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从昏迷恢复量表修订版中获取的哪些信息能对临床诊断和意识恢复提供最可靠的预测?使用机器学习技术的比较研究。

Which information derived from the Coma Recovery Scale-Revised provides the most reliable prediction of clinical diagnosis and recovery of consciousness? A comparative study using machine learning techniques.

机构信息

IRCCS Fondazione Don Carlo Gnocchi onlus, Florence, Italy.

Neurorehabilitation and Brain Research Group, Institute for Human-Centered Technology Research, Polytechnic University of Valencia, Valencia, Spain -

出版信息

Eur J Phys Rehabil Med. 2024 Apr;60(2):190-197. doi: 10.23736/S1973-9087.23.08093-0. Epub 2024 Jan 9.

Abstract

BACKGROUND

The Coma Recovery Scale-Revised (CRS-R) is the most recommended clinical tool to examine the neurobehavioral condition of individuals with disorders of consciousness (DOCs). Different studies have investigated the prognostic value of the information provided by the conventional administration of the scale, while other measures derived from the scale have been proposed to improve the prognosis of DOCs. However, the heterogeneity of the data used in the different studies prevents a reliable comparison of the identified predictors and measures.

AIM

This study investigates which information derived from the CRS-R provides the most reliable prediction of both the clinical diagnosis and recovery of consciousness at the discharge of a long-term neurorehabilitation program.

DESIGN

Retrospective observational multisite study.

SETTING

The enrollment was performed in three neurorehabilitation facilities of the same hospital network.

POPULATION

A total of 171 individuals with DOCs admitted to an inpatient neurorehabilitation program for a minimum of 3 months were enrolled.

METHODS

Machine learning classifiers were trained to predict the clinical diagnosis and recovery of consciousness at discharge using clinical confounders and different metrics extracted from the CRS-R scale.

RESULTS

Results showed that the neurobehavioral state at discharge was predicted with acceptable and comparable predictive value with all the indices and measures derived from the CRS-R, but for the clinical diagnosis and the Consciousness Domain Index, and the recovery of consciousness was predicted with higher accuracy and similarly by all the investigated measures, with the exception of initial clinical diagnosis.

CONCLUSIONS

Interestingly, the total score in the CRS-R and, especially, the total score in its subscales provided the best overall results, in contrast to the clinical diagnosis, which could indicate that a comprehensive measure of the clinical diagnosis rather than the condition of the individuals could provide a more reliable prediction of the neurobehavioral progress of individuals with prolonged DOC.

CLINICAL REHABILITATION IMPACT

The results of this work have important implications in clinical practice, offering a more accurate prognosis of patients and thus giving the possibility to personalize and optimize the rehabilitation plan of patients with DoC using low-cost and easily collectable information.

摘要

背景

昏迷恢复量表修订版(CRS-R)是检查意识障碍(DOC)患者神经行为状态最推荐的临床工具。不同的研究已经调查了量表常规评估提供的信息的预后价值,同时还提出了其他源自量表的措施来改善 DOC 的预后。然而,不同研究中使用的数据的异质性使得对已确定的预测因子和措施的可靠比较变得困难。

目的

本研究旨在探讨从 CRS-R 中获取的哪些信息可以为长期神经康复计划出院时的临床诊断和意识恢复提供最可靠的预测。

设计

回顾性观察性多站点研究。

设置

在同一医院网络的三个神经康复设施中进行了入组。

人群

共纳入 171 名患有 DOC 并接受至少 3 个月住院神经康复计划的患者。

方法

使用机器学习分类器,使用临床混杂因素和从 CRS-R 量表中提取的不同指标,训练来预测出院时的临床诊断和意识恢复。

结果

结果表明,使用 CRS-R 衍生的所有指标和措施,均可对出院时的神经行为状态进行可接受且可比的预测,但对于临床诊断和意识领域指数,以及意识恢复的预测,所有研究措施的准确性都更高,且预测效果相似,除了初始临床诊断。

结论

有趣的是,CRS-R 的总分,尤其是其子量表的总分,提供了最佳的整体结果,与临床诊断相反,这可能表明对个体状况的综合评估而不是临床诊断,可能为长时间患有 DOC 的个体的神经行为进展提供更可靠的预测。

临床康复影响

这项工作的结果在临床实践中具有重要意义,为患者提供更准确的预后,从而有可能使用低成本且易于收集的信息为 DoC 患者个性化和优化康复计划。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a046/11114154/0b8bba9060f0/8093-f1.jpg

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