在急性肺部恶化期间,在患有囊性纤维化的人群中,使用机器学习K-means集群分析进行症状表型化
Eliana R Gill1, Christopher Dill2, Christopher H Goss3
1Division of Biobehavioral Nursing and Health Informatics, Department of Nursing, University of Washington, Seattle, WA, United States.
概括
囊性纤维化肺恶化 (PEx) 显示出不同的症状集群. 高症状 (HS) 现象型患者需要更多的住院治疗,并且与低症状 (LS) 现象型患者相比,症状恢复速度较慢.
科学领域:
- 肺部医学 肺部医学
- 临床研究 临床研究
- 在医疗保健中的数据科学.
背景情况:
- 囊性纤维化 (CF) 患者经常经历与慢性肺病相关的症状.
- 在CF中,肺部恶化 (PEx) 涉及症状增加和肺功能下降.
- 症状集群,即多个症状同时发生的地方,为疾病模式提供了洞察力.
研究的目的:
- 在PEx期间识别CF (PwCF) 患者的症状集群模式.
- 在CF PEx中定义不同的症状表型.
- 分析已识别的PEx表型之间的恢复差异.
主要方法:
- 来自72名用静脉注射抗生素治疗的PwCF的纵向数据的二次分析.
- 症状数据使用CF呼吸道症状日记 (CFRSD) - 慢性呼吸道症状得分 (CRISS) 每天收集21天.
- 针对第一天的症状数据进行了K-means聚类;用于分析的是线性回归和多层次生长模型.
主要成果:
- 症状分为两个主要的表型:低症状 (LS,n=42) 和高症状 (HS,n=30).
- 在HS型患者中,症状明显恶化,CRISS得分明显下降 (p<0.01).
- HS表型与每年5个额外的住院之夜 (p<0.01) 和症状持续时间延长 (p<0.0001) 相关.
结论:
- 症状集群在CFPEX的第1天就显而易见.
- 这种HS类型与住院病例的增加和PEx治疗结束时症状的缓解程度较差有关.
- 通过症状集群对CF PEx的表型鉴定可以为临床管理提供信息并预测结果.
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