Patters of Symptom Trajectory in Patients with Late Stage Nsclc: a Comparison between Never Smokers and Ever Smokers

Lung cancer in never smokers (LCINS) has been suggested as a distinct disease entity. No research has focused on the uniqueness of symptom experiences in never-smoking patients with non-small cell lung cancer (NSCLC). For patients with NSCLC, this three-year project (from 2015-8-1 to 2018-7-31) is aimed to (1) identify the different patterns of trajectories in symptoms, (2) identify the time to deterioration of symptoms, (3) identify patient sub-groups with different initial symptom profiles, (4) examine the predictive effect of the initial symptom profile on 2-year QOL and overall survival, and (5) compare differences in symptom experience (including initial symptom profile, symptom trajectory patterns and time to deterioration) between never smokers and ever smokers of NSCLC. A prospective longitudinal design will be used to repeatedly collect data from 150 newly diagnosed patients with stage III or IV NSCLC. Patients who meet the inclusion criteria will be referred to the research team by the Co-PIs. Patients will be measured for various symptoms and quality of life at enrollment (baseline), every month for six months after enrollment, then every three months until 2 years after enrollment. We will measure seven common symptoms experienced by lung cancer patients: pain (BPI), fatigue (BFI), depression (HADS-D), sleep disturbance (PSQI), dyspnea (CDS), cough (VAS), and cognitive dysfunction (FACT-Cog). QOL will also be measured repeatedly by FACT-L. The above variables will be measured using reliable and valid instruments. All instruments will be built in to an electronic measurement system. In addition, demographic, disease/treatment, and life-style variables will be collected using self-developed questionnaires. Survival status will be monitored every month until death or the end of the study. This study will not cause any physical or psychological risk to patients. Data will be analyzed using Latent Growth Mixture Modeling (LGMM), chi-squares, and Cox proportional-hazards regression model. All analyses will be carried out using SPSS 21.0 and Mplus 7.0.

Project ID:PC10701-0275
External Project ID:MOST105-2314-B182-060-MY3