Distinct Subgroups of Patients With Lung Cancer Receiving Chemotherapy: A Latent Transition Analysis.

Nannan Li,Lili Hou,Shu Li

doi:10.3389/fonc.2020.522407

Abstract

ObjectivesTo identify subgroups of patients with lung cancer receiving chemotherapy based on the severity dimension of symptom experience, and to examine changes in membership between these subgroups over time.MethodsPatients who were scheduled to receive chemotherapy completed the Chinese version of the MD Anderson Symptom Inventory and the revised lung cancer module with a total of 19 symptom items. Data were collected at three time points: two weeks before chemotherapy (T1), after chemotherapy cycle 1 (T2), and after chemotherapy cycle 3 or above (T3). The latent profile analysis and latent transition analysis were used to identify underlying subgroups and describe changes in subgroup membership over time.ResultsFrom the total sample (N = 195), 160 patients completed the symptom assessment at T1, T2, and T3. Two distinct latent symptom profiles of patients could be identified at T1, T2, and T3, which were classified as “Mild” and “Moderate-Severe” profiles. From T1 to T2 and T3, members in the Mild profile were more likely to move to the Moderate-Severe profile. Chemotherapy protocols, prior surgery treatment, and level of education can predict the transitions.ConclusionResults provide a better understanding of the patient’s different symptom experiences and characteristics. These could help clinicians to anticipate symptom patterns and develop interventions in lung cancer patients who were scheduled to receive chemotherapy for the first time.

Highlights

Individuals with lung cancer experience multiple symptoms, which are frequently associated with disease and side effects of treatment [1]
After identifying the optimal latent profile model at each time point, we extended the latent profile analysis (LPA) models to Latent transition analysis (LTA) to examine the transitions in membership between latent profiles over time
A total of four out of the 19 symptoms that occurred in less than 40% at all three time points were excluded in the LPA

Summary

Introduction

Individuals with lung cancer experience multiple symptoms, which are frequently associated with disease and side effects of treatment [1]. Identifying symptom clusters with validity is paramount for developing targeted interventions aimed at improving clinical outcomes of patients. Most common statistical analysis, such as principal components analysis (PCA) [7], factor analysis (FA) [8], and cluster analysis [9] were used to identify symptom clusters in patients with lung cancer. Emotional or psychological and gastrointestinal symptom clusters were commonly identified. The statistical methods mentioned above are advantages in dealing with the cross-sectional data, but for some longitudinal data, they are limited to tracking individual trajectories of symptom experiences over the course of a disease or treatment

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