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利用汇总的个体患者数据加速癌症患者的精准运动医学:北极星计划经验

Accelerating precision exercise medicine in cancer patients using pooled individual patient data: POLARIS experience.

作者信息

Buffart Laurien M, Kenkhuis Marlou-Floor, Newton Robert U, May Anne M, Galvão Daniel A, Courneya Kerry S

机构信息

Department of Medical BioSciences, Radboud University Medical Center, Nijmegen, The Netherlands.

Exercise Medicine Research Institute, Edith Cowan University, Joondalup, WA, Australia.

出版信息

JNCI Cancer Spectr. 2025 Sep 1;9(5). doi: 10.1093/jncics/pkaf078.

Abstract

Numerous exercise oncology trials have been completed, greatly informing exercise recommendations for patients with cancer. Exercise medicine can be administered in various types, doses, and schedules at various time points. Advancing precision exercise medicine requires understanding of how the effects of different exercise interventions vary by characteristics of individual patients. The Predicting OptimaL cAncer RehabIlitation and Supportive care (POLARIS) study provides an international infrastructure and shared database to perform pooled analyses of individual patient data (IPD) from multiple randomized controlled trials. This commentary aims to highlight the value of pooled IPD analyses, summarize key findings from published pooled IPD analyses on the effects of physical exercise on various outcomes, and provide guidance to advance precision exercise medicine for patients with cancer. POLARIS currently includes IPD from 52 exercise trials. Findings to date indicate that exercise interventions in patients with cancer have beneficial effects on physical fitness, fatigue, health-related quality of life, self-reported cognition (posttreatment), sleep disturbances, and symptoms of anxiety and depression. Additionally, it was determined that the exercise effects varied by characteristics of the patients, including the initial value of the outcome, age, marital status, and education level, and by characteristics of the intervention, including exercise supervision and specificity. Future research opportunities to advance precision exercise medicine for patients with cancer include pooling of trial data from understudied populations, data on clinical outcomes, and biomarkers, as well as applying machine learning models for identifying combinations of covariables that modify intervention effects and predictions of individual treatment effects.

摘要

众多运动肿瘤学试验已经完成,这为癌症患者的运动建议提供了大量信息。运动医学可以在不同时间点以各种类型、剂量和时间表进行施用。推进精准运动医学需要了解不同运动干预的效果如何因个体患者的特征而有所不同。预测最佳癌症康复与支持治疗(POLARIS)研究提供了一个国际基础设施和共享数据库,用于对来自多个随机对照试验的个体患者数据(IPD)进行汇总分析。本评论旨在强调汇总IPD分析的价值,总结已发表的关于体育锻炼对各种结果影响的汇总IPD分析的主要发现,并为推进癌症患者的精准运动医学提供指导。POLARIS目前包括来自52项运动试验的IPD。迄今为止的研究结果表明,癌症患者的运动干预对体能、疲劳、健康相关生活质量(治疗后自我报告)、睡眠障碍以及焦虑和抑郁症状都有有益影响。此外,还确定运动效果因患者特征(包括结果的初始值、年龄、婚姻状况和教育水平)以及干预特征(包括运动监督和特异性)而异。推进癌症患者精准运动医学的未来研究机会包括汇总来自研究不足人群的试验数据、临床结果数据和生物标志物,以及应用机器学习模型来识别改变干预效果的协变量组合和个体治疗效果预测。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2641/12401491/5287394f6990/pkaf078f1.jpg

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