Better antimicrobial resistance data analysis and reporting in less time

Objectives: Insights about local antimicrobial resistance (AMR) levels and epidemiology are essential to guide decision-making processes in antimicrobial use. However, dedicated tools for reliable and reproducible AMR data analysis and reporting are often lacking. We aimed to compare traditional data analysis and reporting versus a new approach for reliable and reproducible AMR data analysis in a clinical setting.Methods: Ten professionals who routinely work with AMR data were provided with blood culture test results including antimicrobial susceptibility results. Participants were asked to perform a detailed AMR data analysis in a two-round process: first using their software of choice and next using our newly developed software tool. Accuracy of the results and time spent were compared between both rounds. Finally, participants rated the usability using the System Usability Scale (SUS).Results: The mean time spent on creating the AMR report reduced from 93.7 to 22.4 min (P Conclusions: This study demonstrated the significant improvement in efficiency and accuracy in standard AMR data analysis and reporting workflows through open-source software. Integrating these tools in clinical settings can democratize the access to fast and reliable insights about local microbial epidemiology and associated AMR levels. Thereby, our approach can support evidence-based decision-making processes in the use of antimicrobials

Statistical methods for automated drug susceptibility testing: Bayesian minimum inhibitory concentration prediction from growth curves

Determination of the minimum inhibitory concentration (MIC) of a drug that prevents microbial growth is an important step for managing patients with infections. In this paper we present a novel probabilistic approach that accurately estimates MICs based on a panel of multiple curves reflecting features of bacterial growth. We develop a probabilistic model for determining whether a given dilution of an antimicrobial agent is the MIC given features of the growth curves over time. Because of the potentially large collection of features, we utilize Bayesian model selection to narrow the collection of predictors to the most important variables. In addition to point estimates of MICs, we are able to provide posterior probabilities that each dilution is the MIC based on the observed growth curves. The methods are easily automated and have been incorporated into the Becton--Dickinson PHOENIX automated susceptibility system that rapidly and accurately classifies the resistance of a large number of microorganisms in clinical samples. Over seventy-five studies to date have shown this new method provides improved estimation of MICs over existing approaches.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS217 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Rapid identification of respiratory bacterial pathogens from bronchoalveolar lavage fluid in cattle by MALDI-TOF MS

Respiratory tract infections are a major health problem and indication for antimicrobial use in cattle and in humans. Currently, most antimicrobial treatments are initiated without microbiological results, holding the risk of inappropriate first intention treatment. The main reason for this empirical treatment is the long turnaround time between sampling and availability of identification and susceptibility results. Therefore the objective of the present study was to develop a rapid identification procedure for pathogenic respiratory bacteria in bronchoalveolar lavage fluid (BALf) samples from cattle by MALDI-TOF MS, omitting the cultivation step on agar plates to reduce the turnaround time between sampling and identification of pathogens. The effects of two different liquid growth media and various concentrations of bacitracin were determined to allow optimal growth of Pasteurellaceae and minimise contamination. The best procedure was validated on 100 clinical BALf samples from cattle with conventional bacterial culture as reference test. A correct identification was obtained in 73% of the samples, with 59.1% sensitivity (Se) (47.2-71.0%) and 100% specificity (Sp) (100-100%) after only 6 hours of incubation. For pure and dominant culture samples, the procedure was able to correctly identify 79.2% of the pathogens, with a sensitivity (Se) of 60.5% (45.0-76.1%) and specificity (Sp) of 100% (100-100%). In mixed culture samples, containing >= 2 clinically relevant pathogens, one pathogen could be correctly identified in 57% of the samples with 57.1%Se (38.8-75.5%) and 100% Sp (100-100%). In conclusion, MALDI-TOF MS is a promising tool for rapid pathogen identification in BALf. This new technique drastically reduces turnaround time and may be a valuable decision support tool to rationalize antimicrobial use

Use of whole genome sequencing of commensal Escherichia coli in pigs for antimicrobial resistance surveillance, United Kingdom, 2018

BackgroundSurveillance of commensal Escherichia coli, a possible reservoir of antimicrobial resistance (AMR) genes, is important as they pose a risk to human and animal health. Most surveillance activities rely on phenotypic characterisation, but whole genome sequencing (WGS) presents an alternative.AimIn this retrospective study, we tested 515 E. coli isolated from pigs to evaluate the use of WGS to predict resistance phenotype.MethodsMinimum inhibitory concentration (MIC) was determined for nine antimicrobials of clinical and veterinary importance. Deviation from wild-type, fully-susceptible MIC was assessed using European Committee on Antimicrobial Susceptibility Testing (EUCAST) epidemiological cut-off (ECOFF) values. Presence of AMR genes and mutations were determined using APHA SeqFinder. Statistical two-by-two table analysis and Cohen's kappa (k) test were applied to assess genotype and phenotype concordance.ResultsOverall, correlation of WGS with susceptibility to the nine antimicrobials was 98.9% for test specificity, and 97.5% for the positive predictive value of a test. The overall kappa score (k = 0.914) indicated AMR gene presence was highly predictive of reduced susceptibility and showed excellent correlation with MIC. However, there was variation for each antimicrobial; five showed excellent correlation; four very good and one moderate. Suggested ECOFF adjustments increased concordance between genotypic data and kappa values for four antimicrobials.ConclusionWGS is a powerful tool for accurately predicting AMR that can be used for national surveillance purposes. Additionally, it can detect resistance genes from a wider panel of antimicrobials whose phenotypes are currently not monitored but may be of importance in the future

Discordant bioinformatic predictions of antimicrobial resistance from whole-genome sequencing data of bacterial isolates: an inter-laboratory study.

Antimicrobial resistance (AMR) poses a threat to public health. Clinical microbiology laboratories typically rely on culturing bacteria for antimicrobial-susceptibility testing (AST). As the implementation costs and technical barriers fall, whole-genome sequencing (WGS) has emerged as a 'one-stop' test for epidemiological and predictive AST results. Few published comparisons exist for the myriad analytical pipelines used for predicting AMR. To address this, we performed an inter-laboratory study providing sets of participating researchers with identical short-read WGS data from clinical isolates, allowing us to assess the reproducibility of the bioinformatic prediction of AMR between participants, and identify problem cases and factors that lead to discordant results. We produced ten WGS datasets of varying quality from cultured carbapenem-resistant organisms obtained from clinical samples sequenced on either an Illumina NextSeq or HiSeq instrument. Nine participating teams ('participants') were provided these sequence data without any other contextual information. Each participant used their choice of pipeline to determine the species, the presence of resistance-associated genes, and to predict susceptibility or resistance to amikacin, gentamicin, ciprofloxacin and cefotaxime. We found participants predicted different numbers of AMR-associated genes and different gene variants from the same clinical samples. The quality of the sequence data, choice of bioinformatic pipeline and interpretation of the results all contributed to discordance between participants. Although much of the inaccurate gene variant annotation did not affect genotypic resistance predictions, we observed low specificity when compared to phenotypic AST results, but this improved in samples with higher read depths. Had the results been used to predict AST and guide treatment, a different antibiotic would have been recommended for each isolate by at least one participant. These challenges, at the final analytical stage of using WGS to predict AMR, suggest the need for refinements when using this technology in clinical settings. Comprehensive public resistance sequence databases, full recommendations on sequence data quality and standardization in the comparisons between genotype and resistance phenotypes will all play a fundamental role in the successful implementation of AST prediction using WGS in clinical microbiology laboratories

Impact of rapid mecA polymerase chain reaction rapid diagnostic testing for Staphylococcus aureus in a pediatric setting

Rapid molecular technology can detect the mecA resistance gene in Staphylococcus aureus (SA), predicting methicillin susceptibility in under one hour. In combination with antimicrobial stewardship program interventions in adults with SA bacteremia, rapid mecA testing decreases time to targeted therapy. This intervention has not yet been shown effective in pediatric patients or in the absence of real-time stewardship interventions. The objective of this study was to determine if time to optimal therapy decreased following implementation of GeneXpert rapid diagnostic testing (RDT) in a pediatric institution without a formal antimicrobial stewardship protocol for response. The primary outcome was time to optimal therapy, determined by the number of hours from collection of the blood sample to the initiation of an optimal regimen. Optimal regimens were defined as vancomycin therapy alone for MRSA and nafcillin, oxacillin, or cefazolin alone for MSSA

Aetiology, Antimicrobial Susceptibility and Predictors of\ud Urinary Tract Infection among Febrile Under-Fives at\ud Muhimbili National Hospital, Dar es Salaam-Tanzania

Urinary tract infection (UTI) is a common cause of fever in children and contributes to morbidity and mortality. This study aimed at determining prevalence, aetiology and antimicrobial susceptibility pattern of the isolates at Muhimbili National Hospital (MNH), Dar es Salaam- Tanzania. Demographic data were collected using a pretested questionnaire. 382 febrile children below five years admitted in the general paediatric wards were recruited. Urine specimens were obtained for urinalysis, culture and antimicrobial sensitivity testing. UTI was detected in 16.8% (64/382). Children who presented prolonged duration of fever (7 days or longer) were more likely to have UTI (p< 0.01). Duration of fever, positive leukocyte and nitrite tests were independent predictors of UTI. Isolated bacteria included Escherichia coli (39.1%), Klebsiella spp (31.2%), Staphylococcus epidermidis (6.2%), Staphylococcus aureus (4.7%) and Pseudomonas aeruginosa (4.7%). We observed high resistance of the isolated uropathogens to ampicillin (79.9%), co-trimoxazole (89%) and clavulanate-amoxillin (70.3%). Amikacin had the least resistance (12.5%) from the isolated pathogens

Antimicrobial Resistance in Neisseria gonorrhoeae: Proceedings of the STAR Sexually Transmitted Infection-Clinical Trial Group Programmatic Meeting.

The goal of the Sexually Transmitted Infection Clinical Trial Group's Antimicrobial Resistance (AMR) in Neisseria gonorrhoeae (NG) meeting was to assemble experts from academia, government, nonprofit and industry to discuss the current state of research, gaps and challenges in research and technology and priorities and new directions to address the continued emergence of multidrug-resistant NG infections. Topics discussed at the meeting, which will be the focus of this article, include AMR NG global surveillance initiatives, the use of whole genome sequencing and bioinformatics to understand mutations associated with AMR, mechanisms of AMR, and novel antibiotics, vaccines and other methods to treat AMR NG. Key points highlighted during the meeting include: (i) US and International surveillance programs to understand AMR in NG; (ii) the US National Strategy for combating antimicrobial-resistant bacteria; (iii) surveillance needs, challenges, and novel technologies; (iv) plasmid-mediated and chromosomally mediated mechanisms of AMR in NG; (v) novel therapeutic (eg, sialic acid analogs, factor H [FH]/Fc fusion molecule, monoclonal antibodies, topoisomerase inhibitors, fluoroketolides, LpxC inhibitors) and preventative (eg, peptide mimic) strategies to combat infection. The way forward will require renewed political will, new funding initiatives, and collaborations across academic and commercial research and public health programs