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机器学习在预测下肢再血管化后的伤口愈合和肢体救援结果:预测准确性的系统审查

Abdulkreem Al-Juhani¹, Rodan Desoky², Abdullah Abdullah³

¹General Surgery, King Abdulaziz University Faculty of Medicine, Jeddah, SAU.

|August 27, 2025

概括

机器学习模型在预测慢性四肢威胁性缺血 (CLTI) 和外周动脉疾病 (PAD) 后的伤口愈合和四肢保存方面表现有希望. 在临床广泛采用之前需要进一步验证.

科学领域:

血管外科
医疗信息学
在医疗保健中的机器学习

背景情况:

慢性肢体威胁性缺血 (CLTI) 和外周动脉疾病 (PAD) 往往导致截肢等不良结果,即使在重新血管化后.
目前用于预测患者结果的方法缺乏个性化的准确性.

研究的目的:

系统评估机器学习 (ML) 模型的预测性能,以预测下肢再血管化后的伤口愈合和肢体保存.
将ML模型的疗效与传统的预后工具进行比较.

主要方法:

在主要数据库 (PubMed,Embase,Scopus等) 进行了系统的文献搜索. 从2018年1月到2025年3月.
根据PICO标准,使用ML预测下肢再血管化的结果.
预测模型偏差风险评估工具 (PROBAST) 用于偏差评估;需要量化性能指标 (例如,AUROC).

主要成果:

五项研究符合纳入标准,使用大型注册数据集上的XGBoost和神经网络等ML技术.
与标准后勤回归相比,ML模型显示出更高的预后准确性 (AUROC 0. 78- 0. 95).
三项研究的偏差风险较低,但由于样本大小或缺乏外部验证,两项研究的风险较高/含糊.

结论:

机器学习模型在预测重血管化后的伤口愈合和肢体挽救方面具有显著的潜力,其表现优于传统方法.
在临床实施之前,有限的外部验证和方法的变化需要进一步的前性研究.

关键词:

慢性肢体威胁性缺血症机器学习周围动脉疾病预测模型重新血管化伤口愈合

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