整合阶段纠正信号平均化与机器学习来预测中风相关的感染:一个回顾性队列研究
Yiyang Gao1,2,3, Jiaqi Zhong1,2,3, Tingting Li1,2,3
1Department of Neurology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Frontiers in neurology
|January 29, 2026
概括
一个新的机器学习模型准确地预测了急性缺血性中风 (AIS) 患者的中风相关感染 (SAI),使用阶段纠正信号平均 (PRSA) 指标. 这种工具有助于早期检测和有针对性的抗生素使用,改善患者的治疗结果.
科学领域:
- 神经学 神经学
- 传染性疾病 传染性疾病
- 生物医学工程 生物医学工程
背景情况:
- 与中风相关的感染 (SAI) 在急性缺血性中风 (AIS) 患者的预后恶化.
- 在AIS中缺乏SAI的早期诊断和风险分层工具.
- 这项研究解决了在紧急情况下评估SAI风险的验证工具的需求.
研究的目的:
- 开发和验证一种机器学习 (ML) 模型,用于预测AIS患者的SAI.
- 为了利用阶段纠正信号平均化 (PRSA) 指标进行早期的SAI风险评估.
- 通过及时诊断和干预,改善患者的治疗结果.
主要方法:
- 分析了392名AIS患者 (2021-2023) 的队列.
- 使用了人口,心率变化 (HRV) 和PRSA参数.
- 博鲁塔算法和相关性分析选择了变量;训练和验证了10个ML模型.
主要成果:
- 确定了SAI的16个独立预测因素.
- 分类提升 (CAT) 模型实现了91%的准确性,92%的特异性和0.939 AUC.
- 心脏减速能力 (DC) 和NIHSS得分是SHAP分析发现的关键预测因素.
结论:
- 结合临床和PRSA数据的ML模型准确地预测AIS患者的SAI.
- CAT模型显示了提高早期SAI检测和治疗的潜力.
- PRSA标记可以指导预防策略并优化抗生素使用.
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