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探索新型数据驱动的聚类方法,以发现新生儿经术后温度测量的纵向模式
Stephanie M Helman1, Nathan T Riek2, Susan M Sereika3
1School of Medicine, Department of Medicine, Division of General Internal Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania.
Mayo Clinic proceedings. Digital health
|November 11, 2025
概括
患有先天性心脏缺陷的新生儿接受心肺旁路术 (CPB) 显示出明显的术后温度模式. 在CPB后持续的低温会增加这些婴儿出现并发症的风险.
科学领域:
- 儿童心脏病学 儿童心脏病学
- 新生儿手术 新生儿手术
- 机器学习在医学中的应用
背景情况:
- 术后温度管理对于患有先天性心脏缺陷 (CHD) 的新生儿在心肺绕道 (CPB) 后至关重要.
- 了解不同的温度轨迹可以为风险分层提供信息,并改善患者的治疗结果.
研究的目的:
- 通过无监督机器学习,在CPB后患有CHD的新生儿中识别独特的术后温度模式.
- 在识别这些温度轨迹时,比较不同集群方法 (GBTM,SOM,k-means) 的性能.
- 评估确定温度集群在术后并发症上的预后价值.
主要方法:
- 对接受CPB的450名新生儿的前性数据进行了二次分析.
- 基于组的轨迹建模 (GBTM),自我组织地图 (SOM) 和k-means集群被用来识别3个术后温度集群 (持续低温,缓解低温,正常热).
- 温度集群与术后并发症复合结果之间的关联被使用多变量逻辑回归来评估.
主要成果:
- 这三种聚类方法都确定了不同的温度轨迹:持续低温,缓解低温和正常热量.
- 在GBTM和SOM之间观察到很强的一致性 (κ=0.92),而GBTM和k-means之间的一致性较弱 (κ=0.41).
- 与GBTM (OR 2.8) 和SOM (OR 2.3) 模型中的常温新生儿相比,持久性低温组的新生儿患术后并发症的几率明显更高.
结论:
- 机器学习技术有效地划分了CPB后新生儿的明显的术后温度轨迹.
- 在CPB后的持续性低温症是这种脆弱人群中不良结果的重要预测因素.
- 根据温度模式,GBTM和SOM在识别高风险新生儿方面表现出强烈的一致性和预后价值.
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