Bacteraemia due to non-ESBL-producing Escherichia coli O25b:H4 sequence type 131: insights into risk factors, clinical features and outcomes

2017 ◽  
Vol 49 (4) ◽  
pp. 498-502 ◽  
Author(s):  
Isabel Morales-Barroso ◽  
Lorena López-Cerero ◽  
José Molina ◽  
Mar Bellido ◽  
María Dolores Navarro ◽  
...  
Keyword(s):  
Risk Factors ◽  
Escherichia Coli ◽  
Clinical Features ◽  
Sequence Type

scholarly journals The higher prevalence of extended spectrum beta-lactamases among Escherichia coli ST131 in Southeast Asia is driven by expansion of a single, locally prevalent subclone

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Swaine L. Chen ◽  
Ying Ding ◽  
Anucha Apisarnthanarak ◽  
Shirin Kalimuddin ◽  
Sophia Archuleta ◽  
...  
Keyword(s):  
Risk Factors ◽  
Escherichia Coli ◽  
Antibiotic Resistance ◽  
South America ◽  
Southeast Asia ◽  
Sequence Type ◽  
Beta Lactamases ◽  
Extended Spectrum Beta Lactamases ◽  
Mlst Type ◽  
Excess Resistance

Abstract The ST131 multilocus sequence type (MLST) of Escherichia coli is a globally successful pathogen whose dissemination is increasing rates of antibiotic resistance. Numerous global surveys have demonstrated the pervasiveness of this clone; in some regions ST131 accounts for up to 30% of all E. coli isolates. However, many regions are underrepresented in these published surveys, including Africa, South America, and Asia. We collected consecutive bloodstream E. coli isolates from three countries in Southeast Asia; ST131 was the most common MLST type. As in other studies, the C2/H30Rx clade accounted for the majority of ST131 strains. Clinical risk factors were similar to other reported studies. However, we found that nearly all of the C2 strains in this study were closely related, forming what we denote the SEA-C2 clone. The SEA-C2 clone is enriched for strains from Asia, particularly Southeast Asia and Singapore. The SEA-C2 clone accounts for all of the excess resistance and virulence of ST131 relative to non-ST131 E. coli. The SEA-C2 strains appear to be locally circulating and dominant in Southeast Asia, despite the intuition that high international connectivity and travel would enable frequent opportunities for other strains to establish themselves.

scholarly journals Self-reported risk factors for having Escherichia coli sequence type 131 or its H30 subclone among US Veterans with a clinical E. coli isolate

2018 ◽  
Vol 147 ◽  
Author(s):  
Amee R. Manges ◽  
Paul Thuras ◽  
Stephen Porter ◽  
James R. Johnson
Keyword(s):  
Risk Factors ◽  
Escherichia Coli ◽  
Military Veterans ◽  
Meat Consumption ◽  
Wild Animal ◽  
Sequence Type ◽  
E Coli ◽  
Us Military ◽  
Animal Contact ◽  
Healthcare Associated

AbstractAmong 469 US military veterans with an Escherichia coli clinical isolate (2012–2013), we explored healthcare and non-healthcare risk factors for having E. coli sequence type 131 and its H30 subclone (ST131-H30). Overall, 66 (14%) isolates were ST131; 51 (77%) of these were ST131-H30. After adjustment for healthcare-associated factors, ST131 remained positively associated with medical lines and nursing home residence. After adjustment for environmental factors, ST131 remained associated with wild animal contact (positive), meat consumption (negative) and pet cat exposure (negative). Thus, ST131 was associated predominantly with healthcare-associated exposures, while non-ST131 E. coli were associated with some environmental exposures.

scholarly journals Long-Term Care Facilities Are Reservoirs for Antimicrobial-Resistant Sequence Type 131 Escherichia coli

2015 ◽  
Vol 2 (1) ◽  
Author(s):  
Mary J. Burgess ◽  
James R. Johnson ◽  
Stephen B. Porter ◽  
Brian Johnston ◽  
Connie Clabots ◽  
...  
Keyword(s):  
Risk Factors ◽  
Escherichia Coli ◽  
Gel Electrophoresis ◽  
Long Term Care ◽  
Pulsed Field Gel Electrophoresis ◽  
Sequence Type ◽  
Term Care ◽  
E Coli ◽  
Care Facilities

Abstract Background.  Emerging data implicate long-term care facilities (LTCFs) as reservoirs of fluoroquinolone-resistant (FQ-R) Escherichia coli of sequence type 131 (ST131). We screened for ST131 among LTCF residents, characterized isolates molecularly, and identified risk factors for colonization. Methods.  We conducted a cross-sectional study using a single perianal swab or stool sample per resident in 2 LTCFs in Olmsted County, Minnesota, from April to July 2013. Confirmed FQ-R E. coli isolates underwent polymerase chain reaction-based phylotyping, detection of ST131 and its H30 and H30-Rx subclones, extended virulence genotyping, and pulsed-field gel electrophoresis (PFGE) analysis. Epidemiological data were collected from medical records. Results.  Of 133 fecal samples, 33 (25%) yielded FQ-R E. coli, 32 (97%) of which were ST131. The overall proportion with ST131 intestinal colonization was 32 of 133 (24%), which differed by facility: 17 of 41 (42%) in facility 1 vs 15 of 92 (16%) in facility 2 (P = .002). All ST131 isolates represented the H30 subclone, with virulence gene and PFGE profiles resembling those of previously described ST131 clinical isolates. By PFGE, certain isolates clustered both within and across LTCFs. Multivariable predictors of ST131 colonization included inability to sign consent (odds ratio [OR], 4.16 [P = .005]), decubitus ulcer (OR, 4.87 [ P = .04]), and fecal incontinence (OR, 2.59 [P = .06]). Conclusions.  Approximately one fourth of LTCF residents carried FQ-R ST131 E. coli resembling ST131 clinical isolates. Pulsed-field gel electrophoresis suggested intra- and interfacility transmission. The identified risk factors suggest that LTCF residents who require increased nursing care are at greatest risk for ST131 colonization, possibly due to healthcare-associated transmission.

scholarly journals Comparing Patient Risk Factor-, Sequence Type-, and Resistance Locus Identification-Based Approaches for Predicting Antibiotic Resistance in Escherichia coli Bloodstream Infections

2019 ◽  
Vol 57 (6) ◽  
Author(s):  
Derek R. MacFadden ◽  
Roberto G. Melano ◽  
Bryan Coburn ◽  
Nathalie Tijet ◽  
William P. Hanage ◽  
...  
Keyword(s):  
Risk Factors ◽  
Escherichia Coli ◽  
Antibiotic Resistance ◽  
Risk Factor ◽  
Resistance Gene ◽  
Sequence Type ◽  
Gene Identification ◽  
Data Set ◽  
Epidemiologic Risk ◽  
Epidemiologic Risk Factor

ABSTRACT Rapid diagnostic tests for antibiotic resistance that identify the presence or absence of antibiotic resistance genes/loci are increasingly being developed. However, these approaches usually neglect other sources of predictive information which could be identified over shorter time periods, including patient epidemiologic risk factors for antibiotic resistance and markers of lineage. Using a data set of 414 Escherichia coli isolates recovered from separate episodes of bacteremia at a single academic institution in Toronto, Ontario, Canada, between 2010 and 2015, we compared the potential predictive ability of three approaches (epidemiologic risk factor-, pathogen sequence type [ST]-, and resistance gene identification-based approaches) for classifying phenotypic resistance to three antibiotics representing classes of broad-spectrum antimicrobial therapy (ceftriaxone [a 3rd-generation cephalosporin], ciprofloxacin [a fluoroquinolone], and gentamicin [an aminoglycoside]). We used logistic regression models to generate model receiver operating characteristic (ROC) curves. Predictive discrimination was measured using apparent and corrected (bootstrapped) areas under the curves (AUCs). Epidemiologic risk factor-based models based on two simple risk factors (prior antibiotic exposure and recent prior susceptibility of Gram-negative bacteria) provided a modest predictive discrimination, with AUCs ranging from 0.65 to 0.74. Sequence type-based models demonstrated strong discrimination (AUCs, 0.83 to 0.94) across all three antibiotic classes. The addition of epidemiologic risk factors to sequence type significantly improved the ability to predict resistance for all antibiotics (P < 0.05). Resistance gene identification-based approaches provided the highest degree of discrimination (AUCs, 0.88 to 0.99), with no statistically significant benefit being achieved by adding the patient epidemiologic predictors. In summary, sequence type or other lineage-based approaches could produce an excellent discrimination of antibiotic resistance and may be improved by incorporating readily available patient epidemiologic predictors but are less discriminatory than identification of the presence of known resistance loci.

scholarly journals 1165. Comparing Patient Risk Factors, Sequence Type, and Resistance Loci Identification Approaches for Predicting Antibiotic Resistance in Escherichia coli Bloodstream Infections

2018 ◽  
Vol 5 (suppl_1) ◽  
pp. S351-S351
Author(s):  
Derek MacFadden ◽  
Roberto Melano ◽  
Nathalie Tijet ◽  
William P Hanage ◽  
Nick Daneman
Keyword(s):  
Risk Factors ◽  
Escherichia Coli ◽  
Antibiotic Resistance ◽  
Antibiotic Therapy ◽  
Antibiotic Resistance Genes ◽  
Bloodstream Infections ◽  
Sequence Type ◽  
Empiric Antibiotic Therapy ◽  
Gene Identification ◽  
Epidemiologic Risk

Abstract Background To improve the adequacy of empiric antibiotic therapy, an important predictor of clinical outcome, rapid diagnostic tests of antibiotic resistance are increasingly being developed that identify the presence or absence of antibiotic resistance genes/Loci. Few approaches have utilized other sources of predictive information, which could be identified in shorter time periods, including patient epidemiologic risk factors for antibiotic resistance and markers of lineage (e.g., sequence type). Methods Using a dataset of 414 Escherichia coli isolated from separate episodes of bacteremia at a single academic institution in Toronto, Canada between 2010 and 2015, we compared the potential predictive ability of three approaches (epidemiologic, sequence type, and gene identification) for classifying antibiotic resistance to three commonly used classes of broad-spectrum antibiotic therapy (third-generation cephalosporins, fluoroquinolones, and aminoglycosides). We used logistic regression models with binary predictor variables to generate model receiver operating characteristic curves. Predictive discrimination was measured using apparent and corrected (bootstrapped) area under the curves (AUCs). Results Using two simple epidemiologic risk factors (prior antibiotic exposure and recent prior Gram-negative susceptibility), modest predictive discrimination was achieved (AUCs 0.65–0.74). Sequence type demonstrated strong discrimination (AUCs 0.84–0.94) across all three antibiotic classes. Epidemiologic risk factors significantly improved sequence-type prediction for cephalosporins and aminoglycosides (P &lt; 0.05). Gene identification approaches provided the highest degree of discrimination (AUCs 0.73–0.99), with no statistically significant benefit of adding epidemiologic predictors. Conclusion Rapid identification of sequence type, or other lineage-based classification, could produce excellent discrimination of antibiotic resistance, and may be improved by incorporating readily available epidemiologic predictors. Disclosures All authors: No reported disclosures.

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