Prediction of peak oxygen uptake using interpretable machine learning on routinely available preoperative assessments before lung resection.

Background: Cardiopulmonary exercise testing (CPET) is the reference standard for preoperative functional assessment before lung resection, but its use is limited by resource and practical constraints. This study developed and internally evaluated an interpretable machine learning model to estimate preoperative peak oxygen uptake (VO Methods: This single-centre retrospective study included 320 consecutive patients in South Korea who underwent preoperative treadmill CPET between April 2018 and March 2024. Thirty-three routinely available predictors-including demographic characteristics, anthropometric measures, pulmonary function results, and bioimpedance-derived indices-were used to train predictive models. Model development employed 10 repeats of 5-fold nested cross-validation. The best-performing model was interpreted using Shapley additive explanations. Potential prioritisation performance was assessed by classifying patients with VO Results: Random forest showed the best performance, with a root mean square error of 3.750±0.731, a mean absolute error of 2.901±0.599, and a coefficient of determination of 0.323±0.153, indicating moderate explanatory performance for VO Conclusions: This predictive model showed moderate performance for estimating VO