHOD

This was a single-center, retrospective observational study. We included a total of 116 critically ill patients who underwent FGM for glycemic control. The accuracy of FGM was assessed as follows using blood gas glucose values as a reference: (1) numerical accuracy using the mean absolute relative difference, (2) clinical accuracy using consensus error grid analysis, and (3) stability of accuracy assessing 14-day trends in consensus error grid distribution.

RESULTS

FGM sensors remained in situ for a median of 6 [4, 11] days. We compared 2014 pairs of measurements between the sensor and blood gas analysis. Glucose values from the sensor were consistently lower, with a mean absolute relative difference of 13.8% (±16.0%), than those from blood gas analysis. Consensus error grid analysis demonstrated 99.4% of the readings to be in a clinically acceptable accuracy zone. The accuracy of FGM was stable across the 14 days after device insertion.

CONCLUSIONS

FGM had acceptable reliability and accuracy to arterial blood gas analysis in critically ill patients. In addition, the accuracy of FGM persisted for at least 14 days. Our study promotes the potential usefulness of FGM for glycemic monitoring in critically ill patients.

Evaluation of the FreeStyle Libre, a flash glucose monitoring system, in client-owned cats with diabetes mellitus

Objectives

Home blood glucose monitoring using a portable blood glucose meter is important in the management of feline diabetes mellitus, but taking blood samples may be stressful for owners and cats. A flash glucose monitoring system measuring interstitial glucose, such as the FreeStyle Libre, overcomes some of these drawbacks. The aim of this study was to evaluate the practical use and analytical and clinical accuracy of the FreeStyle Libre in 41 client-owned diabetic cats.

Methods

In this prospective study, interstitial glucose concentrations were measured with the FreeStyle Libre and compared with blood glucose concentrations measured with a portable blood glucose meter (AlphaTRAK) on days 1, 7 or 8 and 14 after application of the device. Cat behaviour during application, location, skin reaction at the attachment site and owner satisfaction were assessed. Accuracy was determined by fulfilment of ISO 15197:2013 criteria, including Bland–Altman plotting and error grid analysis.

Results

Placing the device was easy, with 70% of cats showing no reaction. Most sensors were placed on the thoracic wall. Skin reactions at the attachment site were not present or mild in almost all cats. Owners were very satisfied with the use of the FreeStyle Libre. Median functional life of the sensor was 10 days (range 1–14). Good correlation was found between interstitial and blood glucose measurements (rho[r] = 0.88, P <0.0001). Fifty-three percent of interstitial glucose concentrations were within a maximum deviation of 15% from blood glucose concentrations and 92.7% were within the safe risk zones 0 and 1 of the surveillance error grid.

Conclusions and relevance

The flash glucose monitoring system was easy to use and owners of diabetic cats were satisfied with its use. Although the device did not completely fulfil ISO requirements, it is sufficiently accurate for glucose monitoring in diabetic cats.

Effect of intravenous vitamin C on arterial blood gas analyser and Accu-Chek point-of-care glucose monitoring in critically ill patients

Background: Intravenous vitamin C is known to interfere with some point-of-care blood glucose meters. We aimed to determine the concentrations at which ascorbate interferes with glucose concentrations measured using a point-of-care blood glucose meter. We also compared the point-of-care meter and an arterial blood gas (ABG) analyser in the intensive care unit with laboratory glucose monitoring in septic patients receiving intravenous vitamin C infusions. Methods: Blood samples containing normal, depleted and supplemented glucose and increasing concentrations of ascorbate (0.1-1.0 mmol/L) were tested using an Accu-Chek Inform II (Roche Diagnostics, USA) glucometer. For the in vivo study, 41 individual blood samples were drawn daily from septic patients (n = 16) receiving infusions of 25 mg/kg of vitamin C every 6 hours. The glucose values of matched blood samples were assessed using Accu-Chek, ABG and laboratory glucose methods. Results: For every 1 mmol/L of ascorbate added, the glucose concentration measured by the point-of-care monitor increased by 1.4 mmol/L (95% CI, 1.0-1.8; P < 0.001). Analysis of matched blood samples collected following intravenous vitamin C infusion indicated that 98% of the ABG and 83% of the Accu-Chek values met the International Organization for Standardization (ISO) 15197:2013 accuracy criteria. One patient had severe renal impairment, which contributed to elevated plasma vitamin C concentrations (median, 0.95 mmol/L; range, 0.64-1.10 mmol/L), resulting in elevated Accu-Chek readings and presenting a moderate clinical risk for the highest value. Conclusions: Vitamin C concentrations < 0.8 mmol/L do not interfere with point-of-care glucose monitoring. Intravenous vitamin C infusion of 25 mg/kg every 6 hours does not interfere with point-of-care glucose monitoring unless the patient has renal impairment, in which case laboratory glucose tests should be used.

Blood Glucose Level Prediction: Advanced Deep-Ensemble Learning Approach

Optimal and sustainable control of blood glucose levels (BGLs) is the aim of type-1 diabetes management. The automated prediction of BGL using machine learning (ML) algorithms is considered as a promising tool that can support this aim. In this context, this paper proposes new advanced ML architectures to predict BGL leveraging deep learning and ensemble learning. The deep-ensemble models are developed with novel meta-learning approaches, where the feasibility of changing the dimension of a univariate time series forecasting task is investigated. The models are evaluated regression-wise and clinical-wise. The performance of the proposed ensemble models are compared with benchmark non-ensemble models. The results show the superior performance of the developed ensemble models over developed non-ensemble benchmark models and also show the efficacy of the proposed meta-learning approaches.

Diabetes Complications and Related Comorbidities Impair the Accuracy of FreeStyle Libre, a Flash Continuous Glucose Monitoring System, in Patients with Type 2 Diabetes

BACKGROUND

Although flash continuous glucose monitoring systems (FCGM) accuracy has been extensively studied in diabetes, its accuracy is still not fully evaluated in type 2 diabetes (T2D) patients in real-world settings. In the present study, we aim to assess the effects of diabetes complications and related comorbidities on FCGM accuracy in T2D patients with diabetes complications and related comorbidities in the real world.

METHODS

FCGM data were collected at eight-time points daily (3 AM, 7 AM, 9 AM, 11 AM, 1 PM, 5 PM, 7 PM, and 9 PM) from 742 patients with T2D and compared with simultaneous fingertip capillary blood glucose (reference blood glucose, REF), and the difference was evaluated using Parkes error grid (PEG), surveillance error grid (SEG), and logistic regression analysis.

RESULTS

In total, 25,579 FCGM/REF data pairs were included in the study. The FCGM values were lower than the paired REF values in 75% of the pairs. The maximum bias (-23.0%) and maximum mean absolute relative difference (24.5%) were observed at 3 AM among eight-time points. SEG analysis also demonstrated the highest percentage of paired readings in moderate and great risk zone (C and D) at 3 AM than PEG analysis (7.33% vs 0.43%, P<0.001). According to the SEG classification, hypoglycemia, infection, diabetic foot, diabetic ketoacidosis, and hypertension were independent risk factors that impaired FCGM accuracy in patients.

CONCLUSION

FCGM commonly underestimates blood glucose levels. Compared with PEG, SEG analysis seems more conducive to the analysis of FCGM performance. The present data highlights the impairment of diabetes complications and related comorbidities on the FCGM accuracy in T2D patients.

Products for Monitoring Glucose Levels in the Human Body With Noninvasive Optical, Noninvasive Fluid Sampling, or Minimally Invasive Technologies

BACKGROUND

Conventional home blood glucose measurements require a sample of blood that is obtained by puncturing the skin at the fingertip. To avoid the pain associated with this procedure, there is high demand for medical products that allow glucose monitoring without blood sampling. In this review article, all such products are presented.

METHODS

In order to identify such products, four different sources were used: (1) PubMed, (2) Google Patents, (3) Diabetes Technology Meeting Startup Showcase participants, and (4) experts in the field of glucose monitoring. The information obtained were filtered by using two inclusion criteria: (1) regulatory clearance, and/or (2) significant coverage in Google News starting in the year 2016, unless the article indicated that the product had been discontinued. The identified bloodless monitoring products were classified into three categories: (1) noninvasive optical, (2) noninvasive fluid sampling, and (3) minimally invasive devices.

RESULTS

In total, 28 noninvasive optical, 6 noninvasive fluid sampling, and 31 minimally invasive glucose monitoring products were identified. Subsequently, these products were characterized according to their regulatory, technological, and consumer features. Products with regulatory clearance are described in greater detail according to their advantages and disadvantages, and with design images.

CONCLUSIONS

Based on favorable technological features, consumer features, and other advantages, several bloodless products are commercially available and promise to enhance diabetes management. Paths for future products are discussed with an emphasis on understanding existing barriers related to both technical and non-technical issues.

The hypoglycaemia error grid: A UK-wide consensus on CGM accuracy assessment in hyperinsulinism

OBJECTIVE

Continuous Glucose Monitoring (CGM) is gaining in popularity for patients with paediatric hypoglycaemia disorders such as Congenital Hyperinsulinism (CHI), but no standard measures of accuracy or associated clinical risk are available. The small number of prior assessments of CGM accuracy in CHI have thus been incomplete. We aimed to develop a novel Hypoglycaemia Error Grid (HEG) for CGM assessment for those with CHI based on expert consensus opinion applied to a large paired (CGM/blood glucose) dataset.

DESIGN AND METHODS

Paediatric endocrinology consultants regularly managing CHI in the two UK centres of excellence were asked to complete a questionnaire regarding glucose cutoffs and associated anticipated risks of CGM errors in a hypothetical model. Collated information was utilised to mathematically generate the HEG which was then approved by expert, consensus opinion. Ten patients with CHI underwent 12 weeks of monitoring with a Dexcom G6 CGM and self-monitored blood glucose (SMBG) with a Contour Next One glucometer to test application of the HEG and provide an assessment of accuracy for those with CHI.

RESULTS

CGM performance was suboptimal, based on 1441 paired values of CGM and SMBG showing Mean Absolute Relative Difference (MARD) of 19.3% and hypoglycaemia (glucose <3.5mmol/L (63mg/dL)) sensitivity of only 45%. The HEG provided clinical context to CGM errors with 15% classified as moderate risk by expert consensus when data was restricted to that of practical use. This provides a contrasting risk profile from existing diabetes error grids, reinforcing its utility in the clinical assessment of CGM accuracy in hypoglycaemia.

CONCLUSIONS

The Hypoglycaemia Error Grid, based on UK expert consensus opinion has demonstrated inadequate accuracy of CGM to recommend as a standalone tool for routine clinical use. However, suboptimal accuracy of CGM relative to SMBG does not detract from alternative uses of CGM in this patient group, such as use as a digital phenotyping tool. The HEG is freely available on GitHub for use by other researchers to assess accuracy in their patient populations and validate these findings.

Practical implementation of remote continuous glucose monitoring in hospitalized patients with diabetes

Purpose

Inpatient diabetes management involves frequent assessment of glucose levels for treatment decisions. Here we describe a program for inpatient real-time continuous glucose monitoring (rtCGM) at a community hospital and the accuracy of rtCGM-based glucose estimates.

Methods

Adult inpatients with preexisting diabetes managed with intensive insulin therapy and a diagnosis of coronavirus disease 2019 (COVID-19) were monitored via rtCGM for safety. An rtCGM system transmitted glucose concentration and trending information at 5-minute intervals to nearby smartphones, which relayed the data to a centralized monitoring station. Hypoglycemia alerts were triggered by rtCGM values of ≤85 mg/dL, but rtCGM data were otherwise not used in management decisions; insulin dosing adjustments were based on blood glucose values measured via fingerstick blood sampling. Accuracy was evaluated retrospectively by comparing rtCGM values to contemporaneous point-of-care (POC) blood glucose values.

Results

A total of 238 pairs of rtCGM and POC data points from 10 patients showed an overall mean absolute relative difference (MARD) of 10.3%. Clarke error grid analysis showed 99.2% of points in the clinically acceptable range, and surveillance error grid analysis showed 89.1% of points in the lowest risk category. It was determined that for 25% of the rtCGM values, discordances in rtCGM and POC values would likely have resulted in different insulin doses. Insulin dose recommendations based on rtCGM values differed by 1 to 3 units from POC-based recommendations.

Conclusion

rtCGM for inpatient diabetes monitoring is feasible. Evaluation of individual rtCGM-POC paired values suggested that using rtCGM data for management decisions poses minimal risks to patients. Further studies to establish the safety and cost implications of using rtCGM data for inpatient diabetes management decisions are warranted.

A Review of Non-Invasive Optical Systems for Continuous Blood Glucose Monitoring

The prevalence of diabetes is increasing globally. More than 690 million cases of diabetes are expected worldwide by 2045. Continuous blood glucose monitoring is essential to control the disease and avoid long-term complications. Diabetics suffer on a daily basis with the traditional glucose monitors currently in use, which are invasive, painful, and cost-intensive. Therefore, the demand for non-invasive, painless, economical, and reliable approaches to monitor glucose levels is increasing. Since the last decades, many glucose sensing technologies have been developed. Researchers and scientists have been working on the enhancement of these technologies to achieve better results. This paper provides an updated review of some of the pioneering non-invasive optical techniques for monitoring blood glucose levels that have been proposed in the last six years, including a summary of state-of-the-art error analysis and validation techniques.

Machine learning-based glucose prediction with use of continuous glucose and physical activity monitoring data: The Maastricht Study

BACKGROUND

Closed-loop insulin delivery systems, which integrate continuous glucose monitoring (CGM) and algorithms that continuously guide insulin dosing, have been shown to improve glycaemic control. The ability to predict future glucose values can further optimize such devices. In this study, we used machine learning to train models in predicting future glucose levels based on prior CGM and accelerometry data.

METHODS

We used data from The Maastricht Study, an observational population-based cohort that comprises individuals with normal glucose metabolism, prediabetes, or type 2 diabetes. We included individuals who underwent >48h of CGM (n = 851), most of whom (n = 540) simultaneously wore an accelerometer to assess physical activity. A random subset of individuals was used to train models in predicting glucose levels at 15- and 60-minute intervals based on either CGM data or both CGM and accelerometer data. In the remaining individuals, model performance was evaluated with root-mean-square error (RMSE), Spearman's correlation coefficient (rho) and surveillance error grid. For a proof-of-concept translation, CGM-based prediction models were optimized and validated with the use of data from individuals with type 1 diabetes (OhioT1DM Dataset, n = 6).

RESULTS

Models trained with CGM data were able to accurately predict glucose values at 15 (RMSE: 0.19mmol/L; rho: 0.96) and 60 minutes (RMSE: 0.59mmol/L, rho: 0.72). Model performance was comparable in individuals with type 2 diabetes. Incorporation of accelerometer data only slightly improved prediction. The error grid results indicated that model predictions were clinically safe (15 min: >99%, 60 min >98%). Our prediction models translated well to individuals with type 1 diabetes, which is reflected by high accuracy (RMSEs for 15 and 60 minutes of 0.43 and 1.73 mmol/L, respectively) and clinical safety (15 min: >99%, 60 min: >91%).

CONCLUSIONS

Machine learning-based models are able to accurately and safely predict glucose values at 15- and 60-minute intervals based on CGM data only. Future research should further optimize the models for implementation in closed-loop insulin delivery systems.

Impact of factory-calibrated Freestyle Libre System with new glucose algorithm measurement accuracy and clinical performance in children with type 1 diabetes during summer camp

BACKGROUND

Factory-calibrated intermittently-scanned Continuous Glucose Monitoring (isCGM) device FreeStyle Libre (FSL) has recently received improvements in its glucose tracking algorithm and calibration procedures, which are claimed to have improved its accuracy.

OBJECTIVE

To compare the accuracy of two generations of 14-days FSL devices (A in 2016, B in 2019) to self-monitored blood glucose measurements (SMBG) in children with type 1 diabetes in real-life conditions during a summer camp.

MATERIALS AND METHODS

Two largely independent groups of youth with type 1 diabetes took part in summer camps. In 2016 they used FSL-A, in 2019 FSL-B. On scheduled days, participants performed supervised 8-point glucose profiles with FSL and SMBG. The accuracy vs SMBG was assessed with mean absolute relative difference (MARD) and clinical surveillance error grid (SEG).

RESULTS

We collected 1655 FSL-SMBG measurement pairs from 78 FSL-A patients (age 13 ± 2.3 years old; HbA1c: 7.6 ± 0.8%) and 1796 from 58 in FSL-B group (age 13.8 ± 2.3 years old, HbA1c: 7.5 ± 1.1%)-in total 3451 measurements. FSL-B displayed lower MARD than FSL-A (11.3 ± 3.1% vs 13.7 ± 4.6%, P = .0003), lower SD of errors (20.2 ± 6.7 mg/dL vs 24.1 ± 9.6 mg/dL, P = .0090) but similar bias (-7.6 ± 11.8 mg/dL vs -6.5 ± 8 mg/dL, P = .5240). Both FSL-A and FSL-B showed significantly higher MARD when glycaemia was decreasing >2 mg/dL/min (FSL-A:22.3 ± 18.5%; FSL-B:17.9 ± 15.8%, P < .0001) compared with stable conditions (FSL-A: 11.4 ± 10.4%, FSL-B:10.1 ± 9.1%) and when the system could not define the glycaemic trend (FSL-A:16.5 ± 16.3%; FSL-B:15.2 ± 14.9%, P < .0001). Both generations demonstrated high percentage of A-class and B-class results in SEG (FSL-A: 96.4%, FSL-B: 97.6%) with a significant shift from B (decrease by 3.7%) to A category (increase by 3.9%) between generations (FSL-A: 16/80.4%; FSL-B:12.3/85.3%, P = .0012).

CONCLUSION

FSL-B demonstrated higher accuracy when compared to FSL-A However, when glycemia is decreasing or its trend is uncertain, the verification with a glucose meter is still advisable.

Analysis of a Unique Postmarket Surveillance Dataset That a Glucose Test-Strip Demonstrates no Evidence of Interference and Robust Clinical Accuracy Irrespective of the Prescription Medication Status of a Large Cohort of Patients With Diabetes

BACKGROUND

Despite a marked increase in polypharmacy in patients with diabetes there have been no thorough evaluations of the impact of polypharmacy on the accuracy of any current blood glucose monitoring (BGM) system. This study evaluated the accuracy of a BGM test-strip with respect to polypharmacy using a large clinical registry dataset.

METHODS

Medication profiles were analyzed for 830 subjects (334 with type 1 [T1D] and 496 with type 2 diabetes [T2D]) attending three hospitals. Blood samples were analyzed to determine clinical accuracy of the BGM test-strip compared to a laboratory comparator.

RESULTS

Across the 830 subjects, 473 different medications (41 diabetes and 432 nondiabetes) were recorded. Patients took on average 6.5 (n = 1-23) individual medications and 4 (n = 1-11) unique classes of medication. Clinical accuracy to EN ISO 15197:2015 criteria was met irrespective of increasing average number of individual medication, categorized from 1 to 4, 5 to 8, 9 to 12, and >12 taken per subject (97.7%, 98.4%, 98.1%, and 98.5%, respectively). Clinical accuracy to EN ISO 15197:2015 criteria was also met across 15 classes of medication using the combined dataset (98.1%; 13 003/13 253). Surveillance error grid analysis showed 98.8% (13 079/13 232) of readings presented no clinical risk. No individual class or combination of medication classes impacted clinical accuracy of the BGM test-strip.

CONCLUSIONS

This comprehensive analysis for this specific test-strip platform demonstrated no evidence of interference and robust clinical accuracy of this test strip, irrespective of the prescription medication status of patients with diabetes.

Evidence From a Long-Term, Systematic Post-Market Surveillance Program: Clinical Performance of a Hematocrit-Insensitive Blood Glucose Test Strip

BACKGROUND

Described is a manufacturer's systematic post-market evaluation of the long-term clinical accuracy of a commercially available blood glucose monitoring (BGM) test strip product.

METHODS

Production batches of test strips were routinely and regularly sampled and evaluated in a clinical setting to assess product accuracy. Evaluations were performed on capillary blood samples from a minimum of 100 subjects with diabetes, by clinical staff according to instructions for use. Readings were compared against capillary blood samples collected at the same time and measured by a standard laboratory reference method. Clinical accuracy was calculated according to EN ISO 15197:2015.

RESULTS

A total of 21 115 paired results were obtained, equating to 209 production batches over the >3-year period since test strip launch. Of the results, 97.6% met the accuracy criterion (range: 97.1-98.1% by year), with 98.1% of values presenting zero risk as defined by the surveillance error grid. At the <5th (21.0-33.8%) and >95th (48.3-59.4%) percentile extremes of hematocrit distribution, 97.9% and 96.4% of values were clinically accurate. The product also demonstrated clinical accuracy across all seven glucose ranges ("bins") as defined by the standard. Under conditions of combined hematocrit and glucose (<80 mg/dL and ≥300 mg/dL) extremes, 97.7% of values were clinically accurate.

CONCLUSIONS

Methodologies and results from a manufacturer's self-imposed clinical accuracy surveillance program of a BGM product is presented. Given the publication of sometimes-conflicting data presented within ad hoc BGM clinical accuracy evaluations, usually of limited size, it is advocated that BGM manufacturers adopt similarly robust and systematic surveillance programs to safeguard patients.

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

OBJECTIVE

We 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 METHODS

This 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.

RESULTS

Mean 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.

CONCLUSIONS

Compared 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.

Safety and Accuracy of Factory-Calibrated Continuous Glucose Monitoring in Pediatric Patients Undergoing Hematopoietic Stem Cell Transplantation

Background: Pediatric patients undergoing hematopoietic stem cell transplantation (HSCT) may be at risk for malglycemia and adverse outcomes, including infection, prolonged hospital stays, organ dysfunction, graft-versus-host-disease, delayed hematopoietic recovery, and increased mortality. Continuous glucose monitoring (CGM) may aid in describing and treating malglycemia in this population. However, no studies have demonstrated safety, tolerability, or accuracy of CGM in this uniquely immunocompromised population. Materials and Methods: A prospective observational study was conducted, using the Abbott Freestyle Libre Pro, in patients aged 2-30 undergoing HSCT at Children's Hospital Colorado to evaluate continuous glycemia in this population. CGM occurred up to 7 days before and 60 days after HSCT, during hospitalization only. In a secondary analysis of this data, blood glucoses collected during routine HSCT care were compared with CGM values to evaluate accuracy. Adverse events and patient refusal to wear CGM device were monitored to assess safety and tolerability. Results: Participants (n = 29; median age 13.1 years, [interquartile range] [4.7, 16.6] years) wore 84 sensors for an average of 25 [21.5, 30.0] days per participant. Paired serum-sensor values (n = 893) demonstrated a mean absolute relative difference of 20% ± 14% with Clarke Error Grid analysis showing 99% of pairs in the clinically acceptable Zones (A+B). There were four episodes of self-limited bleeding (4.8% of sensors); no other adverse events occurred. Six patients (20.7%) refused subsequent CGM placements. Conclusions: CGM use appears safe and feasible although with suboptimal accuracy in the hospitalized pediatric HSCT population. Few adverse events occurred, all of which were low grade.

Five-Year Two-Center Retrospective Comparison of Central Laboratory Glucose to GEM 4000 and ABL 800 Blood Glucose: Demonstrating the (In)adequacy of Blood Gas Glucose

PURPOSE

To evaluate the glucose assays of two blood gas analyzers (BGAs) in intensive care unit (ICU) patients by comparing ICU BGA glucoses to central laboratory (CL) glucoses of almost simultaneously drawn specimens.

METHODS

Data repositories provided five years of ICU BGA glucoses and contemporaneously drawn CL glucoses from a Calgary, Alberta ICU equipped with IL GEM 4000 and CL Roche Cobas 8000-C702, and an Edmonton, Alberta ICU equipped with Radiometer ABL 800 and CL Beckman-Coulter DxC. Blood glucose analyzer and CL glucose differences were evaluated if they were both drawn either within ±15 or ±5 minutes. Glucose differences were assessed graphically and quantitatively with simple run charts and the surveillance error grid (SEG) and quantitatively with the 2016 Food and Drug Administration guidance document, with ISO 15197 and SEG statistical summaries. As the GEM glucose exhibits diurnal variation, CL-arterial blood gas (ABG) differences were evaluated according to time of day.

RESULTS

Compared to the GEM glucoses measured between 0200 and 0800, the run charts of (GEM-CL) glucose demonstrate significant outliers between 0800 and 0200 which are identified as moderate to severe clinical outliers by SEG analysis (P < .002 and P < .0005 for 5- and 15-minute intervals). Over the entire 24-hour period, the rates of moderate to severe glucose clinical outliers are 3.5/1000 (GEM) and 0.6/1000 glucoses (ABL), respectively, using the 15-minute interval (P < .0001).

DISCUSSION

The GEM ABG glucose is associated with a higher frequency of moderate to severe glucose clinical outliers, especially between 0800 and 0200, increased CL testing and higher average patient glucoses.

Comparison of Accuracy Guidelines for Hospital Glucose Meters

When used in hospital settings, glucose meter performance issues involve analytic comparability to lab-based testing, patient and sample variables, and clinical affects such as insulin treatment protocol outcomes and morbidity or outcome risk factors. Different tools are available to assess these issues, including accuracy and precision statistics along with clinical risk measures such as error grids or simulation testing. Regulatory, guidance, and professional bodies have advocated a number of varying recommendations for glucose meter performance in different situations and under different patient conditions. These are summarized and compared, but reconciling these guidelines can be confusing or difficult for providers. Blood glucose meters are useful in the management of patients in acute or assisted care facilities, but users must appreciate the variables that affect measurements and provide for oversight that can manage risk factors and maintain meter performance expectations.

Assessment of a Noninvasive Chronic Glucose Monitoring System in Euglycemic and Diabetic Swine (Sus scrofa)

Models of type-I diabetes are well-characterized and commonly used in the preclinical evaluation of drugs and medical devices. The diabetic minipig is an excellent example of a translational model. However, chronic glucose monitoring in this species can be challenging; frequent blood sampling can be technically difficult and poorly tolerated in conscious swine. Skin-patch continuous blood glucose monitors are FDA-approved for human use and offer a potential refinement to cageside blood collection. However, this modality has not been evaluated in pigs. In this study, young adult male STZ-induced diabetic Yucatan minipigs (n = 4) and healthy York pigs (n = 4) were implanted with a 14-d skin-patch continuous glucose monitor. Readings from continuous glucose monitors were time-matched to whole blood samples, with glucose measurements performed using point-of-care blood glucose monitors, serum chemistry or both. The aims of the study were to assess if a continuous glucose monitoring system could accurately detect glucose levels in swine, and to compare the readings toboth point-of-care glucometers and serum chemistry results. We hypothesized that a continuous glucose monitoring system would accurately detect glucose levels in swine in comparison with a validated analyzer and could serve as an animal welfarerefinement for studies of diabetes. We found that the continuous glucose monitor used in this study provided an adequateadjunct for clinical management in the stable diabetic pig and a minimally invasive and inexpensive option for colony maintenanceof chronically diabetic swine.