Materials and Methods: Data from 51 evaluable patients who underwent talc pleurodesis between June 2019 and April 2026 were retrospectively analyzed. Clinical success was defined as the absence of recurrence at the 1-month follow-up. Baseline pleural fluid parameters and post-procedural 2-hour systemic inflammatory markers were recorded. Features were analyzed using a regularized LASSO (L1) logistic regression model under stratified 5-fold cross-validation and visualized via global TreeSHAP analysis. Unsupervised k-means clustering was additionally implemented to identify distinct patient stratification trajectories based on inflammatory kinetics.
Results: The overall success rate was 68.6% (n = 35). Although acute post-procedural shifts occurred across traditional systemic markers, univariate analyses demonstrated no linear differentiation between outcomes. However, in the LASSO multivariate framework, baseline intrapleural total protein emerged as the strongest independent predictor of success, validated by robust AUC and TreeSHAP configurations. Unsupervised k-means clustering successfully stratified patients into distinct phenotypes, revealing that a regulated, stable inflammatory kinetic profile yields significantly superior clinical efficacy compared
Conclusions: While early systemic inflammatory markers lack standalone linear predictive capacity, machine learning uncovers critical non-linear prognostic phenotypes based on acute biomarker kinetics. Baseline intrapleural total protein remains a robust local predictor, highlighting that a pre-existing exudative microenvironment, coupled with a regulated systemic inflammatory response, dictates talc
