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

作者：Yong Yang, Xueqi Tian, Huiling Zhou, Yichao Wang, Yifeng Gu, Qi Ao, Decai Wang, Zhiying Wang, Yabin Gong, Lijing Jiao, Ling Xu · 发表于：Annals of Medicine · 年份：2025 · DOI：10.1080/07853890.2025.2479588 · 被引用次数：7 · 研究领域：Lung Cancer Diagnosis and Treatment、Cancer survivorship and care、Enhanced Recovery After Surgery

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. RESULTS: 205 participants participated in this study. The predominant postoperative complaints included fatigue, shortness of breath, pain, and coughing. Two distinct classes of symptom trajectories were identified: 'severe group' and 'mild group'. Four independent predictors of heterogeneous symptom trajectories were used to develop a scoring model. The area under the receiver operating characteristic curve for this model was 0.742 (95% CI: 0.651-0.832). And the calibration curves demonstrated strong concordance between anticipated probability and actual data (mean absolute error: 0.033). Furthermore, the decision curve analysis (DCA) indicated higher net benefit than other four single factors. SHAP high...