神经疾病危重症患者早期康复介入是临床康复医学的方向

近年来,随着重症监护技术及综合抢救技术的发展,神经疾病危重患者的死亡率已有显著下降。在综合性医院及神经专科医院内建立重症监护病房(ICU)、神经外科重症监护病房(NICU)及卒中病房(strokeunits)已成为国际性趋势。神经疾病危重症患者生命支持系统卓有成效的工作使此类患者生存率显著增高,随之而来存活患者功能状态及生活质量即成为关注的焦点。发达国家针对脑卒中救治而组建的卒中应急分队(stroketeam)及脑外伤救治小组(TBIteam)中除了临床医师、影像诊断专家、神经介入治疗专家、护理人员外,物理治疗师、语言治疗师、神经心理学专…     

神经中央监护系统在重型脑卒中患者救治中的应用

目的探讨神经中央监护系统在重型脑卒中患者救治中的应用技术。方法对164例神经科重症监护室的重型脑卒中患者进行神经中央监护系统监护,随时观察数据和图像的变化,对异常变化及时进行对症处理。结果 164例患者中132例患者好转出院,26例患者家属放弃治疗自动出院,6例患者死亡。结论神经中央监护系统是一种有效的监护工具,有助于早期发现病情变化,从而提高重型脑卒中患者抢救成功率。     

远程ICU的国内外实施现状

远程重症监护(tele-intensive care unit,Tele-ICU),简称远程 ICU,是一种基于信息化技术的重症护理模式,能够通过各种形式的信息化技术对重症监护病房提供远程实时监护,与异地患者和医疗护理人员进行实时视频交流,促进优质重症监护服务资源的共享且保障患者安全[1].近年来,在人口老龄化、慢性病...     

ICU每日质量查检单的优化设计及临床应用

目的通过优化设计ICU每日质量查检单(简称查检单),指导医护人员在临床实践中进行质量查检,以提高监护质量,保证护理安全。方法 2011年12月至2012年2月,将查检单应用于ICU的危重患者,对重症患者的治疗护理措施进行每日查检,观察各具体项目的执行情况。结果重症医疗护理团队对查检单涉及的11项基本治疗及监护措施执行率为85%~99%。结论将查检单应用于ICU重症患者的质量管理,有助于提高ICU的监护质量。     

改良式诺顿评分在重症监护病房肝移植病人压疮预防中的应用

[目的]调查改良式诺顿评分应用于重症监护病房肝移植术后病人临床压疮监控的有效性。[方法]根据肝移植病人的特点,将诺顿评分表进行改良,时肝移植术后重症监护病房病人进行评估,提供个性化护理。[结果]采用改良式诺顿评分表,有效降低了重症监护病房肝移植病人压疮的发生率,提高了基础护理质量。[结论]改良式诺顿评分表可用于预防重症监护病房肝移植病人压疮。     

人工智能在角膜共聚焦显微镜检查角膜神经病变中的应用进展

<正>角膜共聚焦显微镜是目前用来评估角膜神经病变的有力工具,因其操作快速、无创、可重复性高的优点,开始应用于糖尿病周围神经病变的早期诊断中。近年来,随着人工智能的蓬勃发展,利用人工智能分析角膜神经病变的算法日渐成熟,本文对利用角膜共聚焦显微镜分析糖尿病性角膜神经病变的角膜神经改变及多种人工智能分析在角膜共聚焦显微镜检查角膜神经病变的临床应用进展做一综述。1共聚焦显微镜原理共聚焦显微镜是一种快速、非侵入性和重复性的技术,能够以高分辨率对人类角膜进行显微结构评估。     

急诊EICU护士应掌握的技术

EICU亦称急诊重症监护单元或急诊重症监护病房。随着急诊重症监护技术的发展，EICU对护理人员的要求也越来越高。本人结合多年来急诊重症监护工作，感到EICU的护士除具有综合的护理素质、扎实的基本功外，还必须掌握急诊重症监护技术，这在EICU护理工作中至关重要。     

建立重症监护协作网络促进专科护理技术发展

为加强医院重症护理技术建设,提高重症病人的监护技术及护理质量,该院建立了医院重症监护技术协作网络。作者介绍了重症监护技术协作网络的建立、运作及其实施效果。     

改良式诺顿评分在重症监护病房肝移植病人压疮预防中的应用

[目的]调查改良式诺顿评分应用于重症监护病房肝移植术后病人临床压疮监控的有效性.[方法]根据肝移植病人的特点,将诺顿评分表进行改良,对肝移植术后重症监护病房病人进行评估,提供个性化护理.[结果]采用改良式诺顿评分表,有效降低了重症监护病房肝移植病人压疮的发生率,提高了基础护理质量.[结论]改良式诺顿评分表可用于预防重症监护病房肝移植病人压疮.     

科护士长在重症监护协作网络中的管理作用

为突破专科护理工作的局限性,提高医院整体重症监护水平,我院于1997年建立了以ICU为中心的重症监护技术协作网络,利用其急救设备完善、监护技术熟练、临床经验丰富等优势．形成了全院性的重症监护技术帮带指导、交叉协作网络。科护士长在协作网络中发挥着协调反馈、控制质量的作用。现将科护士长在协作网络中的作用介绍如下：1发挥科护士长管理职能,提高重症监护指导效果1．1实施五项工作内容（1）讨论制定重症监护质量标准及实施方案。（2）制定重症监护培训目标。（3）定期对监护质量分析并研究对策。（4）协调重症患者特别护理及护…     

Predictive modeling in neurocritical care using causal artificial intelligence

Artificial intelligence (AI) and digital twin models of various systems have long been used in industry to test products quickly and efficiently. Use of digital twins in clinical medicine caught attention with the development of Archimedes, an AI model of diabetes, in 2003. More recently, AI models have been applied to the fields of cardiology, endocrinology, and undergraduate medical education. The use of digital twins and AI thus far has focused mainly on chronic disease management, their application in the field of critical care medicine remains much less explored. In neurocritical care, current AI technology focuses on interpreting electroencephalography, monitoring intracranial pressure, and prognosticating outcomes. AI models have been developed to interpret electroencephalograms by helping to annotate the tracings, detecting seizures, and identifying brain activation in unresponsive patients. In this mini-review we describe the challenges and opportunities in building an actionable AI model pertinent to neurocritical care that can be used to educate the newer generation of clinicians and augment clinical decision making.     

Nursing Informatics and Implementation of a NICU Portal

Neonatology and informatics are relatively new subspecialties to the health field; however both have made rapid developments over a considerably short period of time. Significant improvements have been made to the care of neonates resulting in a rapid rate of survival of sick and premature neonates. Along with improvements in care there has been the development of technology and with it the field of informatics. This paper offers a review of the development of nursing informatics and its application in changing practice in a tertiary neonatal intensive care unit (NICU).     

深度学习用于宫颈癌诊疗研究进展

宫颈癌(CC)居于女性恶性肿瘤第二位,严重危害女性健康和生命,成为全球关注的健康问题。人工智能(AI)是利用计算机程序模拟、延伸和拓展人的智能行为的科学;以AI为核心技术的智能医学是医学未来发展的重要方向。深度学习(DL)为AI新领域,在图像分析领域展示出巨大应用潜力,用于诊断及鉴别诊断肿瘤、指导治疗及预测预后等具有特有优势。本文对DL用于筛查和诊断CC、指导治疗及判断预后的现状、问题及前景进行综述。     

人工智能医学影像在胸部应用的机遇与挑战

人工智能(Artificial Intelligence,AI)是以深度学习(Deep Learning,DL)、人工神经网络(Artificial Neural Network,ANN)、机器学习(Machine Learning,ML)等新一代核心算法、强大的计算机计算能力和大数据(Big Date)共同促进的产物。也就是说,算法、算力和数据是其三要素。近年来,随着人工智能技术的飞速发展,AI在医学影像领域的应用日新月异,其中AI胸部影像研究最早也最成熟,本综述将从人工智能在胸部影像应用现状、机遇和挑战以及未来发展方向等方面进行阐述。     

Artificial intelligence applications in the intensive care unit

OBJECTIVE: To review the history and current applications of artificial intelligence in the intensive care unit. DATA SOURCES: The MEDLINE database, bibliographies of selected articles, and current texts on the subject. STUDY SELECTION: The studies that were selected for review used artificial intelligence tools for a variety of intensive care applications, including direct patient care and retrospective database analysis. DATA EXTRACTION: All literature relevant to the topic was reviewed. DATA SYNTHESIS: Although some of the earliest artificial intelligence (AI) applications were medically oriented, AI has not been widely accepted in medicine. Despite this, patient demographic, clinical, and billing data are increasingly available in an electronic format and therefore susceptible to analysis by intelligent software. Individual AI tools are specifically suited to different tasks, such as waveform analysis or device control. CONCLUSIONS: The intensive care environment is particularly suited to the implementation of AI tools because of the wealth of available data and the inherent opportunities for increased efficiency in inpatient care. A variety of new AI tools have become available in recent years that can function as intelligent assistants to clinicians, constantly monitoring electronic data streams for important trends, or adjusting the settings of bedside devices. The integration of these tools into the intensive care unit can be expected to reduce costs and improve patient outcomes.     

人工智能分割心外膜脂肪组织研究进展

心外膜脂肪(EAT)属内脏脂肪组织，具有复杂的生理及病理特性，对于多种心血管相关疾病的发生、发展及预后等具有重要影响。随着人工智能(AI)的快速发展，基于AI分割EAT并用于诊疗心血管疾病等已成为研究热点。本文就AI分割EAT研究进展进行综述。     

Data science in the intensive care unit

In this editorial, we comment on the current development and deployment of data science in intensive care units (ICUs). Data in ICUs can be classified into qualitative and quantitative data with different technologies needed to translate and interpret them. Data science, in the form of artificial intelligence (AI), should find the right interaction between physicians, data and algorithm. For individual patients and physicians, sepsis and mechanical ventilation have been two important aspects where AI has been extensively studied. However, major risks of bias, lack of generalizability and poor clinical values remain. AI deployment in the ICUs should be emphasized more to facilitate AI development. For ICU management, AI has a huge potential in transforming resource allocation. The coronavirus disease 2019 pandemic has given opportunities to establish such systems which should be investigated further. Ethical concerns must be addressed when designing such AI.     

The withdrawal of life support in adult intensive care: an evaluative review of the literature

To ensure that intensive care is both cost-effective and humanitarian depends on a willingness to withdraw treatment once the prognosis is recognised as being hopeless, as the process of dying may be prolonged if futile treatment is continued Continuing advances in technology, science and professional care, raising new ethical, economic and legislative dilemmas, mean the decision to withdraw treatment in intensive care has become an issue of renewed concern These advances require both the development of scientific methods to support the decision-making process and an understanding of the ethical balance that underpins such decisions with particular reference to the intensive care nurse Whilst there is recognition that the families of patients who die in intensive care require ongoing support and bereavement care, there is little to suggest that the needs of the multidisciplinary personnel caring for these patients are understood or provided for This paper aims to explore quality end-of-life care, common medical practices, research into the role of the expert nurse in caring for the dying patient and new strategies for the incorporation of palliative care Strategies are proposed to translate the shared ownership, responsibility and accountability of clinical governance regarding these decisions into positive action, consensus and collaboration at both local and national level.     

人工智能在脑卒中神经影像中的应用

近年来，人工智能在计算机科学领域快速崛起。医学成像过程中产生了海量图像信息，因此非常适合采用人工智能技术进行相关数据处理。脑卒中患者神经影像在临床诊断、治疗及随访评估中非常关键，人工智能技术在基于脑卒中影像数据的处理和分析中发挥着越来越重要的作用。本文主要回顾人工智能技术在缺血性与出血性脑卒中神经影像应用中的研究进展，重点关注缺血性脑卒中的自动检测、责任脑区缺血状态判断及治疗评估，以及出血性脑卒中的智能诊断、量化分析及治疗评估；同时对基于脑卒中影像智能诊断系统的临床转化应用现状进行分析，探讨当前人工智能在脑卒中神经影像应用过程中存在的主要挑战，并对未来发展前景进行展望。     

Prise en charge du traumatisme crânien cérébral grave

Management of severe traumatic brain injury (TBI) is based on early resuscitation — aiming to avoid hypoxia and hypotension, the rapid patient transfer to specialised neuroscience centers with neurosurgical availability, early surgical evacuation of acute cerebral lesions and invasive monitoring of intracranial pressure in TBI patients with abnormal admission CT-scan (contusions, hemorrhage, hematoma). Upon intensive care unit admission, treatment will be focused on the prevention/attenuation of secondary brain injuries of intra-cerebral (intracranial hypertension, cerebral ischemia, non-convulsive seizures) and sytemic (hyperthermia, hyperglycemia) etiology, according to a standardised algorithm. The application of such standardised therapeutic strategy has been shown by several studies to improve TBI prognosis and overall care of head-injured patients.