Limited methods for colistin MIC determination are available to clinical microbiology laboratories. The purpose of this study was to evaluate the accuracy of the colistin broth disk elution (CBDE) test compared to that of broth microdilution (BMD) for identifying colistin MICs. CBDE was compared to colistin BMD using a collection of Gram-negative bacilli tested at two U.S. microbiology laboratories. The isolates tested included 121 retrospective clinical isolates, 45 prospective clinical isolates, and 6 mcr-1-positive Escherichia coli isolates. CBDE was performed with four 10-ml cation-adjusted Mueller-Hinton broth tubes per isolate, to which 0, 1, 2, and 4 colistin 10-µg disks were added, generating final concentrations in the tubes of 0 (growth control), 1, 2, and 4 µg/ml, respectively. MICs were evaluated visually and interpreted using Clinical and Laboratory Standards Institute breakpoints. Site 2 also compared CBDE to the reference broth macrodilution (BMAD) method (n = 110 isolates). Overall, CBDE yielded a categorical agreement (CA) and essential agreement (EA) of 98% and 99%, respectively, compared to the results of colistin BMD. Very major errors occurred for mcr-1-producing strains, with MICs fluctuating from 2 to 4 µg/ml on repeat testing. The results for all other isolates were in CA with those of BMD. CBDE versus BMAD had an EA of 100% and a CA of 100%. Compared to currently used techniques, CBDE is an easy and practical method to perform colistin MIC testing. Some mcr-1-producing isolates yielded MICs of 2 µg/ml by CBDE and 4 µg/ml by BMD. As such, the results for isolates with colistin MICs of 2 µg/ml by CBDE should be confirmed by the reference BMD method, and isolates with MICs of ≥2 µg/ml should be evaluated for the presence of mcr genes.

Standard antimicrobial susceptibility testing (AST) approaches lead to delays in the selection of optimal antimicrobial therapy. Here, we sought to determine the accuracy of antimicrobial resistance (AMR) determinants identified by Nanopore whole-genome sequencing in predicting AST results. Using a cohort of 40 clinical isolates (21 carbapenemase-producing carbapenem-resistant Klebsiella pneumoniae, 10 non-carbapenemase-producing carbapenem-resistant K. pneumoniae, and 9 carbapenem-susceptible K. pneumoniae isolates), three separate sequencing and analysis pipelines were performed, as follows: (i) a real-time Nanopore analysis approach identifying acquired AMR genes, (ii) an assembly-based Nanopore approach identifying acquired AMR genes and chromosomal mutations, and (iii) an approach using short-read correction of Nanopore assemblies. The short-read correction of Nanopore assemblies served as the reference standard to determine the accuracy of Nanopore sequencing results. With the real-time analysis approach, full annotation of acquired AMR genes occurred within 8 h from subcultured isolates. Assemblies sufficient for full resistance gene and single-nucleotide polymorphism annotation were available within 14 h from subcultured isolates. The overall agreement of genotypic results and anticipated AST results for the 40 K. pneumoniae isolates was 77% (range, 30% to 100%) and 92% (range, 80% to 100%) for the real-time approach and the assembly approach, respectively. Evaluating the patients contributing the 40 isolates, the real-time approach and assembly approach could shorten the median time to effective antibiotic therapy by 20 h and 26 h, respectively, compared to standard AST. Nanopore sequencing offers a rapid approach to both accurately identify resistance mechanisms and to predict AST results for K. pneumoniae isolates. Bioinformatics improvements enabling real-time alignment, coupled with rapid extraction and library preparation, will further enhance the accuracy and workflow of the Nanopore real-time approach.

Background:Targeted screening for carbapenem-resistant organisms (CROs), including carbapenem-resistant Enterobacteriaceae (CRE) and carbapenemase-producing organisms (CPOs), remains limited; recent data suggest that existing policies miss many carriers.Objective:Our objective was to measure the prevalence of CRO and CPO perirectal colonization at hospital unit admission and to use machine learning methods to predict probability of CRO and/or CPO carriage.Methods:We performed an observational cohort study of all patients admitted to the medical intensive care unit (MICU) or solid organ transplant (SOT) unit at The Johns Hopkins Hospital between July 1, 2016 and July 1, 2017. Admission perirectal swabs were screened for CROs and CPOs. More than 125 variables capturing preadmission clinical and demographic characteristics were collected from the electronic medical record (EMR) system. We developed models to predict colonization probabilities using decision tree learning.Results:Evaluating 2,878 admission swabs from 2,165 patients, we found that 7.5% and 1.3% of swabs were CRO and CPO positive, respectively. Organism and carbapenemase diversity among CPO isolates was high. Despite including many characteristics commonly associated with CRO/CPO carriage or infection, overall, decision tree models poorly predicted CRO and CPO colonization (C statistics, 0.57 and 0.58, respectively). In subgroup analyses, however, models did accurately identify patients with recent CRO-positive cultures who use proton-pump inhibitors as having a high likelihood of CRO colonization.Conclusions:In this inpatient population, CRO carriage was infrequent but was higher than previously published estimates. Despite including many variables associated with CRO/CPO carriage, models poorly predicted colonization status, likely due to significant host and organism heterogeneity.