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癌症患者营养不良的风险预测模型:系统审查和元分析

Jiayan Yu¹, Xin Chu², Dongqing Guo¹

¹School of Nursing, Chengdu University of Traditional Chinese Medicine, Chengdu, China.

Frontiers in nutrition

|December 29, 2025

概括

目前用于预测癌症和营养不良的模型是有希望的,但有方法上的缺陷. 整合经过验证的工具对于早期查和瘤护理中的个性化营养支持至关重要.

科学领域:

在瘤学瘤学.
营养科学营养科学
生物统计学生物统计学

背景情况:

有许多模型可以预测癌症患者的营养不良,但它们的可靠性和临床使用是不确定的.
缺乏系统的评估,阻碍了将早期风险识别纳入瘤学和护理实践.
早期识别对于优化营养干预和改善患者结果至关重要.

研究的目的:

系统地审查和批判性地评估现有的癌症患者营养不良风险预测模型.
为改善未来的模型开发和临床实施提供基于证据的见解.
评估当前营养不良预测模型的方法质量和性能.

主要方法:

在PubMed,Embase,Web of Science,Cochrane图书馆和Scopus数据库中进行系统搜索.
评估了13项研究,包括使用PROBAST检查清单的57个预测模型.
对模型性能曲线下的面积 (AUC) 的元分析.

主要成果:

包括的模型使用了后勤回归和机器学习;报告的营养不良发生率差异很大 (11.9-69.9%).
常见的预测因素包括体重指数 (BMI),年龄和性别;经过验证的模型的AUC是0.85,表明了良好的歧视.
所有研究都表现出高偏差风险,主要是由于数据源问题和报告缺陷.

结论:

现有的癌症营养不良预测模型处于早期阶段,尽管具有良好的辨别能力.
方法的弱点导致偏差的高风险,需要改进.
瘤学和护理实践需要验证的模型,以实现早期查,个性化营养和多学科合作.

关键词:

癌症癌症癌症癌症癌症营养不良营养不良营养不良这是一个元分析.风险预测风险预测系统性审查系统性审查

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