Leveraging Longitudinal Patient-Reported Outcomes Trajectories to Predict Survival in Non-Small-Cell Lung Cancer
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Purpose
Despite their potential, patient-reported outcomes (PROs) are often underutilized in clinical decision-making, especially when improvements in PROs do not align with clinical outcomes. This misalignment may result from insufficient analytical methods that overlook the temporal dynamics and substantial variability of PROs data. To address these gaps, we developed a novel approach to investigate the prognostic value of longitudinal PRO dynamics in non-small-cell lung cancer (NSCLC) using Lung Cancer Symptom Scale (LCSS) data.
Methods
Longitudinal patient-reported LCSS data from 481 NSCLC participants in the placebo arm of a Phase III trial were analyzed. A population modeling approach was applied to describe PRO progression trajectories while accounting for substantial variability in the data. Associations between PRO model parameters and survival outcomes were assessed using Cox proportional hazards models. Model-informed PRO parameters were further used to predict survival via machine learning.
Results
A PRO progression model described LCSS dynamics and predicted a median time to symptom progression of 229 days (95% CI: 15-583). Faster PRO progression rates were significantly associated with poorer survival (HR 1.13, 95% CI: 1.076-1.18), while greater placebo/prior treatment effects correlated with improved survival (HR 0.93, 95% CI: 0.883-0.99). A machine learning model using PRO parameters achieved an AUC-ROC of 0.78, demonstrating their potential to predict overall survival.
Conclusions
This study demonstrates that longitudinal PRO data can provide prognostic insights into survival in NSCLC. The findings support the use of PRO dynamics to improve clinical decision-making and optimize patient-centered treatment strategies.
Translational Relevance
Patient-reported outcomes (PROs) offer a patient-centered, non-invasive approach to assessing health status and symptoms, making them a valuable tool for understanding the patient experience. However, PROs are often underutilized in clinical decision-making, especially when improvements in PROs do not align with clinical outcomes. This misalignment is often driven by analytical methods that fail to account for the temporal dynamics and inherent variability of PROs data. To address these limitations, we have developed a novel computational framework that integrates population modeling with machine learning to leverage longitudinal PRO dynamics for predicting survival in non-small-cell lung cancer patients. This innovative approach can be generalized to other PRO datasets, providing personalized insights into PRO trajectories for each patient. By enhancing the utility of PROs data, our framework holds the potential to significantly improve clinical decision-making, refine patient care strategies, and optimize the design and evaluation of clinical trials, ultimately advancing precision oncology.
