Agreement between non-invasive and invasive arterial blood pressure during surgery in the prone position: an error grid analysis.

PURPOSE: Prone position has recently gained renewed importance as a treatment for acute respiratory distress syndrome and spine and brain surgeries. Our study aimed to perform an error grid analysis to examine the clinical discrepancies between arterial blood pressure (ABP) and non-invasive blood pressure (NIBP) in the prone position and to investigate the risk factors influencing these differences. METHODS: Error grid analysis was performed retrospectively on 1389 pairs of 100 consecutive prone positioning cases. This analysis classifies the difference between the two methods into five clinically relevant zones, from "no risk" to "dangerous risk". Additionally, multivariable ordinal logistic regression analysis was conducted to evaluate the relationship between the risk zones of mean blood pressure (MBP), as classified by error grid analysis and the covariate of interest. RESULTS: Error grid analysis showed that the proportions of measurement pairs in risk zones A-E for systolic blood pressure were 96.8%, 3.2%, 0.1%, 0%, and 0%, respectively. In contrast, the MBP proportions were 74.0%, 25.1%, 0.9%, 0.1%, and 0%. Multivariable ordinal logistic regression analysis revealed that the position of arms (next to the head) was a significant factor (adjusted odds ratio: 4.35, 95% CI: 2.38-8.33, P < 0.001). CONCLUSION: Error grid analysis revealed a clinically unacceptable discrepancy between ABP and NIBP for MBP during prone positioning surgery. The position of the arms next to the head was associated with increased clinical discrepancy between the two MBP measurement methods.

Accuracy of the Nova StatStrip® glucometer in patients undergoing major abdominal surgery: an observational study.

PURPOSE: While the Nova StatStrip® Glucose Hospital Meter System (Nova Biomedical, Waltham, MA, USA) is approved for point-of-care testing (POCT) in critically ill patients, its use during major abdominal surgery has not been evaluated. The purpose of this study was to assess the accuracy of the Nova StatStrip glucometer in patients undergoing major hepatobiliary procedures using the Parkes error grid (ISO15197:2013) and criteria defined by the Clinical and Laboratory Standards Institute (CLSI) POCT12-A3 guideline. METHODS: This study was a post hoc exploratory study of patients participating in a prospective randomized controlled trial on the effects of hyperinsulinemic normoglycemia (HNC) on infectious outcomes after hepatobiliary surgery. Arterial blood samples were collected before surgery and one hour, two hours, and three hours after baseline. Blood glucose levels were analyzed by the Nova StatStrip glucometer and the GEM® PremierTM 5000 blood gas analyzer. Accuracy of the StatStrip glucometer was assessed using the Parkes error grid for type 1 diabetes mellitus (when 99% of samples were within zones A and B on the Parkes error grid and clinical accuracy was acceptable) and the CLSI POCT12-A3 criteria. RESULTS: Blood glucose levels were analyzed in 135 patients, 70 of whom received the HNC. In the Parkes error grid plotted, all samples at all time-points were within zones A and B. The Nova StatStrip glucometer also satisfied CLSI POCT12-A3 criteria at all time-points. CONCLUSION: The Nova StatStrip glucometer was accurate in patients undergoing major upper abdominal surgery, independent of the administration of high-dose insulin therapy. STUDY REGISTRATION: ClinicalTrials.gov (NCT01528189); registered 7 February 2012.

RéSUMé: OBJECTIF: Bien que le système hospitalier de lecture de la glycémie StatStrip® de Nova (Nova Biomedical, Waltham, MA, É.-U.) soit approuvé pour une utilisation au chevet (ou POCT, pour 'Point of Care Testing') chez la patientèle en état critique, son utilisation n'a pas été évaluée en chirurgie abdominale majeure. L'objectif de cette étude était d'évaluer la précision du glucomètre StatStrip de Nova chez la patientèle bénéficiant d'interventions hépatobiliaires majeures à l'aide de la grille d'erreur de Parkes (ISO15197:2013) et des critères définis par la directive POCT12-A3 du Clinical and Laboratory Standards Institute (CLSI). MéTHODE: Il s'agissait d'une étude exploratoire post-hoc auprès de patient·es participant à une étude randomisée contrôlée prospective sur les effets de la normoglycémie hyperinsulinémique (HNC) sur les issues infectieuses après une chirurgie hépatobiliaire. Des échantillons de sang artériel ont été prélevés avant la chirurgie et une heure, deux heures et trois heures après l'échantillon initial. Les taux de glycémie ont été analysés avec le glucomètre StatStrip de Nova et l'analyseur de gaz sanguin GEM® PremierTM 5000. La précision du glucomètre StatStrip a été évaluée à l'aide de la grille d'erreur de Parkes pour le diabète sucré de type 1 (lorsque 99 % des échantillons se trouvaient dans les zones A et B de la grille d'erreur de Parkes et que la précision clinique était acceptable) et des critères POCT12-A3 du CLSI. RéSULTATS: La glycémie a été analysée chez 135 personnes, dont 70 ont reçu une normoglycémie hyperinsulinémique. Dans la grille d'erreur de Parkes tracée, tous les échantillons à tous les points temporels se trouvaient dans les zones A et B. Le glucomètre StatStrip de Nova a également satisfait aux critères POCT12-A3 du CLSI à tous les points temporels. CONCLUSION: Le glucomètre StatStrip de Nova était précis chez la patientèle bénéficiant d'une chirurgie abdominale supérieure majeure, indépendamment de l'administration d'insulinothérapie à forte dose. ENREGISTREMENT DE L'éTUDE: ClinicalTrials.gov (NCT01528189); enregistrée le 7 février 2012.

Use of a Continuous Glucose Monitoring System in High-Risk Hospitalized Noncritically Ill Patients With Diabetes After Cardiac Surgery and During Their Transition of Care From the Intensive Care Unit During COVID-19: A Pilot Study.

OBJECTIVE: Continuous glucose monitoring (CGM) has demonstrated benefits in managing inpatient diabetes. We initiated this single-arm pilot feasibility study during the COVID-19 pandemic in 11 patients with diabetes to determine the feasibility and accuracy of real-time CGM in patients who underwent cardiac surgery and whose care was being transitioned from the intensive care unit. METHODS: A Clarke error grid analysis was used to compare CGM and point-of-care measurements. The mean absolute relative difference (MARD) of the paired measurements was calculated to assess the accuracy of CGM for glucose measurements during the first 24 hours on CGM, the remaining time on CGM, and for different chronic kidney disease (CKD) strata. RESULTS: Overall MARD between point-of-care and CGM measurements was 14.80%. MARD for patients without CKD IV and V with an estimated glomerular filtration rate (eGFR) of ≥20 mL/min/1.73 m2 was 12.13%. Overall, 97% of the CGM values were within the no-risk zone of the Clarke error grid analysis. For the first 24 hours, a sensitivity analysis of the overall MARD for all patients and those with an eGFR of ≥20 mL/min/1.73 m2 was 15.42% ± 14.44% and 12.80% ± 7.85%, respectively. Beyond the first 24 hours, overall MARD for all patients and those with an eGFR of ≥20 mL/min/1.73 m2 was 14.54% ± 13.21% and 11.86% ± 7.64%, respectively. CONCLUSION: CGM has shown great promise in optimizing inpatient diabetes management in the noncritical care setting and after the transition of care from the intensive care unit with high clinical reliability and accuracy. More studies are needed to further assess CGM in patients with advanced CKD.

Commercial and Scientific Solutions for Blood Glucose Monitoring-A Review.

Diabetes is a chronic and, according to the state of the art, an incurable disease. Therefore, to treat diabetes, regular blood glucose monitoring is crucial since it is mandatory to mitigate the risk and incidence of hyperglycemia and hypoglycemia. Nowadays, it is common to use blood glucose meters or continuous glucose monitoring via stinging the skin, which is classified as invasive monitoring. In recent decades, non-invasive monitoring has been regarded as a dominant research field. In this paper, electrochemical and electromagnetic non-invasive blood glucose monitoring approaches will be discussed. Thereby, scientific sensor systems are compared to commercial devices by validating the sensor principle and investigating their performance utilizing the Clarke error grid. Additionally, the opportunities to enhance the overall accuracy and stability of non-invasive glucose sensing and even predict blood glucose development to avoid hyperglycemia and hypoglycemia using post-processing and sensor fusion are presented. Overall, the scientific approaches show a comparable accuracy in the Clarke error grid to that of the commercial ones. However, they are in different stages of development and, therefore, need improvement regarding parameter optimization, temperature dependency, or testing with blood under real conditions. Moreover, the size of scientific sensing solutions must be further reduced for a wearable monitoring system.

Error grid analysis for risk management in the difference between invasive and noninvasive blood pressure measurements.

PURPOSE: Invasive arterial blood pressure (IAP) and noninvasive blood pressure (NIBP) measurements are both common methods. Recently, a new method of error grid analysis was proposed to compare blood pressure obtained using two measurement methods. This study aimed to compare IAP and NIBP measurements using the error grid analysis and investigate potential confounding factors affecting the discrepancies between IAP and NIBP. METHODS: Adult patients who underwent general anesthesia in the supine position with both IAP and NIBP measurements were retrospectively investigated. The error grid analyses were performed to compare IAP and NIBP. In the error grid analysis, the clinical relevance of the discrepancies between IAP and NIBP was evaluated and classified into five zones from no risk (A) to dangerous risk (E). RESULTS: Overall, data of 1934 IAP/NIBP measurement pairs from 100 patients were collected. The error grid analysis revealed that the proportions of zones A-E for systolic blood pressure were 96.4%, 3.5%, 0.05%, 0%, and 0%, respectively. In contrast, the proportions for mean blood pressure were 82.5%, 16.7%, 0.8%, 0%, and 0%, respectively. The multiple regression analysis revealed that continuous phenylephrine administration (p = 0.016) and age (p = 0.044) were the significant factors of an increased clinical risk of the differences in mean blood pressure. CONCLUSIONS: The error grid analysis indicated that the differences between IAP and NIBP for mean blood pressure were not clinically acceptable and had the risk of leading to unnecessary treatments. Continuous phenylephrine administration and age were the significant factors of an increased clinical risk of the discrepancies between IAP and NIBP.

A Noninvasive Accurate Measurement of Blood Glucose Levels with Raman Spectroscopy of Blood in Microvessels.

Raman spectra of human skin obtained by laser excitation have been used to non-invasively detect blood glucose. In previous reports, however, Raman spectra thus obtained were mainly derived from the epidermis and interstitial fluid as a result of the shallow penetration depth of lasers in skin. The physiological process by which glucose in microvessels penetrates into the interstitial fluid introduces a time delay, which inevitably introduces errors in transcutaneous measurements of blood glucose. We focused the laser directly on the microvessels in the superficial layer of the human nailfold, and acquired Raman spectra with multiple characteristic peaks of blood, which indicated that the spectra obtained predominantly originated from blood. Incorporating a multivariate approach combining principal component analysis (PCA) and back propagation artificial neural network (BP-ANN), we performed noninvasive blood glucose measurements on 12 randomly selected volunteers, respectively. The mean prediction performance of the 12 volunteers was obtained as an RMSEP of 0.45 mmol/L and R2 of 0.95. It was no time lag between the predicted blood glucose and the actual blood glucose in the oral glucose tolerance test (OGTT). We also applied the procedure to data from all 12 volunteers regarded as one set, and the total predicted performance was obtained with an RMSEP of 0.27 mmol/L and an R2 of 0.98, which is better than that of the individual model for each volunteer. This suggested that anatomical differences between volunteer fingernails do not reduce the prediction accuracy and 100% of the predicted glucose concentrations fall within Region A and B of the Clarke error grid, allowing acceptable predictions in a clinically relevant range. The Raman spectroscopy detection of blood glucose from microvessels is of great significance of non-invasive blood glucose detection of Raman spectroscopy. This innovative method may also facilitate non-invasive detection of other blood components.

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.

Glu-Ensemble: An ensemble deep learning framework for blood glucose forecasting in type 2 diabetes patients.

Diabetes is a chronic metabolic disorder characterized by elevated blood glucose levels, posing significant health risks such as cardiovascular disease, and nerve, kidney, and eye damage. Effective management of blood glucose is essential for individuals with diabetes to mitigate these risks. This study introduces the Glu-Ensemble, a deep learning framework designed for precise blood glucose forecasting in patients with type 2 diabetes. Unlike other predictive models, Glu-Ensemble addresses challenges related to small sample sizes, data quality issues, reliance on strict statistical assumptions, and the complexity of models. It enhances prediction accuracy and model generalizability by utilizing larger datasets and reduces bias inherent in many predictive models. The framework's unified approach, as opposed to patient-specific models, eliminates the need for initial calibration time, facilitating immediate blood glucose predictions for new patients. The obtained results indicate that Glu-Ensemble surpasses traditional methods in accuracy, as measured by root mean square error, mean absolute error, and error grid analysis. The Glu-Ensemble framework emerges as a promising tool for blood glucose level prediction in type 2 diabetes patients, warranting further investigation in clinical settings for its practical application.

Safety and Accuracy of Professional Continuous Glucose Monitoring in Patients Undergoing Allogeneic Hematopoietic Stem Cell Transplantation.

Hyperglycemia in the early days following allogeneic hematopoietic stem cell transplantation (allo-HSCT) is a well-known risk factor for acute graft-versus-host disease (GVHD) and non-relapse mortality. The FreeStyle Libre Pro, a factory calibrated continuous glucose monitoring (CGM) device, has been used for the retrospective analysis of glucose testing in patients with diabetes. We assessed the safety and accuracy of the device in patients undergoing allo-HSCT. We recruited eight patients who underwent allo-HSCT between August 2017 and March 2020. They wore the FreeStyle Libre Pro on the day before or on the day of transplantation until 28 days after transplantation. Adverse events, especially bleeding and infection, were monitored to assess safety, and blood glucose levels were measured and compared with the device values. None of the eight participants experienced bleeding that was difficult to stop from the sensor site or local infection that required antimicrobial administration. The device value was well correlated with blood glucose (correlation coefficient r=0.795, P<0.01); however, the overall mean absolute relative difference was 32.1%±16.0%. Our study demonstrated the safety of FreeStyle Libre Pro in allo-HSCT patients. However, the sensor results tended to be lower than the blood glucose levels.

In Vitro Validation of a Novel Continuous Intra-Abdominal Pressure Measurement System (TraumaGuard).

Introduction: Intra-abdominal pressure (IAP) has been recognized as an important vital sign in critically ill patients. Due to the high prevalence and incidence of intra-abdominal hypertension in surgical (trauma, burns, cardiac) and medical (sepsis, liver cirrhosis, acute kidney injury) patients, continuous IAP (CIAP) monitoring has been proposed. This research was aimed at validating a new CIAP monitoring device, the TraumaGuard from Sentinel Medical Technologies, against the gold standard (height of a water column) in an in vitro setting and performing a comparative analysis among different CIAP measurement technologies (including two intra-gastric and two intra-bladder measurement devices). A technical and clinical guideline addressing the strengths and weaknesses of each device is provided as well. Methods: Five different CIAP measurement devices (two intra-gastric and three intra-vesical), including the former CiMON, Spiegelberg, Serenno, TraumaGuard, and Accuryn, were validated against the gold standard water column pressure in a bench-top abdominal phantom. The impacts of body temperature and bladder fill volume (for the intra-vesical methods) were evaluated for each system. Subsequently, 48 h of continuous monitoring (n = 2880) on top of intermittent IAP (n = 300) readings were captured for each device. Using Pearson's and Lin's correlations, concordance, and Bland and Altman analyses, the accuracy, precision, percentage error, correlation and concordance coefficients, bias, and limits of agreement were calculated for all the different devices. We also performed error grid analysis on the CIAP measurements to provide an overview of the involved risk level due to wrong IAP measurements and calculated the area under the curve and time above a certain IAP threshold. Lastly, the robustness of each system in tracking the dynamic variations of the raw IAP signal due to respirations and heartbeats was evaluated as well. Results: The TraumaGuard was the only technology able to measure the IAP with an empty artificial bladder. No important temperature dependency was observed for the investigated devices except for the Spiegelberg, which displayed higher IAP values when the temperature was increased, but this could be adjusted through recalibration. All the studied devices showed excellent ability for IAP monitoring, although the intra-vesical IAP measurements seem more reliable. In general, the TraumaGuard, Accuryn, and Serenno showed better accuracy compared to intra-gastric measurement devices. On average, biases of +0.71, +0.93, +0.29, +0.25, and -0.06 mm Hg were observed for the CiMON, Spiegelberg, Serenno, TraumaGuard, and Accuryn, respectively. All of the equipment showed percentage errors smaller than 25%. Regarding the correlation and concordance coefficients, the Serenno and TraumaGuard showed the best results (R2 = 0.98, p = 0.001, concordance coefficient of 99.5%). Error grid analysis based on the Abdominal Compartment Society guidelines showed a very low associated risk level of inappropriate treatment strategies due to erroneous IAP measurements. Regarding the dynamic tracings of the raw IAP signal, all the systems can track respiratory variations and derived parameters; however, the CiMON was slightly superior compared to the other technologies. Conclusions: According to the research guidelines of the Abdominal Compartment Society (WSACS), this in vitro study shows that the TraumaGuard can be used interchangeably with the gold standard for measuring continuous IAP, even in an empty artificial bladder. Confirmation studies with the TraumaGuard in animals and humans are warranted to further validate these findings.

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.

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.

An Analysis of 2019 FDA Adverse Events for Two Insulin Pumps and Two Continuous Glucose Monitors.

US Food and Drug Administration adverse event data for 2019 were analyzed for two insulin pumps and two continuous glucose monitors (CGMs). The analyses were selective-they were guided by the text described in the adverse events. They included (1) percent using auto mode for the Medtronic 670G pump, (2) distributions of hyper and hypo glucose values for Medtronic and Tandem pumps, (3) a Parkes error grid for Dexcom CGM vs glucose meter when the complaint was inaccuracy, and (4) the most frequent events for Abbott Freestyle. We found that for the 670G pump, there were more hypo events when auto mode was on than when auto mode was off. With Dexcom CGMs, users complained about inaccurate result when most results were in the B zone. With the Abbott Freestyle, the most frequent adverse event was an allergic skin reaction.

Comparison of glucose concentrations in canine whole blood, plasma, and serum measured with a veterinary point-of-care glucometer.

Previous studies have determined that, compared to whole blood, serum or plasma used in a portable blood glucometer (PBG) may provide more accurate results. We investigated the accuracy of a veterinary PBG (AlphaTRAK 2; Zoetis) for the measurement of glucose concentrations in serum, plasma, and whole blood compared to plasma glucose concentration measured by a biochemical analyzer. Blood samples from 53 client-owned dogs were collected. Lin concordance correlation coefficient (ρc) and Bland-Altman plots were used to determine correlation and agreement between the results obtained for the different sample types. Glucose concentration in whole blood measured by the veterinary PBG was more strongly correlated with the glucose concentration measured by the biochemical analyzer (ρc = 0.92) compared to plasma and serum glucose concentrations (ρc = 0.59 and 0.57, respectively). The mean differences between the glucose concentrations in whole blood, plasma, and serum measured by the veterinary PBG and the glucose concentration determined by the biochemical analyzer were 1.0, 6.3, and 6.7 mmol/L (18, 113, and 121 mg/dL), respectively. Our findings suggest that, when using this veterinary PBG, the accuracy of a glucose measurement obtained is higher when using whole blood compared to plasma or serum. Use of whole blood allows for more correct assessment and diagnosis, which are necessary for appropriate therapeutic intervention.

Antioxidants other than vitamin C may be detected by glucose meters: Immediate relevance for patients with disorders targeted by antioxidant therapies.

Owing to their ease of use, glucose meters are frequently used in research and medicine. However, little is known of whether other non-glucose molecules, besides vitamin C, interfere with glucometry. Therefore, we sought to determine whether other antioxidants might behave like vitamin C in causing falsely elevated blood glucose levels, potentially exposing patients to glycemic mismanagement by being administered harmful doses of glucose-lowering drugs. To determine whether various antioxidants can be detected by seven commercial glucose meters, human blood samples were spiked with various antioxidants ex vivo and their effect on the glucose results were assessed by Parkes error grid analysis. Several of the glucose meters demonstrated a positive bias in the glucose measurement of blood samples spiked with vitamin C, N-acetylcysteine, and glutathione. With the most interference-sensitive glucose meter, non-blood solutions of 1 mmol/L N-acetylcysteine, glutathione, cysteine, vitamin C, dihydrolipoate, and dithiothreitol mimicked the results seen on that glucose meter for 0.7, 1.0, 1.2, 2.6, 3.7 and 5.5 mmol/L glucose solutions, respectively. Glucose meter users should be alerted that some of these devices might produce spurious glucose results not only in patients on vitamin C therapy but also in those being administered other antioxidants. As discussed herein, the clinical relevance of the data is immediate in view of the current use of antioxidant therapies for disorders such as the metabolic syndrome, diabetes, cardiovascular diseases, and coronavirus disease 2019.

Acute response in vivo of a fiber-optic sensor for continuous glucose monitoring from canine studies on point accuracy.

The objective of this study was to evaluate the acute response of Sencil(™), a fiber-optic sensor, in point accuracy for glucose monitoring in vivo on healthy dogs under anesthesia. A total of four dogs with clinically normal glycemia were implanted with one sensor each in the chest region to measure the interstitial glucose concentration during the ovariohysterectomy procedure. The data was acquired every 10 seconds after initiation, and was compared to the concentration of venous plasma glucose sampled during the surgery procedures for accuracy of agreement analysis. In the four trials with a range of 71-297 mg/dL plasma glucose, the collected 21 pairs of ISF readings from the Sencil™ and the plasma reference showed superior dispersion of residue values than the conventional system, and a linear correlation (the Pearson correlation coefficient is 0.9288 and the y-intercept is 14.22 mg/dL). The MAD (17.6 mg/dL) and RMAD (16.16%) of Sencil™ measurements were in the comparable range of the conventional system. The Clarke error grid analysis indicated that 100% of the paired points were in the clinically acceptable zone A (61.9%) and B (38.1%).

Analysis of a Point-of-Care HbA1c Assay: Is It Time for an HbA1c Error Grid?

In an article in Journal of Diabetes Science and Technology, Arnold et al have presented a thorough study of imprecision components for a point-of-care hemoglobin A1c (HbA1c) assay. An interesting and innovative approach is the combination of data from different studies to arrive at a total error estimate. But total error has the oxymoron feature of estimating performance for most (95%) but not all of the results. An HbA1c error grid would provide the severity for results that exceed the 6% requirement. Since this device is intended for Clinical Laboratory Improvement Amendments waived labs and allows for finger-stick samples, monitoring the Food and Drug Administration adverse event database (MAUDE, Manufacturer and User Facility Device Experience) is recommended.

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.

An insulin-dose error assessment grid: A new tool to evaluate glucose meter performance.

OBJECTIVE: To develop a tool to assess the clinical accuracy of glucose meter performance using an insulin dosing protocol to assess the frequency and extent of error in insulin dose categories. METHODS: Retrospective comparison of 1815 glucose meter and central laboratory glucose results obtained from 1698 critically ill patients was conducted using the Parkes error grid, Surveillance error grid and an insulin dose error assessment grid with a sliding scale insulin dosing protocol used to manage critically ill patients. RESULTS: Parkes error grid and Surveillance error grid analyses indicated little risk conferred with the glucose meter results. Insulin dose error assessment grid complemented the aforementioned consensus error grids by determining quantifiable metrics, insulin dose category errors. Insulin dose error analysis indicated that 76.8% (1395/1815) would not have any change in insulin dose, 99.2% (1800/1815) within ±1 insulin dose category, 99.9% (1814/1815) within ±2 categories and 100% within ±3 insulin dose categories. CONCLUSIONS: Analysis with an insulin dose error grid provides information about the frequency and extent of insulin dose category errors with a specific insulin dosing protocol and describes potential clinical impact of glucose meter error.

Getting More Information From Glucose Meter Evaluations.

Glucose meter evaluations are common in publications and inform whether the meter meets the ISO 15197 specification. The ISO 15197 specifications, which are universally cited, leave 1% of results unspecified, which can be thought of as typical performance of results (99%) versus rare performance (1%). Suggestions are provided to extract more information from these evaluations, including rare performance, since highly discrepant results or failure to obtain a result can be observed in a glucose meter that has met the ISO 15197 specification. It is also recommended that when manufacturers perform evaluations, they analyze adverse events contained in the FDA MAUDE database. Finally, we point out an important problem with the ISO 15197 specifications.