Antibiotic Susceptibility Testing Data Research Articles

Comparison of clinical antibiotic susceptibility testing interpretations to CLSI standard interpretations

Background: Clinical antibiotic susceptibility testing (AST) interpretations based on minimum inhibitory concentrations (MIC) breakpoints are important for both clinical decision making and some reportable condition criteria. Standardization of MIC breakpoints across clinical laboratories is lacking; AST instruments are often validated for outdated Clinical and Laboratory Standards Institute (CLSI) MIC breakpoint guidelines. In this study, we analyzed the agreement between the reported clinical laboratory AST interpretations and the guideline CLSI interpretation. Methods: Clinical laboratory AST data collected from the Multisite Gram-Negative Surveillance Initiative (MuGSI) carbapenem-resistant Enterobacterales (CRE) surveillance program in Tennessee between 2019 and 2021 were utilized. MIC values from the clinical instrument were used to calculate CLSI standard interpretations following the 2019–2021 CLSI M100 guidelines. Agreement between the clinical laboratory and CLSI interpretations of the reported MIC values were measured using a weighted Cohen κ calculated in SAS version 9.4 software. Total matches were isolates with identical CLSI and clinical laboratory interpretations. Results: In total, 14 antibiotics were assessed. Of those, 9 antibiotics had at least moderate agreement (κ > 0.41) between interpretations. Agreement between the clinical laboratory and the CLSI interpretations were near perfect (κ > 0.81) for 3 antibiotics. Agreement between the clinical laboratory and the CLSI interpretations were poor for cefazolin (0.06) and ertapenem (0.14). Cefotaxime (−0.07) was the only antibiotic that suggested no agreement. Conclusions: Of the antibiotics included in the analysis, 36% had less than moderate agreement between clinical laboratory and CLSI AST interpretations. Given the increases in antimicrobial resistance globally and the emphasis placed on antibiotic stewardship, standardization across clinical AST panels should be prioritized. Inconsistencies have the potential to contribute to inappropriate antibiotic use in addition to under- or overidentification of reportable conditions, including CRE.Disclosures: None

Мониторинг ведущей грамотрицательной микрофлоры и антибиотикорезистентности при остеомиелите

Introduction There is an urgent need for a surveillance system of regular monitoring of specific bacteria inducing various types of osteomyelitis to identify resistant isolates and optimize the use of antibiotics. Objective: monitoring of specific gram-negative bacteria and analysis of the antibiotic resistance of the strains isolated from osteomyelitis patients over a three-year period. Results and discussion P. aeruginosa was the first most common pathogen among gram-negative microorganisms isolated from the patients between 2017 and 2019. Prevalence of the isolates identified in 2019 decreased by 9.6 % as compared to 2017. Next frequently encountered clinical isolates were Enterobacter sp., Acinetobacter sp., Klebsiella sp. There was a twofold increase in K. pneumoniae strains isolated in 2019. Analysis of antibiotic susceptibility testing data revealed multiresistance of the Acinetobacter sp. strains in 2019 despite the total decrease in resistant isolates in 2017 and 2018. Among non-fermenting gram-negative rods, the species being resistant to imipenem were shown to increase by 5.4 times. Overall antibiotic resistance was on rise. Increased antimicrobial resistance to beta-lactam antibiotics also combined with BLaC inhibitors was observed in Enterobacteriaceae population. Meropenem was found to be effective against most bacteria with growing drug resistance observed as compared with recent years. The antibiotic resistance profiles of Klebsiella sp. strains appeared to be high at antimicrobial testing. Conclusion Diverse bacterial morphology of gram-negative species and increasing proportion of drug-resistant strains isolated in osteomyelitis cases have necessitated regular monitoring of multiresistant clinical isolates for adjustment of empirical antibiotic therapies.

Surveillance of Healthcare-Associated Bloodstream and Urinary Tract Infections in a National Level Network of Indian Hospitals

Background: Healthcare-associated infections (HAIs) are a major global threat to patient safety. Systematic surveillance is crucial for understanding HAI rates and antimicrobial resistance trends and to guide infection prevention and control (IPC) activities based on local epidemiology. In India, no standardized national HAI surveillance system was in place before 2017. Methods: Public and private hospitals from across 21 states in India were recruited to participate in an HAI surveillance network. Baseline assessments followed by trainings ensured that basic microbiology and IPC implementation capacity existed at all sites. Standardized surveillance protocols for central-line–associated bloodstream infections (CLABSIs) and catheter-associated urinary tract infections (CAUTIs) were modified from the NHSN for the Indian context. IPC nurses were trained to implement surveillance protocols. Data were reported through a locally developed web portal. Standardized external data quality checks were performed to assure data quality. Results: Between May 2017 and April 2019, 109 ICUs from 37 hospitals (29 public and 8 private) enrolled in the network, of which 33 were teaching hospitals with >500 beds. The network recorded 679,109 patient days, 212,081 central-line days, and 387,092 urinary catheter days. Overall, 4,301 bloodstream infection (BSI) events and 1,402 urinary tract infection (UTI) events were reported. The network CLABSI rate was 9.4 per 1,000 central-line days and the CAUTI rate was 3.4 per 1,000 catheter days. The central-line utilization ratio was 0.31 and the urinary catheter utilization ratio was 0.57. Moreover, 3,542 (73%) of 4,742 pathogens reported from BSIs and 868 (53%) of 1,644 pathogens reported from UTIs were gram negative. Also, 1,680 (26.3%) of all 6,386 pathogens reported were Enterobacteriaceae. Of 1,486 Enterobacteriaceae with complete antibiotic susceptibility testing data reported, 832 (57%) were carbapenem resistant. Of 951 Enterobacteriaceae subjected to colistin broth microdilution testing, 62 (7%) were colistin resistant. The surveillance platform identified 2 separate hospital-level HAI outbreaks; one caused by colistin-resistant K. pneumoniae and another due to Burkholderia cepacia. Phased expansion of surveillance to additional hospitals continues. Conclusions: HAI surveillance was successfully implemented across a national network of diverse hospitals using modified NHSN protocols. Surveillance data are being used to understand HAI burden and trends at the facility and national levels, to inform public policy, and to direct efforts to implement effective hospital IPC activities. This network approach to HAI surveillance may provide lessons to other countries or contexts with limited surveillance capacity.Funding: NoneDisclosures: None

Accuracy of Infection Control Surveillance in Identifying Genomically Confirmed Cross Transmission Clusters

Background: Infection prevention surveillance for cross transmission is often performed by manual review of microbiologic culture results to identify geotemporally related clusters. However, the sensitivity and specificity of this approach remains uncertain. Whole-genome sequencing (WGS) analysis can help provide a gold-standard for identifying cross-transmission events. Objective: We employed a published WGS program, the Philips IntelliSpace Epidemiology platform, to compare accuracy of two surveillance methods: (i.) a virtual infection practitioner (VIP) with perfect recall and automated analysis of antibiotic susceptibility testing (AST), sample collection timing, and patient location data and (ii) a novel clinical matching (CM) algorithm that provides cluster suggestions based on a nuanced weighted analysis of AST data, timing of sample collection, and shared location stays between patients. Methods: WGS was performed routinely on inpatient and emergency department isolates of Enterobacter cloacae, Enterococcus faecium, Klebsiella pneumoniae, and Pseudomonas aeruginosa at an academic medical center. Single-nucleotide variants (SNVs) were compared within core genome regions on a per-species basis to determine cross-transmission clusters. Moreover, one unique strain per patient was included within each analysis, and duplicates were excluded from the final results. Results: Between May 2018 and April 2019, clinical data from 121 patients were paired with WGS data from 28 E. cloacae, 21 E. faecium, 61 K. pneumoniae, and 46 P. aeruginosa isolates. Previously published SNV relatedness thresholds were applied to define genomically related isolates. Mapping of genomic relatedness defined clusters as follows: 4 patients in 2 E. faecium clusters and 2 patients in 1 P. aeruginosa cluster. The VIP method identified 12 potential clusters involving 28 patients, all of which were “pseudoclusters.” Importantly, the CM method identified 7 clusters consisting of 27 patients, which included 1 true E. faecium cluster of 2 patients with genomically related isolates. Conclusions: In light of the WGS data, all of the potential clusters identified by the VIP were pseudoclusters, lacking sufficient genomic relatedness. In contrast, the CM method showed increased sensitivity and specificity: it decreased the percentage of pseudoclusters by 14% and it identified a related genomic cluster of E. faecium. These findings suggest that integrating clinical data analytics and WGS is likely to benefit institutions in limiting expenditure of resources on pseudoclusters. Therefore, WGS combined with more sophisticated surveillance approaches, over standard methods as modeled by the VIP, are needed to better identify and address true cross-transmission events.Funding: This study was supported by Philips Healthcare.Disclosures: None

Routinely available antimicrobial susceptibility information can be used to increase the efficiency of screening for carbapenemase-producing Enterobacteriaceae.

Introduction. Increased carbapenem resistance is often caused by carbapenemase production.Aim. The objective of our study was to assess which antibiotic susceptibility patterns, as tested by automated systems, are highly associated with the absence of carbapenemase production in Enterobacteriaceae isolates, and could therefore be used as a screening tool.Methodology. Routine antibiotic susceptibility testing data from 42 medical microbiology laboratories in the Netherlands in the period between January 2011 and June 2017 were obtained from the national antimicrobial resistance surveillance programme. Data on Enterobacteriaceae isolates that had an elevated minimum inhibitory concentration (MIC) for carbapenems (meropenem >0.25 mg l-1 or imipenem >1.0 mg l-1) were selected and subjected to phenotypic or genotypic carbapenemase production testing. Routinely available amoxicillin/clavulanic acid, piperacillin/tazobactam, cefuroxime and ceftriaxone/cefotaxime susceptibilities were studied in relation to carbapenemase production by calculating the negative predictive value.Results. No evidence for carbapenemase-producing Enterobacteriaceae (CPE) was found in 767 of 1007 (76 %) isolates. The negative predictive value was highest for amoxicillin/clavulanic acid (99.6 %) and piperacillin/tazobactam (98.8 %).Conclusion. Enterobacteriaceae isolates with elevated carbapenem MICs that are susceptible to amoxicillin/clavulanic acid or piperacillin/tazobactam are unlikely to be carbapenemase producers. Preselection based on this susceptibility pattern may lead to increased laboratory efficiency and reduction of costs. Whether this is also true for countries with a different distribution of CPE species and types or a higher prevalence of CPE needs to be studied.

Hospital surveillance predicts community pneumococcal antibiotic resistance in Vietnam.

In Vietnam, Streptococcus pneumoniae is a leading cause of disease, including meningitis. Antibiotics are available without physician prescription at community pharmacies and rates of antibiotic non-susceptibility are high. Appropriate treatment and antibiotic stewardship need to be informed by surveillance data. To report community-based pneumococcal antibiotic susceptibility testing data from children enrolled in a pneumococcal conjugate vaccine trial in Ho Chi Minh City [the Vietnam Pneumococcal Project (ViPP)] and compare these with published hospital-based data from the nationwide Survey of Antibiotic Resistance (SOAR) to determine whether hospital surveillance data provide an informative estimate of circulating pneumococcal resistance. Pneumococcal isolates from 234 nasopharyngeal swabs collected from ViPP participants at 12 months of age underwent antibiotic susceptibility testing using CLSI methods and the data were compared with SOAR data. Antibiotic susceptibility testing identified penicillin-non-susceptible pneumococci in 93.6% of pneumococcus-positive ViPP swabs (oral, non-meningitis breakpoints). Non-susceptibility to erythromycin, trimethoprim/sulfamethoxazole, clindamycin and tetracycline also exceeded 79%. MDR, defined as non-susceptibility to three or more classes of antibiotic, was common (94.4% of swabs). Low or no resistance was detected for ceftriaxone (non-meningitis breakpoints), ofloxacin and vancomycin. Antibiotic non-susceptibility rates in ViPP and SOAR were similar for several antibiotics tested. A very high proportion of pneumococci carried in the community are MDR. Despite wide disparities in population demographics between ViPP and SOAR, the non-susceptibility rates for several antibiotics were comparable. Thus, with some qualification, hospital antibiotic susceptibility testing data in Vietnam can inform circulating pneumococcal antibiotic non-susceptibility in young children, the group at highest risk of pneumococcal disease, to guide antibiotic prescribing and support surveillance strategies.

2467. Inferring Strain Type Attribution from Antibiotic Resistance Profiles among E. coli Causing Healthcare-Associated Infections in the United States, 2013–2017

BackgroundE. coli is a leading cause of healthcare-associated infections; clonal group ST131, which has expanded worldwide with notable increased severity of infections, is commonly resistant to extended-spectrum cephalosporins (ESC) and fluoroquinolones (FQ). Herein, we relate ESC and FQ resistance profiles from CDC’s National Healthcare Safety Network (NHSN) with specific strain types from CDC laboratory surveillance collections.MethodsNHSN isolate and antibiotic susceptibility testing data were collected from all E. coli associated with central line-associated bloodstream infections, catheter-associated urinary tract infections, ventilator-associated events, or surgical site infections from 2013–2017. Resistance was scored as non-susceptibility to at least one drug per class [susceptible (S); resistant (R)]. ESC and FQ susceptibilities and multilocus sequence types (ST) using the Achtman 7 loci scheme were determined for a contemporaneous set of E. coli isolates collected through CDC laboratory surveillance.ResultsOf 96,672 E. coli infections reported to NHSN, 13% were ESC-R/FQ-R, 23% ESC-S/FQ-R, 4% ESC-R/FQ-S, and 60% were ESC-S/FQ-S. Among 105 ESC-R/FQ-R and 21 ESC-S/FQ-R laboratory isolates, the majority (67.6% and 52.4%, respectively) were ST131, whereas of 38 ESC-R/FQ-S and 53 ESC-S/FQ-S isolates, ST131 was a minority (18.4% and 7.5%, respectively). The odds of an isolate being ST131 were 10.5 if FQ-R (P < 0.001), 3.4 if ESC-R (P < 0.001), and 6.0 if ESC-R/FQ-R (P < 0.001). Using the national distribution of resistance combinations from NHSN, and assuming static ST-resistance distribution, we can infer that ST131 was responsible for 25.8% (95% CI, 23.9%-27.6%) of all E.coli healthcare-associated infections in the United States in 2013–2017.ConclusionMolecular inferences generated by applying laboratory data to resistance signature data in reportable datasets may make national E. coli ST burden estimates possible. Further characterization of resistance combinations with strain type, infection rates, and clinical outcomes may inform targeted prevention strategies at the local/regional level.Disclosures All authors: No reported disclosures.

1831. Machine Learning Approaches to Predicting Resistance in Pseudomonas aeruginosa

BackgroundMulti-drug-resistant (MDR) P. aeruginosa (PA) infections continue to cause significant morbidity and mortality in various patient groups including those with malignancies. Predicting antimicrobial resistance (AMR) from whole-genome sequencing data if done rapidly, could aid in providing optimal care to patients.MethodsTo better understand the connections between DNA variation and phenotypic AMR in PA, we developed a new algorithm, variant mapping and prediction of antibiotic resistance (VAMPr), to build association and machine learning prediction models of AMR based on publicly available whole-genome sequencing and antibiotic susceptibility testing (AST) data. A validation cohort of contemporary PA bloodstream isolates was sequenced and AST was performed. Accuracy of predicting AMR for various PA–drug combinations was calculated.ResultsVAMPr was built from 3,393 bacterial isolates (83 PA isolates included) from 9 species that contained AST data for 29 antibiotics. 14,615 variant genotypes were identified within the dataset and 93 association and prediction models were built. 120 PA bloodstream isolates from cancer patients were included for analysis in the validation cohort. ~15% of isolates were carbapenem resistant and ~20% were quinolone resistant. For drug-isolate combinations where >100 isolates were available, machine-learning prediction accuracies ranged from 75.6% (PA and ceftazidime; 90/119 correctly predicted) to 98.1% (PA and amikacin; 105/107 correctly predicted). Machine learning accurately identified known variants that strongly predicted resistance to various antibiotic classes. Examples included specific gyrA mutations (T83I; P < 0.00001) and quinolone resistance.ConclusionMachine learning predicted AMR in P. aeruginosa across a number of antibiotics with high accuracy. Given the genomic heterogeneity of PA, increased genomic data for this pathogen will aid in further improving prediction accuracy across all antibiotic classes.DisclosuresSamuel L. Aitken, PharmD, Melinta Therapeutoics: Grant/Research Support, Research Grant; Merck, Sharpe, and Dohme: Advisory Board; Shionogi: Advisory Board

Prevalence of etiologic agents causing invasive bacterial disease and evaluation of their antibiotic susceptibility pattern

Invasive bacterial diseases (IBD) cause significant morbidity and mortality which leads to enormous health suffering and economic frustration in Bangladesh especially in people with resource poor region. The prevalence of IBD may vary even in different regions in a country. To understand the prevalence of etiologic agents causing IBD and determining their response to commonly used standard antibiotics we conducted a retrospective cross sectional study in rural town of Bangladesh on patient attended for blood culture with IBD sign-symptoms in well reputed microbiology lab. We considered IBD patients (118) of all age groups (0-85 years) both gender (male 54% and female 46%). 25% (30) blood culture of all IBD patients found bacterial growth positive including contaminant Staphylococcus sp. (10). The isolated IBD etiologic agents are S. aureus (10), Salmonella sp. (5), Salmonella Paratyphi (1), E. coli (2), Shigella sp. (1), Pseudomonas sp. (1). We could have retrieved antibiotic susceptibility testing data of five isolates including S. aureus (1), Salmonella sp. (2), Salmonella Paratyphi (1), Pseudomonas sp. (1); which showed except Salmonella sp. all isolates are sensitive to tested commonly used standard antibiotics. Two non typhoidal Salmonella sp. showed intermediate sensitivity to ciprofloxacin (5 ìg) which indicate reconsideration of choosing non typhoidal Salomonella sp. infection with ciprofloxacin (5 ìg). As it is a retrospective, not well organized cross sectional study; these findings may not represent the entire actual scenario of IBD in the region. As a consequence, a well organizing and adequately powered study must need to be conducted&#x0D; Asian J. Med. Biol. Res. December 2018, 4(4): 330-336

In Vitro Antibiotic Susceptibility Pattern of Non-diphtheriae Corynebacterium Isolates in Ontario, Canada, from 2011 to 2016.

Non-diphtheriae Corynebacterium-associated disease has been increasingly observed and often presents a conundrum to the treating physician. Analysis of antibiotic susceptibility testing data for 1,970 clinical Corynebacterium isolates received between 2011 and 2016 revealed that empirical drug treatment options are limited to vancomycin and linezolid. Corynebacterium striatum was the most frequently observed species during this study period, along with C. amycolatum and C. pseudodiphtheriticum/C. propinquum Low levels of susceptibility to penicillin (14.5%), erythromycin (15.1%), and clindamycin (8.7%) were observed for non-diphtheriae Corynebacterium species, while 3.0% of isolates were not susceptible to daptomycin. Similarly, 26.9% and 38.1% of Corynebacterium isolates were susceptible to ciprofloxacin and trimethoprim-sulfamethoxazole, respectively. Our data show much lower susceptibility to penicillin than previously reported in the literature and an increasing number of isolates resistant to daptomycin, highlighting the need for continued antibiotic surveillance studies for appropriate patient management and treatment success.

Francisella tularensis Susceptibility to Antibiotics: A Comprehensive Review of the Data Obtained In vitro and in Animal Models.

The antibiotic classes that are recommended for tularaemia treatment are the aminoglycosides, the fluoroquinolones and the tetracyclines. However, cure rates vary between 60 and 100% depending on the antibiotic used, the time to appropriate antibiotic therapy setup and its duration, and the presence of complications, such as lymph node suppuration. Thus, antibiotic susceptibility testing (AST) of F. tularensis strains remains of primary importance for detection of the emergence of antibiotic resistances to first-line drugs, and to test new therapeutic alternatives. However, the AST methods reported in the literature were poorly standardized between studies and AST data have not been previously evaluated in a comprehensive and comparative way. The aim of the present review was to summarize experimental data on antibiotic susceptibilities of F. tularensis obtained in acellular media, cell models and animal models since the introduction of fluoroquinolones in the treatment of tularaemia in 1989. We compiled MIC data of 33 antibiotics (including aminoglycosides, fluoroquinolones, tetracyclines, macrolides, β-lactams, chloramphenicol, rifampicin, and linezolid) against 900 F. tularensis strains (504 human strains), including 107 subsp. tularensis (type A), 789 subsp. holarctica (type B) and four subsp. mediasiatica strains, using various AST methods. Specific culture media were identified or confirmed as unsuitable for AST of F. tularensis. Overall, MICs were the lowest for ciprofloxacin (≤ 0.002–0.125 mg/L) and levofloxacin, and ranged from ≤ 0.016 to 2 mg/L for gentamicin, and 0.064 to 4 mg/L for doxycycline. No resistant strain to any of these antibiotics was reported. Fluoroquinolones also exhibited a bactericidal activity against intracellular F. tularensis and lower relapse rates in animal models when compared with the bacteriostatic compound doxycycline. As expected, lower MIC values were found for macrolides against type A and biovar I type B strains, compared to biovar II type B strains. The macrolides were more effective against F. tularensis grown in phagocytic cells than in acellular media.

Rapid Antibiotic Susceptibility Testing in a Microfluidic pH Sensor

For appropriate selection of antibiotics in the treatment of pathogen infection, rapid antibiotic susceptibility testing (AST) is urgently needed in clinical practice. This study reports the utilization of a microfluidic pH sensor for monitoring bacterial growth rate in culture media spiked with different kinds of antibiotics. The microfluidic pH sensor was fabricated by integration of pH-sensitive chitosan hydrogel with poly(dimethylsiloxane) (PDMS) microfluidic channels. For facilitating the reflectometric interference spectroscopic measurements, the chitosan hydrogel was coated on an electrochemically etched porous silicon chip, which was used as the substrate of the microfluidic channel. Real-time observation of the pH change in the microchannel can be realized by Fourier transform reflectometric interference spectroscopy (FT-RIFS), in which the effective optical thickness (EOT) was selected as the optical signal for indicating the reversible swelling process of chitosan hydrogel stimulated by pH change. With this microfluidic pH sensor, we demonstrate that confinement of bacterial cells in a nanoliter size channel allows rapid accumulation of metabolic products and eliminates the need for long-time preincubation, thus reducing the whole detection time. On the basis of this technology, the whole bacterial growth curve can be obtained in less than 2 h, and consequently rapid AST can be realized. Compared with conventional methods, the AST data acquired from the bacterial growth curve can provide more detailed information for studying the antimicrobial behavior of antibiotics during different stages. Furthermore, the new technology also provides a convenient method for rapid minimal inhibition concentration (MIC) determination of individual antibiotics or the combinations of antibiotics against human pathogens that will find application in clinical and point-of-care medicine.