A matched case-control study on Escherichia coli factors contributing to sepsis and septic shock in bacteraemic patients

Abstract

Background: One third of patients with Escherichia coli bacteraemia develop a dysregulated inflammatory response (sepsis/septic shock). Our objective was to investigate whether specific microbiological determinants of E. coli are associated to presentation with sepsis/shock.

Methods: A matched case-control study was performed; 101 case-patients with E. coli bacteraemia presenting with sepsis (SEPSIS-3 criteria) and 101 control-patients with E. coli bacteraemia without sepsis were matched by service, sex, age, Charlson index, acquisition and source of the bacteraemia and empirical treatment. Whole genome sequencing of E. coli isolates was performed (Illumina MiSeq Inc.). Sequence type, serotype, fimH type, virulence factors, antibiotic resistance genes, plasmid replicons pathogenicity islands and prophages were determined. A multivariate model was built for presentation with sepsis/septic shock using conditional logistic regression. The predictive capacity on the observed data was measured with the area under the ROC curve (AUROC) with 95% confidence intervals (CI).

Results: Here we show that in the multivariate model (adjusted OR; 95% CI), the ST69 clone (7.53; 1.06-35.05) and pic gene (4.38; 1.53-12.54) are associated to presentation with sepsis/shock, while the genes papC (0.30; 0.12-0.74) and fdeC (0.18; 0.03-1.32) show a protective effect. The AUROC of this model is 0.81 (95% CI 0.74-0.87).

Conclusions: We identify E. coli bacterial factors associated with severe clinical presentation in patients with bacteraemia. Further studies would be needed to consider these factors as potential preventive or therapeutic targets.

Fig. 1. Phylogenomic reconstruction of bacteraemic isolates of Escherichia coli from patients presenting as sepsis/septic shock (cases) and without sepsis (controls).

Phylogenomic reconstruction of case-control Escherichia coli isolates based on a 99% core-genome alignment. The maximum likelihood (ML) phylogenomic tree based on 1000 rapid bootstrap inferences (GTR substitution model) was obtained with RAxML. Then, a recombination-free ML tree was generated with ClonalFrameML and visualised with the Tree of Life interactive tool (iTOL). Sequence types and phylogroups, represented in colored square boxes, respectively. Isolates collected from ‘control’ patients are labelled with a brown star, while those from ‘case’ patients are labelled with a yellow star.

Fig. 2. Clustering of Escherichia coli isolates based on the virulence genome.

Principal coordinate analysis (PCoA) using a Euclidean distance matrix of all virulence genes shown in two dimensions. In total 32.4% of the variation between isolates was explained by the first two axes. Virulence clusters were identified using K-means clustering, with ellipses covering 95% of isolates in each cluster.

Fig. 3. Performance of machine learning models for classifying septic shock using different sets of bacterial genomic predictors.

Boxplots (median, interquartile range [IQR], whiskers = 1.5 × IQR and outliers) showing the test-set performance of random forest models trained to classify sepsis/septic shock in patients with Escherichia coli bacteraemia. Each model is based on a different combination of bacterial genomic predictors. The plot displays the distribution of area under the receiver operating characteristic curve (AUROC) across 1000 random splits of the study population into training and test sets. The dashed line indicates the AUROC = 0.50 (random performance). Model 1: feature-selected predictors (genes or elements) (n = 11) from the full set of 225 available predictors, including virulence genes, pathogenicity islands, sequence types, phylogroups, plasmids and resistance genes. Model 2: feature-selected predictors (n = 7) from virulence genes and pathogenicity islands (subset of n = 109). Model 3: virulence clusters. Model 4: sequence types. Model 5: phylogroups. Model 6: plasmid types. Model 7: resistance genes and mutations. Model 8: all virulence genes and pathogenicity islands (n = 109). Model 9: all bacterial genomic predictors (n = 225).