ABSTRACT
Antecedentes: La prevalencia de malnutrición es elevada entre la población mayor. El ingreso hospitalario es una ventana de oportunidad para su detección. Objetivo: Valorar la concordancia de distintas escalas nutricionales en pacientes hospitalizados.Método: Estudio prospectivo en pacientes mayores de 65años no institucionalizados ingresados en un servicio de Medicina Interna. Se compararon 5 encuestas de cribado de malnutrición (MNA, MST, MUST, NRS-2000 y CONUT) y 3 encuestas de cribado de riesgo nutricional (SCREEN3, 8 y 14). Como patrón de referencia se utilizó la definición de malnutrición de la Iniciativa Global para el Liderazgo en Malnutrición (GLIM). Resultados: Se incluyeron 85 pacientes (37% mujeres, mediana de edad 83años). El 48% (IC95%: 38-59%) de los pacientes fueron clasificados como malnutridos según criterios GLIM. La escala SCREEN3 fue la más sensible (93%; IC95%: 87-98) y MUST la más específica (91%; IC95%: 85-99). La escala más eficaz para excluir la sospecha de malnutrición fue SCREEN3 (LR− 0,17; IC95%: 0,05-0,53) y la mejor para confirmarla fue MST (LR+ 7,08; IC95%: 3,06-16,39). La concordancia entre las distintas escalas fue baja o muy baja, con índices kappa entre 0,082 y 0,465.Conclusiones: Se precisa un abordaje integral para detectar la malnutrición en adultos mayores ingresados. Las escalas más sensibles son más útiles en el cribado inicial. Las herramientas de riesgo nutricional podrían ser eficaces en esta etapa. En un segundo paso se debe confirmar la malnutrición de acuerdo con criterios establecidos como los de la GLIM.(AU)
Background: The prevalence of malnutrition is high among the elderly population. Hospital admission is a window of opportunity for its detection. Objective: To assess the concordance of different nutritional scales in hospitalized patients. Methods: Prospective study in non-institutionalized patients over 65years of age admitted to an internal medicine department. Five malnutrition screening surveys (MNA, MST, MUST, NRS-2000 and CONUT) and three nutritional risk screening surveys (SCREEN3, 8 and 14) were compared. As gold standard we use the Global Leadership Initiative for Malnutrition (GLIM) definition of malnutrition. Results: Eighty-five patients (37% female, median age 83years) were included. Forty-eight percent (95%CI: 38-59%) of patients were classified as malnourished according to GLIM criteria. The SCREEN3 scale was the most sensitive (93%; 95%CI: 87-98) and MUST the most specific (91%; 95%CI: 85-99). The most effective scale for excluding suspected malnutrition was SCREEN3 (LR− 0.17; 95%CI: 0.05-0.53) and the best for confirming it was MST (LR+ 7.08; 95%CI: 3.06-16.39). Concordance between the different scales was low or very low with kappa indices between 0.082 and 0.465. Conclusions: A comprehensive approach is needed to detect malnutrition in hospitalized patients. More sensitive scales are more useful in initial screening. Nutritional risk tools could be effective at this stage. In a second step, malnutrition should be confirmed according to established criteria such as GLIM.(AU)
Subject(s)
Humans , Male , Female , Middle Aged , Aged , Malnutrition , Health of Institutionalized Elderly , Sarcopenia , Sensitivity and Specificity , Nutrition Assessment , Prospective Studies , Surveys and Questionnaires , Health of the ElderlyABSTRACT
BACKGROUND: The prevalence of malnutrition is high among the elderly population. Hospital admission is a window of opportunity for its detection. OBJECTIVE: To assess the concordance of different nutritional scales in hospitalized patients. METHODS: Prospective study in non-institutionalized patients over 65 years of age admitted to an internal medicine department. Five malnutrition screening surveys (MNA, MST, MUST, NRS-2000 and CONUT) and three nutritional risk screening surveys (SCREEN 3, 8 and 14) were compared. As gold standard we use the Global Malnutrition Leadership Initiative for Malnutrition (GLIM) definition of malnutrition. RESULTS: Eighty-five patients (37% female, median age 83 years) were included. Forty-eight percent (95% CI 38-59%) of patients were classified as malnourished according to GLIM criteria. The SCREEN 3 scale was the most sensitive (93%; 95% CI 87-98) and MUST the most specific (91%; CI 85-99). The most effective scale for excluding suspected malnutrition was SCREEN 3 (LR- 0.17; 95% CI 0.05-0.53) and the best for confirming it was MST (LR+ 7.08; 95% CI 3.06-16.39). Concordance between the different scales was low or very low with kappa indices between 0.082 and 0.465. CONCLUSIONS: A comprehensive approach is needed to detect malnutrition in hospitalized patients. More sensitive scales are more useful in initial screening. Nutritional risk tools could be effective at this stage. In a second step, malnutrition should be confirmed according to established criteria such as GLIM.
Subject(s)
Malnutrition , Nutrition Assessment , Humans , Female , Aged , Aged, 80 and over , Male , Prospective Studies , Malnutrition/diagnosis , Malnutrition/epidemiology , Hospitalization , Mass Screening , LeadershipABSTRACT
La relación entre ética e inteligencia artificial en medicina es un tema crucial y complejo y se encuadra en su contexto más amplio. Así, la ética en inteligencia artificial médica implica asegurar que las tecnologías sean seguras, justas y respeten la privacidad de los pacientes. Esto incluye preocuparse de la precisión de los diagnósticos proporcionados por la inteligencia artificial, la equidad en el tratamiento de pacientes y la protección de los datos personales de salud. Los avances en inteligencia artificial pueden mejorar significativamente la atención médica, desde diagnósticos más precisos hasta tratamientos personalizados. Sin embargo, es esencial que los desarrollos en inteligencia artificial médica se realicen con una consideración ética fuerte, involucrando a los pacientes, profesionales de la salud e inteligencia artificial y especialistas en ética para guiar y supervisar su implementación. Por último, es fundamental la transparencia en los algoritmos de inteligencia artificial y la formación continua para los profesionales médicos. (AU)
The relationship between ethics and artificial intelligence in medicine is a crucial and complex topic that falls within its broader context. Ethics in medical artificial intelligence involves ensuring that technologies are safe, fair, and respect patient privacy. This includes concerns about the accuracy of diagnoses provided by artificial intelligence, fairness in patient treatment, and protection of personal health data. Advances in artificial intelligence can significantly improve healthcare, from more accurate diagnoses to personalized treatments. However, it is essential that developments in medical artificial intelligence are carried out with strong ethical consideration, involving healthcare professionals, artificial intelligence experts, patients, and ethics specialists to guide and oversee their implementation. Finally, transparency in artificial intelligence algorithms and ongoing training for medical professionals are fundamental. (AU)
Subject(s)
Artificial Intelligence/ethics , Artificial Intelligence/trends , Ethics, MedicalABSTRACT
The relationship between ethics and artificial intelligence in medicine is a crucial and complex topic that falls within its broader context. Ethics in medical artificial intelligence (AI) involves ensuring that technologies are safe, fair, and respect patient privacy. This includes concerns about the accuracy of diagnoses provided by artificial intelligence, fairness in patient treatment, and protection of personal health data. Advances in artificial intelligence can significantly improve healthcare, from more accurate diagnoses to personalized treatments. However, it is essential that developments in medical artificial intelligence are carried out with strong ethical consideration, involving healthcare professionals, artificial intelligence experts, patients, and ethics specialists to guide and oversee their implementation. Finally, transparency in artificial intelligence algorithms and ongoing training for medical professionals are fundamental.
Subject(s)
Artificial Intelligence , Medicine , Humans , Algorithms , Health Facilities , Health PersonnelABSTRACT
Introduction: social media is increasingly used in medical education, but its real educational effectiveness is unclear. In this study we assess the effectiveness of Twitter threads (TTS) in improving electrocardiogram (ECG) basic reading skills (ECGBRS). Materials and Methods: Seven TTS describing ECGBRS were published from October 28, 2021, to November 24, 2021. Tests were used to assess medical students ECGBRS pre and post intervention. All third and sixth-year medical students were invited to participate. Sixty-three students were enrolled (33 third year and 30 sixth year). Nine (14.3%) participants dropped out. Results: Sixth year medical students had higher ECGBRS at baseline. The number of correct items increased after the Twitter intervention; median correct pre-test items were 20 out of 56, (interquartile range (IQR) 14-23), and median post-test were 29 out of 56, (IQR 21-36) (p < 0.001). The improvement in sixth year students was greater than for third year students; 10 more correct items (IQR 4-14) vs. 7 (IQR 1-14) items (p = 0.045). The more TTS followed, the greater the improvement in ECGBRS (p = 0.004). The QRS axis calculation was the ECG reading skill with the lowest scores. Most medical students were definitely (35%) or very probably (46%) interested in repeating another on-line learning experience and found the TTS extremely (39%) or very (46%) interesting. Conclusions: The use of specifically designed TTS was associated with improvement in medical students' interpretation of ECGs. The effectiveness of the threads was higher in the final years of medical school when basic skills had already been acquired. Supplementary Information: The online version contains supplementary material available at 10.1007/s40670-023-01885-x.
