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预测中风患者的疼痛及其与死亡率的关联。

Predicting pain and its association with mortality in patients with stroke.

作者信息

Viktorisson Adam, Hashem Aref Haj, S Sunnerhagen Katharina, Abzhandadze Tamar

机构信息

Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.

Department of Rehabilitation Medicine, Sahlgrenska University Hospital, Gothenburg, Sweden.

出版信息

BMC Neurol. 2025 Jan 7;25(1):10. doi: 10.1186/s12883-024-04011-5.

DOI:10.1186/s12883-024-04011-5
PMID:39773356
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11705892/
Abstract

BACKGROUND AND OBJECTIVES

Poststroke pain (PSP) is a prevalent and severe consequence of stroke, encompassing central, neuropathic, and nonneuropathic pain. In this study, we aimed to investigate clinical factors associated with PSP three months after stroke and concurrently explore the association between PSP and one-year mortality.

METHODS

This registry-based study comprised data from stroke patients admitted to three hospitals in Sweden between November 2014 and June 2019. The outcome was PSP three months after stroke. Twelve (out of 28) predictor variables were selected by three machine learning methods, and a multivariable binary logistic regression model was fitted for predicting PSP. The association between PSP and one-year poststroke mortality was examined using Cox proportional hazards models.

RESULTS

Among 4,160 stroke patients participating in the three-month follow-up, 54.7% reported PSP. Antiplatelet use, diabetes, hemiparesis, sensory deficits, and need for assistance before stroke were significant predictors of PSP. Male sex, being born in Sweden, higher income, and regular prestroke physical activity predicted the absence of PSP. After adjustment for age, sex, region of birth, and stroke severity, patients experiencing PSP had a significantly higher one-year mortality rate than those without pain, and the most severe level of pain (constant pain) was associated with the highest cumulative mortality.

CONCLUSION

The study findings indicate treatable factors associated with PSP, which highlight areas of improvement in management strategies. Clinicians should recognize that PSP is associated with increased one-year mortality, emphasizing the importance of pain prevention and treatment for enhanced poststroke outcomes.

摘要

背景与目的

中风后疼痛(PSP)是中风常见且严重的后果,包括中枢性、神经性和非神经性疼痛。在本研究中,我们旨在调查中风后三个月与PSP相关的临床因素,并同时探讨PSP与一年死亡率之间的关联。

方法

这项基于登记处的研究纳入了2014年11月至2019年6月期间在瑞典三家医院住院的中风患者的数据。结局指标是中风后三个月的PSP。通过三种机器学习方法从28个预测变量中选择了12个,并拟合了多变量二元逻辑回归模型来预测PSP。使用Cox比例风险模型检查PSP与中风后一年死亡率之间的关联。

结果

在参与三个月随访的4160例中风患者中,54.7%报告有PSP。使用抗血小板药物、糖尿病、偏瘫、感觉障碍以及中风前需要帮助是PSP的显著预测因素。男性、出生在瑞典、收入较高以及中风前有规律的体育活动预测无PSP。在调整年龄、性别、出生地区和中风严重程度后,经历PSP的患者一年死亡率显著高于无疼痛的患者,最严重程度的疼痛(持续性疼痛)与最高累积死亡率相关。

结论

研究结果表明了与PSP相关的可治疗因素,突出了管理策略中需要改进的方面。临床医生应认识到PSP与一年死亡率增加有关,强调疼痛预防和治疗对改善中风后结局的重要性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a04a/11705892/cc664e5b933a/12883_2024_4011_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a04a/11705892/cba36d3ea6bf/12883_2024_4011_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a04a/11705892/cc664e5b933a/12883_2024_4011_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a04a/11705892/cba36d3ea6bf/12883_2024_4011_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a04a/11705892/cc664e5b933a/12883_2024_4011_Fig2_HTML.jpg

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本文引用的文献

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Stroke. 2023 Dec;54(12):3107-3116. doi: 10.1161/STROKEAHA.123.043355. Epub 2023 Nov 2.
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Machine learning algorithms for identifying predictive variables of mortality risk following dementia diagnosis: a longitudinal cohort study.机器学习算法在识别痴呆症诊断后死亡风险预测变量中的应用:一项纵向队列研究。
Sci Rep. 2023 Jun 10;13(1):9480. doi: 10.1038/s41598-023-36362-3.
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Artificial intelligence and machine learning in pain research: a data scientometric analysis.
疼痛研究中的人工智能与机器学习:一项数据科学计量分析。
Pain Rep. 2022 Nov 3;7(6):e1044. doi: 10.1097/PR9.0000000000001044. eCollection 2022 Nov-Dec.
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Role of Artificial Intelligence and Machine Learning in the prediction of the pain: A scoping systematic review.人工智能和机器学习在疼痛预测中的作用:范围系统评价。
Proc Inst Mech Eng H. 2022 Oct;236(10):1478-1491. doi: 10.1177/09544119221122012. Epub 2022 Sep 23.
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Efficacy of rehabilitative techniques in reducing hemiplegic shoulder pain in stroke: Systematic review and meta-analysis.康复技术在减少脑卒中偏瘫肩痛中的疗效:系统评价和荟萃分析。
Ann Phys Rehabil Med. 2022 Sep;65(5):101602. doi: 10.1016/j.rehab.2021.101602. Epub 2021 Nov 19.
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Machine Learning in Medicine: Will This Time Be Different?医学中的机器学习:这次会有所不同吗?
Circulation. 2020 Oct 20;142(16):1521-1523. doi: 10.1161/CIRCULATIONAHA.120.050583. Epub 2020 Oct 19.
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Evaluation of machine learning methods to stroke outcome prediction using a nationwide disease registry.利用全国性疾病登记系统评估机器学习方法对脑卒中结局的预测。
Comput Methods Programs Biomed. 2020 Jul;190:105381. doi: 10.1016/j.cmpb.2020.105381. Epub 2020 Feb 1.
8
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Artificial intelligence, machine learning and health systems.人工智能、机器学习与卫生系统。
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Agreed definitions and a shared vision for new standards in stroke recovery research: The Stroke Recovery and Rehabilitation Roundtable taskforce.达成关于卒中康复研究新标准的共识定义和共同愿景:卒中康复和康复圆桌会议工作组。
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