Nivel de conocimientos de estudiantes de medicina sobre diagnóstico y manejo del infarto agudo del miocardio

Introduction: acute myocardial infarction is a disease with high morbidity and mortality.Objective: to determine the knowledge level of medical students about the diagnosis and management of acute myocardial infarction.Method: an observational, descriptive and cross-sectional study was carried out between January and February 2022 in medical students from the University of Medical Sciences of Pinar del Río who participated in the provincial update workshop on acute myocardial infarction. Through intentional sampling, a sample of 92 students was selected. To collect the information, a survey was used using Google Forms.Results: the female sex (65,21%), the age group from 21 to 22 years (65,21%) and the fourth-year students (50%) prevailed. Hypertension was the most identified risk factor (97,98%). 97,82% of the students identified precordial pain as the main clinical manifestation. 100% identified the presentation with complications, where sudden death was the most identified (81,52%). 100% point to the electrocardiogram as the main complementary, where ST alterations were the most identified (84,78%). 95,65% of the students indicated constant monitoring of vital parameters and cardiovascular function as the management measure.Conclusions: Medicine students belonging to the clinical area at the University of Medical Sciences of Pinar del Río have an adequate level of knowledge about the diagnosis and management of acute myocardial infarction.Introducción: el infarto agudo del miocardio constituye una enfermedad con elevada morbilidad y mortalidad.Objetivo: determinar el nivel de conocimientos de estudiantes de medicina sobre el diagnóstico y manejo del infarto agudo del miocardioMétodo: se realizó un estudio observacional, descriptivo y transversal entre enero y febrero de 2022 en estudiantes de Medicina de la Universidad de Ciencias Médicas de Pinar del Río del ciclo clínico que participaron en el Taller provincial de actualización sobre infarto agudo de miocardio. Mediante un muestreo intencional se seleccionó una muestra de 92 estudiantes. Para la recolección de la información se empleó una encuesta mediante Google Forms.Resultados: predominó el sexo femenino (65,21 %), el grupo etario de 21 a 22 años (65,21 %) y los estudiantes de cuarto año (50 %). La hipertensión fue el factor de riesgo más identificado (97,98 %). El 97,82 % de los estudiantes identificó el dolor precordial como principal manifestación clínica. El 100 % identificó la presentación con complicaciones, donde la muerte súbita fue la más identificada (81,52 %). El 100 % señala al electrocardiograma como principal complementario, donde las alteraciones del ST fueron las más identificada (84,78 %). El 95,65 % de los estudiantes indicaron la monitorización constante de los parámetros vitales y función cardiovascular como la medida de manejo.Conclusiones: los estudiantes de Medicina pertenecientes al área clínica en la Universidad de Ciencias Médicas de Pinar del Río poseen un adecuado nivel de conocimientos sobre el diagnóstico y manejo del infarto agudo del miocardio.  

Role of Endocrinologists in Eliminating Health Care Disparities

Objective: To review some of the persistent disparities in health and health care in the United States related to race, ethnicity, and socioeconomic status, with a focus on diabetes mellitus and obesity, and to discuss the role of endocrinologists in preventing these disparities. Methods: Some of the efforts made by the US government, such as public health strategies, to address health disparities are outlined, and statistics about diabetes and obesity are presented. Results: The elimination of health disparities, recognized as a national challenge for decades, is a national priority as defined in the national goals for Healthy People 2010. Health disparities refer to the differences in the quality of health and health care access and outcomes across racial, ethnic, and socioeconomic groups. Such disparities may be related to the patient (education, socioeconomic status, environment, language), the health care system (location, structural barriers, financial resources), or the provider, including a lack of diversity in the health care workforce. Endocrinologists are responsible for the care of many patients with chronic diseases, including obesity and diabetes mellitus. Both of these chronic diseases are diagnosed with increased frequency in minority populations and are preventable, difficult to manage, and associated with many complications and high health care costs. Conclusion: The role of endocrinologists is to provide equitable, affordable, accessible, high-quality, timely, cost-effective, and culturally sensitive health care. They must be involved in population health decisions and development of optimal health care policy so that endocrine disorders can ultimately be prevented. In addition, they must educate themselves, their patients, and the community regarding maintenance of healthy lifestyles to prevent complications

sj-pdf-1-dst-10.1177_19322968221085273 – Supplemental material for A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings

Supplemental material, sj-pdf-1-dst-10.1177_19322968221085273 for A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings by David C. Klonoff, Jing Wang, David Rodbard, Michael A. Kohn, Chengdong Li, Dorian Liepmann, David Kerr, David Ahn, Anne L. Peters, Guillermo E. Umpierrez, Jane Jeffrie Seley, Nicole Y. Xu, Kevin T. Nguyen, Gregg Simonson, Michael S. D. Agus, Mohammed E. Al-Sofiani, Gustavo Armaiz-Pena, Timothy S. Bailey, Ananda Basu, Tadej Battelino, Sewagegn Yeshiwas Bekele, Pierre-Yves Benhamou, B. Wayne Bequette, Thomas Blevins, Marc D. Breton, Jessica R. Castle, James Geoffrey Chase, Kong Y. Chen, Pratik Choudhary, Mark A. Clements, Kelly L. Close, Curtiss B. Cook, Thomas Danne, Francis J. Doyle, Angela Drincic, Kathleen M. Dungan, Steven V. Edelman, Niels Ejskjaer, Juan C. Espinoza, G. Alexander Fleming, Gregory P. Forlenza, Guido Freckmann, Rodolfo J. Galindo, Ana Maria Gomez, Hanna A. Gutow, Lutz Heinemann, Irl B. Hirsch, Thanh D. Hoang, Roman Hovorka, Johan H. Jendle, Linong Ji, Shashank R. Joshi, Michael Joubert, Suneil K. Koliwad, Rayhan A. Lal, M. Cecilia Lansang, Wei-An (Andy) Lee, Lalantha Leelarathna, Lawrence A. Leiter, Marcus Lind, Michelle L. Litchman, Julia K. Mader, Katherine M. Mahoney, Boris Mankovsky, Umesh Masharani, Nestoras N. Mathioudakis, Alexander Mayorov, Jordan Messler, Joshua D. Miller, Viswanathan Mohan, James H. Nichols, Kirsten Nørgaard, David N. O’Neal, Francisco J. Pasquel, Athena Philis-Tsimikas, Thomas Pieber, Moshe Phillip, William H. Polonsky, Rodica Pop-Busui, Gerry Rayman, Eun-Jung Rhee, Steven J. Russell, Viral N. Shah, Jennifer L. Sherr, Koji Sode, Elias K. Spanakis, Deborah J. Wake, Kayo Waki, Amisha Wallia, Melissa E. Weinberg, Howard Wolpert, Eugene E. Wright, Mihail Zilbermint and Boris Kovatchev in Journal of Diabetes Science and Technolog

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

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 clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments

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

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 clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments