昏迷和意识障碍的神经预后。

Q2 Medicine
Shubham Biyani, Henry Chang, Vishank A Shah
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引用次数: 0

摘要

昏迷和意识障碍(DoC)是主要由严重急性脑损伤引起的临床综合征,其恢复轨迹不确定,通常需要长期的支持性治疗。这给患者、护理人员和社会带来了重大的社会经济负担。预测昏迷患者的恢复是神经危重症护理的一个重要方面,虽然目前的预测严重依赖于临床评估,如瞳孔反应和运动,这远非精确,但当代的预测已经整合了更先进的技术,如神经成像和脑电图(EEG)。尽管如此,神经系统预测仍然充满了不确定性和显著的不准确性,并受到几种形式的预测偏差的影响,包括自我实现预言偏差、情感预测和临床医生治疗偏差等。然而,意识障碍患者的神经系统预后影响改变生活的决定,包括继续治疗干预或退出维持生命治疗(WLST),这对严重急性脑损伤后的生存和恢复有直接影响。近年来,神经监测技术、人工智能(AI)和机器学习(ML)的进步改变了预测领域。这些技术有潜力处理大量临床数据并确定可靠的预后标记,提高对心脏骤停、脑出血和创伤性脑损伤(TBI)等疾病的预测准确性。例如,AI/ML建模已经导致识别新的意识状态,如隐蔽意识和认知运动分离,这可能对严重脑损伤后的预后具有重要意义。本章回顾了昏迷和DoC中神经系统预测的发展前景,强调了当前的陷阱和偏见,并总结了临床检查、神经影像学、生物标志物和神经生理学工具在特定疾病状态下预测的整合。我们将进一步讨论神经系统预测的未来,重点是人工智能和机器学习技术的整合,以提供更加个性化和准确的预测,最终改善患者的预后和神经危重症护理的决策过程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Neurologic prognostication in coma and disorders of consciousness.

Coma and disorders of consciousness (DoC) are clinical syndromes primarily resulting from severe acute brain injury, with uncertain recovery trajectories that often necessitate prolonged supportive care. This imposes significant socioeconomic burdens on patients, caregivers, and society. Predicting recovery in comatose patients is a critical aspect of neurocritical care, and while current prognostication heavily relies on clinical assessments, such as pupillary responses and motor movements, which are far from precise, contemporary prognostication has integrated more advanced technologies like neuroimaging and electroencephalogram (EEG). Nonetheless, neurologic prognostication remains fraught with uncertainty and significant inaccuracies and is impacted by several forms of prognostication biases, including self-fulfilling prophecy bias, affective forecasting, and clinician treatment biases, among others. However, neurologic prognostication in patients with disorders of consciousness impacts life-altering decisions including continuation of treatment interventions vs withdrawal of life-sustaining therapies (WLST), which have a direct influence on survival and recovery after severe acute brain injury. In recent years, advancements in neuro-monitoring technologies, artificial intelligence (AI), and machine learning (ML) have transformed the field of prognostication. These technologies have the potential to process vast amounts of clinical data and identify reliable prognostic markers, enhancing prediction accuracy in conditions such as cardiac arrest, intracerebral hemorrhage, and traumatic brain injury (TBI). For example, AI/ML modeling has led to the identification of new states of consciousness such as covert consciousness and cognitive motor dissociation, which may have important prognostic significance after severe brain injury. This chapter reviews the evolving landscape of neurologic prognostication in coma and DoC, highlights current pitfalls and biases, and summarizes the integration of clinical examination, neuroimaging, biomarkers, and neurophysiologic tools for prognostication in specific disease states. We will further discuss the future of neurologic prognostication, focusing on the integration of AI and ML techniques to deliver more individualized and accurate prognostication, ultimately improving patient outcomes and decision-making process in neurocritical care.

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来源期刊
Handbook of clinical neurology
Handbook of clinical neurology Medicine-Neurology (clinical)
CiteScore
4.10
自引率
0.00%
发文量
302
期刊介绍: The Handbook of Clinical Neurology (HCN) was originally conceived and edited by Pierre Vinken and George Bruyn as a prestigious, multivolume reference work that would cover all the disorders encountered by clinicians and researchers engaged in neurology and allied fields. The first series of the Handbook (Volumes 1-44) was published between 1968 and 1982 and was followed by a second series (Volumes 45-78), guided by the same editors, which concluded in 2002. By that time, the Handbook had come to represent one of the largest scientific works ever published. In 2002, Professors Michael J. Aminoff, François Boller, and Dick F. Swaab took on the responsibility of supervising the third (current) series, the first volumes of which published in 2003. They have designed this series to encompass both clinical neurology and also the basic and clinical neurosciences that are its underpinning. Given the enormity and complexity of the accumulating literature, it is almost impossible to keep abreast of developments in the field, thus providing the raison d''être for the series. The series will thus appeal to clinicians and investigators alike, providing to each an added dimension. Now, more than 140 volumes after it began, the Handbook of Clinical Neurology series has an unparalleled reputation for providing the latest information on fundamental research on the operation of the nervous system in health and disease, comprehensive clinical information on neurological and related disorders, and up-to-date treatment protocols.
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