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Xu, Shiwu, Chih-Cheng Chen, Yi Wu, Xufang Wang, and Fen Wei. "Adaptive Residual Weighted K-Nearest Neighbor Fingerprint Positioning Algorithm Based on Visible Light Communication." Sensors 20, no. 16 (August 8, 2020): 4432. http://dx.doi.org/10.3390/s20164432.
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The weighted K-nearest neighbor (WKNN) algorithm is a commonly used fingerprint positioning, the difficulty of which lies in how to optimize the value of K to obtain the minimum positioning error. In this paper, we propose an adaptive residual weighted K-nearest neighbor (ARWKNN) fingerprint positioning algorithm based on visible light communication. Firstly, the target matches the fingerprints according to the received signal strength indication (RSSI) vector. Secondly, K is a dynamic value according to the matched RSSI residual. Simulation results show the ARWKNN algorithm presents a reduced average positioning error when compared with random forest (81.82%), extreme learning machine (83.93%), artificial neural network (86.06%), grid-independent least square (60.15%), self-adaptive WKNN (43.84%), WKNN (47.81%), and KNN (73.36%). These results were obtained when the signal-to-noise ratio was set to 20 dB, and Manhattan distance was used in a two-dimensional (2-D) space. The ARWKNN algorithm based on Clark distance and minimum maximum distance metrics produces the minimum average positioning error in 2-D and 3-D, respectively. Compared with self-adaptive WKNN (SAWKNN), WKNN and KNN algorithms, the ARWKNN algorithm achieves a significant reduction in the average positioning error while maintaining similar algorithm complexity.
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Novirza, Resti, and Muldarisnur Muldarisnur. "Pengaruh Panjang Pengupasan Terhadap Sensitivitas dan Akurasi Sensor Gula Darah Menggunakan Serat Optik Singlemode." Jurnal Fisika Unand 8, no. 1 (January 2, 2019): 72–76. http://dx.doi.org/10.25077/jfu.8.1.72-76.2019.
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Telah dilakukan optimasi panjang pengupasan sensor serat optik singlemode untuk memperolehsensitivitas dan akurasi yang tinggi pada sensor pengukuran gula darah. Panjang pengupasan divariasikan antara 1 cm hingga 5 cm dengan interval 1 cm. Serat optik digunakan untuk memandu cahaya dari sumber laser dioda merah (λ=650nm) ke detektor fotodioda. Interaksi antara gelombang evanescent dan molekul glukosa dalam darah meningkat karena sebagian cladding pada serat optik dikupas. Perbedaan kedalaman penetrasi karena panjang pengupasan mempengaruhi sensitivitas dan akurasi sensor yang telah dirancang. Sensitivitas tertinggi yaitu 1,034 mV/(mg/dL) yang diperoleh pada panjang pengupasan 3 cm. Sensitivitas terendah diperoleh pada panjang pengupasan 5 cm dengan nilai 0,453 mV/(mg/dL). Akurasi tertinggi terdapat pada panjang pengupasan 2 cm dengan nilai 95,23% dan akurasi terendah pada panjang pengupasan 1 cm dengan nilai 93,35%. Clark Error Grid Analisis menunjukkan bahwa data tersebar 95% di daerah A dan 0,5% di daerah B yang mempenyai error yang kecil, artinya optimasi sensor gula darah akurat dan dapat diandalkan.Kata kunci: sensor gula darah, gelombang evanescent, panjang pengupasan, sensitivitas, akurasi.
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Al-dhaheri, Mustafa Ayesh, Nasr-Eddine Mekkakia-Maaza, Hassan Mouhadjer, and Abdelghani Lakhdari. "Noninvasive blood glucose monitoring system based on near-infrared method." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 2 (April 1, 2020): 1736. http://dx.doi.org/10.11591/ijece.v10i2.pp1736-1746.
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Diabetes is considered one of the life-threatening diseases in the world which need continuous monitoring to avoid the complication of diabetes. There is a need to develop a non-invasive monitoring system that avoids the risk of infection problems and pain caused by invasive monitoring techniques. This paper presents a method for developing a noninvasive technique to predict the blood glucose concentration (BCG) based on the Near-infrared (NIR) light sensor. A prototype is developed using a finger sensor based on LED of 940 nm wavelength to collect photoplethysmography (PPG) signal which is variable depending on the glucose concentration variance, a module circuit to preprocess PPG signals is realized, which includes an amplifier and analog filter circuits, an Arduino UNO is used to analog-to-digital conversion. A digital Butterworth filterer is used to remove PPG signal trends, then detect the PPG data peaks to determine the relationship between the PPG signal and (BCG) and use it as input parameters to build the calibration model based on linear regression. Experiments show that the Root Mean Squares Error (RMSE) of the prediction is between 8.264mg/dL and 13.166 mg/dL, the average of RMSE is about 10.44mg/dL with a correlation coefficient (R^2) of 0.839, it is observed that the prediction of glucose concentration is in the clinically acceptable region of the standard Clark Error Grid (CEG).
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Clarke, William L. "The Original Clarke Error Grid Analysis (EGA)." Diabetes Technology & Therapeutics 7, no. 5 (October 2005): 776–79. http://dx.doi.org/10.1089/dia.2005.7.776.
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Faruqui, Syed Hasib Akhter, Yan Du, Rajitha Meka, Adel Alaeddini, Chengdong Li, Sara Shirinkam, and Jing Wang. "Development of a Deep Learning Model for Dynamic Forecasting of Blood Glucose Level for Type 2 Diabetes Mellitus: Secondary Analysis of a Randomized Controlled Trial." JMIR mHealth and uHealth 7, no. 11 (November 1, 2019): e14452. http://dx.doi.org/10.2196/14452.
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Background Type 2 diabetes mellitus (T2DM) is a major public health burden. Self-management of diabetes including maintaining a healthy lifestyle is essential for glycemic control and to prevent diabetes complications. Mobile-based health data can play an important role in the forecasting of blood glucose levels for lifestyle management and control of T2DM. Objective The objective of this work was to dynamically forecast daily glucose levels in patients with T2DM based on their daily mobile health lifestyle data including diet, physical activity, weight, and glucose level from the day before. Methods We used data from 10 T2DM patients who were overweight or obese in a behavioral lifestyle intervention using mobile tools for daily monitoring of diet, physical activity, weight, and blood glucose over 6 months. We developed a deep learning model based on long short-term memory–based recurrent neural networks to forecast the next-day glucose levels in individual patients. The neural network used several layers of computational nodes to model how mobile health data (food intake including consumed calories, fat, and carbohydrates; exercise; and weight) were progressing from one day to another from noisy data. Results The model was validated based on a data set of 10 patients who had been monitored daily for over 6 months. The proposed deep learning model demonstrated considerable accuracy in predicting the next day glucose level based on Clark Error Grid and ±10% range of the actual values. Conclusions Using machine learning methodologies may leverage mobile health lifestyle data to develop effective individualized prediction plans for T2DM management. However, predicting future glucose levels is challenging as glucose level is determined by multiple factors. Future study with more rigorous study design is warranted to better predict future glucose levels for T2DM management.
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Mondal, Himel, and Shaikat Mondal. "Clarke Error Grid Analysis on Graph Paper and Microsoft Excel." Journal of Diabetes Science and Technology 14, no. 2 (November 28, 2019): 499. http://dx.doi.org/10.1177/1932296819890875.
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Anand, Pradeep Kumar, Dong Ryeol Shin, and Mudasar Latif Memon. "Adaptive Boosting Based Personalized Glucose Monitoring System (PGMS) for Non-Invasive Blood Glucose Prediction with Improved Accuracy." Diagnostics 10, no. 5 (May 7, 2020): 285. http://dx.doi.org/10.3390/diagnostics10050285.
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In this paper, we present an architecture of a personalized glucose monitoring system (PGMS). PGMS consists of both invasive and non-invasive sensors on a single device. Initially, blood glucose is measured invasively and non-invasively, to train the machine learning models. Then, paired data and corresponding errors are divided scientifically into six different clusters based on blood glucose ranges as per the patient’s diabetic conditions. Each cluster is trained to build the unique error prediction model using an adaptive boosting (AdaBoost) algorithm. Later, these error prediction models undergo personalized calibration based on the patient’s characteristics. Once, the errors in predicted non-invasive values are within the acceptable error range, the device gets personalized for a patient to measure the blood glucose non-invasively. We verify PGMS on two different datasets. Performance analysis shows that the mean absolute relative difference (MARD) is reduced exceptionally to 7.3% and 7.1% for predicted values as compared to 25.4% and 18.4% for measured non-invasive glucose values. The Clarke error grid analysis (CEGA) plot for non-invasive predicted values shows 97% data in Zone A and 3% data in Zone B for dataset 1. Moreover, for dataset 2 results echoed with 98% and 2% in Zones A and B, respectively.
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Wentholt, I. M., J. B. Hoekstra, and J. H. DeVries. "A Critical Appraisal of the Continuous Glucose-Error Grid Analysis: Response to Clarke et al." Diabetes Care 30, no. 2 (January 26, 2007): 450–51. http://dx.doi.org/10.2337/dc06-2157.
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Segev, Natalie, Lindsey N. Hornung, Siobhan E. Tellez, Joshua D. Courter, Sarah A. Lawson, Jaimie D. Nathan, Maisam Abu-El-Haija, and Deborah A. Elder. "Continuous Glucose Monitoring in the Intensive Care Unit Following Total Pancreatectomy with Islet Autotransplantation in Children: Establishing Accuracy of the Dexcom G6 Model." Journal of Clinical Medicine 10, no. 9 (April 27, 2021): 1893. http://dx.doi.org/10.3390/jcm10091893.
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Hyperglycemia is detrimental to postoperative islet cell survival in patients undergoing total pancreatectomy with islet autotransplantation (TPIAT). This makes continuous glucose monitoring (CGM) a useful management tool. We evaluated the accuracy of the Dexcom G6 CGM in pediatric intensive care unit patients following TPIAT. Twenty-five patients who underwent TPIAT had Dexcom G6 glucose values compared to paired serum glucose values. All paired glucose samples were obtained within 5 minutes of each other during the first seven days post TPIAT. Data were evaluated using mean absolute difference (MAD), mean absolute relative difference (MARD), %20/20, %15/15 accuracy, and Clarke Error Grid analysis. Exclusions included analysis during the CGM “warm-up” period and hydroxyurea administration (known drug interference). A total of 183 time-matched samples were reviewed during postoperative days 2–7. MAD was 14.7 mg/dL and MARD was 13.4%, with values of 15.2%, 14.0%, 12.1%, 11.4%, 13.2% and 14.1% at days 2, 3, 4, 5, 6 and 7, respectively. Dexcom G6 had a %20/20 accuracy of 78%, and a %15/15 accuracy of 64%. Clarke Error Grid analysis showed that 77% of time-matched values were clinically accurate, and 100% were clinically acceptable. The Dexcom G6 CGM may be an accurate tool producing clinically acceptable values to make reliable clinical decisions in the immediate post-TPIAT period.
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Umar, Usman, Risnawaty Alyah, and Imran Amin. "Analisa Keakuratan Kadar Glukosa Darah Menggunakan Clarke-Error Grid Analisis pada Alat Ukur Non-invasive menggunakan Sensor Photoacoustic." Lontara 1, no. 2 (December 7, 2020): 125–35. http://dx.doi.org/10.53861/lontarariset.v1i2.80.
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Blood glucose is a very important element in the human body, but if it is deficient or excessive, it can cause chronic disease that can lead to death. To prevent this, it is necessary to monitor cholesterol and blood glucose levels regularly, at this time the tool for measuring blood glucose levels is still an invasive method by taking a blood sample at the fingertip by injuring it. This study aims to develop a non-invasive blood glucose measuring device using a Photoacoustic Spectroscopy sensor in the range of values from a laser pulse source (λ = 650 nm) which can detect glucose signals in the blood. The method of developing this research is by designing a non-invasive measuring instrument and measuring blood glucose levels in male and female participants to create a linearity equation between blood glucose levels and the output voltage from the sensor, then mathematically obtained a polynominal equation to convert the voltage to values. blood glucose level. Validation of measuring instruments designed by comparing invasive measuring instruments as a reference, using the Clarke EGA to determine accuracy based on the classification of values of blood glucose and blood cholesterol levels based on reference. The results of the Clarke EGA analysis show that all measurement data is in Zone A so that the measuring instrument with a photoacoustic sensor can be accepted.
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Izah, Rofiatul, Subiyanto Subiyanto, and Dhidik Prastiyanto. "Improvement of DSOGI PLL Synchronization Algorithm with Filter on Three-Phase Grid-connected Photovoltaic System." Jurnal Elektronika dan Telekomunikasi 18, no. 1 (August 31, 2018): 35. http://dx.doi.org/10.14203/jet.v18.35-45.
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Synchronous Reference Frame Phase Locked Loop (SRF PLL) has been widely used for synchronization three-phase grid-connected photovoltaic (PV) system. On the grid fault, SRF PLL distorted by negative sequence component and grid harmonic that caused an error in estimating parameter because of ripple and oscillation. This work combined SRF PLL with Dual Second Order Generalized Integrator (DSOGI) and filter to minimize ripple and minimize oscillation in the phase estimation and frequency estimation. DSOGI was used for filtering and obtaining the 90o shifted versions from the vαβ signals. These signals (vαβ) were generated from three phase grid voltage signal using Clarke transform. The vαβ signal was the inputs to the positive-sequence calculator (PSC). The positive-sequence vαβ was transformed to the dq synchronous reference frame and became an input to SRF-PLL to create the estimation frequency. This estimation frequency from SRF PLL was filtered by the low-pass filter to decrease grid harmonic. Moreover, the output of low-pass filter was a frequency adaptive. The performance of DSOGI PLL with filter is compared with DSOGI PLL, SRF PLL, and IEEE standard 1547(TM)-2003. The improvement of DSOGI PLL with filter gave better performances than DSOGI PLL and SRF PLLbecause it minimized ripples and oscillations in the phase and frequency estimations.
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Carugati, Ignacio, Carlos M. Orallo, Sebastian Maestri, Patricio G. Donato, and Daniel Carrica. "Error analysis of phase detector based on Clarke transform and arctangent function in polluted grids." Electric Power Systems Research 127 (October 2015): 160–64. http://dx.doi.org/10.1016/j.epsr.2015.05.027.
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Yan, Rengna, Huiqin Li, Xiaocen Kong, Xiaofang Zhai, Maoyuan Chen, Yixuan Sun, Lei Ye, Xiaofei Su, and Jianhua Ma. "The Accuracy and Precision of the Continuously Stored Data from Flash Glucose Monitoring System in Type 2 Diabetes Patients during Standard Meal Tolerance Test." International Journal of Endocrinology 2020 (January 4, 2020): 1–6. http://dx.doi.org/10.1155/2020/5947680.
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Background. The purpose of this study was to investigate the accuracy of the continuously stored data from the Abbott FreeStyle Libre flash glucose monitoring (FGM) system in Chinese diabetes patients during standard meal tests when glucose concentrations were rapidly changing. Subjects and Methods. Interstitial glucose levels were monitored for 14 days in 26 insulin-treated patients with type 2 diabetes using the FGM system. Standard meal tests were conducted to induce large glucose swings. Venous blood glucose (VBG) was tested at 0, 30, 60, and 120 min after standard meal tests in one middle day of the first and second weeks, respectively. The corresponding sensor glucose values were obtained from interpolating continuously stored data points. Assessment of accuracy was according to recent consensus recommendations with median absolute relative difference (MARD) and Clarke and Parkes error grid analysis (CEG and PEG). Results. Among 208 paired sensor-reference values, 100% were falling within zones A and B of the Clarke and Parkes error grid analysis. The overall MARD was 10.7% (SD, 7.8%). Weighted least squares regression analysis resulted in high agreement between the FGM sensor glucose and VBG readings. The overall MTT results showed that FGM was lower than actual VBG, with MAD of 22.1 mg/dL (1.2 mmol/L). At VBG rates of change of -1 to 0, 0 to 1, 1 to 2, and 2 to 3 mg/dl/min, MARD results were 11.4% (SD, 8.7%), 9.4% (SD, 6.5%), 9.9% (SD, 7.5%), and 9.5% (SD, 7.7%). At rapidly changing VBG concentrations (>3 mg/dl/min), MARD increased to 19.0%, which was significantly higher than slow changing BG groups. Conclusions. Continuously stored interstitial glucose measurements with the FGM system were found to be acceptable to evaluate VBG in terms of clinical decision during standard meal tests. The continuously stored data from the FGM system appeared to underestimate venous glucose and performed less well during rapid glucose changes.
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Worsley, Graham J., Guilhem A. Tourniaire, Kathryn E. S. Medlock, Felicity K. Sartain, Hazel E. Harmer, Michael Thatcher, Adrian M. Horgan, and John Pritchard. "Continuous Blood Glucose Monitoring with a Thin-Film Optical Sensor." Clinical Chemistry 53, no. 10 (October 1, 2007): 1820–26. http://dx.doi.org/10.1373/clinchem.2007.091629.
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Abstract Background: We recently described a holographic optical sensor with improved selectivity for glucose over fructose that was based on a thin-film polymer hydrogel containing phenylboronic acid receptors. The aim of the present work was to measure glucose in human blood plasma as opposed to simple buffers and track changes in concentration at a rate mimicking glucose changes in vivo. Methods: We used holographic sensors containing acrylamide, N,N′-methylenebisacrylamide, 3-acrylamidophenylboronic acid, and (3-acrylamidopropyl)trimethylammonium chloride to measure 7 human blood plasma samples at different glucose concentrations (3–33 mmol/L) in static mode. Separately, using a flow cell, the glucose concentration was varied at approximately 0.17–0.28 mmol−1 · L−1 · min−1, and the sensor’s ability to continuously monitor glucose was investigated over an extended period. Results: We subjected the results of the ex vivo static measurements to error grid analysis. Of 46 measurements, 42 (91.3%) fell in zone A of a Clarke error grid, and the remainder (8.7%) fell in zone B. The ex vivo flow experiments showed that the sensor is able to accurately track changes in concentration occurring in real time without lag or evidence of hysteresis. Conclusions: We demonstrate the ability of a phenylboronic acid–based sensor to measure glucose in human blood plasma for the 1st time in vitro. Holographic glucose sensors can be used without recourse to recalibration. Their robust nature, coupled with their format flexibility, makes them an attractive alternative to conventional electrochemical enzyme-based methods of glucose monitoring for people with diabetes.
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Alarcón-Paredes, Antonio, Victor Francisco-García, Iris P. Guzmán-Guzmán, Jessica Cantillo-Negrete, René E. Cuevas-Valencia, and Gustavo A. Alonso-Silverio. "An IoT-Based Non-Invasive Glucose Level Monitoring System Using Raspberry Pi." Applied Sciences 9, no. 15 (July 28, 2019): 3046. http://dx.doi.org/10.3390/app9153046.
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Patients diagnosed with diabetes mellitus must monitor their blood glucose levels in order to control the glycaemia. Consequently, they must perform a capillary test at least three times per day and, besides that, a laboratory test once or twice per month. These standard methods pose difficulty for patients since they need to prick their finger in order to determine the glucose concentration, yielding discomfort and distress. In this paper, an Internet of Things (IoT)-based framework for non-invasive blood glucose monitoring is described. The system is based on Raspberry Pi Zero (RPi) energised with a power bank, using a visible laser beam and a Raspberry Pi Camera, all implemented in a glove. Data for the non-invasive monitoring is acquired by the RPi Zero taking a set of pictures of the user fingertip and computing their histograms. Generated data is processed by an artificial neural network (ANN) implemented on a Flask microservice using the Tensorflow libraries. In this paper, all measurements were performed in vivo and the obtained data was validated against laboratory blood tests by means of the mean absolute error (10.37%) and Clarke grid error (90.32% in zone A). Estimated glucose values can be harvested by an end device such as a smartphone for monitoring purposes.
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Fitzgerald, Oisin, Oscar Perez-Concha, Blanca Gallego, Manoj K. Saxena, Lachlan Rudd, Alejandro Metke-Jimenez, and Louisa Jorm. "Incorporating real-world evidence into the development of patient blood glucose prediction algorithms for the ICU." Journal of the American Medical Informatics Association 28, no. 8 (April 19, 2021): 1642–50. http://dx.doi.org/10.1093/jamia/ocab060.
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Abstract Objective Glycemic control is an important component of critical care. We present a data-driven method for predicting intensive care unit (ICU) patient response to glycemic control protocols while accounting for patient heterogeneity and variations in care. Materials and Methods Using electronic medical records (EMRs) of 18 961 ICU admissions from the MIMIC-III dataset, including 318 574 blood glucose measurements, we train and validate a gradient boosted tree machine learning (ML) algorithm to forecast patient blood glucose and a 95% prediction interval at 2-hour intervals. The model uses as inputs irregular multivariate time series data relating to recent in-patient medical history and glycemic control, including previous blood glucose, nutrition, and insulin dosing. Results Our forecasting model using routinely collected EMRs achieves performance comparable to previous models developed in planned research studies using continuous blood glucose monitoring. Model error, expressed as mean absolute percentage error is 16.5%–16.8%, with Clarke error grid analysis demonstrating that 97% of predictions would be clinically acceptable. The 95% prediction intervals achieve near intended coverage at 93%–94%. Discussion ML algorithms built on observational data sources, such as EMRs, present a promising approach for personalization and automation of glycemic control in critical care. Future research may benefit from applying a combination of methodologies and data sources to develop robust methodologies that account for the variations seen in ICU patients and difficultly in detecting the extremes of observed blood glucose values. Conclusion We demonstrate that EMRs can be used to train ML algorithms that may be suitable for incorporation into ICU decision support systems.
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Elder, Craig T., Tera Thigpin, Rachel Karlnoski, David Smith, David Mozingo, and Joshua S. Carson. "Results of a Multicenter Feasibility Study of an Automated Bedside Glucose Monitoring System in the Burn Intensive Care Setting." Journal of Burn Care & Research 41, no. 3 (October 21, 2019): 535–38. http://dx.doi.org/10.1093/jbcr/irz171.
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Abstract Intensive blood glucose regimens required for tight glycemic control in critically ill burn patients carry risk of hypoglycemia and are ultimately limited by the frequency of which serum glucose measurements can be feasibly monitored. Continuous inline glucose monitoring has the potential to significantly increase the frequency of serum glucose measurement. The objective of this study was to assess the accuracy of a continuous glucose monitor with inline capability (Optiscanner) in the burn intensive care setting. A multicenter, observational study was conducted at two academic burn centers. One hundred and six paired blood samples were collected from 10 patients and measured on the Optiscanner and the Yellow Springs Instrument. Values were plotted on a Clarke Error Grid and mean absolute relative difference calculated. Treatment was guided by existing hospital protocols using separately obtained values. 97.2% of results obtained from Optiscanner were within 25% of corresponding Yellow Springs Instrument values and 100% were within 30%. Mean absolute relative difference was calculated at 9.6%. Our findings suggest that a continuous glucose monitor with inline capability provides accurate blood glucose measurements among critically ill burn patients.
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Akkaya, Ibrahim, Erman Selim, Mert Altintas, and Mehmet Engin. "Power spectral density-based nearinfrared sub-band detection for noninvasive blood glucose prediction in both in-vitro and in-vivo studies." Journal of Innovative Optical Health Sciences 11, no. 06 (November 2018): 1850035. http://dx.doi.org/10.1142/s1793545818500359.
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Diabetes is a widespread and serious disease and noninvasive measurement has been in high demand. To address this problem, a power spectral density-based method was offered for determining glucose sensitive sub-bands in the nearinfrared (NIR) spectrum. The experiments were conducted using phantoms of different optical properties in-vitro conditions. The optical bands 1200–1300[Formula: see text]nm and 2100–2200[Formula: see text]nm were found feasible for measuring blood glucose. After that, a photoplethysmography (PPG)-based low cost and portable optical system was designed. It has six different NIR wavelength LEDs for illumination and an InGaAs photodiode for detection. Optical density values were calculated through the system and used as independent variables for multiple linear regression analysis. The results of blood glucose levels for 24 known healthy subjects showed that the optical system prediction was nearly 80% in the A zone and 20% in the B zone according to the Clarke Error Grid analysis. It was shown that a promising easy-use, continuous, and compact optical system had been designed.
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Magarian, Peggy, and Bernhard Sterling. "Plasma-Generating Glucose Monitor Accuracy Demonstrated in an Animal Model." Journal of Diabetes Science and Technology 3, no. 6 (November 2009): 1411–18. http://dx.doi.org/10.1177/193229680900300622.
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Introduction: Four randomized controlled trials have compared mortality and morbidity of tight glycemic control versus conventional glucose for intensive care unit (ICU) patients. Two trials showed a positive outcome. However, one single-center trial and a large multicenter trial had negative results. The positive trials used accurate portable lab analyzers. The negative trial allowed the use of meters. The portable analyzer measures in filtered plasma, minimizing the interference effects. OptiScan Biomedical Corporation is developing a continuous glucose monitor using centrifuged plasma and mid-infrared spectroscopy for use in ICU medicine. The OptiScanner draws approximately 0.1 ml of blood every 15 min and creates a centrifuged plasma sample. Internal quality control minimizes sample preparation error. Interference adjustment using this technique has been presented at the Society of Critical Care Medicine in separate studies since 2006. Method: A good laboratory practice study was conducted on three Yorkshire pigs using a central venous catheter over 6 h while performing a glucose challenge. Matching Yellow Springs Instrument glucose readings were obtained. Results: Some 95.7% of the predicted values were in the Clarke Error Grid A zone and 4.3% in the B zone. Of those in the B zone, all were within 3.3% of the A zone boundaries. The coefficient of determination ( R2) was 0.993. The coefficient of variance was 5.02%. Animal necropsy and blood panels demonstrated safety. Conclusion: The OptiScanner investigational device performed safely and accurately in an animal model. Human studies using the device will begin soon.
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Li, Nan, Hang Zang, Huimin Sun, Xianzhi Jiao, Kangkang Wang, Timon Cheng-Yi Liu, and Yaoyong Meng. "A Noninvasive Accurate Measurement of Blood Glucose Levels with Raman Spectroscopy of Blood in Microvessels." Molecules 24, no. 8 (April 17, 2019): 1500. http://dx.doi.org/10.3390/molecules24081500.
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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.
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Acciaroli, Giada, Mattia Zanon, Andrea Facchinetti, Andreas Caduff, and Giovanni Sparacino. "Retrospective Continuous-Time Blood Glucose Estimation in Free Living Conditions with a Non-Invasive Multisensor Device." Sensors 19, no. 17 (August 24, 2019): 3677. http://dx.doi.org/10.3390/s19173677.
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Even if still at an early stage of development, non-invasive continuous glucose monitoring (NI-CGM) sensors represent a promising technology for optimizing diabetes therapy. Recent studies showed that the Multisensor provides useful information about glucose dynamics with a mean absolute relative difference (MARD) of 35.4% in a fully prospective setting. Here we propose a method that, exploiting the same Multisensor measurements, but in a retrospective setting, achieves a much better accuracy. Data acquired by the Multisensor during a long-term study are retrospectively processed following a two-step procedure. First, the raw data are transformed to a blood glucose (BG) estimate by a multiple linear regression model. Then, an enhancing module is applied in cascade to the regression model to improve the accuracy of the glucose estimation by retrofitting available BG references through a time-varying linear model. MARD between the retrospectively reconstructed BG time-series and reference values is 20%. Here, 94% of values fall in zone A or B of the Clarke Error Grid. The proposed algorithm achieved a level of accuracy that could make this device a potential complementary tool for diabetes management and also for guiding prediabetic or nondiabetic users through life-style changes.
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Ocvirk, Gregor, Martin Hajnsek, Ralph Gillen, Arnfried Guenther, Gernot Hochmuth, Ulrike Kamecke, Karl-Heinz Koelker, et al. "The Clinical Research Tool: A High-Performance Microdialysis-Based System for Reliably Measuring Interstitial Fluid Glucose Concentration." Journal of Diabetes Science and Technology 3, no. 3 (May 2009): 468–77. http://dx.doi.org/10.1177/193229680900300310.
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Background: A novel microdialysis-based continuous glucose monitoring system, the so-called Clinical Research Tool (CRT), is presented. The CRT was designed exclusively for investigational use to offer high analytical accuracy and reliability. The CRT was built to avoid signal artifacts due to catheter clogging, flow obstruction by air bubbles, and flow variation caused by inconstant pumping. For differentiation between physiological events and system artifacts, the sensor current, counter electrode and polarization voltage, battery voltage, sensor temperature, and flow rate are recorded at a rate of 1 Hz. Method: In vitro characterization with buffered glucose solutions (cglucose = 0 − 26 × 10−3 mol liter−1) over 120 h yielded a mean absolute relative error (MARE) of 2.9 ± 0.9% and a recorded mean flow rate of 330 ± 48 nl/min with periodic flow rate variation amounting to 24 ± 7%. The first 120 h in vivo testing was conducted with five type 1 diabetes subjects wearing two systems each. A mean flow rate of 350 ± 59 nl/min and a periodic variation of 22 ± 6% were recorded. Results: Utilizing 3 blood glucose measurements per day and a physical lag time of 1980 s, retrospective calibration of the 10 in vivo experiments yielded a MARE value of 12.4 ± 5.7. Clarke error grid analysis resulted in 81.0%, 16.6%, 0.8%, 1.6%, and 0% in regions A, B, C, D, and E, respectively. Conclusion: The CRT demonstrates exceptional reliability of system operation and very good measurement performance. The ability to differentiate between artifacts and physiological effects suggests the use of the CRT as a reference tool in clinical investigations.
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Zisser, Howard C., Timothy S. Bailey, Sherwyn Schwartz, Robert E. Ratner, and Jonathan Wise. "Accuracy of the SEVEN® Continuous Glucose Monitoring System: Comparison with Frequently Sampled Venous Glucose Measurements." Journal of Diabetes Science and Technology 3, no. 5 (September 2009): 1146–54. http://dx.doi.org/10.1177/193229680900300519.
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Background: The purpose of this study was to compare the accuracy of measurements obtained from the DexCom™ SEVEN® system with Yellow Springs Instrument (YSI) laboratory measurements of venous blood glucose. Methods: Seventy-two subjects with insulin-requiring diabetes, aged 18–71, were enrolled in a multicenter, prospective single-arm study. All participants wore the SEVEN continuous glucose monitoring (CGM) system for one, 7-day wear period. Calibration with capillary finger stick measurements was performed 2 hours after sensor insertion and once every 12 hours thereafter. A subset of subjects (28) wore two systems simultaneously to assess precision. All subjects participated in one, 10-hour in-clinic session on day 1, 4, or 7 of the study to compare CGM measurements against a laboratory method (YSI analyzer) using venous measurements taken once every 20 minutes. Carbohydrate consumption and insulin dosing were adjusted in order to obtain a broad range of glucose values. Results: Comparison of CGM measurements with the laboratory reference method ( n = 2318) gave mean and median absolute relative differences (ARDs) of 16.7 and 13.2%, respectively. The percentage was 70.4% in the clinically accurate Clarke error grid A zone and 27.5% in the benign error B zone. Performance of the SEVEN system was consistent over time with mean and median ARD lowest on day 7 as compared to YSI (13.3 and 10.2%, respectively). Average sensor time lag was 5 minutes. Conclusions: Measurements of the DexCom SEVEN system were found to be consistent and accurate compared with venous measurements made using a laboratory reference method over 7 days of wear.
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Kopecký, Petr, Miloš Mráz, Jan Bláha, Jaroslav Lindner, Štĕpán Svačina, Roman Hovorka, and Martin Haluzík. "The Use of Continuous Glucose Monitoring Combined with Computer-Based eMPC Algorithm for Tight Glucose Control in Cardiosurgical ICU." BioMed Research International 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/186439.
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Aim. In postcardiac surgery patients, we assessed the performance of a system for intensive intravenous insulin therapy using continuous glucose monitoring (CGM) and enhanced model predictive control (eMPC) algorithm.Methods. Glucose control in eMPC-CGM group (n=12) was compared with a control (C) group (n=12) treated by intravenous insulin infusion adjusted according to eMPC protocol with a variable sampling interval alone. In the eMPC-CGM group glucose measured with a REAL-Time CGM system (Guardian RT) served as input for the eMPC adjusting insulin infusion every 15 minutes. The accuracy of CGM was evaluated hourly using reference arterial glucose and Clarke error-grid analysis (C-EGA). Target glucose range was 4.4–6.1 mmol/L.Results. Of the 277 paired CGM-reference glycemic values, 270 (97.5%) were in clinically acceptable zones of C-EGA and only 7 (2.5%) were in unacceptable D zone. Glucose control in eMPC-CGM group was comparable to C group in all measured values (average glycemia, percentage of time above, within, and below target range,). No episode of hypoglycemia (<2.9 mmol) occurred in eMPC-CGM group compared to 2 in C group.Conclusion. Our data show that the combination of eMPC algorithm with CGM is reliable and accurate enough to test this approach in a larger study population.
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Bahartan, Karnit, Keren Horman, Avner Gal, Andrew Drexler, Yulia Mayzel, and Tamar Lin. "Assessing the Performance of a Noninvasive Glucose Monitor in People with Type 2 Diabetes with Different Demographic Profiles." Journal of Diabetes Research 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/4393497.
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Background. Noninvasive glucose-monitoring devices represent an exciting frontier in diabetes research. GlucoTrack® is a noninvasive device that indirectly measures glucose fluctuation in the earlobe tissue. However, GlucoTrack measurements may be susceptible to effects of quasi-stable factors that may be affected by demographic profiles. The current study, thus, examined device performances in people with type 2 diabetes with different demographic profiles, focusing on age, gender, body mass, and whether the earlobe is pierced. Materials and Methods. Clinical trials were conducted on 172 type 2 adult diabetic subjects. Device performance was clinically evaluated using the Clarke error grid (CEG) analysis and statistically assessed using absolute relative difference (ARD). Results. CEG analysis revealed that 97.6% of glucose readings were within the clinically acceptable CEG A + B zones. Mean and median ARD were 22.3% and 18.8%, respectively. Likelihood ratio and parametric bootstrap tests revealed that there were no significant differences in ARD values across age, gender, body mass, and whether the earlobe was pierced, indicating that the accuracy of GlucoTrack remains consistent across the tested demographic profiles. Conclusions. Our results suggest that GlucoTrack performance does not depend on demographic profiles of its users and it is thus suitable for various people with type 2 diabetes.
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Moser, Othmar, Norbert Tripolt, Peter Pferschy, Anna Obermayer, Harald Kojzar, Alexander Mueller, Hakan Yildirim, Caren Sourij, Max Eckstein, and Harald Sourij. "Performance of the Intermittently Scanned Continuous Glucose Monitoring (isCGM) System during a High Oral Glucose Challenge in Adults with Type 1 Diabetes—A Prospective Secondary Outcome Analysis." Biosensors 11, no. 1 (January 15, 2021): 22. http://dx.doi.org/10.3390/bios11010022.
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To assess intermittently scanned continuous glucose monitoring (isCGM) performance for different rates of change in plasma glucose (RCPG) during glycemic challenges in type 1 diabetes (T1D). Nineteen people with T1D (7 females; age 35 ± 11 years; HbA1c 7.3 ± 0.6% (56 ± 7 mmol/mol)) performing two glycemic challenges (OGTT) were included. During OGTTs, plasma glucose was compared against sensor glucose for timepoints 0 min (pre-OGTT), +15 min, +30 min, +60 min, +120 min, +180 min, and +240 min by means of median absolute (relative) difference (MARD and MAD) and Clarke Error Grid (CEG), then was stratified for RCPG and glycemic ranges. Overall, MARD was 8.3% (4.0–14.8) during hypoglycemia level 1 18.8% (15.8–22.0), euglycemia 9.5% (4.3–15.1), hyperglycemia level 1 9.4% (4.0–17.2), and hyperglycemia level 2 7.1% (3.3–11.9). The MARD was associated with the RCPG (p < 0.0001), detailing significant differences in comparison of low, moderate, high, and very high RCPG (p = 0.014). Overall, CEG resulted in 88% (212 values) of comparison points in zone A, 12% (29 values) in zone B, and 0.4% (1 value) in zone D. The isCGM system was accurate during OGTTs. Its performance was dependent on the RCPG and showed an overestimation of the actual reference glucose during hypoglycemia.
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Sadhu, Archana R., Ivan Alexander Serrano, Jiaqiong Xu, Tariq Nisar, Jessica Lucier, Anjani R. Pandya, and Bhargavi Patham. "Continuous Glucose Monitoring in Critically Ill Patients With COVID-19: Results of an Emergent Pilot Study." Journal of Diabetes Science and Technology 14, no. 6 (October 16, 2020): 1065–73. http://dx.doi.org/10.1177/1932296820964264.
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Background: Amidst the coronavirus disease 2019 (COVID-19) pandemic, continuous glucose monitoring (CGM) has emerged as an alternative for inpatient point-of-care blood glucose (POC-BG) monitoring. We performed a feasibility pilot study using CGM in critically ill patients with COVID-19 in the intensive care unit (ICU). Methods: Single-center, retrospective study of glucose monitoring in critically ill patients with COVID-19 on insulin therapy using Medtronic Guardian Connect and Dexcom G6 CGM systems. Primary outcomes were feasibility and accuracy for trending POC-BG. Secondary outcomes included reliability and nurse acceptance. Sensor glucose (SG) was used for trends between POC-BG with nursing guidance to reduce POC-BG frequency from one to two hours to four hours when the SG was in the target range. Mean absolute relative difference (MARD), Clarke error grids analysis (EGA), and Bland-Altman (B&A) plots were calculated for accuracy of paired SG and POC-BG measurements. Results: CGM devices were placed on 11 patients: Medtronic ( n = 6) and Dexcom G6 ( n = 5). Both systems were feasible and reliable with good nurse acceptance. To determine accuracy, 437 paired SG and POC-BG readings were analyzed. For Medtronic, the MARD was 13.1% with 100% of readings in zones A and B on Clarke EGA. For Dexcom, MARD was 11.1% with 98% of readings in zones A and B. B&A plots had a mean bias of −17.76 mg/dL (Medtronic) and −1.94 mg/dL (Dexcom), with wide 95% limits of agreement. Conclusions: During the COVID-19 pandemic, CGM is feasible in critically ill patients and has acceptable accuracy to identify trends and guide intermittent blood glucose monitoring with insulin therapy.
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Schierenbeck, Fanny, Anders Franco-Cereceda, and Jan Liska. "Accuracy of 2 Different Continuous Glucose Monitoring Systems in Patients Undergoing Cardiac Surgery." Journal of Diabetes Science and Technology 11, no. 1 (July 9, 2016): 108–16. http://dx.doi.org/10.1177/1932296816651632.
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Background: Continuous glucose monitoring (CGM) is today provided by various techniques. This study aims to compare two different CGM-systems: the FreeStyle Libre subcutaneous continuous glucose monitoring system (SC-CGM) and the Eirus intravascular microdialysis continuous glucose monitoring system (MD-CGM) in patients undergoing cardiac surgery. Methods: A total of 26 patients were equipped with both the SC-CGM and the MD-CGM systems. The SC-CGM system was placed subcutaneously in the left upper-arm and the MD-CGM system was placed in the superior vena cava. Reference blood glucose values were obtained by analyzing arterial blood in a blood gas analyzer. Reference glucose values were then paired with glucose values from both CGM-systems and analyzed for accuracy. Results: In all, 514 paired MD-CGM/arterial blood gas glucose values and 578 paired SC-CGM/arterial blood gas glucose values were obtained. Mean difference (SD) for the MD-CGM system was 0.9 (15.1) mg/dl and for the SC-CGM system −43.4 (20) mg/dl. ISO criteria (ISO15197:2013) were not met by either CGM system. In the Clarke error grid, all paired samples were within the zones AB for the MD-CGM system, and 94% in zone A. For the SC-CGM system, 99.1% of the paired samples were within zones AB, and 18.9% in zone A. Both the MD-CGM and the SC-CGM systems were reliable and used without complications. Conclusions: These results indicate that the Eirus intravascular microdialysis system monitors glucose continuously with superior accuracy compared to the FreeStyle Libre subcutaneous glucose monitoring system, which repeatedly measured a glucose value that was lower than the reference method.
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Wu, Ming-Hsun, Mei-Yen Fang, Lin-Ni Jen, Hung-Chan Hsiao, Andreas Müller, and Cheng-Teng Hsu. "Clinical Evaluation of Bionime Rightest GM310 Biosensors with a Simplified Electrode Fabrication for Alternative-Site Blood Glucose Tests." Clinical Chemistry 54, no. 10 (October 1, 2008): 1689–95. http://dx.doi.org/10.1373/clinchem.2008.106328.
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Abstract Background: Most processes for fabricating biosensors applied to screen-printed carbon electrodes (SPCEs) are complex. This study presents a novel one-step process for manufacturing electrodes for injection-molding biosensors. Methods: During the sensor-fabrication process, barrel-plated gold electrodes were inserted into an injection-molded base. The electrode directly touched the electrical contact of a meter. We analyzed technical measurements for this biosensor, including tests of the measurement range, within-run imprecision, and between-meter imprecision. In clinical trials, experienced technicians tested 3 alternative sites (fingertip, palm, and arm). The results were simultaneously compared with plasma values obtained with the hexokinase method on the Olympus AU640 instrument. Analytical results were evaluated according to International Standards Organization 15197 (ISO 15197:2003) criteria and by Clarke error grid analysis (EGA), and CVs were calculated to evaluate within-run imprecision. Results: The glucose measurement range was 0.6– 33.3 mmol/L (y = 0.96x + 0.07 mmol/L; r2 = 0.9977). The CVs in the within-run imprecision test were 1.7%–3.5%, and the overall CV was 2.1%, indicating good reproducibility of results. The Student t-tests of mean values from 5 meters revealed statistically insignificant differences (P > 0.05). In clinical trials, the agreement of the Rightest GM310 meter results with those of a laboratory method complied with ISO 15197:2003 criteria. In the EGA, 100% of the values were within the acceptable zones (A + B), and the proportion of values within zone A exceeded 95%. Conclusions: The Bionime Rightest GM310 meter applied a simplified process for biosensor fabrication and displayed acceptable performance for monitoring glucose concentrations at alternative test sites.
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Kurnikova, Irina A., Aigerim U. Ualihanova, Leonid Y. Morgunov, Elvira R. Mavlyalieva, and Marya A. Surikova. "Evaluation of the efficiency of using glucose monitoring devices upon unsatisfactory diabetes compensation." Problems of Endocrinology 63, no. 1 (February 4, 2017): 23–29. http://dx.doi.org/10.14341/probl201763123-29.
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Rationale. The key purpose of modern glucose meters is to ensure regular self-monitoring of glucose level when receiving outpatient management, provided that fair diabetes compensation is achieved. Although glucose meters are not intended for assessing the glucose level in severe metabolic disorders (ketosis, ketoacidosis), since these conditions have a negative effect on device accuracy, in actual life a patient (or a physician) can face a situation when a glucose meter is the only tool for evaluating carbohydrate metabolism disorders.
Objective. To evaluate clinical accuracy of Satellite Express PKG-03 glucose meter in measuring the glucose level in patients with type 1 and 2 diabetes mellitus (DM) receiving insulin therapy when the disease course is complicated by ketosis or ketoacidosis.
Material and methods. Capillary blood was simultaneously collected in two groups of patients receiving insulin therapy from the same drop to evaluate the glucose blood level using the Satellite Express glucose meter and a SUPER GL laboratory analyzer of glucose and lactate levels. Acid-base imbalance was the key criterion for distributing patients into groups: no disorders were detected in group 1 patients, while group 2 patients had ketosis or ketoacidosis. The results were evaluated using the Clarke error grid.
Results. Comparative analysis of blood samples collected from 77 patients showed that all deviations in glucose level indices measured using the Satellite Express glucose meter from the reference values belonged to zones A (the clinically valid values) and B (safe deviations) in patients without acid-base imbalance. In patients hospitalized for ketosis and ketoacidosis (group 2), the deviations from the reference values lay in zones A and B in 97%, while lying on the boundary between zones B and C only in 3%.
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Breteler, Martine J. M., Eline J. KleinJan, Daan A. J. Dohmen, Luke P. H. Leenen, Richard van Hillegersberg, Jelle P. Ruurda, Kim van Loon, Taco J. Blokhuis, and Cor J. Kalkman. "Vital Signs Monitoring with Wearable Sensors in High-risk Surgical Patients." Anesthesiology 132, no. 3 (March 1, 2020): 424–39. http://dx.doi.org/10.1097/aln.0000000000003029.
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Abstract Background Vital signs are usually recorded once every 8 h in patients at the hospital ward. Early signs of deterioration may therefore be missed. Wireless sensors have been developed that may capture patient deterioration earlier. The objective of this study was to determine whether two wearable patch sensors (SensiumVitals [Sensium Healthcare Ltd., United Kingdom] and HealthPatch [VitalConnect, USA]), a bed-based system (EarlySense [EarlySense Ltd., Israel]), and a patient-worn monitor (Masimo Radius-7 [Masimo Corporation, USA]) can reliably measure heart rate (HR) and respiratory rate (RR) continuously in patients recovering from major surgery. Methods In an observational method comparison study, HR and RR of high-risk surgical patients admitted to a step-down unit were simultaneously recorded with the devices under test and compared with an intensive care unit–grade monitoring system (XPREZZON [Spacelabs Healthcare, USA]) until transition to the ward. Outcome measures were 95% limits of agreement and bias. Clarke Error Grid analysis was performed to assess the ability to assist with correct treatment decisions. In addition, data loss and duration of data gaps were analyzed. Results Twenty-five high-risk surgical patients were included. More than 700 h of data were available for analysis. For HR, bias and limits of agreement were 1.0 (–6.3, 8.4), 1.3 (–0.5, 3.3), –1.4 (–5.1, 2.3), and –0.4 (–4.0, 3.1) for SensiumVitals, HealthPatch, EarlySense, and Masimo, respectively. For RR, these values were –0.8 (–7.4, 5.6), 0.4 (–3.9, 4.7), and 0.2 (–4.7, 4.4) respectively. HealthPatch overestimated RR, with a bias of 4.4 (limits: –4.4 to 13.3) breaths/minute. Data loss from wireless transmission varied from 13% (83 of 633 h) to 34% (122 of 360 h) for RR and 6% (47 of 727 h) to 27% (182 of 664 h) for HR. Conclusions All sensors were highly accurate for HR. For RR, the EarlySense, SensiumVitals sensor, and Masimo Radius-7 were reasonably accurate for RR. The accuracy for RR of the HealthPatch sensor was outside acceptable limits. Trend monitoring with wearable sensors could be valuable to timely detect patient deterioration. Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New
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Dreval', A. V., T. P. Shestakova, A. A. Manukyan, and O. G. Brezhneva. "The individualized statistical analysis of the continuous glucose monitoring data." Almanac of Clinical Medicine 48, no. 7 (December 31, 2020): 459–68. http://dx.doi.org/10.18786/2072-0505-2020-48-068.
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Background: Continuous glucose monitoring (CGM) has shown its benefits in pregnant women with diabetes. Flash glucose monitoring (FGM), as one of the CGM types, has not been well assessed in this patient group. The interpretation of a big volume of information on glycaemia obtained with various CGM devices is possible with statistical analysis according to the algorithms proposed by manufacturers. While these algorithms cannot be comprehensive, evaluation of alternative approaches to the CGM data statistical analysis and comparison of the results obtained with different devices seem reasonable. No unified algorithm for modification of antidiabetic treatment according to the CGM results has been yet developed. This study was performed in a pregnant patient with type 1 diabetes mellitus (T1DM) to demonstrate the methods to individualized analysis of the data from various devices (CGM, FGM, glucometer) that could be used in routine clinical practice.Aim: To evaluate the individual advantages and disadvantages of the simultaneous use of FGM, CGM and SMBG in a pregnant woman with type 1 diabetes.Materials and methods: This was an observational case study with a retrospective assessment of the patient's data obtained with FGM, CGM and a glucometer in a 31-year female patient with T1DM of 6-year duration and 9 weeks of gestation, who had been on pump insulin therapy for one year and had an HbA1c level of 5.4%. During the study the patient continued her pump therapy and performed blood glucose self-monitoring (BGSM) and simultaneously used FGM and CGM. The following FGM data were compared with CGM and glucometer results: measurement numbers, time in range, mean daily glucose, mean absolute difference (MAD), and mean absolute relative difference (MARD).Results: The FGM-derived mean daily glucose was lower than that measured with the glucometer: 5.1±1.9 mmol/L vs 6.4±2.2 mmol/L (p<0.001). The number of measurements with FGM was 32.0±12.9 times daily and with a glucometer 15.1±5.5 times daily (p<0.001). MAD values were minimal in the hypoglycemic range (0.5±0.3 mmol/L) and maximal in the hyperglycemic range (1.6±1.2 mmol/L, р<0.001). The MARD values were significantly smaller in the hyperglycemic than in the normoglycemic (16.6±12.6% vs 21.3±14.0%, р=0.035). The highest MAD and MARD were observed on the Day 1 of the sensor installation. The comparison of FGM and the glucometer readings with the Clarke consensus error grid showed that 82% of the FGM readings were in zone A or B. The FGM accuracy was higher from Day 2 to Day 9 (72.5% of the FGM readings in zone A). MAD between FGM and CGM readings was not different from that between FGM and the glucometer: 1.3±1.0 mmol/L and 1.2±0.9 mmol/L, respectively (p=0.09). MARD for the FGM and CGM comparison was higher than that for FGM and glucometer comparison: 24.4±23.0% and 18.8±13.5%, respectively (р<0.001). The Pearson's correlation coefficient FGM and CGM seemed lower than that between FGM and the glucometer (0.837 and 0.889, respectively). FGM has identified more hypoglycemic events compared to CGM: time below range was 29.4% and 8.8%, respectively, p<0.001).Conclusion: The FGM readings highly correlate with the glucometer. The FGM difference with the glucometer was lower in the hypo- and hyperglycemic ranges. FGM shows higher values for time below range than CGM. It is necessary to continue the study of the clinical acceptability of FGM in pregnant women and determination of its optimal regimen for the treatment of this patient category, as well as to develop an algorithm for treatment modification based on the results of FGM.
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Tiberi, Eloisa, Francesco Cota, Giovanni Barone, Alessandro Perri, Valerio Romano, Rossella Iannotta, Costantino Romagnoli, and Enrico Zecca. "Continuous glucose monitoring in preterm infants: evaluation by a modified Clarke error grid." Italian Journal of Pediatrics 42, no. 1 (March 9, 2016). http://dx.doi.org/10.1186/s13052-016-0236-9.
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Sengupta, Sohini, Anil Handoo, Inaamul Haq, Karamvir Dahiya, Sanjay Mehta, and Mradul Kaushik. "Clarke Error Grid Analysis for Performance Evaluation of Glucometers in a Tertiary Care Referral Hospital." Indian Journal of Clinical Biochemistry, March 25, 2021. http://dx.doi.org/10.1007/s12291-021-00971-4.
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Dorsaf, Ghozzi. "Near Infrared Spectrum for Non-Invasive Glucose Measurement." Current Research in Diabetes & Obesity Journal 11, no. 1 (June 12, 2019). http://dx.doi.org/10.19080/crdoj.2019.11.555801.
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This paper deals with novel approach for non-invasive glucose monitoring based on NIR spectroscopy. The technique was demonstrated on 300 human serums of different concentrations range of 08-297 mg/dl. In order to compare the proposed approach to a standard one, a regression analysis was performed and used to predict glucose concentration overall range of values. In vitro experiments showed a strong correlation between noninvasively device result and real glucose concentration. The correlation was 0.97 and Clarke error grid analysis showed that 97.33% of the measured fall within the clinically acceptable regions. Results showed that the created model can open a new path to a real-time, painless and a portable device that would well-being the lives of millions of diabetics in the world.
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Kos, Snježana, Arie van Meerkerk, Joke van der Linden, Theo Stiphout, and Remi Wulkan. "Validation of a new generation POCT glucose device with emphasis on aspects important for glycemic control in the hospital care." Clinical Chemistry and Laboratory Medicine (CCLM) 50, no. 9 (September 1, 2012). http://dx.doi.org/10.1515/cclm-2011-0900.
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AbstractPoint-of-care (POC) glucose devices are widely used for insulin-dosage decision-making although such an application is not always permitted. In this study, we have evaluated a new generation of POC glucose device, the HemoCueThis study was performed according to the CLSI/STARD criteria. The 201DMRT was compared to the laboratory hexokinase glucose method (Siemens Dimension VistaThe 201DMRT showed a good agreement with the laboratory reference method. This was examined using Deming regression analysis, percentage Bland-Altman plot and a modified Clarke-error grid. The total analytical error at the clinically critical glucose concentrations of 5.6, 7.0 and 11.1 mmol/L (101, 126 and 200 mg/dL) was 6.4%, 4.3% and 3.0%, respectively. The total error among the different POC devices and among different cuvette lot numbers was <6.5%. Glucose measurements on the 201DMRT were not affected by changes in partial pressure of oxygen, whereas changes in hematocrit had influence on the results (3.4% for every 0.10 L/L change in hematocrit).The 201DMRT device can be used for glycemic control based on analytical results presented. However, the clinical applicability for tight glycemic control must be confirmed in a clinical study.
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Thomas, Spencer, and Robert Hitchcock. "Continuous In Vivo Monitoring of a Flexible Subcutaneous Sensor in Freely Moving Diabetic Rats." Journal of Medical Devices 2, no. 2 (June 1, 2008). http://dx.doi.org/10.1115/1.2936213.
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Subcutaneous tissue is frequently the target site for placement of continuous, real-time metabolic sensors. Since the 1960s, numerous research groups have developed needle-like sensor designs, patterned after the Clarke Electrode, to monitor glucose in subcutaneous tissue. These designs perform well in vitro but often fail in vivo due to sensor instability and tissue response. None of these studies focused on the mechanical properties of implanted sensors and how these properties may affect in vivo performance. To investigate the role of sensor stiffness on short term functionality we developed a low stiffness subcutaneous sensor patterned after the Clarke Electrode and tested it in rodents. The purpose of this study was two-fold. The first goal was to demonstrate the in vivo functionality of the flexible sensor. The second goal was to evaluate the effect of stiffness on functionality by co-implanting stiff and flexible sensors. In the first series of studies the low stiffness sensors provided glucose level measurements that fell within the A and B regions of the Clarke Error Grid 93.0% of the time. The results of the second study yielded similar accuracy; however, no performance difference was seen between the stiff and flexible sensors. We concluded that the flexible sensor works for at least 3days after implantation in the subcutaneous tissue of freely moving rats and that the key property of low stiffness has no differential effect on the accuracy of the sensor in the freely moving rodent model of these studies.
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Mambelli, Emanuele, Stefania Cristino, Giovanni Mosconi, Christian Göbl, and Andrea Tura. "Flash Glucose Monitoring to Assess Glycemic Control and Variability in Hemodialysis Patients: The GIOTTO Study." Frontiers in Medicine 8 (July 30, 2021). http://dx.doi.org/10.3389/fmed.2021.617891.
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Background: Flash glucose monitoring (FGM) is a technology with considerable differences compared to continuous glucose monitoring (CGM), but it has been scarcely studied in hemodialysis patients. Thus, we aimed assessing the performance of FGM in such patients by comparison to self-monitoring of blood glucose (SMBG). We will also focus on estimation of glycemic control and variability, and their relationships with parameters of glucose homeostasis.Methods: Thirty-one patients (20 with type 2 diabetes, T2DM, 11 diabetes-free, NODM) collected readings by FGM and SMBG for about 12 days on average. Readings by FGM and SMBG were compared by linear regression, Clarke error grid, and Bland-Altman analyses. Several indices of glycemic control and variability were computed. Ten patients also underwent oral glucose tolerance test (OGTT) for assessment of insulin sensitivity/resistance and insulin secretion/beta-cell function.Results: Flash glucose monitoring and SMBG readings showed very good agreement in both T2DM and NODM (on average, 97 and 99% of readings during hemodialysis in A+B Clarke regions, respectively). Some glycemic control and variability indices were similar by FGM and SMBG (p = 0.06–0.9), whereas others were different (p = 0.0001–0.03). The majority of control and variability indices were higher in T2DM than in NODM, according to both FGM and SMBG (p = 0.0005–0.03). OGTT-based insulin secretion was inversely related to some variability indices according to FGM (R < −0.72, p < 0.02).Conclusions: Based on our dataset, FGM appeared acceptable for glucose monitoring in hemodialysis patients, though partial disagreement with SMBG in glycemic control/variability assessment needs further investigations.
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"Performance and User Experience Evaluation of a Non-Invasive Glucose Monitoring Device." International Journal of Diabetes & Metabolic Disorders 1, no. 2 (December 12, 2016). http://dx.doi.org/10.33140/ijdmd/00009.
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Background: An accurate, low-maintenance, comfortable and easy-to-use glucose monitoring device might be the key to successful diabetes management. This research evaluated the performance of user experience with GlucoTrack®, a commercially available non-invasive device. Specifically, following one individual calibration, accuracy was assessed during a six month period equivalent to device sensors’ lifespan. Materials and Methods: GlucoTrack’s accuracy during six months was evaluated in 17 type-2 diabetic patients. User experience and device acceptance were assessed using questionnaires obtained from 95 naïve people with diabetes who used GlucoTrack at home. Results: GlucoTrack’s overall mean absolute relative difference (ARD) was 22.8% and 98.0% of points were in the clinically acceptable zones A and B of the Clarke Error Grid. The 95% confidence intervals of ARD standard deviation values of the first and sixth months (15.3-17.2% and 16.6-18.7%, respectively) overlapped. A favorable response to the easiness of device use and measurement performance, as well as to the comfort of the device and its screen, were reported in 75%, 86%, 87% and 95% of the users, respectively. These results did not depend on age, gender and level of education. Additionally, 83% of users expressed willingness to use the device regularly and 75% stated they would measure their glucose more frequently compared to the use of invasive device. Conclusions: GlucoTrack maintained its accuracy for six months, pointing to its low maintenance. The device was also highly accepted among diabetic patients. These findings attest the potential of GlucoTrack to enhance diabetic patients’ glucose monitoring routine.
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Da Prato, G., S. Pasquini, E. Rinaldi, T. Lucianer, S. Donà, L. Santi, C. Negri, E. Bonora, P. Moghetti, and M. Trombetta. "Accuracy of CGM Systems During Continuous and Interval Exercise in Adults with Type 1 Diabetes." Journal of Diabetes Science and Technology, June 11, 2021, 193229682110235. http://dx.doi.org/10.1177/19322968211023522.
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Background: 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). Method: 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). Results: 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. Conclusions: 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.
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Johnson-Rabbett, Brianna, Hiba Hashmi, Ryan Lyerla, and Laura Lafave. "SAT-639 Is the Freestyle Libre Flash Glucose Monitor Accurate in the Critically Ill?" Journal of the Endocrine Society 4, Supplement_1 (April 2020). http://dx.doi.org/10.1210/jendso/bvaa046.1808.
Full textAbstract:
Abstract The FreeStyle Libre flash glucose monitor (FGM) has made the use of continuous glucose monitors more accessible to the typical diabetes patient in an outpatient setting given the significantly lower cost and ease of use of FGM as compared to other systems. However, FGM is not labeled for use in a critically ill population. The critical care department at our institution queried the endocrine department about studying the use of FGM in critically ill patients. The interest of the critical care department was due to the potential of decrease in patient discomfort and decrease in time and effort of nursing and support staff related to the performance of fingerstick capillary glucoses if FGM was an adequate replacement measure. As of yet, there has been only minimal study of flash glucose monitoring in critically ill patients. One Australian study evaluated 8 patients in an ICU setting and determined that as compared with arterial blood glucose monitoring, flash glucose monitoring provided acceptable numerical and clinical accuracy.1 A Swedish study evaluated a total of 26 patients undergoing cardiac surgery and compared the use of FGM to use of a microdialysis intravascular system and concluded that the microdialysis system was more accurate, though in this study, only 25% of patients had diabetes. 2 To further investigate use of FGM in a critically ill population, we plan to undertake a single center, prospective, single arm study enrolling at least 20 and up to 40 patients. Inclusion criteria include a known diagnosis of type 1 or type 2 diabetes, age of 18 or older, and admission to the medical intensive care unit (MICU) with expected MICU stay of at least 48 hours. Participating subjects will have a sensor applied by a study investigator. After confirmation that the sensor is operational, the investigator will place opaque tape over the monitor to blind the monitor data. Nurses or medical assistants will conduct the standard of care fingerstick glucose monitoring per hospital protocol but will also have been notified of request to also pass FGM reader over the sensor at time of fingerstick glucose data collection. The primary objectives are to determine numerical accuracy in a critical care setting using the mean absolute relative difference and to determine clinical accuracy in a critical care setting using the surveillance error grid and the clarke error grid analyses. Preliminary data should be available by March, 2020. 1. Ancona P, Eastwood GM, Lucchetta L, Ekinci EI, Bellomo R, Martensson J. The performance of flash glucose monitoring in critically ill patients with diabetes. Crit Care Resusc 2017; 19: 167-174, June 2017. 2. Schierenbeck F, Franco-Cereceda A, Liska J. Accuracy of 2 Different Continuous Glucose Monitoring Systems in Patients Undergoing Cardiac Surgery: Intravascular Microdialysis Versus Subcutaneous Tissue Monitoring. Journal of Diabetes Science and Technology 2017, Vol. 11(1) 108–116
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Hughes, Jonathan, Thibault Gautier, Patricio Colmegna, Chiara Fabris, and Marc D. Breton. "Replay Simulations with Personalized Metabolic Model for Treatment Design and Evaluation in Type 1 Diabetes." Journal of Diabetes Science and Technology, November 20, 2020, 193229682097319. http://dx.doi.org/10.1177/1932296820973193.
Full textAbstract:
Background: The capacity to replay data collected in real life by people with type 1 diabetes mellitus (T1DM) would lead to individualized (vs population) assessment of treatment strategies to control blood glucose and possibly true personalization. Patek et al introduced such a technique, relying on regularized deconvolution of a population glucose homeostasis model to estimate a residual additive signal and reproduce the experimental data; therefore, allowing the subject-specific replay of what-if scenarios by altering the model inputs (eg, insulin). This early method was shown to have a limited domain of validity. We propose and test in silico a similar approach and extend the method applicability. Methods: A subject-specific model personalization of insulin sensitivity and meal-absorption parameters is performed. The University of Virginia (UVa)/Padova T1DM simulator is used to generate experimental scenarios and test the ability of the methodology to accurately reproduce changes in glucose concentration to alteration in meal and insulin inputs. Method performance is assessed by comparing true (UVa/Padova simulator) and replayed glucose traces, using the mean absolute relative difference (MARD) and the Clarke error grid analysis (CEGA). Results: Model personalization led to a 9.08 and 6.07 decrease in MARD over a prior published method of replaying altered insulin scenarios for basal and bolus changes, respectively. Replay simulations achieved high accuracy, with MARD <10% and more than 95% of readings falling in the CEGA A-B zones for a wide range of interventions. Conclusions: In silico studies demonstrate that the proposed method for replay simulation is numerically and clinically valid over broad changes in scenario inputs, indicating possible use in treatment optimization.