症状网络分析和未经监督的瘤患者集群确定了症状负担的驱动因素和具有明显症状模式的患者子组
Brandon H Bergsneider1,2, Terri S Armstrong1, Yvette P Conley3
1Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA.
Cancer medicine
|October 8, 2024
概括
患者的症状体验有很大的不同. 计算分析在瘤病患者中发现了不同的症状模式和子组,强调了基于个体患者特征的个性化症状管理策略的需要.
科学领域:
- 在瘤学瘤学.
- 计算生物学 计算生物学
- 医疗保健服务研究 医疗服务研究
背景情况:
- 症状体验的个体间变异性对瘤学的有针对性的干预提出了挑战.
- 计算方法对于理解患者症状负担的异质性至关重要.
研究的目的:
- 无偏见地描述瘤病人的症状经历中的异质性.
- 用计算方法识别症状模式和症状负担的驱动因素.
主要方法:
- 在3088名瘤学患者中,分析了32种来自Memory症状评估尺度的症状.
- 为队列和患者子组构建高斯图形模型症状网络.
- 无监督聚类以根据症状共同严重性模式识别患者子组.
主要成果:
- 确定了三个症状集群:宪法性,胃肠表皮性和心理性.
- 缺乏能量成为影响整体体经验的中心症状.
- 确定了六个具有独特模式和特征的患者子组,症状优先级不同.
结论:
- 计算方法有效地捕捉了患者在症状体验中的异质性.
- 识别子组的核心症状和临床决定因素可以为精确的症状管理提供信息.
- 进一步验证可能会导致针对不同瘤患者群体量身定制的干预措施.
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