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Association of microbiological factors with mortality in Escherichia coli bacteraemia presenting with sepsis/septic shock: A prospective cohort study.
Clinical Microbiology and Infection 2024 April 8
OBJECTIVES: This study aimed to determine the association of E. coli microbiological factors with 30-day mortality in BSI patients presenting with a dysregulated response to infection (i.e., sepsis or septic shock).
METHODS: Whole genome sequencing was performed on 224 E. coli isolates of patients with sepsis/septic shock, from 22 Spanish hospitals. Phylogroup, sequence type, virulence, antibiotic resistance and pathogenicity islands were assessed. A multivariable model for 30-day mortality including clinical and epidemiological variables was built, to which microbiological variables were hierarchically added. The predictive capacity of the models was estimated by the area under the receiver operating characteristic curve (AUROC) with 95% confidence intervals (CI).
RESULTS: Mortality at day 30 was 31% (69 patients). The clinical model for mortality included (adjusted OR; 95% CI) age (1.04; 1.02-1.07), Charlson index ≥3 (1.78; 0.95-3.32), urinary BSI source (0.30; 0.16-0.57) and active empirical treatment (0.36; 0.11-1.14) with an AUROC of 0.73 (95% CI, 0.67-0.80). Addition of microbiological factors selected clone ST95 (3.64; 0.94-14.04), eilA gene (2.62; 1.14-6.02) and astA gene (2.39; 0.87-6.59) as associated with mortality, with an AUROC of 0.76 (0.69-0.82).
CONCLUSIONS: Despite having a modest overall contribution, some microbiological factors were associated with increased odds of death and would deserve being studied as potential therapeutic or preventive targets.
METHODS: Whole genome sequencing was performed on 224 E. coli isolates of patients with sepsis/septic shock, from 22 Spanish hospitals. Phylogroup, sequence type, virulence, antibiotic resistance and pathogenicity islands were assessed. A multivariable model for 30-day mortality including clinical and epidemiological variables was built, to which microbiological variables were hierarchically added. The predictive capacity of the models was estimated by the area under the receiver operating characteristic curve (AUROC) with 95% confidence intervals (CI).
RESULTS: Mortality at day 30 was 31% (69 patients). The clinical model for mortality included (adjusted OR; 95% CI) age (1.04; 1.02-1.07), Charlson index ≥3 (1.78; 0.95-3.32), urinary BSI source (0.30; 0.16-0.57) and active empirical treatment (0.36; 0.11-1.14) with an AUROC of 0.73 (95% CI, 0.67-0.80). Addition of microbiological factors selected clone ST95 (3.64; 0.94-14.04), eilA gene (2.62; 1.14-6.02) and astA gene (2.39; 0.87-6.59) as associated with mortality, with an AUROC of 0.76 (0.69-0.82).
CONCLUSIONS: Despite having a modest overall contribution, some microbiological factors were associated with increased odds of death and would deserve being studied as potential therapeutic or preventive targets.
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