Dataset Information

Predicting Antibiotic Resistance in Gram-Negative Bacilli from Resistance Genes.

ABSTRACT: We developed a rapid high-throughput PCR test and evaluated highly antibiotic-resistant clinical isolates of Escherichia coli (n?= 2,919), Klebsiella pneumoniae (n?= 1,974), Proteus mirabilis (n?= 1,150), and Pseudomonas aeruginosa (n?= 1,484) for several antibiotic resistance genes for comparison with phenotypic resistance across penicillins, cephalosporins, carbapenems, aminoglycosides, trimethoprim-sulfamethoxazole, fluoroquinolones, and macrolides. The isolates originated from hospitals in North America (34%), Europe (23%), Asia (13%), South America (12%), Africa (7%), or Oceania (1%) or were of unknown origin (9%). We developed statistical methods to predict phenotypic resistance from resistance genes for 49 antibiotic-organism combinations, including gentamicin, tobramycin, ciprofloxacin, levofloxacin, trimethoprim-sulfamethoxazole, ertapenem, imipenem, cefazolin, cefepime, cefotaxime, ceftazidime, ceftriaxone, ampicillin, and aztreonam. Average positive predictive values for genotypic prediction of phenotypic resistance were 91% for E. coli, 93% for K. pneumoniae, 87% for P. mirabilis, and 92% for P. aeruginosa across the various antibiotics for this highly resistant cohort of bacterial isolates.

SUBMITTER: Walker GT

PROVIDER: S-EPMC6496154 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Publications

Predicting Antibiotic Resistance in Gram-Negative Bacilli from Resistance Genes.

Walker G Terrance GT Quan Julia J Higgins Stephen G SG Toraskar Nikhil N Chang Weizhong W Saeed Alexander A Sapiro Vadim V Pitzer Kelsey K Whitfield Natalie N Lopansri Bert K BK Motyl Mary M Sahm Daniel D

Antimicrobial agents and chemotherapy 20190327 4

We developed a rapid high-throughput PCR test and evaluated highly antibiotic-resistant clinical isolates of Escherichia coli (n = 2,919), Klebsiella pneumoniae (n = 1,974), Proteus mirabilis (n = 1,150), and Pseudomonas aeruginosa (n = 1,484) for several antibiotic resistance genes for comparison with phenotypic resistance across penicillins, cephalosporins, carbapenems, aminoglycosides, trimethoprim-sulfamethoxazole, fluoroquinolones, and ...[more]

PMID: 30917985

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Project description:IntroductionVentilator-associated pneumonias (VAPs) are a complication of mechanical ventilation in the intensive care unit (ICU) that increase length of stay, morbidity, and mortality. While identifying and treating infections early is paramount to improving patient outcomes, more and more data demonstrate limited courses of antibiotics improve outcomes. Prolonged (10-14 day) courses of antibiotics have remained the standard of care for pneumonia due to gram-negative bacilli (GNR). We aimed to review our GNR VAPs to assess risk factors for recurrent GNR infections.MethodsWe reviewed trauma patients who developed VAP from 02/2019 through 05/2022. Demographics, injury characteristics, and outcomes were reviewed with a focus on pneumonia details including the cultured pathogen(s), antibiotic(s) used, treatment duration, and presence of recurrent infections. We then compared single episode VAPs to multiple episode VAPs among patients infected by GNRs.ResultsEleven of the fifty trauma patients admitted to the ICU suffered a VAP caused by a GNR. Of these eleven patients, six experienced a recurrent infection, four of which were caused by Pseudomonas aeruginosa and two of which were caused by Enterobacter aerogenes. Among the patients who received ten days of antibiotic treatment, half suffered a recurrence. Although, there was no difference in the microbiology or antibiotic duration between the recurrences and single episodes.ConclusionDespite prolonged use of antibiotics, we found that the risk of recurrent or persistent infections was high among patients with VAP due to GNB. Further study is needed to determine optimal treatment to minimize the risk of these recurrences.Key messageVentilator-associated pneumonia due to gram-negative bacilli is a rare but high morbidity complication in intensive care units. Despite prolonged duration of therapy, these infections still appear to account for many recurrent infections and further study into optimal therapy is warranted.

Project description:BackgroundEmerging worldwide in the past decade, there has been a significant increase in multidrug-resistant bacteria from serious nosocomial infections, especially carbapenemase-producing Gram-negative bacilli that have emerged worldwide. The objective of this study is to investigate carbapenem resistance in Gram-negative bacilli bacteria using phenotypic detection, antimicrobial resistance profiles and genotypic characterisation methods.Methods200 Gram-negative bacilli isolates were collected from different clinical specimens. All clinical samples were exposed to isolation and identification of significant pathogens applying bacteriological examination and an automated Vitek-2 system. The isolates were subjected to susceptibility tests by the Vitek-2 automated system and those isolates that were resistant to beta-lactam drugs, including carbapenems, third-generation cephalosporines or cefoxitin, were selected for phenotyping using Carba plus disc system assay for detection of carbapenemase-producing isolates. These isolates were further confirmed by molecular detection. PCR was used for the detection carbapenem-resistant genes (OXA-48, IMP, NDM, VIM, and KPC).Results110 (55%) of 200 Gram-negative bacilli were identified as beta-lactam-resistant isolates. The frequency of carbapenem-resistant isolates was calculated to be 30.9% (n = 34/110). A collection totalling 65/110 (59%) isolates were identified as carbapenemase producers by phenotypic method. Moreover, among the 65 carbapenemase-producing Gram-negative isolates with a positive phenotype-based result, 30 (46%), 20 (30%) and 18 (27%) isolates were positive for OXA-48, KPC and MBL enzymes, respectively, as well as the production of 27% of AmpC with porin loss. Tigecycline was the most effective antibiotic that affected 70% of MDR isolates, but high rates of resistance were detected to other tested antimicrobials. Of interest, a high incidence of MDR, XDR and PDR profiles were observed among all carbapenemase-producing isolates. 36% (24/65) of the tested isolates were MDR to 3 to 5 antimicrobial classes. 29% (17/65) of the recovered isolates were XDR to 6 to 7 antimicrobial classes. Alarmingly, 24% (16/65) of isolates displayed PDR to all the tested 8 antimicrobial classes. Genotype assay, including 53 phenotypically confirmed carbapenemase-producing isolates of Gram-negative bacilli, found 51(96%) isolates were harbouring one or more genes. The most common carbapenemase gene was bla NDM 83% (44/53) followed by bla OXA-48 75% (40/53), bla VIM 49% (26/53) and bla IMP 43% (23/53), while the gene bla KPC was least frequent 7% (4/53). 92% (46/51) of isolates were involved in the production of more than one carbapenemase gene.ConclusionThis study demonstrated the emergence of carbapenemase-producing Gram-negative pathogens implicated in healthcare-related infections. Accurate identification of carbapenem-resistant bacterial pathogens is essential for patient treatment, as well as the development of appropriate contamination control measures to limit the rapid spread of pathogens. Tigecycline exhibited potent antimicrobial activity against MDR, XDR and PDR-producing strains that establish a threatening alert which indicates the complex therapy of infections caused by these pathogens.

Project description:The time-to-result for culture-based microorganism recovery and phenotypic antimicrobial susceptibility testing necessitates initial use of empiric (frequently broad-spectrum) antimicrobial therapy. If the empiric therapy is not optimal, this can lead to adverse patient outcomes and contribute to increasing antibiotic resistance in pathogens. New, more rapid technologies are emerging to meet this need. Many of these are based on identifying resistance genes, rather than directly assaying resistance phenotypes, and thus require interpretation to translate the genotype into treatment recommendations. These interpretations, like other parts of clinical diagnostic workflows, are likely to be increasingly automated in the future. We set out to evaluate the two major approaches that could be amenable to automation pipelines: rules-based methods and machine learning methods. The rules-based algorithm makes predictions based upon current, curated knowledge of Enterobacteriaceae resistance genes. The machine-learning algorithm predicts resistance and susceptibility based on a model built from a training set of variably resistant isolates. As our test set, we used whole genome sequence data from 78 clinical Enterobacteriaceae isolates, previously identified to represent a variety of phenotypes, from fully-susceptible to pan-resistant strains for the antibiotics tested. We tested three antibiotic resistance determinant databases for their utility in identifying the complete resistome for each isolate. The predictions of the rules-based and machine learning algorithms for these isolates were compared to results of phenotype-based diagnostics. The rules based and machine-learning predictions achieved agreement with standard-of-care phenotypic diagnostics of 89.0 and 90.3%, respectively, across twelve antibiotic agents from six major antibiotic classes. Several sources of disagreement between the algorithms were identified. Novel variants of known resistance factors and incomplete genome assembly confounded the rules-based algorithm, resulting in predictions based on gene family, rather than on knowledge of the specific variant found. Low-frequency resistance caused errors in the machine-learning algorithm because those genes were not seen or seen infrequently in the test set. We also identified an example of variability in the phenotype-based results that led to disagreement with both genotype-based methods. Genotype-based antimicrobial susceptibility testing shows great promise as a diagnostic tool, and we outline specific research goals to further refine this methodology.

Dataset Information

Predicting Antibiotic Resistance in Gram-Negative Bacilli from Resistance Genes.

Publications

Predicting Antibiotic Resistance in Gram-Negative Bacilli from Resistance Genes.

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