Development and validation of a nomogram for predicting heterogeneous patient-reported outcome recovery trajectories after lung cancer surgery: a retrospective study

Postoperative recovery in lung cancer patients is heterogeneous, yet traditional assessments often fail to capture this variability. This study aimed to identify distinct patient-reported outcome (PRO) recovery trajectories for core postoperative symptoms, validate them against objective pulmonary function, and develop a preoperative nomogram to predict unfavorable recovery. A single-center retrospective cohort study was conducted including 528 patients who underwent minimally invasive surgery for non-small cell lung cancer. Longitudinal PROs (cough, dyspnea, pain, fatigue) were collected over four postoperative weeks via a digital platform. Growth mixture modeling (GMM) was used to identify recovery trajectories, which were dichotomized into favorable and unfavorable groups. The trajectories were validated against the postoperative change in forced expiratory volume in 1 s (△FEV1%). A predictive nomogram was developed using LASSO regression and multivariable logistic regression, with internal validation via bootstrapping. GMM identified three distinct trajectories per symptom, consistently including a persistent high-symptom group. The composite unfavorable trajectory group showed significantly worse △FEV1% than the favorable group (5.2% vs. 12.8%, p < 0.001). The final nomogram incorporated five predictors: age, Charlson Comorbidity Index, preoperative handgrip strength, preoperative depression/anxiety, and extent of resection. The model demonstrated excellent discrimination (C-index = 0.845, bootstrap-corrected 0.832), good calibration, and clinical utility across a wide threshold range. This study confirms the heterogeneity of postoperative recovery and provides a validated, easy-to-use nomogram for preoperative prediction of unfavorable PRO recovery trajectories. The tool may facilitate early risk stratification and personalized interventions to improve recovery outcomes.