University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
Pediatr Blood Cancer. 2014 Jul;61(7):1282-8. doi: 10.1002/pbc.25029. Epub 2014 Mar 15.
Children with cancer experience multiple symptoms due to their disease and as a result of treatment. The purpose of this study was to demonstrate the feasibility and potential utility of using latent profile analysis (LPA), a type of cluster analysis, in children with cancer to identify groups of patients who experience similar levels of symptom severity and impairment of physical function.
We analyzed patient-reported symptom and functional data previously collected using the Pediatric Patient Reported Outcomes Measurement Information System (PROMIS). LPA was used to identify and characterize groups of patients who reported similar levels of symptom severity and functional impairment. We then used the multinomial logit model to examine demographic and disease characteristics associated with symptom/function profile membership.
The analysis included 200 patients in treatment or in survivorship. We identified four symptom/function profiles; children currently receiving cancer treatment and those with at least one other medical problem were more likely to be members of the profile with the highest levels of symptom severity and functional impairment. Gender, age, race/ethnicity, and tumor type were not associated with profile membership.
LPA is a cluster research methodology that provides clinically useful results in pediatric oncology patients. Future studies of children with cancer using LPA could potentially lead to development of clinical scoring systems that predict patients' risk of developing more severe symptoms and functional impairments, allowing clinicians, patients, and parents to better anticipate and prevent the multiple symptoms that occur during and after treatment for childhood cancer.
儿童癌症患者由于疾病和治疗会出现多种症状。本研究旨在展示使用潜在剖面分析（LPA）的可行性和潜在效用，LPA 是一种聚类分析方法，用于识别癌症患儿中具有相似严重程度和身体功能障碍水平的患者群体。
我们分析了先前使用儿科患者报告结局测量信息系统（PROMIS）收集的患者报告的症状和功能数据。LPA 用于识别和描述报告相似症状严重程度和功能障碍水平的患者群体。然后，我们使用多项逻辑回归模型检查与症状/功能特征群成员相关的人口统计学和疾病特征。
该分析纳入了 200 名正在接受治疗或处于生存随访阶段的患者。我们确定了四个症状/功能特征群；正在接受癌症治疗的儿童和至少存在其他一种医疗问题的儿童更有可能属于症状严重程度和功能障碍最高水平的特征群。性别、年龄、种族/民族和肿瘤类型与特征群成员身份无关。
LPA 是一种聚类研究方法，可为儿科肿瘤患者提供临床有用的结果。未来使用 LPA 对癌症儿童进行的研究可能会导致开发出预测患者出现更严重症状和功能障碍风险的临床评分系统，从而使临床医生、患者和家长能够更好地预测和预防儿童癌症治疗期间和之后发生的多种症状。
