Author Affiliations: School of Nursing, College of Medicine, National Taiwan University (Dr Wu); Institute of Hospital and Health Care Administration, Yang-Ming University (Dr Lin); School of Nursing, National Taipei University of Nursing and Health Sciences (Dr Liang); and Department of Medicine, National Taiwan University (Dr Jou), Taipei, Taiwan.
Cancer Nurs. 2019 May/Jun;42(3):198-207. doi: 10.1097/NCC.0000000000000587.
Prior studies identifying symptom clusters used a symptom-centered approach to demonstrate the relationship among symptoms. Latent profile analysis (LPA) is a patient-centered approach that classifies individuals from a heterogeneous population into homogeneous subgroups, helping prioritize interventions to focus on clusters with the most severe symptom burden.
The aim of this study was to use LPA to determine the best-fit models and to identify phenotypes of severe symptom distress profiles for adolescents with cancer who are undergoing treatment and in survivorship.
We used estimated means generated by the LPA to predict the probability of an individual symptom occurring across on- and off-treatment groups for 200 adolescents with cancer.
The 3-profile solution was considered the best fit to the data for both on- and off-treatment groups. Adolescents on treatment and classified into the severe profile were most likely to report distress in appetite, fatigue, appearance, nausea, and concentration. Adolescents off treatment and classified into the severe profile were most likely to report distress in fatigue, pain frequency, and concentration.
Latent profile analysis provided a cluster methodology that uncovered hidden profiles from observed symptoms. This made it possible to directly compare the phenotypes of severe profiles between different treatment statuses.
The co-occurring 13-item Symptom Distress Scale symptoms found in the severe symptom distress profiles could be used as items in a prespecified severe symptom distress cluster, helping evaluate a patient's risk of developing varying degrees of symptom distress.
先前的研究通过以症状为中心的方法确定症状群，以证明症状之间的关系。潜在剖面分析（LPA）是一种以患者为中心的方法，可将来自异质人群的个体分为同质亚组，有助于优先干预以关注症状负担最严重的集群。
本研究旨在使用 LPA 确定最佳拟合模型，并确定正在接受治疗和生存的青少年癌症患者严重症状困扰的表型。
我们使用 LPA 生成的估计均值来预测 200 名癌症青少年在治疗期间和治疗结束后个体症状发生的概率。
对于治疗期间和治疗结束后的组，三剖面解决方案被认为最适合数据。接受治疗并被归类为严重组的青少年最有可能报告食欲、疲劳、外貌、恶心和注意力方面的困扰。接受治疗并被归类为严重组的青少年最有可能报告疲劳、疼痛频率和注意力方面的困扰。
潜在剖面分析提供了一种聚类方法，可以从观察到的症状中发现隐藏的表型。这使得比较不同治疗状态下严重表型的表型成为可能。
严重症状困扰剖面中共同出现的 13 项症状困扰量表症状可用作预先指定的严重症状困扰集群中的项目，有助于评估患者出现不同程度症状困扰的风险。
