Continuous Glucose Monitoring Systems Systems Research Articles

CORRELATION BETWEEN 1,5-ANHYDROGLUCITOL LEVELS AND GLYCEMIC VARIABILITY IN TYPE 2 DIABETIC PATIENTS USING CONTINUOUS GLUCOSE MONITORING

CORRELATION BETWEEN 1,5-ANHYDROGLUCITOL LEVELS AND GLYCEMIC VARIABILITY IN TYPE 2 DIABETIC PATIENTS USING CONTINUOUS GLUCOSE MONITORING

Two novel GCK mutations in Chinese patients with maturity-onset diabetes of the young.

Heterozygous inactivating mutations in the glucokinase (GCK) gene result in the asymptomatic fasting hyperglycemia named as GCK-MODY or MODY2. The genetic testing can effectively avoid the misdiagnosis and inappropriate treatment for GCK-MODY. A total of 25 unrelated families with MODY were screened for mutations in coding region of GCK by using direct sequencing. Three different bioinformatics tools such as PolyPhen2, Mutation Taster and PROVEAN were performed to predict the function of mutant proteins. The glucose profile was recorded by continuous glucose monitoring system (CGMS) to evaluate the glycemic variability for the GCK-MODY patient. Our study identified five GCK mutations in 24% of the families (6/25): two novel mutations (I126fs and G385A) and three already described mutations (G44S, H50fs and S383L). In silico analyses predicted that these mutations altered structural conformational changes. The values of mean amplitude of glycemic excursions (MAGE), an important index of blood glucose fluctuation in CGMS system, were 0.81 in the first 24 h and 1.61 in the second 24 h record in the patient with GCK-MODY (F3), suggesting little glucose fluctuation. The genetic testing is suggested to be important to differentiate GCK-MODY from other types of diabetes. CGMS might be used to screen GCK-MODY cases prior to genetic testing.

Reproducibility of continuous glucose monitoring results under real-life conditions in an adult population: a functional data analysis

Continuous glucose monitoring systems (CGM) are a very useful tool to understand the behaviour of glucose in different situations and populations. Despite the widespread use of CGM systems in both clinical practice and research, our understanding of the reproducibility of CGM data remains limited. The present work examines the reproducibility of the results provided by a CGM system in a random sample of a free-living adult population, from a functional data analysis approach. Functional intraclass correlation coefficients (ICCs) and their 95% confidence intervals (CI) were calculated to assess the reproducibility of CGM results in 581 individuals. 62% were females 581 participants (62% women) mean age 48 years (range 18–87) were included, 12% had previously been diagnosed with diabetes. The inter-day reproducibility of the CGM results was greater for subjects with diabetes (ICC 0.46 [CI 0.39–0.55]) than for normoglycaemic subjects (ICC 0.30 [CI 0.27–0.33]); the value for prediabetic subjects was intermediate (ICC 0.37 [CI 0.31–0.42]). For normoglycaemic subjects, inter-day reproducibility was poorer among the younger (ICC 0.26 [CI 0.21–0.30]) than the older subjects (ICC 0.39 [CI 0.32–0.45]). Inter-day reproducibility was poorest among normoglycaemic subjects, especially younger normoglycaemic subjects, suggesting the need to monitor some patient groups more often than others.

Accuracy of CGM Systems During Continuous and Interval Exercise in Adults with Type 1 Diabetes.

continuous glucose monitoring systems (CGMs) play an important role in the management of T1D, but their accuracy may reduce during rapid glucose excursions. The aim of study was to assess the accuracy of recent rt-CGMs available in Italy, in subjects with T1D during 2 sessions of physical activity: moderate continuous (CON) and interval exercise (IE). we recruited 22 patients with T1D, on CSII associated or integrated with a CGM, to which a second different sensor was applied. Data recorded by CGMs were compared with the corresponding plasma glucose (PG) values, measured every 5 minutes with the glucose analyzer. To assess the accuracy of the CGMs, we evaluated the Sensor Bias (SB), the Mean Absolute Relative Difference (MARD) and the Clarke error grid (CEG). a total of 2355 plasma-sensor glucose paired points were collected. Both average plasma and interstitial glucose concentrations did not significantly differ during CON and IE. During CON: 1. PG change at the end of exercise was greater than during IE (P = .034); 2. all sensors overestimated PG more than during IE, as shown by SB (P < .001) and MARD (P < .001) comparisons. Classifying the performance according to the CEG, significant differences were found between the 2 sessions in distribution of points in A and B zones. the exercise affects the accuracy of currently available CGMs, especially during CON, suggesting, in this circumstance, the need to maintain blood glucose in a "prudent" range, above that generally recommended. Further studies are needed to investigate additional types of activities.

Comparing effects of continuous glucose monitoring systems (CGMs) and self-monitoring of blood glucose (SMBG) amongst adults with type 2 diabetes mellitus: a systematic review protocol

BackgroundContinuous glucose monitorings (CGMs) have been used to manage diabetes with reasonable glucose control amongst patients with type 2 diabetes (T2D) in recent decades. CGMs measure interstitial fluid glucose levels to provide information about glucose levels, which identify fluctuation that would not have been identified with conventional self-monitoring. Self-monitoring of blood glucose (SMBG) is a classical tool to measure glycaemic changes. However, the effectiveness of glucose control, hypoglycemia, weight change, quality of life and user satisfaction, are needed to evaluate and compare CGMs and SMBG amongst adults with T2D.MethodsThe review will compare the various forms of CGM systems (i.e flash CGM, real-time CGM, retrospective CGM) versus SMBG or usual intervention regarding diabetes management amongst adults with T2D. The following databases will be searched: Cochrane Library, PubMed, EMBASE, CINAHL, PsycINFO, Scopus and grey literature (ClinicalTrials.gov, PsycEXTRA, ProQuest Dissertations, Google Scholar and Theses Global) for the identification of studies. The studies involving adults (aged ≥ 18 years old) will be included. We will only include and summarise randomised clinical trials (RCTs) with respect to authors, publication type, year, status and type of devices. Studies published in English between February 2010 and March 2020, will be included as the field of CGMs amongst T2D patients has emerged over the last decade. Primary outcomes will be HbA1c (glycosylated haemoglobin level) (mmol/L), body weight (kg), time spent with hypoglycaemia (< 70 mg/dl) or hyperglycaemia (≥ 180 mg/dl), blood pressure (< 140/90 mmHg is considered as good management) and quality of life (understanding and feeling of living situation based on culture and value system). Secondary outcome measures will be user satisfaction (patient or treatment/intervention satisfaction or satisfaction scale) and barriers (physical and mental difficulties or issues). Study selection, data extraction and risk of bias assessment will be conducted independently by at least two reviewers. A third reviewer will determine and resolve discrepancies. Moreover, the quality of the evidence of the review will be assessed according to the Grading of Recommendations Assessment, Development and Evaluation tool (GRADE).DiscussionThe review will synthesise evidence on the comparison between using CGMs and SMBG. The results will support researchers and health professionals to determine the most effective methods/technologies in the overall diabetes management.Systematic review registrationPROSPERO CRD42020149212

MON-086 Various Subcutaneous Continuous Glucose Monitors Comparably Lower HbA1c in Children

Background: Preliminary studies have demonstrated improvement in metabolic control of patients (PTs) using subcutaneous Continuous Glucose Monitoring systems (CGMs). In this study, we investigated the effect of CGMs on PTs’ glycemic control and compared the change in patient HbA1c levels between sensors. Objective: To determine how CGMs affect metabolic control in PTs and the effect of different sensors on glycemic control. Patients and Methods: 33 PTs with Type 1 diabetes mellitus (DM) who began using a CGM between 2017 and 2019 were selected for inclusion. CGM systems used included DexcomG6™, DexcomG5™, DexcomG4™, Enlite™, Guardian 3™, or Medtronic Sure-T™ sensors. Results: The mean (MN) age of PTs at initial visit was 15.3 ± 5.1 yrs and the MN age at second visit was 15.8 ± 5.1 yrs. The MN time between visits was 5.0 ± 2.4 months (mos). 6 PTs had follow up (F/U) times less than 3 mos, 18 PTs had F/U times between 3 and 6 mos, 6 PTs had F/U times between 6 and 9 mos, and 3 PTs had F/U times greater than 9 mos. The MN and median (MD) HbA1c at the initial visit for all PTs was 8.28% ± 1.48 and 8.10%, respectively. The MN and MD HbA1c at final F/U for all PTs was 7.57% ± 1.11 and 7.50%, respectively. The difference in MN HbA1c was significant (p<0.001).The MN and MD HbA1c at the initial visit for PTs with a F/U time less than 3 mos was 7.55% ± 0.77 and 7.75%, respectively. The MN and MD HbA1c at F/U for these PTs was 7.20% ± 0.79 and 7.20%, respectively. The difference in MN HbA1c was significant (p<0.05).The MN and MD HbA1c at the initial visit for all PTs with a F/U time greater than 3 mos was 8.44% ± 1.53 and 8.10%, respectively. The MN and MD HbA1c at F/U for these PTs was 7.66% ± 1.15 and 7.50%, respectively. The difference in MN HbA1c was significant (p<0.001).The MN change of HbA1c between visits was not significant between PTs who had 3–6 mo, 6–9 mo, and 9+ mo F/U times (p=0.96)15 PTs had HbA1c levels less than or equal to 8.0%. The MN and MD HbA1c at initial visit for these PTs was 7.20% ± 0.41 and 7.30%, respectively. The MN and MD HbA1c at F/U for these PTs was 6.75% ± 0.47 and 6.80%, respectively. The difference in MN HbA1c was significant (p<0.001).20 PTs had HbA1c levels greater than 8.0% at initial visit. The MN and MD HbA1c at the initial visit for these PTs was 9.18% ± 1.47 and 8.80%, respectively. The MN and MD HbA1c at F/U for these PTs was 8.26% ± 1.03 and 8.00%, respectively. The difference in MN HbA1c was significant (p<0.001). The MN change in HbA1c between the high HbA1c group (-.92% ± 1.02) and low HbA1c group (-0.45% ± 0.32) was not significant (p>0.05).25 PTs used a Dexcom™ sensor while 8 PTs used a Medtronic™ sensor. The MN change in HbA1c was not significant between these brands (p>0.05). Conclusion: CGMs improve metabolic control in pediatric PTs with Type 1 DM regardless of initial HbA1c. Further, this improved control is sustained over time. Sensor brands appear to be equally effective at achieving this goal.

Prediction of Children Diabetes by Autoregressive Integrated Moving Averages Model Using Big Data and Not Only SQL

Enormous efforts have been made by the health care organizations to assess the frequency and occurrence of diabetes among children. The epidemiology of diabetes is estimated with different methods. However, to effectively manage and estimate the diabetes, monitoring systems like glucose meters and Continuous Glucose Monitoring Systems (CGM) can be used. CGM is a way to determine glucose levels right through the day and night. The data obtained from such systems can be utilized effectively to manage as well to predict the diabetes. As the glucose level of the patient is monitored throughout the day, it results in an enormous amount of data. It is difficult to analyze large datasets using SQL, therefore NoSQL is used for handling big data based prediction. One such NoSQL tool known as ArangoDB is used to process the dataset with Arango Query Language (AQL). Investigations relevant to selection of attributes required for the model are discussed. In this paper, ARIMA model has been implemented to predict the diabetes among children. The model is evaluated in terms of moving average of glucose value of a particular person on a specific day. The results show that ARIMA model is appropriate for predicting Time-Series data especially like data obtained by CGM systems.

POTENTIALS TO PREDICT THE DEVELOPMENT OF DIABETES 1 TYPE COMPLICATIONS BY GLUCOSE CONTROL INDICATORS

Diabetes mellitus is recognized as a new non-infectious «epidemic of the XXI century» due to its steady increase in morbidity and a number of medical and social problems. These problems are associated with disability and mortality of patients resulted from the development of chronic complications of the disease. Hyperglycemia plays a major role in the development of diabetic complications. Diabetic microangiopathies predetermine the course and prognosis of the disease. HbA1c level and glucose variability are the complementary characteristics of glucose control. The aim of the study was to develop a mathematical model for predicting the development of diabetic microangiopathy in patients with diabetes type 1 by using continuous glucose monitoring system (CGMS). 62 patients (aged 18–45 years) with type 1 diabetes mellitus were examined. Clinical laboratory examination included: assessment of the of HbA1c level, C-peptide level, levels of blood creatinine and albuminuria. Patients were divided into groups: group 1 had HbA1c≤7.0% (n = 18), group 2 had HbA1c&gt; 7.0% (n = 44). Long-term monitoring of blood glucose levels was conducted with using the CGMS system during 6 days. Maximum blood glucose level, minimum blood glucose level and the difference of maximum and minimum blood glucose levels were accounted. The mathematical equation was obtained by using the simple linear regression analysis. This mathematical equation shows relationship between the level of albuminuria and the difference between maximum and minimum blood glucose levels. It can be used to predict the progression of diabetic nephropathy in patients with type 1 diabetes. We suggested the method for prognosticating the development and progression of diabetic microangiopathy (on an example of diabetic nephropathy) in patients with diabetes mellitus type 1 that does not require special software. This calculation may be performed using self-monitoring of blood glucose in clinical practice.

Continuous subcutaneous glucose monitoring is accurate in term and near-term infants at risk of hypoglycaemia.

Postnatal hypoglycaemia increases the risk of adverse neurological outcomes in newborn infants, and adequate glucose control requires repetitive and painful blood sampling. This study evaluated a continuous glucose monitoring system (CGMS) that aims to improve glucose control and decrease the frequency of blood samples taken from neonates. CGMS sensors, which measure glucose values every five minutes and require calibration twice a day, were placed on 20 infants at risk of hypoglycaemia. The infants also underwent blood glucose sampling, and the blood glucose values were compared with CGMS values six times during the first 30 minutes after sampling. We used 97/264 (37%) of the blood glucose values taken for the CGMS calibration. The highest accuracy, a mean of 0.22 (95% confidence interval 0.13-0.30 mmol/L), was found 15-19 minutes after sampling, due to the calibration process. No significant subcutaneous glucose time lag was detectable. The CGMS system was an accurate and feasible method for glucose control, provided earlier detection of hypoglycaemia in newborn infants and reduced their exposure to procedural pain. The delay in calibration in infants was a new finding and needs to be taken into account when comparing CGMS readings to blood glucose values.

Sensor Monitoring of Physical Activity to Improve Glucose Management in Diabetic Patients: A Review.

Diabetic individuals need to tightly control their blood glucose concentration. Several methods have been developed for this purpose, such as the finger-prick or continuous glucose monitoring systems (CGMs). However, these methods present the disadvantage of being invasive. Moreover, CGMs have limited accuracy, notably to detect hypoglycemia. It is also known that physical exercise, and even daily activity, disrupt glucose dynamics and can generate problems with blood glucose regulation during and after exercise. In order to deal with these challenges, devices for monitoring patients’ physical activity are currently under development. This review focuses on non-invasive sensors using physiological parameters related to physical exercise that were used to improve glucose monitoring in type 1 diabetes (T1DM) patients. These devices are promising for diabetes management. Indeed they permit to estimate glucose concentration either based solely on physical activity parameters or in conjunction with CGM or non-invasive CGM (NI-CGM) systems. In these last cases, the vital signals are used to modulate glucose estimations provided by the CGM and NI-CGM devices. Finally, this review indicates possible limitations of these new biosensors and outlines directions for future technologic developments.

Detection of Glycemic Abnormalities in Young Adult Patients with Beta Thalassemia Major Using Continuous Glucose Monitoring System (CGMS) and Oral Glucose Tolerance Test (OGTT)

Abstract▪2158▪This icon denotes a clinically relevant abstract Introduction:Both insulin deficiency and insulin resistance are reported in patients with β thalassemia major (BTM). The use of continuous blood glucose monitoring system (CGMS) among the different methods for early detection of glycaemic abnormalities has not been studied thoroughly in these patients. Aims:The aims of this study were: 1. to detect glycaemic abnormalities, if any, in young adults with BTM using fasting blood glucose (FBG), oral glucose tolerance test (OGTT), 72-h continuous glucose concentration by CGMS system, and serum insulin and C-peptide concentrations 2. To compare the results of these two methods in detecting glycaemic abnormalities in these patients and 3. To calculate homeostatic model assessment (HOMA), and the quantitative insulin sensitivity check index (QUICKI) in these patients. In order to evaluate whether glycaemic abnormalities are due to insulin deficiency and/or resistance. Materials and methods:Randomly selected young adults (n = 14) with BTM were the subjects of this study. All patients were investigated using a standard oral glucose tolerance test (OGTT) (using 75 gram of glucose) and 72-h continuous glucose concentration by CGM system (Medtronic system). Fasting serum insulin and C-peptide concentrations were measured and HOMA-B, HOMA-IR were calculated accordingly. Results:Using OGTT, 5 patients had impaired fasting glucose (IFG) (Fasting BG from 5.6 to 6.9 mmol/L). Two of them had impaired glucose tolerance IGT (BG from 7.8 and < 11.1 mmol/L) and one had BG = 16.2 mmol/L after 2-hrs (diabetic). Using CGMS in addition to the glucose data measured by glucometer (3–5 times/ day), 6 patients had IFG. The maximum (postprandial) BG recorded exceeded 11.1 mmol/L in 4 patients (28.5%) (Diabetics) and was > 7.8 but < 11.1 mmol/L in 8 patients (57%) (IGT).The mean values of HOMA and QUICKI in patients with BTM were < 2.6 (1.6± 0.8) and > 0.33 (0.36±0.03) respectively ruling out significant insulin resistance in these adolescents. There was a significant negative correlation between the β-cell function (B %) on the one hand and the fasting and the 2-h BG (r= −0.6, and − 0.48, P< 0.01 respectively) on the other hand. Serum insulin concentrations were not correlated with fasting BG or ferritin levels. The average and maximum BG levels recorded by CGMS were significantly correlated with the fasting BG (r= 0.69 and 0.6 respectively with P < 0.01) and with the BG at 2-hour after oral glucose intake (r= 0.87and 0.86 respectively with P < 0.01). Ferritin concentrations were positively correlated with the fasting BG and the 2-h BG levels in the OGTT (r= 0.69, 0.43 respectively, P < 0.001) as well as with the average and the maximum BG recorded by CGM (r =0.75, and 0.64 respectively with P < 0.01). Ferritin concentrations were negatively correlated with the β-cell function (r= −0.41, P< 0.01). Conclusion:CGMS has proved to be superior to OGTT for the diagnosis of glycaemic abnormalities in young adult patients with BTM. In our patients, defective β-cell function rather than insulin resistance appeared to be the cause for these abnormalities. The significant correlations between serum ferritin concentrations and the beta cell functions suggested the importance of adequate chelation to prevent β-cell dysfunction Disclosures:No relevant conflicts of interest to declare.

Correlations of Glucose Levels in Interstitial Fluid Estimated by Continuous Glucose Monitoring Systems and Venous Plasma

Continuous glucose monitoring systems (CGMS) are relatively new technology that measures interstitial glucose every 5 minute over a period of 72 hours [1]. Continuous glucose monitoring by CGMS provides accurate data to patients and physicians about blood glucose variability during the measurement period. Two types of CGMS devices, intended for professional and personal use, are available. Professional devices allow physicians to download and analyze data and for use in clinical decision making. Personal devices allow patients to monitor real time glucose data and use such data for self-monitoring and medication adjustments. For this reason, the American Association of Clinical Endocrinologists has recommended the use of personal devices for glucose control and prevention of hypoglycemia in patients with type 1 diabetes [2,3] and pregnancy with diabetes [4,5]. The article, correlation and accuracy of glucose levels between interstitial fluid and venous plasma by continuous glucose monitoring system [6] clearly showed a glucose time lag between blood levels and subcutaneous interstitial levels. Blood and interstitial fluid had different peak times and peak values after meal loading, but the trends of glucose excursion were very similar. In addition, CGMS data showed delayed peaks and lowered peak values compared to venous blood sugar levels after the ingestion of different kinds of food. These lags between the two compartments did not differ by food. The use of CGMS devices, which provide accurate control and monitoring of blood glucose levels, is suitable for use in uncontrolled diabetic patients and to help physicians make decisions regarding treatment modalities. In the treatment of diabetes, glucose variability, especially postprandial hyperglycemia, is a major risk factor for cardiovascular complications. By using CGMS devices, clinicians can easily access glucose variability in patients. Although patients more easily achieved target HbA1c levels when practicing self monitoring of glucose using the CGMS device than by other methods, systemic reviews comparing the results of CGMS use and intermittent fingertip glucose monitoring do not indicate significantly superior benefits of CGMS. However, they do support the improved detection of asymptomatic nocturnal hypoglycemia [7]. CGMS systems offer many advantages to patients and physicians. However, the sensitivity of glucose sensing decreases over time. After 3 to 5 days, the sensing amplitude of CGMS sensors becomes unreliable. The accuracy of sensors is affected by localized tissue reactions and fibrous encapsulation [8]. For these reasons, patients should calibrate sensing amplitudes through comparisons with fingertip glucose values regularly. Methods of blocking fibrous tissue aggregation and reducing tissue reactions, such as corticosteroid sprays or anticancer drug coatings, were used in attempts to address these problems [9,10]. Also, if we placed the sensor in subcutaneous space during continuous monitoring, small glucose diffusion gap from blood level to subcutaneous tissue fluid was another limitation. Recently, ubiquitous health systems have been introduced to help outpatients to control their blood sugar levels. If patients send their blood glucose profiles to physicians over the internet, their physicians can provide advice to meet their patients' needs. The use of CGMS systems connected to Internet networks has the potential to provide effective, evidence-based support to clinicians in their daily efforts to optimize glycemic control [11]. The closed loop pump is a state of the art technology [12,13]. But major pitfall of this closed loop pump is sensing problems of glucose sensor and limited space of insulin reservoir in transplanted pump. Closed loop pumps are expected to replace open loop pumps and decrease the pain associated with insulin injections. Permanent usable blood glucose sensors and interstitial glucose sensors are also expected to be developed [9,10]. Rapid development of information technology devices and techniques will solve the relevant technological problems, and closed loop systems will be available to patients in the future.

Glycemic excursions in Indian pregnant women monitored by continuous glucose monitoring system—A pilot study

Summary Objective To assess the glycemic profile in pregnant women consuming south Indian diet by continuous glucose monitoring system (CGMS) and to ascertain the time to monitor peak post-prandial plasma glucose. Research design and methods This study included 12 women with gestational diabetes mellitus (GDM) and an equal number with normal glucose tolerance (NGT) at entry. Minimed sylmer (CA, USA) CGMS system was used. They were advised to continue their usual diet and lifestyle during the study period. Results There was no difference in the time to peak between the two groups with all three meals (p > 0.05) but there was a significant increase in the peak plasma glucose (PG) in the GDM compared to NGT women (p 0.05). Conclusions Flexibility in the monitoring time between 60 and 90 min is an ethically convenient option for a pregnant woman.

Putative Delays in Interstitial Fluid (ISF) Glucose Kinetics Can Be Attributed to the Glucose Sensing Systems Used to Measure Them Rather than the Delay in ISF Glucose Itself

Since the advent of subcutaneous glucose sensors, there has been intense focus on characterizing the delay in the interstitial fluid (ISF) glucose response and the effect of insulin to alter the plasma-to-ISF glucose gradient. The Medtronic MiniMed continuous glucose monitoring system (CGMS) has often been used for this purpose; however, many of the studies have used experimental conditions that fall outside its intended use, for example, studies that have assessed the delay during rapid glucose excursions brought about by intravenous infusion of glucose or insulin. Under these conditions, it is possible that the rate of glucose change may exceed that allowed by CGMS filtering routines. If so, the estimated delay may be because of the filter rather than the ISF. Also, sensor characteristics, such as nonspecific offset current or stability, may have been inadvertently attributed to changes in the plasma-to-ISF gradient. The potential for these issues to have confounded the understanding of ISF glucose delay and gradient is investigated. An in vitro preparation in which no delay or gradient exists between sensor and measurement solution was used to recreate a rapidly changing glucose profile from a previously published in vivo study. The CGMS system (N = 6 sensors) was then used to estimate any artifactual delay and gradient introduced by the system per se. One-point calibration resulted in an apparent change in gradient as glucose was lowered from approximately 100 to 50 mg/dl. After a two-point calibration, sensor glucose followed the glucose profile as it was decreased slowly from approximately 100 to approximately 60 mg/dl; however, when the glucose level was subsequently increased rapidly to approximately 150 mg/dl, CGMS filtering routines limited the rate of change of sensor glucose and introduced a delay similar to that previously attributed to ISF glucose equilibration delay. Studies that have previously used the Medtronic MiniMed CGMS system to assess changes in the plasma-to-ISF glucose gradient may need to be reassessed to ensure that the offset current was estimated accurately. Studies that have used the system to assess ISF glucose delay during rapid, unphysiologic changes in glucose and did not remove the CGMS smoothing filters may have attributed CGMS filter delay to ISF glucose equilibration.

Accuracy and feasibility of point-of-care and continuous blood glucose analysis in critically ill ICU patients

IntroductionTo obtain strict glucose regulation, an accurate and feasible bedside glucometry method is essential. We evaluated three different types of point-of-care glucometry in seriously ill intensive care unit (ICU) patients. The study was performed as a single-centre, prospective, observational study in a 12-bed medical ICU of a university hospital.MethodsPatients with an expected ICU stay of more than 48 hours were included. Because the reference laboratory delivers glucose values after approximately 30 to 60 minutes, which is too slow to use in a glucose regulation protocol and for calibration of the subcutaneous continuous glucose monitoring system (CGMS) (CGMS System Gold), we first validated the ICU-based blood gas/glucose analyser ABL715 (part 1 of the study). Subsequently, part 2 was performed: after inserting (and calibrating) the subcutaneous CGMS, heparinised arterial blood samples were drawn from an arterial line every 6 hours and analysed on both the Precision PCx point-of-care meter using test strips and on the blood gas/glucose analyser ABL715. CGMS glucose data were downloaded after 24 to 72 hours. The results of the paired measurements were analysed as a scatter plot by the method of Bland and Altman and were expressed as a correlation coefficient.ResultsPart 1: Four hundred and twenty-four blood samples were drawn from 45 critically ill ICU patients. The ICU-based blood gas/glucose analyser ABL715 provided a good estimate of conventional laboratory glucose assessment: the correlation coefficient was 0.95. In the Clarke error grid, 96.8% of the paired measurements were in the clinically acceptable zones A and B. Part 2: One hundred sixty-five paired samples were drawn from 19 ICU patients. The Precision PCx point-of-care meter showed a correlation coefficient of 0.89. Ninety-eight point seven percent of measurements were within zones A and B. The correlation coefficient for the subcutaneous CGMS System Gold was 0.89. One hundred percent of measurements were within zones A and B.ConclusionThe ICU-based blood glucose analyser ABL715 is a rapid and accurate alternative for laboratory glucose determination and can serve as a standard for ICU blood glucose measurements. The Precision PCx is a good alternative, but feasibility may be limited because of the blood sample handling. The subcutaneous CGMS System Gold is promising, but real-time glucose level reporting is necessary before it can be of clinical use in the ICU. When implementing a glucose-insulin algorithm in patient care or research, one should realise that the absolute glucose level may differ systematically among various measuring methods, influencing targeted glucose levels.