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Your search keyword '"Galindo, Rodolfo J."' showing total 3 results
Start Over Author "Galindo, Rodolfo J." Publication Type Electronic Resources
3 results on '"Galindo, Rodolfo J."'

1. Accuracy and Potential Interferences of Continuous Glucose Monitoring Sensors in the Hospital

Author

Bellido, Virginia, Freckman, Guido, Pérez, Antonio, Galindo, Rodolfo J., Bellido, Virginia, Freckman, Guido, Pérez, Antonio, and Galindo, Rodolfo J.

Abstract

For years, the standard of care for monitoring dysglycemia in hospitalized patients was capillary blood glucose (CBG) testing with point-of-care glucose meters. Recently, there has been a revolution in novel factory-calibrated continuous glucose monitoring (CGM) systems. Newer CGMs are smaller and less expensive, have improved accuracy and longer wear time, and do not require fingerstick CBG for calibration, resulting in increased utilization in ambulatory settings. Consequently, hospitals have noticed increased usability of CGMs among hospitalized patients and expect a progressive continued increase. During the COVID-19 pandemic, there was a critical need for innovative approaches to glycemic monitoring, with several pilot implementation projects using CGM in the intensive care unit and non-intensive care unit settings, further boosting the evidence in this area. Hence, recent guidelines have provided recommendations for the use of CGM in specific hospital scenarios and highlighted the potential of CGM to overcome CBG limitations for glucose monitoring in the inpatient setting. In this review, we provide the following: 1) an up-to-date review of the accuracy of the newer CGMs in hospitalized patients, 2) a discussion of standards for CGM accuracy metrics, 3) a contemporary overview of potential interferences that may cause inaccuracies or poor CGM performance, and 4) required steps for full regulatory approval of CGMs in the hospital and future research steps to advance the field forward.

Published
2023

2. A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings

Author

Klonoff, David C., Wang, Jing, Rodbard, David, Kohn, Michael A., Li, Chengdong, Liepmann, Dorian, Kerr, David, Ahn, David, Peters, Anne L., Umpierrez, Guillermo E., Seley, Jane Jeffrie, Xu, Nicole Y., Nguyen, Kevin T., Simonson, Gregg, Agus, Michael S.D., Al-Sofiani, Mohammed E., Armaiz-Pena, Gustavo, Bailey, Timothy S., Basu, Ananda, Battelino, Tadej, Bekele, Sewagegn Yeshiwas, Benhamou, Pierre Yves, Bequette, B. Wayne, Blevins, Thomas, Breton, Marc D., Castle, Jessica R., Chase, James Geoffrey, Chen, Kong Y., Choudhary, Pratik, Clements, Mark A., Close, Kelly L., Cook, Curtiss B., Danne, Thomas, Doyle, Francis J., Drincic, Angela, Dungan, Kathleen M., Edelman, Steven V., Ejskjaer, Niels, Espinoza, Juan C., Fleming, G. Alexander, Forlenza, Gregory P., Freckmann, Guido, Galindo, Rodolfo J., Gomez, Ana Maria, Gutow, Hanna A., Heinemann, Lutz, Hirsch, Irl B., Hoang, Thanh D., Hovorka, Roman, Jendle, Johan H., Ji, Linong, Joshi, Shashank R., Joubert, Michael, Koliwad, Suneil K., Lal, Rayhan A., Lansang, M. Cecilia, Lee, Wei An, Leelarathna, Lalantha, Leiter, Lawrence A., Lind, Marcus, Litchman, Michelle L., Mader, Julia K., Mahoney, Katherine M., Mankovsky, Boris, Masharani, Umesh, Mathioudakis, Nestoras N., Mayorov, Alexander, Messler, Jordan, Miller, Joshua D., Mohan, Viswanathan, Nichols, James H., Nørgaard, Kirsten, O’Neal, David N., Pasquel, Francisco J., Philis-Tsimikas, Athena, Pieber, Thomas, Phillip, Moshe, Polonsky, William H., Pop-Busui, Rodica, Rayman, Gerry, Rhee, Eun Jung, Russell, Steven J., Shah, Viral N., Sherr, Jennifer L., Sode, Koji, Spanakis, Elias K., Wake, Deborah J., Waki, Kayo, Wallia, Amisha, Weinberg, Melissa E., Wolpert, Howard, Wright, Eugene E., Zilbermint, Mihail, Kovatchev, Boris, Klonoff, David C., Wang, Jing, Rodbard, David, Kohn, Michael A., Li, Chengdong, Liepmann, Dorian, Kerr, David, Ahn, David, Peters, Anne L., Umpierrez, Guillermo E., Seley, Jane Jeffrie, Xu, Nicole Y., Nguyen, Kevin T., Simonson, Gregg, Agus, Michael S.D., Al-Sofiani, Mohammed E., Armaiz-Pena, Gustavo, Bailey, Timothy S., Basu, Ananda, Battelino, Tadej, Bekele, Sewagegn Yeshiwas, Benhamou, Pierre Yves, Bequette, B. Wayne, Blevins, Thomas, Breton, Marc D., Castle, Jessica R., Chase, James Geoffrey, Chen, Kong Y., Choudhary, Pratik, Clements, Mark A., Close, Kelly L., Cook, Curtiss B., Danne, Thomas, Doyle, Francis J., Drincic, Angela, Dungan, Kathleen M., Edelman, Steven V., Ejskjaer, Niels, Espinoza, Juan C., Fleming, G. Alexander, Forlenza, Gregory P., Freckmann, Guido, Galindo, Rodolfo J., Gomez, Ana Maria, Gutow, Hanna A., Heinemann, Lutz, Hirsch, Irl B., Hoang, Thanh D., Hovorka, Roman, Jendle, Johan H., Ji, Linong, Joshi, Shashank R., Joubert, Michael, Koliwad, Suneil K., Lal, Rayhan A., Lansang, M. Cecilia, Lee, Wei An, Leelarathna, Lalantha, Leiter, Lawrence A., Lind, Marcus, Litchman, Michelle L., Mader, Julia K., Mahoney, Katherine M., Mankovsky, Boris, Masharani, Umesh, Mathioudakis, Nestoras N., Mayorov, Alexander, Messler, Jordan, Miller, Joshua D., Mohan, Viswanathan, Nichols, James H., Nørgaard, Kirsten, O’Neal, David N., Pasquel, Francisco J., Philis-Tsimikas, Athena, Pieber, Thomas, Phillip, Moshe, Polonsky, William H., Pop-Busui, Rodica, Rayman, Gerry, Rhee, Eun Jung, Russell, Steven J., Shah, Viral N., Sherr, Jennifer L., Sode, Koji, Spanakis, Elias K., Wake, Deborah J., Waki, Kayo, Wallia, Amisha, Weinberg, Melissa E., Wolpert, Howard, Wright, Eugene E., Zilbermint, Mihail, and Kovatchev, Boris

Abstract

Background: A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data. Methods: We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low–glucose and low-glucose hypoglycemia; very high–glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation. Results: The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals. Conclusion: The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by cl

Published
2023

3. Diabetes Technology Meeting 2020.

Author

Shang, Trisha, Shang, Trisha, Zhang, Jennifer Y, Bequette, B Wayne, Raymond, Jennifer K, Coté, Gerard, Sherr, Jennifer L, Castle, Jessica, Pickup, John, Pavlovic, Yarmela, Espinoza, Juan, Messer, Laurel H, Heise, Tim, Mendez, Carlos E, Kim, Sarah, Ginsberg, Barry H, Masharani, Umesh, Galindo, Rodolfo J, Klonoff, David C, Shang, Trisha, Shang, Trisha, Zhang, Jennifer Y, Bequette, B Wayne, Raymond, Jennifer K, Coté, Gerard, Sherr, Jennifer L, Castle, Jessica, Pickup, John, Pavlovic, Yarmela, Espinoza, Juan, Messer, Laurel H, Heise, Tim, Mendez, Carlos E, Kim, Sarah, Ginsberg, Barry H, Masharani, Umesh, Galindo, Rodolfo J, and Klonoff, David C

Abstract

Diabetes Technology Society hosted its annual Diabetes Technology Meeting on November 12 to November 14, 2020. This meeting brought together speakers to cover various perspectives about the field of diabetes technology. The meeting topics included artificial intelligence, digital health, telemedicine, glucose monitoring, regulatory trends, metrics for expressing glycemia, pharmaceuticals, automated insulin delivery systems, novel insulins, metrics for diabetes monitoring, and discriminatory aspects of diabetes technology. A live demonstration was presented.

Published
2021

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