Evaluating Conformal Reliability of Pathway-Level Transcriptomic Signatures Under Cross-Cohort Shift in Sepsis Mortality Prediction

Blood transcriptomic profiling enables prognostic modeling by capturing the host immune response at the molecular level. Yet, the within-cohort evaluation strategies employed by many transcriptomic models inadequately reflect deployment across independent hospitals. Outside deployment scenarios introduce a cohort shift that can substantially degrade predictive performance and reliability of uncertainty estimates. We present a framework for evaluating transcriptomic sepsis mortality prediction under realistic cross-cohort deployment, systematically comparing gene-level, pathway-level and hybrid molecular representations. Four publicly available whole-blood transcriptomic cohorts consisting of 936 patients and 248 mortality events were harmonized into a shared 7,660-gene feature space and evaluated under leave-one-cohort-out validation using logistic regression, random forests, XGBoost and LightGBM. Beyond AUROC and AUPRC, model behavior was evaluated via conformal prediction, calibration analysis, selective prediction and the proposed Pathway Stability Index. Gene-level and hybrid representations were found to generally achieve the strongest discriminative performance, whereas pathway-level representations exhibited greater robustness across model families, more reliable uncertainty behavior under cross-cohort shift and stable molecular signatures enriched for immune and host-defense processes identified through Gene Ontology and KEGG enrichment analyses. These findings demonstrate that molecular representation influences not only predictive discrimination but also calibration, uncertainty reliability, biological coherence and transferability under external validation.