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.
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.
Funding was provided by the Ministry of Science and Technology, Taiwan (grant MOST 103-2314-B-002-192-MY3).
The authors have no conflicts of interest to disclose.
Correspondence: Wei-Wen Wu, PhD, RN, School of Nursing, College of Medicine, National Taiwan University, No. 1, Jen-Ai Road, Sec. 1, Taipei 10051, Taiwan (email@example.com).
Accepted for publication December 12, 2017.