Genomic virulence markers are associated with severe outcomes in patients with Pseudomonas aeruginosa bloodstream infection.

Background: Pseudomonas aeruginosa (PA) bloodstream infection (BSI) is a common healthcare-associated complication linked to antimicrobial resistance and high mortality. Ongoing clinical trials are exploring novel anti-virulence agents, yet studies on how bacterial virulence affects PA infection outcomes is conflicting and data from real-world clinical populations is limited.

Methods: We studied a multicentre cohort of 773 adult patients with PA BSI consecutively collected during 7-years from sites in Europe and Australia.

Predicting sepsis onset using a machine learned causal probabilistic network algorithm based on electronic health records data.

Sepsis is a leading cause of mortality and early identification improves survival. With increasing digitalization of health care data automated sepsis prediction models hold promise to aid in prompt recognition. Most previous studies have focused on the intensive care unit (ICU) setting.