Microbial traits have minimal power for predicting death from bloodstream infection

Bloodstream infection (BSI) is a life-threatening condition where the risk of dying is affected by both patient- and pathogen-related factors. Certain microbial traits (e.g., virulence genes) are thought to affect survival rates from BSIs, but the magnitude of their impact on human mortality remains unclear. To address this, we analyzed the microbial genomes, quantitative proteomes, and electronic medical records (EMRs) from 35,746 BSIs from Calgary, Canada (2006-2022). We show that EMR-based machine learning models accurately predict 30-day mortality (AUC=0.83), but surprisingly, integrating

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