Abstract / Summary
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 >70,000 microbial genes/proteins does not improve this performance. We demonstrate that microbial factors co-segregate with specific patient cohorts and are therefore not independent predictors of risk. We conclude that microbial virulence factors have minimal impact on mortality rates from BSIs.