一种多中心大数据方法,用于准确的PICC-RVT预后和临床实践中主要风险因素的识别
Yue Li1,2, Ting Li1, Hengjie Su1
1Institute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Heliyon
|November 5, 2024
概括
一个新的深度学习模型准确地预测了与外周插入的中央导管 (PICC) 相关的静脉血栓形成 (PICC-RVT) 风险,即使在无症状患者中也是如此. 这种预后工具有助于早期干预,并减少了患者的负担.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 血管医学 血管医学
背景情况:
- 周边插入的中央导管 (PICC) 对于静脉治疗至关重要,但可能导致严重的静脉血栓形成 (PICC-RVT).
- 目前的诊断方法,如彩色多普勒流成像 (CDFI),对于早期的PICC-RVT,耗时且效率较低.
- 需要一种预后工具来及时诊断和治疗,特别是在资源有限的环境中.
研究的目的:
- 开发和评估一个有效的预后模型,PRAD (通过深度学习进行PICC-RVT评估),用于量化PICC-RVT风险.
- 使用机器学习识别与PICC-RVT相关的关键风险因素.
主要方法:
- 使用了来自27个中国医疗保健中心的5272名患者的综合数据集.
- 超过14885个机器学习模型被选,以确定最佳的预后模型.
- 采用数据驱动方法系统地开发PRAD模型.
主要成果:
- 该PRAD模型实现了高预测准确度,准确度为86.4%,AUC为0.837.
- 体重分析确定了关键风险因素,包括白蛋白,初级诊断,血红蛋白,血小板和教育水平.
- 该模型在预测早期PICC-RVT方面表现出色,包括无症状个体.
结论:
- PRAD模型为PICC-RVT的早期预测提供了一个有价值的工具,特别是在无症状患者中.
- 识别可控制的风险因素可以进行有针对性的干预,从而减少患者负担.
- 这种深度学习方法促进了PICC-RVT的风险分层和及时管理.
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