Symptom profiles in lung cancer survivors: A latent class approach

Tehreem Hussain, Elisa H Son, Gwenyth R Wallen, Li Yang and Lena J Lee

PLOS ONE, 2025, vol. 20, issue 10, 1-14

Abstract: Lung cancer survivors experience multiple concurrent symptoms after cancer treatments. However, the majority of symptom research has focused on assessing and managing individual symptoms. Furthermore, little is known about the risk factors and adverse outcomes of complex symptoms in lung cancer survivors. The purpose of the study was to: (1) identify symptom profiles in lung cancer survivors; (2) determine influencing factors of the symptom profiles; and (3) examine differences in health outcomes among the symptom profiles. A cross-sectional secondary analysis of data from the Measuring Your Health (MY-Health) study was conducted with 526 lung cancer survivors. Symptom profiles were identified using latent profile analysis based on four patient-reported symptoms (pain, fatigue, sleep disturbance, and depression) with custom PROMIS® short forms. We conducted multinomial logistic regression analysis to determine influencing factors of the symptom profiles and multivariate analysis of variance to examine differences in physical function and social function among the symptom profiles. Four latent class symptom profiles were identified: (1) Within Normal Limits (Class 1), (2) Pain with Fatigue and Sleep Disturbance (Class 2), (3) Depression with Fatigue and Sleep disturbance (Class 3), and (4) All High Symptom Burden (Class 4). Age, income, employment status, and number of comorbidities were the influencing factors of the symptom profiles. There were significant differences in physical function and social function among the symptom profiles. This study found that the influencing factors of the symptom profiles in lung cancer survivors tended to be more sociodemographic in nature, rather than clinical. Researchers and healthcare providers use findings such as these when establishing symptom management strategies for lung cancer survivors by integrating demographic and socioeconomic determinants of health in conjunction with targeted clinical variables.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0309272

DOI: 10.1371/journal.pone.0309272

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