A score prediction model for predicting the heterogeneity symptom trajectories among lung cancer patients during perioperative period: a longitudinal observational study.

ABSTRACT:

Introduction

Patients undergoing video-assisted thoracoscopic surgery (VATs) for lung cancer (LC) frequently experience prolonged symptoms that can significantly affect their quality of life (QoL).

Patients and methods

This study employed a longitudinal observational design. The MDASI and QLQ-C30 were utilized to evaluate symptoms and QoL one day before surgery, as well as at 1 day, 2 weeks, and 1, 2, and 3 months post-surgery. Latent class growth modeling (LCGM) was employed to identify heterogeneous trajectories. By Logistic regression analysis, a score prediction model was developed based on predictive factors, which was internally validated utilizing 1000 bootstrap samples. The SHaply Additive Explanations (SHAP) was used to calculating the contribution of each factor.

SUBMITTER: Yang Y 

PROVIDER: S-EPMC11934189 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature