A tabular prior-data fitted network-based prediction model integrating ferritin and total iron-binding capacity for 30-day all-cause mortality in critically ill cancer patients.

Objectives: Accurately predicting outcomes for critically ill cancer patients remains challenging. This study aimed to integrate biologically relevant iron metabolism markers into short-term mortality prediction and to develop an interpretable, externally validated model based on a tabular prior-data fitted network (TabPFN) for risk stratification in this population. Materials and Methods: We conducted a retrospective cohort study using data from Medical Information Mart for Intensive Care IV (MIMIC-IV) (training and internal validation) and eICU Collaborative Research Database (eICU-CRD) (external validation), including critically ill adult patients with cancer. Associations between iron metabolism markers (ferritin, serum iron, and total iron-binding capacity [TIBC]) and 30-day all-cause mortality were assessed using the Kaplan-Meier curves, multivariable Cox regression, and restricted cubic splines. A TabPFN-based prediction model was developed and compared with 8 other machine-learning algorithms, with the least absolute shrinkage and selection operator used for feature selection. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), calibration, and decision curve analysis. SHapley Additive exPlanation values provided model interpretability. Results: Among 1137 patients in the MIMIC-IV cohort, 293 (26.0%) died within 30 days. Elevated ferritin (hazard ratio [HR] = 1.18, 95% CI, 1.09-1.27) and decreased TIBC (HR = 0.92, 95% CI, 0.85-0.99) were independently associated with mortality and exhibited non-linear relationships. Serum iron showed no prognostic value. The TabPFN model achieved the best performance, with an AUROC of 0.865 (95% CI, 0.812-0.919) in the internal validation set and 0.772 (95% CI, 0.744-0.814) in the external validation set, along with good calibration (Brier score = 0.121) and clinical net benefit. SHapley Additive exPlanation analysis identified lymphocyte count, platelet count, lactate, and ferritin as the most influential predictors. Conclusion: Iron metabolism dysregulation-particularly altered ferritin and TIBC levels-has important prognostic value in critically ill cancer patients. By integrating these biomarkers with a TabPFN framework, this study provides an accurate, interpretable, and externally validated tool for 30-day all-cause mortality prediction that may support clinical decision-making.