Continuum (Minneap Minn). 2024 Jun 1;30(3):878-903. doi: 10.1212/CON.0000000000001433.
This article synthesizes the current literature on prognostication in neurocritical care, identifies existing challenges, and proposes future research directions to reduce variability and enhance scientific and patient-centered approaches to neuroprognostication.
Patients with severe acute brain injury often lack the capacity to make their own medical decisions, leaving surrogate decision makers responsible for life-or-death choices. These decisions heavily rely on clinicians' prognostication, which is still considered an art because of the previous lack of specific guidelines. Consequently, there is significant variability in neuroprognostication practices. This article examines various aspects of neuroprognostication. It explores the cognitive approach to prognostication, highlights the use of statistical modeling such as Bayesian models and machine learning, emphasizes the importance of clinician-family communication during prognostic disclosures, and proposes shared decision making for more patient-centered care.
This article identifies ongoing challenges in the field and emphasizes the need for future research to ameliorate variability in neuroprognostication. By focusing on scientific methodologies and patient-centered approaches, this research aims to provide guidance and tools that may enhance neuroprognostication in neurocritical care.
本文综合了神经危重症预后预测方面的现有文献，确定了当前存在的挑战，并提出了未来的研究方向，以减少变异性，增强神经预后预测的科学性和以患者为中心的方法。
患有严重急性脑损伤的患者通常缺乏做出自己医疗决策的能力，这使得替代决策人负责生死攸关的选择。这些决策严重依赖于临床医生的预后预测，由于之前缺乏具体的指南，这仍然被认为是一门艺术。因此，神经预后预测的实践存在很大的变异性。本文研究了神经预后预测的各个方面。它探讨了预后预测的认知方法，强调了贝叶斯模型和机器学习等统计建模的使用，强调了在预后披露过程中临床医生-家属沟通的重要性，并提出了共享决策以提供更以患者为中心的护理。
本文确定了该领域当前存在的挑战，并强调需要未来的研究来改善神经预后预测的变异性。通过关注科学方法和以患者为中心的方法，这项研究旨在提供指导和工具，可能会增强神经危重症护理中的神经预后预测。
