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预测移植后患者和移植品存活率的模型的比较性能:系统性审查
Joris van de Klundert1, Francisco Perez-Galarce2, Marcelo Olivares3
1Business School, Universidad Adolfo Ibanez, Santiago, Chile.
Transplantation reviews (Orlando, Fla.)
|May 8, 2025
概括
这次审查发现,移植存活预测模型之间没有显著的性能差异. 尽管有新的机器学习方法,传统的Cox比例危险模型仍然具有竞争力,尽管研究质量令人担忧.
科学领域:
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
- 腎臟病學 (nephrology) 是一種醫學.
背景情况:
- 考克斯比例危险模型是用于预测移植存活率的标准.
- 包括机器学习在内的新型预测模型已经出现.
- 这些模型的比较性能需要调查.
研究的目的:
- 系统地审查和比较各种移植生存模型的预测性能.
- 评估新型模型是否优于传统的考克斯比例危险模型.
主要方法:
- 按照PROBAST和CHARMS的指导方针进行系统审查.
- 包括报告比较预测性能的研究.
- 偏差风险评估和比较性绩效分析.
主要成果:
- 审查了37篇出版物,其中包括134项比较研究.
- 许多研究都有高偏差风险,影响了可靠性.
- 没有一个模型类型,包括机器学习,始终优于其他模型; 考克斯模型没有显著优于其他模型.
结论:
- 目前的证据不支持新型模型在脏移植生存方面表现优于考克斯的比例危险模型.
- 现有研究中存在偏差的高风险需要更好地设计研究.
- 未来的研究应侧重于强大的设计和模型的重复使用,以澄清性能差异.
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