评估帕金森病预后的多变量预测模型:一个范围审查协议
Lynn Eickholt1, Megan Super1, Whitley Aamodt2,3
1Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
BMJ open
|December 30, 2025
概括
这次范围审查将确定和评估现有的帕金森病 (PD) 预后模型. 这些发现将指导开发更好的预测工具,用于个别患者的结果.
科学领域:
- 神经学 神经学
- 生物统计学 生物统计学
- 数据科学数据科学数据科学
背景情况:
- 帕金森病 (PD) 呈现出各种运动和非运动症状,影响个体预后.
- 目前对PD临床结果的预测模型缺乏全面的开发,验证和证明的临床效用.
- 需要对现有的预后模型进行系统评估,以指导未来的研究和临床实践.
研究的目的:
- 开发一种系统的方法来识别,审查和评估帕金森病的多变量预后模型.
- 总结有关PD预后模型的当前文献,并确定知识差距.
- 为PD个体预后改进的临床预测模型的开发和验证提供信息.
主要方法:
- 以PRISMA-ScR方法论为指导的范围审查.
- 包括使用传统统计或机器学习预测PD进展的多变量模型,不包括单变量模型.
- 在2025年之前对PubMed,EMBASE,Web of Science和Scopus数据库进行全面搜索,并使用Covidence进行数据提取和双审核员选.
- 使用TRIPOD+AI和PROBAST指南评估所包含的模型.
主要成果:
- 该审查将综合确定多变量预测模型的发现,并按临床结果分类.
- 通过系统评估,将确定现有PD预后模型的缺陷和改进领域.
- 这项研究将全面概述目前的PD预后建模格局.
结论:
- 这一范围审查将为了解帕金森病预后建模的当前状态奠定基础.
- 这些发现将指导开发更强大和临床上有用的预测模型,用于个人PD患者的预后.
- 通过出版物和会议传播将有助于临床医生基于证据的决策,并为未来的研究方向提供信息.
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