Composition-aware and leakage-controlled sparse log-ratio learning for mortality classification from respiratory microbiome profiles.

BACKGROUND: Respiratory microbiome profiles contain compositional information that may complement clinical variables for mortality classification in hospitalized patients with COVID-19. However, microbiome-based classifiers remain vulnerable to compositional artifacts, patient-level leakage, and cohort-specific feature construction. OBJECTIVE: To develop and externally evaluate a leakage-controlled sparse log-ratio learning framework for mortality classification using respiratory microbiome profiles. METHODS: This retrospective secondary analysis used three public COVID-19 respiratory microbiome cohorts. The Sparse Log-Ratio Risk Network (SLRRN) was developed using patient-level nested validation, with genus filtering, zero replacement, balance construction, model tuning, and threshold selection restricted to training partitions. The model learned 16 signed genus balances and integrated four clinical covariates available at the first eligible respiratory sampling landmark. The final specification and threshold were frozen before external evaluation. RESULTS: The development cohort included 82 patients (20 deaths) from PRJNA678105 and PRJNA683617, and frozen external evaluation included 76 independent patients (38 fatal and 38 severe nonfatal cases) from PRJNA726205. Internal AUROC was 0.838 (95% CI, 0.765-0.911), and AUPRC was 0.674 (0.577-0.771). The paired AUROC difference versus XGBoost was 0.041 (0.009-0.073). Without refitting or threshold revision, external AUROC was 0.821 (0.738-0.904), with sensitivity of 0.816 and specificity of 0.842 at the frozen threshold of 0.37. In the outcome-enriched external cohort, the Brier score was 0.166 and the ECE was 0.065; these estimates describe the selected study sample. CONCLUSION: SLRRN provides a composition-aware and leakage-controlled framework for respiratory microbiome-based mortality classification. The findings support further prospective evaluation with standardized sampling and direct comparison with established clinical models before clinical translation.