慢性阻塞性肺病疾病疾病进展和结果的预后模型:系统性审查和元分析
Deborah Testa1, Pietro Magnoni1, Caterina Fanizza2
1Unità di Epidemiologia, Agenzia di Tutela della Salute (ATS) della Città Metropolitana di Milano, Via Conca del Naviglio 45, 20123 Milan, Italy.
Journal of clinical medicine
|December 30, 2025
概括
这项系统性审查发现,虽然对于慢性阻塞性肺病 (COPD) 存在许多预后模型,但大多数使用的是传统方法,而不是机器学习 (ML). 方法上的缺陷和异质性限制了这些COPD进展和恶化预测工具的可靠性.
科学领域:
- 肺部医学 肺部医学
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- 慢性阻塞性肺病 (COPD) 是一个日益严重的全球健康挑战.
- 现有的COPD进展和恶化风险的预后模型显示不一致的严谨性和性能.
- 机器学习 (ML) 具有潜力,但其在COPD预后中的应用有限.
研究的目的:
- 在成年人中系统审查COPD疾病进展和恶化风险的预后模型.
- 评估现有模型的方法质量,通用性和预测性能.
- 在COPD预后中,将传统的基于回归的方法与ML技术进行比较.
主要方法:
- 对COPD (1-5年窗口) 的预后模型进行PubMed和Embase的系统文献搜索.
- 包括死亡率,恶化和住院的模型;使用PROBAST进行评估.
- 对外部验证模型的模型性能和歧视 (c-统计) 的元分析的描述性摘要.
主要成果:
- 包括87项研究中的193个预后模型;只有7%的人使用了ML.
- 所有原因的死亡率和严重恶化是研究的最常见的结果.
- 对恶化模型的元分析受到异质性的阻碍;BODE指数在死亡率方面表现最好 (共计c 0.75).
- 超过40%的研究显示存在偏差的高风险.
结论:
- 有意义的定量综合仅限于外部验证的死亡率模型,因为存在相当大的异质性.
- 基于回归的模型占主导地位,在COPD预后中对ML的采用有限.
- 持续的方法限制需要开发更强大的,验证的模型复杂的患者数据.
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