Dataset Information

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

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?

SUBMITTER: MacFadden DR

PROVIDER: S-EPMC6535602 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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

MacFadden Derek R DR Melano Roberto G RG Coburn Bryan B Tijet Nathalie N Hanage William P WP Daneman Nick N

Journal of clinical microbiology 20190524 6

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 <i>Escherichia coli</i> isolates recovered from separate episodes of bacteremia at a sing ...[more]

PMID: 30894438

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Project description:BackgroundEscherichia coli is an important pathogen in humans and is the most common cause of bacterial bloodstream infections (BSIs). The objectives of our study were to determine factors associated with E. coli BSI incidence rate and third-generation cephalosporin resistance in a multinational population-based cohort.MethodsWe included all incident E. coli BSIs (2014-2018) from national (Finland) and regional (Australia [Canberra], Sweden [Skaraborg], and Canada [Calgary, Sherbrooke, and western interior]) surveillance. Incidence rates were directly age and sex standardized to the European Union 28-country 2018 population. Multivariable negative binomial and logistic regression models estimated factors significantly associated with E. coli BSI incidence rate and third-generation cephalosporin resistance, respectively. The explanatory variables considered for inclusion in both models were year (2014-2018), region (six areas), age (< 70-years-old and ≥ 70-years-old), and sex (female and male).ResultsWe identified 31,889 E. coli BSIs from 40.7 million person-years of surveillance. Overall and third-generation cephalosporin-resistant standardized rates were 87.1 and 6.6 cases/100,000 person-years, respectively, and increased 14.0% and 40.1% over the five-year study. Overall, 7.8% (2483/31889) of E. coli BSIs were third-generation cephalosporin-resistant. Calgary, Canberra, Sherbrooke, and western interior had significantly lower E. coli BSI rates compared to Finland. The significant association between age and E. coli BSI rate varied with sex. Calgary, Canberra, and western interior had significantly greater odds of third-generation cephalosporin-resistant E. coli BSIs compared to Finland. Compared to 2014, the odds of third-generation cephalosporin-resistant E. coli BSIs were significantly increased in 2016, 2017, and 2018. The significant association between age and the odds of having a third-generation cephalosporin-resistant E. coli BSI varied with sex.ConclusionsIncreases in overall and third-generation cephalosporin-resistant standardized E. coli BSI rates were clinically important. Overall, E. coli BSI incidence rates were 40-104% greater than previous investigations from the same study areas. Region, sex, and age are important variables when analyzing E. coli BSI rates and third-generation cephalosporin resistance in E. coli BSIs. Considering E. coli is the most common cause of BSIs, this increasing burden and evolving third-generation cephalosporin resistance will have an important impact on human health, especially in aging populations.

Project description:A 7.8-kbp fragment of chromosomal DNA from a region controlling multiple antibiotic resistance (Mar) in Escherichia coli has been sequenced. Within the fragment is a potential divergent promoter region including marO, which contains two pairs of direct repeats, suggesting possible operator-regulatory sites. To the left of marO (region I) are one or two transcriptional units with three putative open reading frames (ORFs) encoding 64, 157, and 70 amino acids. To the right (region II) is a transcriptional unit containing three putative ORFs (ORF125/144, ORF129, and ORF72). Of six independent Mar mutants, four had mutations within the ORF encoding the first putative protein (ORF125/144) downstream of marO, including three different single-point mutations and an IS2 insertion. One of the other mutations occurred in marO (20-bp duplication), and the other occurred in a site in marO or ORF144 (a 1-bp change). All six mutations led to increased transcription of the region II transcript. High-copy-number plasmids containing marO and the adjacent ORF125/144 region from a wild-type source but not from a Mar mutant reduced the antibiotic resistance of a Mar mutant to levels comparable to those of wild-type cells. High-copy-number plasmids containing wild-type marO alone caused an increase in resistance to tetracycline, chloramphenicol, and norfloxacin in a wild-type strain. The nature of the Mar mutations and the results of the complementation studies suggest that ORF125/144 encodes a repressor (designated MarR) which acts at marO. The second ORF (ORF129), designated marA, would encode a protein, MarA, whose sequence shows strong similarity to those of a family of positive transcriptional regulators. A Tn5 insertion in marA inactivated the multiresistance phenotype of Mar mutants. The function of ORF72, designated marB, encoding the third putative protein in the operon, and that of other ORFs detected within the 7.8-kb fragment have not yet been determined.

Project description:BackgroundEscherichia coli is the most common cause of bloodstream infections (BSIs) and mortality is an important aspect of burden of disease. Using a multinational population-based cohort of E. coli BSIs, our objectives were to evaluate 30-day case fatality risk and mortality rate, and determine factors associated with each.MethodsDuring 2014-2018, we identified 30-day deaths from all incident E. coli BSIs from surveillance nationally in Finland, and regionally in Sweden (Skaraborg) and Canada (Calgary, Sherbrooke, western interior). We used a multivariable logistic regression model to estimate factors associated with 30-day case fatality risk. The explanatory variables considered for inclusion were year (2014-2018), region (five areas), age (< 70-years-old, ≥70-years-old), sex (female, male), third-generation cephalosporin (3GC) resistance (susceptible, resistant), and location of onset (community-onset, hospital-onset). The European Union 28-country 2018 population was used to directly age and sex standardize mortality rates. We used a multivariable Poisson model to estimate factors associated with mortality rate, and year, region, age and sex were considered for inclusion.ResultsFrom 38.7 million person-years of surveillance, we identified 2961 30-day deaths in 30,923 incident E. coli BSIs. The overall 30-day case fatality risk was 9.6% (2961/30923). Calgary, Skaraborg, and western interior had significantly increased odds of 30-day mortality compared to Finland. Hospital-onset and 3GC-resistant E. coli BSIs had significantly increased odds of mortality compared to community-onset and 3GC-susceptible. The significant association between age and odds of mortality varied with sex, and contrasts were used to interpret this interaction relationship. The overall standardized 30-day mortality rate was 8.5 deaths/100,000 person-years. Sherbrooke had a significantly lower 30-day mortality rate compared to Finland. Patients that were either ≥70-years-old or male both experienced significantly higher mortality rates than those < 70-years-old or female.ConclusionsIn our study populations, region, age, and sex were significantly associated with both 30-day case fatality risk and mortality rate. Additionally, 3GC resistance and location of onset were significantly associated with 30-day case fatality risk. Escherichia coli BSIs caused a considerable burden of disease from 30-day mortality. When analyzing population-based mortality data, it is important to explore mortality through two lenses, mortality rate and case fatality risk.

Dataset Information

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

Publications

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

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