关键词: Critical illness myopathy Critical illness polyneuromyopathy Critical illness polyneuropathy Early mobilization Intensive care unit-acquired weakness Lung transplantation Nutritional rehabilitation Prolonged ventilation

来  源:   DOI:10.12998/wjcc.v12.i19.3665   PDF(Pubmed)

Abstract:
In this editorial, comments are made on an interesting article in the recent issue of the World Journal of Clinical Cases by Wang and Long. The authors describe the use of neural network model to identify risk factors for the development of intensive care unit (ICU)-acquired weakness. This condition has now become common with an increasing number of patients treated in ICUs and continues to be a source of morbidity and mortality. Despite identification of certain risk factors and corrective measures thereof, lacunae still exist in our understanding of this clinical entity. Numerous possible pathogenetic mechanisms at a molecular level have been described and these continue to be increasing. The amount of retrievable data for analysis from the ICU patients for study can be huge and enormous. Machine learning techniques to identify patterns in vast amounts of data are well known and may well provide pointers to bridge the knowledge gap in this condition. This editorial discusses the current knowledge of the condition including pathogenesis, diagnosis, risk factors, preventive measures, and therapy. Furthermore, it looks specifically at ICU acquired weakness in recipients of lung transplantation, because - unlike other solid organ transplants- muscular strength plays a vital role in the preservation and survival of the transplanted lung. Lungs differ from other solid organ transplants in that the proper function of the allograft is dependent on muscle function. Muscular weakness especially diaphragmatic weakness may lead to prolonged ventilation which has deleterious effects on the transplanted lung - ranging from ventilator associated pneumonia to bronchial anastomotic complications due to prolonged positive pressure on the anastomosis.
摘要:
在这篇社论中,Wang和Long在最近一期的《世界临床病例杂志》上发表了一篇有趣的文章。作者描述了使用神经网络模型来识别重症监护病房(ICU)获得性弱点发展的危险因素。这种情况现在已经随着在ICU中治疗的患者数量的增加而变得普遍,并且继续成为发病率和死亡率的来源。尽管发现了某些风险因素并采取了纠正措施,在我们对这个临床实体的理解中仍然存在腔隙。已经描述了分子水平上的许多可能的致病机制,并且这些机制继续增加。用于从ICU患者进行研究的分析的可检索数据量可能是巨大的。识别大量数据中的模式的机器学习技术是众所周知的,并且可以很好地提供指针来弥合这种情况下的知识差距。这篇社论讨论了当前的知识,包括发病机理,诊断,危险因素,预防措施,和治疗。此外,它特别关注肺移植接受者的ICU获得性弱点,因为与其他实体器官移植不同,肌肉力量在移植肺的保存和存活中起着至关重要的作用。肺与其他实体器官移植的不同之处在于同种异体移植的正常功能取决于肌肉功能。肌肉无力,尤其是diaphragm肌无力,可能导致长时间的通气,这对移植的肺产生有害影响-从呼吸机相关肺炎到由于吻合口长期正压而引起的支气管吻合并发症。
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