[Medical nutrition therapy in amyotrophic lateral sclerosis - Do we act or react? A case report and multidisciplinary review]. / Tratamiento médico nutricional en la esclerosis lateral amiotrófica: ¿Actuamos o reaccionamos? Un caso clínico y revisión multidisciplinar.

Introduction: Background: amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease with a progressive course. The current prevalence is between 3 and 6 cases/100,000. Malnutrition is closely related to patient prognosis in ALS. The implications of this conditions have been that we should recommend patient care in a multidisciplinary unit. Case report: the case presented shows the evolution of a patient with ALS. The patient was referred to different clinical departments after neurological evaluation and her nutritional, functional and respiratory status were assessed. There was no nutritional deterioration at diagnosis; however, intake was below energy-protein requirements. The clinical evolution of the patient showed a decrease in muscle mass with preservation of weight and fat mass. "Aggressive" measures to control nutritional status such as gastrostomy were rejected in the initial stages of the disease, but had to be carried out after development of dysphagia and associated malnutrition. This situation of progressive morphofunctional deterioration and the development of disease-related complications made essential the participation of different health services and professionals in its control. Dicussion: the management of ALS in a multidisciplinary manner allows to improve the course of the disease and the quality of life of both the patients and their families. Patient follow-up is based on the adjustment and management of complications. The basis of the relationship with these patients includes maintaining an adequate communication with them and their families, and ensuring joint decision-making about their condition.

Introducción: Introducción: la esclerosis lateral amiotrófica (ELA) es una enfermedad neurodegenerativa cuya prevalencia en la actualidad está entre 3 y 6 casos/100.000. La desnutrición está íntimamente relacionada con el pronóstico en el paciente con ELA. Las implicaciones de esta enfermedad hacen que se deba recomendar al paciente la asistencia en una unidad multidisciplinar. Caso clínico: el caso presentado muestra la evolución de una paciente con esclerosis lateral amiotrófica desde el diagnóstico. Tras la valoración por parte de Neurología, se remitió a la paciente a los distintos servicios de seguimiento (Endocrinología, Rehabilitación, Neumología). No se observó deterioro nutricional al diagnóstico; no obstante, la ingesta se encontraba por debajo de los requerimientos. En la progresión de la enfermedad se observó un deterioro de la masa muscular con estabilidad ponderal y de la masa grasa, pero la paciente desarrolló disfagia, síntoma típico de la enfermedad. El planteamiento de medidas "agresivas" para controlar el estado nutricional, como la gastrostomía, fue rechazado al inicio, pero hubo que realizarlas tras la progresión de la disfagia y la desnutrición asociada. Esta situación de deterioro morfofuncional y el desarrollo de complicaciones plantearon la participación de distintos profesionales sanitarios en su control. Discusión: el manejo de la ELA de manera multidisciplinar permite mejorar la evolución de la enfermedad y la calidad de vida del paciente y sus familiares. El seguimiento se basa en el ajuste y el manejo de las complicaciones, en mantener una adecuada comunicación con el paciente y sus familiares, y en tomar de manera conjunta las decisiones sobre su patología.

Análisis de la actividad de la unidad de monitorización intermedios respiratorios en el contexto de pandemia / Analysis of the activity of the interme-diate respiratory monitoring unit in the context of a pandemic

Objetivo principal: Describir la actividad y carga asistencial de una unidad de monitorización de intermedios respiratorios (UMIR) en el contexto de pandemia por Covid-19, así como características de los pacientes ingresados en ella. Metodología: Estudio prospectivo y observacional entre enero y diciembre de 2021. Se evaluaron características del paciente, características de su ingreso y la carga asistencial medida a través de la escala TISS-28. Resultados principales: Ingresaron 127 pacientes. La edad media fue de 69,52 ± 11,61 años, y la gran mayoría eran hombres. Causa principal de ingreso fue insuficiencia respiratoria aguda. La puntuación media de la escala TISS-28 fue de 21,59 ± 2,37. Conclusión principal: La UMIR demostró ser un buen recuso hospitalario para responder a las necesidades de la pandemia por Covid-19, así como reducir la estancia media en otros servicios más especializados. Se debe tener en cuenta la elevada carga asistencial existente.(AU)

Objective: To describe the activity and care load of a respiratory intermediate monitoring unit (UMIR) in the context of the Covid-19 pandemic, as well as the characteristics of the patients admitted to it. Methods: Prospective and observational study between January and December 2021. Characteristics of the patient, characteristics of their admission and the care burden measured through the TISS-28 scale were evaluated. Results: 127 patients were admitted. The mean age was 69.52 ± 11.61 years, and the vast majority were men. Main cause of admission was acute respiratory failure. The mean score on the TISS-28 scale was 21.59 ± 2.37. Conclusion: The UMIR proved to be a good hospital resource to respond to the needs of the Covid-19 pandemic, as well as reduce the average stay in other more specialized services. The existing high care burden must be taken into account.(AU)

Patient Management Assisted by a Neural Network Reduces Mortality in an Intermediate Care Unit.

INTRODUCTION: Mortality risk prediction for Intermediate Respiratory Care Unit's (IRCU) patients can facilitate optimal treatment in high-risk patients. While Intensive Care Units (ICUs) have a long term experience in using algorithms for this purpose, due to the special features of the IRCUs, the same strategics are not applicable. The aim of this study is to develop an IRCU specific mortality predictor tool using machine learning methods. METHODS: Vital signs of patients were recorded from 1966 patients admitted from 2007 to 2017 in the Jiménez Díaz Foundation University Hospital's IRCU. A neural network was used to select the variables that better predict mortality status. Multivariate logistic regression provided us cut-off points that best discriminated the mortality status for each of the parameters. A new guideline for risk assessment was applied and mortality was recorded during one year. RESULTS: Our algorithm shows that thrombocytopenia, metabolic acidosis, anemia, tachypnea, age, sodium levels, hypoxemia, leukocytopenia and hyperkalemia are the most relevant parameters associated with mortality. First year with this decision scene showed a decrease in failure rate of a 50%. CONCLUSIONS: We have generated a neural network model capable of identifying and classifying mortality predictors in the IRCU of a general hospital. Combined with multivariate regression analysis, it has provided us with an useful tool for the real-time monitoring of patients to detect specific mortality risks. The overall algorithm can be scaled to any type of unit offering personalized results and will increase accuracy over time when more patients are included to the cohorts.