Blood Gas Analyzer Accuracy of Glucose Measurements

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.

Leukocyte glucose index as a novel biomarker for COVID-19 severity

The severity of coronavirus disease 2019 (COVID-19) quickly progresses with unfavorable outcomes due to the host immune response and metabolism alteration. Hence, we hypothesized that leukocyte glucose index (LGI) is a biomarker for severe COVID-19. This study involved 109 patients and the usefulness of LGI was evaluated and compared with other risk factors to predict COVID 19 severity. LGI was identified as an independent risk factor (odds ratio [OR] = 1.727, 95% confidence interval [CI]: 1.026-3.048, P = 0.041), with an area under the curve (AUC) of 0.749 (95% CI: 0.642-0.857, P < 0.0001). Interestingly, LGI was a potential risk factor (OR = 2.694, 95% CI: 1.575-5.283, Pcorrected < 0.05) for severe COVID-19 in female but not in male patients. In addition, LGI proved to be a strong predictor of the severity in patients with diabetes (AUC = 0.915 (95% CI: 0.830-1), sensitivity = 0.833, and specificity = 0.931). The AUC of LGI, together with the respiratory rate (LGI + RR), showed a considerable improvement (AUC = 0.894, 95% CI: 0.835-0.954) compared to the other biochemical and respiratory parameters analyzed. Together, these findings indicate that LGI could potentially be used as a biomarker of severity in COVID-19 patients.

Stability of Glucose Concentrations in Frozen Plasma

BACKGROUND

Conflicting information is available regarding the stability of glucose concentrations in frozen plasma samples. Clinical trials could benefit from such long-term storage because it would allow usage of a central laboratory with higher-quality laboratory analyzers in contrast to mobile analyzers in a decentralized setting.

METHODS

In this study, venous blood samples were collected in lithium-heparin gel tubes. Plasma was separated immediately after blood was drawn, and from each of the 21 plasma samples, 6 aliquots were prepared for measurement at 6 time points: immediately and after 2, 4, 6, 8, and 12 weeks. Between sampling and measurement, aliquots were stored at less than -20°C. Transport on dry ice was simulated by placing aliquots in a -80°C freezer for 5 days between weeks 8 and 12. Measurements were performed on a hexokinase-based laboratory analyzer.Average relative differences and corresponding 99% confidence intervals (CIs) were calculated between the stored aliquots' and the immediately measured aliquots' glucose concentrations. Glucose concentrations were deemed stable as long as average relative differences were ≤±2.5%.

RESULTS

Over the whole 12-weeks duration, the largest average relative difference was -1.82% (99% CI: -2.25% to -1.39%). Shorter storage durations tended to lead to less bias.

CONCLUSION

In this study, the stability of glucose concentrations in frozen plasma samples obtained with lithium-heparin gel tubes could be shown for up to 12 weeks. Future studies should be performed to assess whether this is independent of the glucose analyzer and the type of sampling tube used.

Hospital Diabetes Meeting 2022

The annual Virtual Hospital Diabetes Meeting was hosted by Diabetes Technology Society on April 1 and April 2, 2022. This meeting brought together experts in diabetes technology to discuss various new developments in the field of managing diabetes in hospitalized patients. Meeting topics included (1) digital health and the hospital, (2) blood glucose targets, (3) software for inpatient diabetes, (4) surgery, (5) transitions, (6) coronavirus disease and diabetes in the hospital, (7) drugs for diabetes, (8) continuous glucose monitoring, (9) quality improvement, (10) diabetes care and educatinon, and (11) uniting people, process, and technology to achieve optimal glycemic management. This meeting covered new technology that will enable better care of people with diabetes if they are hospitalized.

Glycemic targets in critically ill adults: A mini-review

Illness-induced hyperglycemia impairs neutrophil function, increases pro-inflammatory cytokines, inhibits fibrinolysis, and promotes cellular damage. In turn, these mechanisms lead to pneumonia and surgical site infections, prolonged mechanical ventilation, prolonged hospitalization, and increased mortality. For optimal glucose control, blood glucose measurements need to be done accurately, frequently, and promptly. When choosing glycemic targets, one should keep the glycemic variability < 4 mmol/L and avoid targeting a lower limit of blood glucose < 4.4 mmol/L. The upper limit of blood glucose should be set according to casemix and the quality of glucose control. A lower glycemic target range (i.e., blood glucose 4.5-7.8 mmol/L) would be favored for patients without diabetes mellitus, with traumatic brain injury, or who are at risk of surgical site infection. To avoid harm from hypoglycemia, strict adherence to glycemic control protocols and timely glucose measurements are required. In contrast, a higher glycemic target range (i.e., blood glucose 7.8-10 mmol/L) would be favored as a default choice for medical-surgical patients and patients with diabetes mellitus. These targets may be modified if technical advances for blood glucose measurement and control can be achieved.

Retrospective Analysis of Quality Control Data Using Pooled Blood to Compare Agreement within Two Models of Blood Gas Analyzers

BACKGROUND

Two previous reports from the same group concluded that the analytical reliability of Instrumentation Laboratory GEM4000 analyzers (GEM4K) deteriorated during a 24-hour period, based on results between samples from the same patient but collected at different times. Our routine blood gas Between-Laboratory Survey is done every 2 weeks using a freshly pooled heparinized blood sample taken to each analyzer location to verify comparability among our GEM4K and Radiometer ABL90 (Rad90) blood gas analyzers. Because another report found a few very large differences in glucose results between the GEM4K and central laboratory analyzers, we reviewed the glucose results on plasma from our Chemistry Between-Laboratory Surveys that includes comparisons between our central laboratory analyzers (Beckman DxC800; DxC800) and our GEM4K and Rad90 blood gas analyzers.

METHOD

Using data from our Blood Gas and Chemistry Surveys over a 55-week period, we calculated the mean, standard deviation (SD), and concentration intervals of the 27 sets of results by the GEM4K or Rad90 analyzers. Agreement in plasma glucose between DxC800 and blood gas analyzers was evaluated by the limits of agreement and intraclass correlation coefficient analysis.

RESULTS

For each analyte, the Rad90 had lower average SD than the GEM4K for the 55-week period, although both brands of analyzers showed acceptable performance. For plasma glucose results on our Chemistry Survey, the GEM4K results agreed more closely with the DxC800 results than the Rad90 results.

CONCLUSIONS

Based on both our Blood Gas and Chemistry Surveys, we conclude that both brands of analyzers performed within analytically and clinically acceptable limits throughout the year, with no evidence for the type of errors reported previously.

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.

Perioperative Considerations for Evolving Artificial Pancreas Devices

Type 1 diabetes mellitus is a lifelong condition. It requires intensive patient involvement including frequent glucose measurements and subcutaneous insulin dosing to provide optimal glycemic control to decrease short- and long-term complications of diabetes mellitus without causing hypoglycemia. Variations in insulin pharmacokinetics and responsiveness over time in addition to illness, stress, and a myriad of other factors make ideal glucose control a challenge. Control-to-range and control-to-target artificial pancreas devices (closed-loop artificial pancreas devices [C-APDs]) consist of a continuous glucose monitor, response algorithm, and insulin delivery device that work together to automate much of the glycemic management for an individual while continually adjusting insulin dosing toward a glycemic target. In this way, a C-APD can improve glycemic control and decrease the rate of hypoglycemia. The MiniMed 670G (Medtronic, Fridley, MN) system is currently the only Food and Drug Administration-cleared C-APD in the United States. In this system, insulin delivery is continually adjusted to a glucose concentration, and the patient inputs meal-time information to modify insulin delivery as needed. Data thus far suggest improved glycemic control and decreased hypoglycemic events using the system, with decreased need for patient self-management. Thus, the anticipated use of these devices is likely to increase dramatically over time. There are limited case reports of safe intraoperative use of C-APDs, but the Food and Drug Administration has not cleared any device for such use. Nonetheless, C-APDs may offer an opportunity to improve patient safety and outcomes through enhanced intraoperative glycemic control. Anesthesiologists should become familiar with C-APD technology to help develop safe and effective protocols for their intraoperative use. We provide an overview of C-APDs and propose an introductory strategy for intraoperative study of these devices.

Near-Continuous Glucose Monitoring Makes Glycemic Control Safer in ICU Patients

OBJECTIVES

Tight glycemic control using intermittent blood glucose measurements is associated with a risk of hypoglycemia. Glucose concentrations can now be measured near continuously (every 5-15 min). We assessed the quality and safety of glycemic control guided by a near-continuous glucose monitoring system in ICU patients.

DESIGN

Prospective, cluster-randomized, crossover study.

SETTING

Thirty-five-bed medico-surgical department of intensive care with four separate ICUs.

PATIENTS

Adult patients admitted to the department and expected to stay for at least 3 days were considered for inclusion if they had persistent hyperglycemia (blood glucose > 150 mg/dL) up to 6 hours after admission and/or were receiving insulin therapy.

INTERVENTIONS

A peripheral venous catheter was inserted in all patients and connected to a continuous glucose monitoring sensor (GlucoClear; Edwards Lifesciences, Irvine, CA). The four ICUs were randomized in pairs in a crossover design to glycemic control using unblinded or blinded continuous glucose monitoring monitors. The insulin infusion rate was adjusted to keep blood glucose between 90 and 150 mg/dL using the blood glucose values displayed on the continuous glucose monitor (continuous glucose monitoring group-unblinded units) or according to intermittent blood glucose readings (intermittent glucose monitoring group-blinded units).

MEASUREMENTS AND MAIN RESULTS

The quality and safety of glycemic control were assessed using the proportion of time in range, the frequency of blood glucose less than 70 mg/dL, and the time spent with blood glucose less than 70 mg/dL (TB70), using blood glucose values measured by the continuous glucose monitoring device. Seventy-seven patients were enrolled: 39 in the continuous glucose monitoring group and 38 in the intermittent glucose monitoring group. A total of 43,107 blood glucose values were recorded. The time in range was similar in the two groups. The incidence of hypoglycemia (8/39 [20.5%] vs 15/38 [39.5%]) and the TB70 (0.4% ± 0.9% vs 1.6% ± 3.4%; p < 0.05) was lower in the continuous glucose monitoring than in the intermittent glucose monitoring group.

CONCLUSIONS

Use of a continuous glucose monitoring-based strategy decreased the incidence and severity of hypoglycemia, thus improving the safety of glycemic control.

Glucose Management Technologies for the Critically Ill

Hyperglycemia is common in the intensive care unit (ICU) both in patients with and without a previous diagnosis of diabetes. The optimal glucose range in the ICU population is still a matter of debate. Given the risk of hypoglycemia associated with intensive insulin therapy, current recommendations include treating hyperglycemia after two consecutive glucose >180 mg/dL with target levels of 140-180 mg/dL for most patients. The optimal method of sampling glucose and delivery of insulin in critically ill patients remains elusive. While point of care glucose meters are not consistently accurate and have to be used with caution, continuous glucose monitoring (CGM) is not standard of care, nor is it generally recommended for inpatient use. Intravenous insulin therapy using paper or electronic protocols remains the preferred approach for critically ill patients. The advent of new technologies, such as electronic glucose management, CGM, and closed-loop systems, promises to improve inpatient glycemic control in the critically ill with lower rates of hypoglycemia.

The Progress of Glucose Monitoring-A Review of Invasive to Minimally and Non-Invasive Techniques, Devices and Sensors

Current glucose monitoring methods for the ever-increasing number of diabetic people around the world are invasive, painful, time-consuming, and a constant burden for the household budget. The non-invasive glucose monitoring technology overcomes these limitations, for which this topic is significantly being researched and represents an exciting and highly sought after market for many companies. This review aims to offer an up-to-date report on the leading technologies for non-invasive (NI) and minimally-invasive (MI) glucose monitoring sensors, devices currently available in the market, regulatory framework for accuracy assessment, new approaches currently under study by representative groups and developers, and algorithm types for signal enhancement and value prediction. The review also discusses the future trend of glucose detection by analyzing the usage of the different bands in the electromagnetic spectrum. The review concludes that the adoption and use of new technologies for glucose detection is unavoidable and closer to become a reality.

Recent advances in perioperative glucose monitoring

PURPOSE OF REVIEW

There is ongoing controversy surrounding the use of glucose monitoring in the perioperative setting. It is an important aspect of patient care, but the best way to go about monitoring this parameter is still up for debate. This article will review previously established data and new developments in this field.

RECENT FINDINGS

Several different methods exist to measure blood glucose levels in the perioperative setting, including central laboratory devices, blood gas analyzers, and point-of-care devices. However, it has been recommended that point-of-care devices not be used on 'critically ill' patients, which throws into question the common use of these devices perioperatively. Recently, the Centers for Medicare and Medicaid placed a moratorium on this recommendation, and these devices continue to be a staple in the perioperative setting, but there are other methods of glucose monitoring that can be employed.

SUMMARY

The monitoring of blood glucose levels in the perioperative patient remains an important part of patient care; however, debate still exist on how best to reliably measure blood glucose levels in the most effective manner.

Assessment of the performance of blood glucose monitoring systems for monitoring dysglycaemia in neonatal patients

OBJECTIVE

To validate a three-step protocol that assesses the clinical risk associated with using blood glucose monitoring systems (BGMS) in neonates for the management of dysglycaemia.

METHOD

The three-step validation approach included confirmation of the accuracy of the reference method using National Institute of Standards and Technology (NIST) glucose standards, assessment of analytical risk performed on whole blood collected from paediatric patients routinely tested for glucose and a clinical risk assessment performed using heel stick capillary samples collected from 147 new-born babies and neonates admitted to intensive care. BGMS glucose measurements were compared with the NIST aligned laboratory reference method.

RESULTS

The accuracy of the laboratory reference method was confirmed with the NIST standards. Specificity studies demonstrated that the accuracy of one of the BGMS was affected, particularly, in the hypoglycaemic range, by known interference factors including haematocrit, ascorbic acid, lactose, galactose, N-acetylcysteine and glutathione. The accuracy of the other BGMS was unaffected. The clinical performance of this BGMS in neonates met the system accuracy criteria of Clinical and Laboratory Standards Institute (CLSI) POCT 12-A3 standard for evaluating hospital BGMS with 95.1% of glucose measurements within±0.67 mmol/L for samples ≤5.55 mmol/L and 95.6% within±12.5% for samples>5.55 mmol/L.

CONCLUSIONS

This three-step validation protocol provides a challenging approach for determining the accuracy and reliability of BGMS for managing dysglycaemia in neonates. StatStrip BGMS achieved analytical and clinical performance criteria confirming its suitability for use in neonates. We advocate that this validation approach should be considered for performance evaluations of both BGMS and continuous glucose monitoring systems going forward.