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Objectives: This study aimed to determine the association of Escherichia coli microbiological factors with 30-day mortality in patients with bloodstream infection (BSI) 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)., Discussion: Despite having a modest overall contribution, some microbiological factors were associated with increased odds of death and deserve to be studied as potential therapeutic or preventive targets., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Objective: To validate the unsupervised cluster model (USCM) developed during the first pandemic wave in a cohort of critically ill patients from the second and third pandemic waves., Design: Observational, retrospective, multicentre study., Setting: Intensive Care Unit (ICU)., Patients: Adult patients admitted with COVID-19 and respiratory failure during the second and third pandemic waves., Interventions: None., Main Variables of Interest: Collected data included demographic and clinical characteristics, comorbidities, laboratory tests and ICU outcomes. To validate our original USCM, we assigned a phenotype to each patient of the validation cohort. The performance of the classification was determined by Silhouette coefficient (SC) and general linear modelling. In a post-hoc analysis we developed and validated a USCM specific to the validation set. The model's performance was measured using accuracy test and area under curve (AUC) ROC., Results: A total of 2330 patients (mean age 63 [53-82] years, 1643 (70.5%) male, median APACHE II score (12 [9-16]) and SOFA score (4 [3-6]) were included. The ICU mortality was 27.2%. The USCM classified patients into 3 clinical phenotypes: A (n = 1206 patients, 51.8%); B (n = 618 patients, 26.5%), and C (n = 506 patients, 21.7%). The characteristics of patients within each phenotype were significantly different from the original population. The SC was -0.007 and the inclusion of phenotype classification in a regression model did not improve the model performance (0.79 and 0.78 ROC for original and validation model). The post-hoc model performed better than the validation model (SC -0.08)., Conclusion: Models developed using machine learning techniques during the first pandemic wave cannot be applied with adequate performance to patients admitted in subsequent waves without prior validation., (Copyright © 2024 Elsevier España, S.L.U. and SEMICYUC. All rights reserved.)