Anemia no idoso com doença renal crónica

Trabalho final de mestrado integrado em Medicina, apresentado à Faculdade de Medicina da Universidade de Coimbra.A anemia constitui uma complicação importante e frequente da doença renal crónica, verificando-se um aumento da incidência destas duas condições com o avançar da idade. Algumas das alterações na função e vasculatura renal podem ser atribuída ao próprio processo de envelhecimento. Em idosos saudáveis parece existir um aumento paradoxal da produção renal de eritropoietina, sugerindo que, ao longo dos anos, os percursores eritroides medulares poderão tornar-se menos sensíveis à estimulação da eritropoiese pela eritropoietina. No entanto, em doentes renais crónicos, a falência renal ocasiona uma produção inadequada de eritropoietina face às necessidades orgânicas com consequente diminuição da produção medular de eritrócitos. A anemia pode instalar-se precocemente e tende a agravar no decurso da evolução da doença renal crónica, frequentemente subdividida em cinco estadios de acordo com a constatação da existência de lesão renal e determinação da taxa de filtração glomerular. Apesar de se encontrarem bem esclarecidas as consequências potencialmente graves e a sua representatividade cada vez mais importante na população idosa, a anemia em doentes renais crónicos permanece como uma condição sub-valorizada, possivelmente devido à carência de diretrizes que estabeleçam a abordagem diagnóstica adequada a adotar e esquemas terapêuticos seguros a instituir nesta faixa etária. Apesar da produção inadequada de eritropoietina constituir a causa major de anemia na doença renal crónica, a sua etiologia é, na verdade, multifatorial podendo estar envolvidos diversos outros fatores como, por exemplo, um défice funcional de ferro frequentemente encontrado em condições inflamatórias. Consequentemente, o tratamento poderá passar não só pela administração de agentes estimuladores da eritropoiese, como pela necessidade de suplementação de ferro, quer pela eventual existência de um défice funcional quer pelo desenvolvimento de um défice absoluto induzido pela estimulação da eritropoiese. Este artigo tem como objetivo fazer uma revisao da literatura mais recente acerca da temática “anemia no idoso com doença renal crónica”, abordando os aspetos referidos anteriormente, bastante relevantes nesta população.Anemia is a frequent and major complication of chronic kidney disease and both have rising incidence over lifetime. Some of the changes in function and vascular network of the kidney may also be linked with the aging process. In healthy elders, kidney’s erythropoietin production seems to increase, suggesting that over the years, marrow’s red line stem cells become less sensitive to erythropoietin’s stimulus. However, in those with chronic kidney disease, kidney failure leads to insufficient erythropoietin’s production which ends with decreased red blood cells’ production. Anemia may come up early and tends to aggravate during the evolution of chronic kidney disease, dispart into five stages defined by evidence of kidney damage and level of renal function as measured by glomerular filtration rate. Despite the well known consequences and notorious prevalence of anemia in the elderly with chronic kidney disease, it still is an undervalued issue maybe due to the lack of guidelines that provide proper diagnostic approach and safe treatment regimens in this age group. Even though inappropriate erythropoietin’s production remains the major cause of anemia in chronic kidney disease, its’ actual aetiology is multifactorial as many other conditions may be involved like functional iron deficiency, often found in inflammatory status. Thereafter, anemia’s treatment may imply more than erythropoiesis stimulating agents. An iron supply may also be necessary because of the functional or absolute deficiency due to the stimulated erythropoiesis. The purpose of this article is to review the most recent literature on “Anemia in the elderly with Chronic Kidney Disease”. Previously mentioned subjects are approached concerning its’ relevance in this age group

Machine learning for the real-time assessment of left ventricular ejection fraction in critically ill patients: a bedside evaluation by novices and experts in echocardiography

© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.Background: Machine learning algorithms have recently been developed to enable the automatic and real-time echocardiographic assessment of left ventricular ejection fraction (LVEF) and have not been evaluated in critically ill patients. Methods: Real-time LVEF was prospectively measured in 95 ICU patients with a machine learning algorithm installed on a cart-based ultrasound system. Real-time measurements taken by novices (LVEFNov) and by experts (LVEFExp) were compared with LVEF reference measurements (LVEFRef) taken manually by echo experts. Results: LVEFRef ranged from 26 to 80% (mean 54 ± 12%), and the reproducibility of measurements was 9 ± 6%. Thirty patients (32%) had a LVEFRef < 50% (left ventricular systolic dysfunction). Real-time LVEFExp and LVEFNov measurements ranged from 31 to 68% (mean 54 ± 10%) and from 28 to 70% (mean 54 ± 9%), respectively. The reproducibility of measurements was comparable for LVEFExp (5 ± 4%) and for LVEFNov (6 ± 5%) and significantly better than for reference measurements (p < 0.001). We observed a strong relationship between LVEFRef and both real-time LVEFExp (r = 0.86, p < 0.001) and LVEFNov (r = 0.81, p < 0.001). The average difference (bias) between real time and reference measurements was 0 ± 6% for LVEFExp and 0 ± 7% for LVEFNov. The sensitivity to detect systolic dysfunction was 70% for real-time LVEFExp and 73% for LVEFNov. The specificity to detect systolic dysfunction was 98% both for LVEFExp and LVEFNov. Conclusion: Machine learning-enabled real-time measurements of LVEF were strongly correlated with manual measurements obtained by experts. The accuracy of real-time LVEF measurements was excellent, and the precision was fair. The reproducibility of LVEF measurements was better with the machine learning system. The specificity to detect left ventricular dysfunction was excellent both for experts and for novices, whereas the sensitivity could be improved.info:eu-repo/semantics/publishedVersio

Automation of sub-aortic velocity time integral measurements by transthoracic echocardiography: clinical evaluation of an artificial intelligence-enabled tool in critically ill patients

© 2022 British Journal of Anaesthesia. Published by Elsevier Ltd. All rights reserved.Point-of-care ultrasound techniques are increasingly used for the bedside assessment of cardiac function and haemodynamics in critically ill patients. The sub-aortic or left ventricular outflow tract velocity time integral (VTI) can be measured using pulsed-Doppler ultrasonography from a transthoracic apical 5-chamber view. Quantifying VTI is useful to discriminate between vasoplegic states (hypotension with normal/high VTI) and low flow states (low VTI). Measuring VTI is also useful to predict fluid responsiveness, either by quantifying the respiratory swings in VTI when patients are mechanically ventilated, or by quantifying VTI changes during a passive leg raising manoeuvre or a fluid challenge.info:eu-repo/semantics/publishedVersio