Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA.
School of Medicine, Stanford University, Stanford, California, USA.
Cancer Med. 2024 Oct;13(19):e70278. doi: 10.1002/cam4.70278.
Interindividual variability in oncology patients' symptom experiences poses significant challenges in prioritizing symptoms for targeted intervention(s). In this study, computational approaches were used to unbiasedly characterize the heterogeneity of the symptom experience of oncology patients to elucidate symptom patterns and drivers of symptom burden.
Severity ratings for 32 symptoms on the Memorial Symptom Assessment Scale from 3088 oncology patients were analyzed. Gaussian Graphical Model symptom networks were constructed for the entire cohort and patient subgroups identified through unsupervised clustering of symptom co-severity patterns. Network characteristics were analyzed and compared using permutation-based statistical tests. Differences in demographic and clinical characteristics between subgroups were assessed using multinomial logistic regression.
Network analysis of the entire cohort revealed three symptom clusters: constitutional, gastrointestinal-epithelial, and psychological. Lack of energy was identified as central to the network which suggests that it plays a pivotal role in patients' overall symptom experience. Unsupervised clustering of patients based on shared symptom co-severity patterns identified six patient subgroups with distinct symptom patterns and demographic and clinical characteristics. The centrality of individual symptoms across the subgroup networks differed which suggests that different symptoms need to be prioritized for treatment within each subgroup. Age, treatment status, and performance status were the strongest determinants of subgroup membership.
Computational approaches that combine unbiased stratification of patients and in-depth modeling of symptom relationships can capture the heterogeneity in patients' symptom experiences. When validated, the core symptoms for each of the subgroups and the associated clinical determinants may inform precision-based symptom management.
肿瘤患者症状体验的个体间变异性对优先考虑针对特定干预措施的症状提出了重大挑战。在这项研究中，使用计算方法来公正地描述肿瘤患者症状体验的异质性，以阐明症状模式和症状负担的驱动因素。
对 3088 例肿瘤患者的 Memorial Symptom Assessment Scale 上的 32 个症状的严重程度评分进行了分析。对整个队列和通过症状共同严重程度模式的无监督聚类确定的患者亚组构建了高斯图形模型症状网络。使用基于置换的统计检验分析和比较网络特征。使用多项逻辑回归评估亚组之间人口统计学和临床特征的差异。
整个队列的网络分析显示出三个症状簇：体质、胃肠上皮和心理。缺乏能量被确定为网络的核心，这表明它在患者的整体症状体验中起着关键作用。基于共同症状严重程度模式对患者进行无监督聚类，确定了六个具有不同症状模式和人口统计学及临床特征的患者亚组。个体症状在亚组网络中的中心性不同，这表明需要针对每个亚组中的不同症状进行治疗。年龄、治疗状态和表现状态是亚组归属的最强决定因素。
结合患者无偏分层和症状关系深入建模的计算方法可以捕获患者症状体验的异质性。在验证后，每个亚组的核心症状和相关的临床决定因素可能为基于精准的症状管理提供信息。
