Abstract
BACKGROUND: Lung cancer is among the most prevalent and lethal malignancies worldwide. Non-intubated video-assisted thoracoscopic surgery (VATS) has demonstrated advantages in reducing hospital length of stay (LOS). However, clinical practice indicates that a substantial proportion of patients still experience prolonged length of stay (PLOS). Currently, no risk prediction model exists specifically for PLOS following non-intubated VATS in lung cancer patients. This study aims to analyze clinical data to identify risk factors associated with PLOS and to develop a predictive model. METHODS: A retrospective cohort study was conducted on patients undergoing non-intubated VATS lung cancer surgery between January 2024 and June 2025 at Shandong Provincial Hospital Affiliated to Shandong First Medical University. Data were collected via the Hospital Information System (HIS) and telephone follow-up electronic questionnaires. Categorical variables were analyzed using χ(2) tests, and continuous variables were assessed with t-tests in univariate analyses. Variables with statistical significance in univariate analysis were entered into multivariable logistic regression to identify independent predictors and construct the prediction model. A nomogram was created for visualization. Model discrimination was assessed using the area under the receiver operating characteristic (ROC) curve, and calibration was evaluated with calibration plots. RESULTS: Of 742 patients analyzed, 216 had a prolonged LOS (≥8 days). PLOS was associated with significantly higher comorbidity burdens, more complex surgeries, and worse postoperative outcomes, including a greater complication rate (48.6% vs. 20.0%) than the normal LOS group (all P<0.001). Multivariable analysis identified older age [odds ratio (OR) =1.053], longer preoperative wait (OR =7.729), postoperative complications (OR =2.970), and chest tube drainage >200 mL as independent risk factors for PLOS, while body mass index (BMI) ≥30.0 kg/m(2) was protective (OR =0.043). The resulting predictive nomogram demonstrated excellent discrimination with an area under the curve (AUC) of 0.943. CONCLUSIONS: This prediction model shows robust accuracy in identifying lung cancer patients at high risk of PLOS after non-intubated VATS. It provides a theoretical basis for early identification and timely intervention by clinical staff.