机器学习模型对脑卒中患者脑水肿风险的预测价值:一项meta分析

IF 2.6 3区 心理学 Q2 BEHAVIORAL SCIENCES
Qi Deng, Yu Yang, Hongyu Bai, Fei Li, Wenluo Zhang, Rong He, Yuming Li
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引用次数: 0

摘要

脑卒中患者发生脑水肿的风险很高,这可能会产生严重的后果。然而,目前很少有有效的工具来早期识别或预测这种风险。随着机器学习(ML)在临床实践中的应用越来越多,人们一直在探索其在脑卒中患者脑水肿风险预测中的有效性。然而,缺乏关于其预测价值的系统证据挑战了简单和用户友好的风险评估工具的更新。因此,我们进行了一项系统综述,以评估ML对脑卒中患者脑水肿的预测效用。方法:检索PubMed、Embase、Web of Science和Cochrane数据库,检索时间截止到2024年2月21日。使用预测模型的偏倚评估工具对选定研究的偏倚风险进行评估。荟萃分析综合了验证集的结果。结果:我们纳入了22项研究,涉及25,096例卒中患者和25个模型,这些模型采用常见和可解释的临床特征构建。在验证队列中,模型预测脑卒中后脑水肿的一致性指数(c-index)为0.840 (95% CI: 0.810-0.871),敏感性为0.76 (95% CI: 0.72-0.79),特异性为0.87 (95% CI: 0.83-0.90)。结论:ML模型在预测脑卒中后脑水肿方面具有重要意义,为临床医生提供了一种强有力的预后工具。然而,基于放射组学的研究并未包括在内。我们期待放射组学研究的进步,以提高ML对脑卒中后脑水肿的预测能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Predictive Value of Machine Learning Models for Cerebral Edema Risk in Stroke Patients: A Meta-Analysis

Predictive Value of Machine Learning Models for Cerebral Edema Risk in Stroke Patients: A Meta-Analysis

Introduction

Stroke patients are at high risk of developing cerebral edema, which can have severe consequences. However, there are currently few effective tools for early identification or prediction of this risk. As machine learning (ML) is increasingly used in clinical practice, its effectiveness in predicting cerebral edema risk in stroke patients has been explored. Nonetheless, the lack of systematic evidence on its predictive value challenges the update of simple and user-friendly risk assessment tools. Therefore, we conducted a systematic review to evaluate the predictive utility of ML for cerebral edema in stroke patients.

Methods

We searched PubMed, Embase, Web of Science, and the Cochrane Database up to February 21, 2024. The risk of bias in selected studies was assessed using a bias assessment tool for predictive models. Meta-analysis synthesized results from validation sets.

Results

We included 22 studies with 25,096 stroke patients and 25 models, which were constructed using common and interpretable clinical features. In the validation cohort, the models achieved a concordance index (c-index) of 0.840 (95% CI: 0.810–0.871) for predicting poststroke cerebral edema, with a sensitivity of 0.76 (95% CI: 0.72–0.79) and a specificity of 0.87 (95% CI: 0.83–0.90).

Conclusion

ML models are significant in predicting poststroke cerebral edema, providing clinicians with a powerful prognostic tool. However, radiomics-based research was not included. We anticipate advancements in radiomics research to enhance the predictive power of ML for poststroke cerebral edema.

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来源期刊
Brain and Behavior
Brain and Behavior BEHAVIORAL SCIENCES-NEUROSCIENCES
CiteScore
5.30
自引率
0.00%
发文量
352
审稿时长
14 weeks
期刊介绍: Brain and Behavior is supported by other journals published by Wiley, including a number of society-owned journals. The journals listed below support Brain and Behavior and participate in the Manuscript Transfer Program by referring articles of suitable quality and offering authors the option to have their paper, with any peer review reports, automatically transferred to Brain and Behavior. * [Acta Psychiatrica Scandinavica](https://publons.com/journal/1366/acta-psychiatrica-scandinavica) * [Addiction Biology](https://publons.com/journal/1523/addiction-biology) * [Aggressive Behavior](https://publons.com/journal/3611/aggressive-behavior) * [Brain Pathology](https://publons.com/journal/1787/brain-pathology) * [Child: Care, Health and Development](https://publons.com/journal/6111/child-care-health-and-development) * [Criminal Behaviour and Mental Health](https://publons.com/journal/3839/criminal-behaviour-and-mental-health) * [Depression and Anxiety](https://publons.com/journal/1528/depression-and-anxiety) * Developmental Neurobiology * [Developmental Science](https://publons.com/journal/1069/developmental-science) * [European Journal of Neuroscience](https://publons.com/journal/1441/european-journal-of-neuroscience) * [Genes, Brain and Behavior](https://publons.com/journal/1635/genes-brain-and-behavior) * [GLIA](https://publons.com/journal/1287/glia) * [Hippocampus](https://publons.com/journal/1056/hippocampus) * [Human Brain Mapping](https://publons.com/journal/500/human-brain-mapping) * [Journal for the Theory of Social Behaviour](https://publons.com/journal/7330/journal-for-the-theory-of-social-behaviour) * [Journal of Comparative Neurology](https://publons.com/journal/1306/journal-of-comparative-neurology) * [Journal of Neuroimaging](https://publons.com/journal/6379/journal-of-neuroimaging) * [Journal of Neuroscience Research](https://publons.com/journal/2778/journal-of-neuroscience-research) * [Journal of Organizational Behavior](https://publons.com/journal/1123/journal-of-organizational-behavior) * [Journal of the Peripheral Nervous System](https://publons.com/journal/3929/journal-of-the-peripheral-nervous-system) * [Muscle & Nerve](https://publons.com/journal/4448/muscle-and-nerve) * [Neural Pathology and Applied Neurobiology](https://publons.com/journal/2401/neuropathology-and-applied-neurobiology)
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