913-P: Good Concordance between Home Kit A1C, Glucose Management Indicator, and Point-of-Care A1C: Potential Solutions to Glycemic Assessment during the SARS CoV-2 Pandemic

Diabetes ◽  
2021 ◽  
Vol 70 (Supplement 1) ◽  
pp. 913-P
Author(s):  
DESSI ZAHARIEVA ◽  
ANANTA ADDALA ◽  
PRIYA PRAHALAD ◽  
BRIANNA LEVERENZ ◽  
VICTORIA DING ◽  
...  
Keyword(s):  
Point Of Care ◽  
Good Concordance ◽  
Glucose Management

scholarly journals Glucose Management Technologies for the Critically Ill

2019 ◽  
Vol 13 (4) ◽  
pp. 682-690 ◽  
Author(s):  
Pedro D. Salinas ◽  
Carlos E. Mendez
Keyword(s):  
Insulin Therapy ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
New Technologies ◽  
Point Of Care ◽  
Glucose Monitoring ◽  
Standard Of Care ◽  
Optimal Method ◽  
Intravenous Insulin ◽  
Glucose Management

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

Glycaemic control in critical illness

2016 ◽  
Author(s):  
Simon Finfer
Keyword(s):  
Blood Glucose ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Blood Glucose Concentration ◽  
Point Of Care ◽  
Glucose Variability ◽  
Severe Illness ◽  
Loop Control ◽  
Considerable Uncertainty ◽  
Glucose Management

Hyperglycaemia is a near universal occurrence in critically-ill patients. In the last 10 years, control of blood glucose has been one of the most intensively studied areas of critical care medicine. It has become clear that control of blood glucose has the potential to affect both morbidity and mortality, and considerable uncertainty remains over many aspects of blood glucose management. Both hyperglycaemia and hypoglycaemia are associated with increased mortality and should be avoided wherever possible. Wide fluctuations in blood glucose concentration (referred to as increased glucose variability) are also associated with increased mortality, but may indicate more severe illness. Increased interest in blood glucose management has demonstrated that point-of-care glucose meters designed for ambulatory use by patient with diabetes are not sufficiently accurate for use in critically-ill patients. More accurate analysers should be used in the intensive care unit and management guided by computerized. Future developments may see the introduction of accurate continuous or near continuous blood glucose analysers, but safe and effective closed loop control of blood glucose remains an elusive goal.

scholarly journals Challenges to Glycemic Measurement in the Perioperative and Critically Ill Patient: A Review

2009 ◽  
Vol 3 (6) ◽  
pp. 1270-1281 ◽  
Author(s):  
Andrew D. Pitkin ◽  
Mark J. Rice
Keyword(s):  
Point Of Care ◽  
Turnaround Time ◽  
Sample Volume ◽  
Hospital Environment ◽  
Glucose Measurement ◽  
Critically Ill Patient ◽  
Central Laboratory ◽  
Patients With Diabetes ◽  
Measurement Devices ◽  
Glucose Management

Accurate monitoring of glucose in the perioperative environment has become increasingly important over the last few years. Because of increased cost, turnaround time, and sample volume, the use of central laboratory devices for glucose measurement has been somewhat supplanted by point-of-care (POC) glucose devices. The trade-off in moving to these POC systems has been a reduction in accuracy, especially in the hypoglycemic range. Furthermore, many of these POC devices were originally developed, marketed, and received Food and Drug Administration regulatory clearance as home use devices for patients with diabetes. Without further review, many of these POC glucose measurement devices have found their way into the hospital environment and are used frequently for measurement during intense insulin therapy, where accurate measurements are critical. This review covers the technology behind glucose measurement and the evidence questioning the use of many POC devices for perioperative glucose management.

scholarly journals Multiplexed, quantitative serological profiling of COVID-19 from blood by a point-of-care test

2021 ◽  
Vol 7 (26) ◽  
pp. eabg4901
Author(s):  
Jacob T. Heggestad ◽  
David S. Kinnamon ◽  
Lyra B. Olson ◽  
Jason Liu ◽  
Garrett Kelly ◽  
...  
Keyword(s):  
Prognostic Biomarker ◽  
Point Of Care ◽  
High Sensitivity ◽  
Interferon Γ ◽  
Live Virus ◽  
Point Of Care Test ◽  
Highly Sensitive ◽  
Good Concordance ◽  
Longitudinal Sampling ◽  
User Intervention

Highly sensitive, specific, and point-of-care (POC) serological assays are an essential tool to manage coronavirus disease 2019 (COVID-19). Here, we report on a microfluidic POC test that can profile the antibody response against multiple severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antigens—spike S1 (S1), nucleocapsid (N), and the receptor binding domain (RBD)—simultaneously from 60 μl of blood, plasma, or serum. We assessed the levels of antibodies in plasma samples from 31 individuals (with longitudinal sampling) with severe COVID-19, 41 healthy individuals, and 18 individuals with seasonal coronavirus infections. This POC assay achieved high sensitivity and specificity, tracked seroconversion, and showed good concordance with a live virus microneutralization assay. We can also detect a prognostic biomarker of severity, IP-10 (interferon-γ–induced protein 10), on the same chip. Because our test requires minimal user intervention and is read by a handheld detector, it can be globally deployed to combat COVID-19.

Hyper-G: An Artificial Intelligence Tool for Optimal Decision-Making and Management of Blood Glucose Levels in Surgery Patients

2019 ◽  
Vol 58 (02/03) ◽  
pp. 079-085
Author(s):  
Akira A. Nair ◽  
Mihir Velagapudi ◽  
Lakshmana Behara ◽  
Ravitheja Venigandla ◽  
Christine T. Fong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Point Of Care ◽  
Optimal Decision ◽  
Learning Models ◽  
Glucose Levels ◽  
Optimal Decision Making ◽  
Glucose Management ◽  
Log Ratio ◽  
Machine Learning Models

Abstract Background Hyperglycemia or high blood glucose during surgery is associated with poor postoperative outcome. Knowing in advance which patients may develop hyperglycemia allows optimal assignment of resources and earlier initiation of glucose management plan. Objective To develop predictive models to estimate peak glucose levels in surgical patients and to implement the best performing model as a point-of-care clinical tool to assist the surgical team to optimally manage glucose levels. Methods Using a large perioperative dataset (6,579 patients) of patient- and surgery-specific parameters, we developed and validated linear regression and machine learning models (random forest, extreme gradient boosting [Xg Boost], classification and regression trees [CART], and neural network) to predict the peak glucose levels during surgery. The model performances were compared in terms of mean absolute percentage error (MAPE), logarithm of the ratio of the predicted to actual value (log ratio), median prediction error, and interquartile error range. The best performing model was implemented as part of a web-based application for optimal decision-making toward glucose management during surgery. Results Accuracy of the machine learning models were higher (MAPE = 17%, log ratio = 0.029 for Xg Boost) when compared with that of the linear regression model (MAPE = 22%, log ratio = 0.041). The Xg Boost model had the smallest median prediction error (5.4 mg/dL) and the narrowest interquartile error range (−17 to 24 mg/dL) as compared with the other models. The best performing model, Xg Boost, was implemented as a web application, Hyper-G, which the perioperative providers can use at the point of care to estimate peak glucose levels during surgery. Conclusions Machine learning models are able to accurately predict peak glucose levels during surgery. Implementation of such a model as a web-based application can facilitate optimal decision-making and advance planning of glucose management strategies.

scholarly journals Glucose as the 5th Vital Sign: A Randomized Controlled Trial of Continuous Glucose Monitoring in a non-ICU Hospital Setting

2020 ◽  
Author(s):  
Addie L. Fortmann ◽  
Samantha R Spierling Bagsic ◽  
Laura Talavera ◽  
Isabel Maria Garcia ◽  
Haley Sandoval ◽  
...  
Keyword(s):  
Randomized Controlled Trial ◽  
Vital Sign ◽  
Point Of Care ◽  
Controlled Trial ◽  
Glucose Monitoring ◽  
Hospital Setting ◽  
Current Standard ◽  
Randomized Controlled ◽  
Glucose Management ◽  
Design And Methods

OBJECTIVE: The current standard for hospital glucose management is point-of-care (POC) testing. We conducted a randomized controlled trial comparing real-time CGM (RT-CGM) to POC in a non-ICU hospital setting. <p>RESEARCH DESIGN AND METHODS: <i>N</i>=110 adults with type 2 diabetes (T2D) on a non-ICU floor received RT-CGM with Dexcom G6 vs usual care (UC). RT-CGM data were wirelessly transmitted from the bedside. Hospital telemetry monitored RT-CGM data and notified bedside nursing of glucose alerts and trends. Standardized protocols were used for interventions.</p> <p>RESULTS: The RT-CGM group demonstrated significantly lower mean glucose (M∆= -18.5 mg/dL) and percentage of time in hyperglycemia >250 mg/dL (-11.41%), and higher median TIR 70-250 mg/dL (+11.26%) compared with UC (<i>p</i>s<0.05). Percentage of time in hypoglycemia was very low. </p> <p>CONCLUSION: RT-CGM can be used successfully in community-based hospital non-ICU settings to improve glucose management; continuously streaming glucose readings may truly be the 5<sup>th</sup> vital sign.</p>

scholarly journals Glucose as the 5th Vital Sign: A Randomized Controlled Trial of Continuous Glucose Monitoring in a non-ICU Hospital Setting

2020 ◽  
Author(s):  
Addie L. Fortmann ◽  
Samantha R Spierling Bagsic ◽  
Laura Talavera ◽  
Isabel Maria Garcia ◽  
Haley Sandoval ◽  
...  
Keyword(s):  
Randomized Controlled Trial ◽  
Vital Sign ◽  
Point Of Care ◽  
Controlled Trial ◽  
Glucose Monitoring ◽  
Hospital Setting ◽  
Current Standard ◽  
Randomized Controlled ◽  
Glucose Management ◽  
Design And Methods

OBJECTIVE: The current standard for hospital glucose management is point-of-care (POC) testing. We conducted a randomized controlled trial comparing real-time CGM (RT-CGM) to POC in a non-ICU hospital setting. <p>RESEARCH DESIGN AND METHODS: <i>N</i>=110 adults with type 2 diabetes (T2D) on a non-ICU floor received RT-CGM with Dexcom G6 vs usual care (UC). RT-CGM data were wirelessly transmitted from the bedside. Hospital telemetry monitored RT-CGM data and notified bedside nursing of glucose alerts and trends. Standardized protocols were used for interventions.</p> <p>RESULTS: The RT-CGM group demonstrated significantly lower mean glucose (M∆= -18.5 mg/dL) and percentage of time in hyperglycemia >250 mg/dL (-11.41%), and higher median TIR 70-250 mg/dL (+11.26%) compared with UC (<i>p</i>s<0.05). Percentage of time in hypoglycemia was very low. </p> <p>CONCLUSION: RT-CGM can be used successfully in community-based hospital non-ICU settings to improve glucose management; continuously streaming glucose readings may truly be the 5<sup>th</sup> vital sign.</p>

scholarly journals Multiplexed, quantitative serological profiling of COVID-19 from a drop of blood by a point-of-care test

2020 ◽  
Author(s):  
Jacob T. Heggestad ◽  
David S. Kinnamon ◽  
Lyra B. Olson ◽  
Jason Liu ◽  
Garrett Kelly ◽  
...  
Keyword(s):  
Antibody Response ◽  
Point Of Care ◽  
High Sensitivity ◽  
Multiple Time ◽  
Live Virus ◽  
Point Of Care Test ◽  
Highly Sensitive ◽  
Good Concordance ◽  
Multiple Time Points ◽  
User Intervention

AbstractHighly sensitive, specific, and point-of-care (POC) serological assays are an essential tool to manage the COVID-19 pandemic. Here, we report on a microfluidic, multiplexed POC test that can profile the antibody response against multiple SARS-CoV-2 antigens—Spike S1 (S1), Nucleocapsid (N), and the receptor binding domain (RBD)—simultaneously from a 60 µL drop of blood, plasma, or serum. We assessed the levels of anti-SARS-CoV-2 antibodies in plasma samples from 19 individuals (at multiple time points) with COVID-19 that required admission to the intensive care unit and from 10 healthy individuals. This POC assay shows good concordance with a live virus microneutralization assay, achieved high sensitivity (100%) and specificity (100%), and successfully tracked the longitudinal evolution of the antibody response in infected individuals. We also demonstrated that we can detect a chemokine, IP-10, on the same chip, which may provide prognostic insight into patient outcomes. Because our test requires minimal user intervention and is read by a handheld detector, it can be globally deployed in the fight against COVID-19 by democratizing access to laboratory quality tests.

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