Publications by authors named "John Stelling"

26 Publications

  • Page 1 of 1

Exploring the value of MALDI-TOF MS for the detection of clonal outbreaks of Burkholderia contaminans.

J Microbiol Methods 2021 Feb 29;181:106130. Epub 2020 Dec 29.

Department of Medicine, Division of Infectious Diseases, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA; Department of Medicine, Harvard Medical School 25 Shattuck Street, Boston, MA 02115, USA.

Background: Molecular genetics has risen in both output and affordability to become the gold standard in diagnosis, however it is not yet available for most routine clinical microbiology due to cost and the level of skill it requires. Matrix assisted laser desorption/ionisation - time of flight mass spectrometry (MALDI-TOF MS) approaches may be useful in bridging the gap between low-resolution phenotypic methods and bulky genotypic methods in the goal of epidemiological source-typing of microbes. Burkholderia has been shown to be identifiable at the subspecies level using MALDI-TOF MS. There have not yet been studies assessing the ability of MALDI-TOF MS to source-type Burkholderia contaminans isolates into epidemiologically relevant outbreak clusters.

Methods: 55 well-characterised B. contaminans isolates were used to create a panel for analysis of MALDI-TOF MS biomarker peaks and their relation to outbreak strains, location, source, patient, diagnosis and isolate genetics. Unsupervised clustering was performed and classification models were generated using biostatistical analysis software.

Results: B. contaminans spectra derived from MALDI-TOF MS were of sufficiently high resolution to identify 100% of isolates. Unsupervised clustering methods showed poor evidence of spectra clustering by all characteristics measured. Classification algorithms were discriminatory, with Genetic Algorithm models showing 100% recognition capability for all outbreaks, the pulsed-field gel electrophoresis (PFGE) typeability model, and 96.63% recognition for the location model. A consistent peak at m/z of approximately 6943 was identified in all non-typeable strains but in none of the typeable strains.

Conclusions: MALDI-TOF MS successfully discriminates B. contaminans isolates into clonal, epidemiological clusters, and can recognise isolates non-typeable by PFGE. Further work should investigate this capability, and include peptide studies and genomic sequencing to identify individual proteins or genes responsible for this non-typeablity, particularly at the peak weight identified.
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http://dx.doi.org/10.1016/j.mimet.2020.106130DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7962913PMC
February 2021

Staphylococcus aureus antimicrobial susceptibility trends and cluster detection in Vermont: 2012-2018.

Expert Rev Anti Infect Ther 2021 Jan 8:1-9. Epub 2021 Jan 8.

Department of Medicine, Division of Infectious Diseases, Brigham and Women's Hospital , Boston, MA, USA.

: This study presents demographic and temporal trends in the isolation of Staphylococcus aureus in Vermont clinical microbiology laboratories and explores the use of statistical algorithms and multi-resistance phenotypes to improve outbreak detection. : Routine microbiology test results downloaded from Vermont clinical laboratory information systems were used to monitor S. aureus antimicrobial resistance trends. The integrated WHONET-SaTScan software used multi-resistance phenotypes to identify possible acute outbreaks with the space-time permutation model and slowly emerging geographic clusters using the spatial-only multinomial model. : Data were provided from seven hospital laboratories from 2012 to 2018 for 19,224 S. aureus isolates from 14,939 patients. Statistically significant differences (p ≤ 0.05) in methicillin-resistant S. aureus (MRSA) isolation were seen by age group, specimen type, and health-care setting. Among MRSA, multi-resistance profiles permitted the recognition and tracking of 6 common and 21 rare 'phenotypic clones.' We identified 43 acute MRSA clusters and 7 significant geographic clusters (p ≤ 0.05). : There was significant heterogeneity in MRSA strains between facilities and the use of multi-resistance phenotypes facilitated the recognition of possible outbreaks. Comprehensive electronic surveillance of antimicrobial resistance utilizing routine clinical microbiology data with free software tools offers early recognition and tracking of emerging resistance threats.
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http://dx.doi.org/10.1080/14787210.2021.1845653DOI Listing
January 2021

Automating the Generation of Antimicrobial Resistance Surveillance Reports: Proof-of-Concept Study Involving Seven Hospitals in Seven Countries.

J Med Internet Res 2020 10 2;22(10):e19762. Epub 2020 Oct 2.

Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.

Background: Reporting cumulative antimicrobial susceptibility testing data on a regular basis is crucial to inform antimicrobial resistance (AMR) action plans at local, national, and global levels. However, analyzing data and generating a report are time consuming and often require trained personnel.

Objective: This study aimed to develop and test an application that can support a local hospital to analyze routinely collected electronic data independently and generate AMR surveillance reports rapidly.

Methods: An offline application to generate standardized AMR surveillance reports from routinely available microbiology and hospital data files was written in the R programming language (R Project for Statistical Computing). The application can be run by double clicking on the application file without any further user input. The data analysis procedure and report content were developed based on the recommendations of the World Health Organization Global Antimicrobial Resistance Surveillance System (WHO GLASS). The application was tested on Microsoft Windows 10 and 7 using open access example data sets. We then independently tested the application in seven hospitals in Cambodia, Lao People's Democratic Republic, Myanmar, Nepal, Thailand, the United Kingdom, and Vietnam.

Results: We developed the AutoMated tool for Antimicrobial resistance Surveillance System (AMASS), which can support clinical microbiology laboratories to analyze their microbiology and hospital data files (in CSV or Excel format) onsite and promptly generate AMR surveillance reports (in PDF and CSV formats). The data files could be those exported from WHONET or other laboratory information systems. The automatically generated reports contain only summary data without patient identifiers. The AMASS application is downloadable from https://www.amass.website/. The participating hospitals tested the application and deposited their AMR surveillance reports in an open access data repository.

Conclusions: The AMASS is a useful tool to support the generation and sharing of AMR surveillance reports.
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http://dx.doi.org/10.2196/19762DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7568216PMC
October 2020

Global health and data-driven policies for emergency responses to infectious disease outbreaks.

Lancet Glob Health 2020 11 10;8(11):e1361-e1363. Epub 2020 Aug 10.

Brigham and Women's Hospital, Microbiology Laboratory, Boston, MA 02115, USA. Electronic address:

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http://dx.doi.org/10.1016/S2214-109X(20)30361-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7417143PMC
November 2020

Comparison of de-duplication methods used by WHO Global Antimicrobial Resistance Surveillance System (GLASS) and Japan Nosocomial Infections Surveillance (JANIS) in the surveillance of antimicrobial resistance.

PLoS One 2020 26;15(6):e0228234. Epub 2020 Jun 26.

Antimicrobial Resistance Research Center, National Institute of Infectious Diseases, Higashimurayama, Tokyo, Japan.

A major issue in the surveillance of antimicrobial resistance (AMR) is "de-duplication" or removal of repeated isolates, for which there exist multiple methods. The World Health Organization (WHO) Global Antimicrobial Resistance Surveillance System (GLASS) requires de-duplication by selecting only the first isolate of a given bacterial species per patient per surveillance period per specimen type per age group, gender, and infection origin stratification. However, no study on the comparative application of this method has been reported. The objective of this study was to evaluate differences in data tabulation between the WHO GLASS and the Japan Nosocomial Infections Surveillance (JANIS) system, which counts both patients and isolates after removing repeated isolates of the same bacterial species isolated from a patient within 30 days, regardless of specimen type, but distinguishing isolates with change of antimicrobial resistance phenotype. All bacterial data, consisting of approximately 8 million samples from 1795 Japanese hospitals in 2017 were exported from the JANIS database, and were tabulated using either the de-duplication algorithm of GLASS, or JANIS. We compared the tabulated results of the total number of patients whose blood and urine cultures were taken and of the percentage of resistant isolates of Escherichia coli for each priority antibiotic. The number of patients per specimen type tabulated by the JANIS method was always smaller than that of GLASS. There was a small (< 3%) difference in the percentage of resistance of E. coli for any antibiotic between the two methods in both out- and inpatient settings and blood and urine isolates. The two tabulation methods did not show considerable differences in terms of the tabulated percentages of resistance for E. coli. We further discuss how the use of GLASS tabulations to create a public software and website that could help to facilitate the understanding of and treatment against AMR.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0228234PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7319286PMC
August 2020

Editorial: Clinical Microbiology in Low Resource Settings.

Front Med (Lausanne) 2020 10;7:258. Epub 2020 Jun 10.

Microbiology Laboratory, Women's Hospital, Boston, MA, United States.

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http://dx.doi.org/10.3389/fmed.2020.00258DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7297910PMC
June 2020

Surveillance of antimicrobial resistance and evolving microbial populations in Vermont: 2011-2018.

Expert Rev Anti Infect Ther 2020 10 18;18(10):1055-1062. Epub 2020 Jun 18.

Vermont Department of Health, Infectious Disease Epidemiology , Burlington, VT, USA.

Objective: This study presents trends in organism isolation and antimicrobial resistance in routine microbiology test results from acute-care hospital microbiology laboratories in Vermont.

Methods: Organism identifications and antimicrobial susceptibility test results were captured from acute-care hospital laboratories to monitor geographic and temporal trends in resistance and emerging microbial threats with the free WHONET software.

Results: Data were provided from 12 acute care hospital laboratories from 2011 through 2018 for 318,833 isolates from 148,994 patients (70% female, 74% outpatient, and 63% urine). Significant differences (p < 0.05) in age, gender, and antimicrobial susceptibility results (e.g. and levofloxacin) between outpatient and inpatient isolates were identified with temporal increases in certain species (e.g. ) and resistance (e.g. and erythromycin). The use of multi-resistance phenotypes demonstrated significant heterogeneity (p < 0.05) in MRSA strains between facilities, for example resistant to six priority antimicrobials were found in no critical access hospitals (fewer than 25 inpatient beds) but in all non-critical access hospitals.

Conclusions: Comprehensive electronic surveillance of antimicrobial resistance utilizing routine clinical microbiology data with free software tools offers early recognition and tracking of emerging community and healthcare resistance threats at the local and state level.
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http://dx.doi.org/10.1080/14787210.2020.1776114DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7554058PMC
October 2020

Automated outbreak detection of hospital-associated pathogens: Value to infection prevention programs.

Infect Control Hosp Epidemiol 2020 09 10;41(9):1016-1021. Epub 2020 Jun 10.

University of California Irvine School of Medicine, Orange, California.

Objective: To assess the utility of an automated, statistically-based outbreak detection system to identify clusters of hospital-acquired microorganisms.

Design: Multicenter retrospective cohort study.

Setting: The study included 43 hospitals using a common infection prevention surveillance system.

Methods: A space-time permutation scan statistic was applied to hospital microbiology, admission, discharge, and transfer data to identify clustering of microorganisms within hospital locations and services. Infection preventionists were asked to rate the importance of each cluster. A convenience sample of 10 hospitals also provided information about clusters previously identified through their usual surveillance methods.

Results: We identified 230 clusters in 43 hospitals involving Gram-positive and -negative bacteria and fungi. Half of the clusters progressed after initial detection, suggesting that early detection could trigger interventions to curtail further spread. Infection preventionists reported that they would have wanted to be alerted about 81% of these clusters. Factors associated with clusters judged to be moderately or highly concerning included high statistical significance, large size, and clusters involving Clostridioides difficile or multidrug-resistant organisms. Based on comparison data provided by the convenience sample of hospitals, only 9 (18%) of 51 clusters detected by usual surveillance met statistical significance, and of the 70 clusters not previously detected, 58 (83%) involved organisms not routinely targeted by the hospitals' surveillance programs. All infection prevention programs felt that an automated outbreak detection tool would improve their ability to detect outbreaks and streamline their work.

Conclusions: Automated, statistically-based outbreak detection can increase the consistency, scope, and comprehensiveness of detecting hospital-associated transmission.
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http://dx.doi.org/10.1017/ice.2020.233DOI Listing
September 2020

Integrating whole-genome sequencing within the National Antimicrobial Resistance Surveillance Program in the Philippines.

Nat Commun 2020 06 1;11(1):2719. Epub 2020 Jun 1.

Antimicrobial Resistance Surveillance Reference Laboratory, Research Institute for Tropical Medicine, Muntinlupa, The Philippines.

National networks of laboratory-based surveillance of antimicrobial resistance (AMR) monitor resistance trends and disseminate these data to AMR stakeholders. Whole-genome sequencing (WGS) can support surveillance by pinpointing resistance mechanisms and uncovering transmission patterns. However, genomic surveillance is rare in low- and middle-income countries. Here, we implement WGS within the established Antimicrobial Resistance Surveillance Program of the Philippines via a binational collaboration. In parallel, we characterize bacterial populations of key bug-drug combinations via a retrospective sequencing survey. By linking the resistance phenotypes to genomic data, we reveal the interplay of genetic lineages (strains), AMR mechanisms, and AMR vehicles underlying the expansion of specific resistance phenotypes that coincide with the growing carbapenem resistance rates observed since 2010. Our results enhance our understanding of the drivers of carbapenem resistance in the Philippines, while also serving as the genetic background to contextualize ongoing local prospective surveillance.
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http://dx.doi.org/10.1038/s41467-020-16322-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7264328PMC
June 2020

Why surveillance of antimicrobial resistance needs to be automated and comprehensive.

J Glob Antimicrob Resist 2019 06 13;17:8-15. Epub 2018 Oct 13.

Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. Electronic address:

Objectives: Surveillance of antimicrobial resistance (AMR) can now be automated to analyse the reports of microbiology laboratories continually without operator assistance. It can also be made comprehensive to monitor all the reports of all the world's microbiology laboratories.

Methods And Results: As illustrated through examples provided in this work, each clinical report can be scanned automatically by algorithms to suspect emerging problems and to prompt sampling to confirm such problems, now increasingly by nucleotide sequencing. An emerging problem may be an excess (clustering) of similar microbes owing to their spread among patients who are interrelated in some way, as by shared locations, caregivers or food products. Or it might be a microbe new to an area or to a laboratory but already seen nearby, such as Elizabethkingia anophelis or mcr-1-positive Escherichia coli. Automated early alerting of responders enables them to contain spread sooner and to avert infections downstream. 'Big Data' informatics now also enables surveillance of AMR to be made comprehensive, to monitor all reports of all the world's microbiology laboratories. Such orders of magnitude increase in analysed data would accordingly increase its granularity and thus detect many more global problems sooner. It would also reduce surveillance-blind areas where problems may now emerge and spread undetected.

Conclusions: The world's microbiology laboratories need to integrate and analyse all of their reports for surveillance to make their own patients safer from existing and approaching problems otherwise hard to notice. Making automated surveillance an easy-to-adopt laboratory standard of care can make it comprehensive.
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http://dx.doi.org/10.1016/j.jgar.2018.10.011DOI Listing
June 2019

Implementation and evaluation of an automated surveillance system to detect hospital outbreak.

Am J Infect Control 2017 Dec 23;45(12):1372-1377. Epub 2017 Aug 23.

Infection Prevention and Control, New York University Langone Health System, New York, NY; Division of Infectious Diseases & Immunology, New York University Langone School of Medicine, New York, NY.

Background: The timely identification of a cluster is a critical requirement for infection prevention and control (IPC) departments because these events may represent transmission of pathogens within the health care setting. Given the issues with manual review of hospital infections, a surveillance system to detect clusters in health care settings must use automated data capture, validated statistical methods, and include all significant pathogens, antimicrobial susceptibility patterns, patient care locations, and health care teams.

Methods: We describe the use of SaTScan statistical software to identify clusters, WHONET software to manage microbiology laboratory data, and electronic health record data to create a comprehensive outbreak detection system in our hospital. We also evaluated the system using the Centers for Disease Control and Prevention's guidelines.

Results: During an 8-month surveillance time period, 168 clusters were detected, 45 of which met criteria for investigation, and 6 were considered transmission events. The system was felt to be flexible, timely, accepted by the department and hospital, useful, and sensitive, but it required significant resources and has a low positive predictive value.

Conclusions: WHONET-SaTScan is a useful addition to a robust IPC program. Although the resources required were significant, this prospective, real-time cluster detection surveillance system represents an improvement over historical methods. We detected several episodes of transmission which would have eluded us previously, and allowed us to focus infection prevention efforts and improve patient safety.
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http://dx.doi.org/10.1016/j.ajic.2017.06.031DOI Listing
December 2017

Use of WHONET-SaTScan system for simulated real-time detection of antimicrobial resistance clusters in a hospital in Italy, 2012 to 2014.

Euro Surveill 2017 Mar;22(11)

National Surveillance Centre of Epidemiology, Surveillance and Health Promotion (CNESPS), Department of Epidemiology and Infectious Diseases, Istituto Superiore di Sanità, Rome, Italy.

Resistant pathogens infections cause in healthcare settings, higher patient mortality, longer hospitalisation times and higher costs for treatments. Strengthening and coordinating local, national and international surveillance systems is the cornerstone for the control of antimicrobial resistance (AMR). In this study, the WHONET-SaTScan software was applied in a hospital in Italy to identify potential outbreaks of AMR. Data from San Filippo Neri Hospital in Rome between 2012 and 2014 were extracted from the national surveillance system for antimicrobial resistance (AR-ISS) and analysed using the simulated prospective analysis for real-time cluster detection included in the WHONET-SaTScan software. Results were compared with the hospital infection prevention and control system. The WHONET-SaTScan identified 71 statistically significant clusters, some involving pathogens carrying multiple resistance phenotypes. Of these 71, three were also detected by the hospital system, while a further 15, detected by WHONET-SaTScan only, were considered of relevant importance and worth further investigation by the hospital infection control team. In this study, the WHONET-SaTScan system was applied for the first time to the surveillance of AMR in Italy as a tool to strengthen this surveillance to allow more timely intervention strategies both at local and national level, using data regularly collected by the Italian national surveillance system.
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http://dx.doi.org/10.2807/1560-7917.ES.2017.22.11.30484DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5356424PMC
March 2017

A Review on Nano-Antimicrobials: Metal Nanoparticles, Methods and Mechanisms.

Curr Drug Metab 2017 ;18(2):120-128

King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia.

Nanotechnology is a scientific and engineering technology conducted at the nano-scale, such as in the fields of compound fabric manufacturing, food processing, agricultural processing, and engineering, as well as in medical and medicinal applications. In recent decade, nanomaterial applications for antimicrobial works have of prime interest of by many researchers. Available reports show that some of the metal oxide nanoparticles (NPs) including Al2O3, TiO2, ZnO, CuO, Co3O4, In2O3, MgO, SiO2, ZrO2, Cr2O3, Ni2O3, Mn2O3, CoO, and Nickel oxide have toxicity toward several microorganisms and they could successfully kill numerous bacteria. Based on our literature review there are some effective factors that can influence the ability of nanomaterials in reducing or killing the cells, and there are mechanisms for nanomaterial against bacteria, which are briefly listed as follows: surface charge of the metal nanomaterial, shape, type and material, concentration of nanomaterial, dispersion and contact of nanomaterial to the bacterial cell, presence of active oxygen, liberation of antimicrobial ions, medium components and pH, physicochemical properties, specific surface-area-to-volume ratios, size, role of growth rate, role of biofilm formation, cell wall of bacteria, and effect of UV illumination. It can be considered that in the use of nanomaterials as antimicrobial agents, consideration of many factors remain principal. Antibacterial resistance to common chemical antibacterial agents can be due to long production-consumption cycle, thereby reducing their efficiency, and use of poor quality or fake medicines in undeveloped and developing countries. NPs as antimicrobial agents have become an emerging approach against this challenge, which can establish an effective nanostructure to deliver the antimicrobial agents for targeting the bacterial community efficiently. In addition, they are so potent that microbial pathogens cannot develop resistance to wards them. On the other hand, most of the metal oxide NPs have no toxicity toward humans at effective concentrations used to kill bacterial cells, which thus becomes an advantage for using them in a full scale. However, over the present decade, several studies have suggested that NPs are excellent antibacterial agents, at least at the research level.
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http://dx.doi.org/10.2174/1389200217666161201111146DOI Listing
September 2018

Statistical detection of geographic clusters of resistant Escherichia coli in a regional network with WHONET and SaTScan.

Expert Rev Anti Infect Ther 2016 11 6;14(11):1097-1107. Epub 2016 Sep 6.

a Brigham and Women's Hospital, Department of Medicine , Division of Infectious Diseases , Boston , MA , USA.

Background: While antimicrobial resistance threatens the prevention, treatment, and control of infectious diseases, systematic analysis of routine microbiology laboratory test results worldwide can alert new threats and promote timely response. This study explores statistical algorithms for recognizing geographic clustering of multi-resistant microbes within a healthcare network and monitoring the dissemination of new strains over time.

Methods: Escherichia coli antimicrobial susceptibility data from a three-year period stored in WHONET were analyzed across ten facilities in a healthcare network utilizing SaTScan's spatial multinomial model with two models for defining geographic proximity. We explored geographic clustering of multi-resistance phenotypes within the network and changes in clustering over time.

Results: Geographic clustering identified from both latitude/longitude and non-parametric facility groupings geographic models were similar, while the latter was offers greater flexibility and generalizability. Iterative application of the clustering algorithms suggested the possible recognition of the initial appearance of invasive E. coli ST131 in the clinical database of a single hospital and subsequent dissemination to others.

Conclusion: Systematic analysis of routine antimicrobial resistance susceptibility test results supports the recognition of geographic clustering of microbial phenotypic subpopulations with WHONET and SaTScan, and iterative application of these algorithms can detect the initial appearance in and dissemination across a region prompting early investigation, response, and containment measures.
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http://dx.doi.org/10.1080/14787210.2016.1220303DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5109973PMC
November 2016

World Health Organization Ranking of Antimicrobials According to Their Importance in Human Medicine: A Critical Step for Developing Risk Management Strategies to Control Antimicrobial Resistance From Food Animal Production.

Clin Infect Dis 2016 10 20;63(8):1087-1093. Epub 2016 Jul 20.

Department of Food Safety and Zoonosis, World Health Organization, Geneva, Switzerland.

Antimicrobial use in food animals selects for antimicrobial resistance in bacteria, which can spread to people. Reducing use of antimicrobials-particularly those deemed to be critically important for human medicine-in food production animals continues to be an important step for preserving the benefits of these antimicrobials for people. The World Health Organization ranking of antimicrobials according to their relative importance in human medicine was recently updated. Antimicrobials considered the highest priority among the critically important antimicrobials were quinolones, third- and fourth-generation cephalosporins, macrolides and ketolides, and glycopeptides. The updated ranking allows stakeholders in the agriculture sector and regulatory agencies to focus risk management efforts on drugs used in food animals that are the most important to human medicine. In particular, the current large-scale use of fluoroquinolones, macrolides, and third-generation cephalosporins and any potential use of glycopeptides and carbapenems need to be addressed urgently.
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http://dx.doi.org/10.1093/cid/ciw475DOI Listing
October 2016

The world's microbiology laboratories can be a global microbial sensor network.

Biomedica 2014 Apr;34 Suppl 1:9-15

Department of Medicine, Brigham and Women´s Hospital and Harvard Medical School, Boston, MA, USA.

The microbes that infect us spread in global and local epidemics, and the resistance genes that block their treatment spread within and between them. All we can know about where they are to track and contain them comes from the only places that can see them, the world's microbiology laboratories, but most report each patient's microbe only to that patient's caregiver. Sensors, ranging from instruments to birdwatchers, are now being linked in electronic networks to monitor and interpret algorithmically in real-time ocean currents, atmospheric carbon, supply-chain inventory, bird migration, etc. To so link the world's microbiology laboratories as exquisite sensors in a truly lifesaving real-time network their data must be accessed and fully subtyped. Microbiology laboratories put individual reports into inaccessible paper or mutually incompatible electronic reporting systems, but those from more than 2,200 laboratories in more than 108 countries worldwide are now accessed and translated into compatible WHONET files. These increasingly web-based files could initiate a global microbial sensor network. Unused microbiology laboratory byproduct data, now from drug susceptibility and biochemical testing but increasingly from new technologies (genotyping, MALDI-TOF, etc.), can be reused to subtype microbes of each genus/species into sub-groupings that are discriminated and traced with greater sensitivity. Ongoing statistical delineation of subtypes from global sensor network data will improve detection of movement into any patient of a microbe or resistance gene from another patient, medical center or country. Growing data on clinical manifestations and global distributions of subtypes can automate comments for patient's reports, select microbes to genotype and alert responders.
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http://dx.doi.org/10.1590/S0120-41572014000500002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4331131PMC
April 2014

Biochemical phenotypes to discriminate microbial subpopulations and improve outbreak detection.

PLoS One 2013 31;8(12):e84313. Epub 2013 Dec 31.

WHO Collaborating Centre for Surveillance of Antimicrobial Resistance, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.

Background: Clinical microbiology laboratories worldwide constitute an invaluable resource for monitoring emerging threats and the spread of antimicrobial resistance. We studied the growing number of biochemical tests routinely performed on clinical isolates to explore their value as epidemiological markers.

Methodology/principal Findings: Microbiology laboratory results from January 2009 through December 2011 from a 793-bed hospital stored in WHONET were examined. Variables included patient location, collection date, organism, and 47 biochemical and 17 antimicrobial susceptibility test results reported by Vitek 2. To identify biochemical tests that were particularly valuable (stable with repeat testing, but good variability across the species) or problematic (inconsistent results with repeat testing), three types of variance analyses were performed on isolates of K. pneumonia: descriptive analysis of discordant biochemical results in same-day isolates, an average within-patient variance index, and generalized linear mixed model variance component analysis.

Results: 4,200 isolates of K. pneumoniae were identified from 2,485 patients, 32% of whom had multiple isolates. The first two variance analyses highlighted SUCT, TyrA, GlyA, and GGT as "nuisance" biochemicals for which discordant within-patient test results impacted a high proportion of patient results, while dTAG had relatively good within-patient stability with good heterogeneity across the species. Variance component analyses confirmed the relative stability of dTAG, and identified additional biochemicals such as PHOS with a large between patient to within patient variance ratio. A reduced subset of biochemicals improved the robustness of strain definition for carbapenem-resistant K. pneumoniae. Surveillance analyses suggest that the reduced biochemical profile could improve the timeliness and specificity of outbreak detection algorithms.

Conclusions: The statistical approaches explored can improve the robust recognition of microbial subpopulations with routinely available biochemical test results, of value in the timely detection of outbreak clones and evolutionarily important genetic events.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0084313PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3877295PMC
September 2014

Laboratory-based prospective surveillance for community outbreaks of Shigella spp. in Argentina.

PLoS Negl Trop Dis 2013 12;7(12):e2521. Epub 2013 Dec 12.

Departamento Bacteriología, Instituto Nacional de Enfermedades Infecciosas ANLIS "Dr C. G. Malbrán", Buenos Aires, Argentina.

Background: To implement effective control measures, timely outbreak detection is essential. Shigella is the most common cause of bacterial diarrhea in Argentina. Highly resistant clones of Shigella have emerged, and outbreaks have been recognized in closed settings and in whole communities. We hereby report our experience with an evolving, integrated, laboratory-based, near real-time surveillance system operating in six contiguous provinces of Argentina during April 2009 to March 2012.

Methodology: To detect localized shigellosis outbreaks timely, we used the prospective space-time permutation scan statistic algorithm of SaTScan, embedded in WHONET software. Twenty three laboratories sent updated Shigella data on a weekly basis to the National Reference Laboratory. Cluster detection analysis was performed at several taxonomic levels: for all Shigella spp., for serotypes within species and for antimicrobial resistance phenotypes within species. Shigella isolates associated with statistically significant signals (clusters in time/space with recurrence interval ≥365 days) were subtyped by pulsed field gel electrophoresis (PFGE) using PulseNet protocols.

Principal Findings: In three years of active surveillance, our system detected 32 statistically significant events, 26 of them identified before hospital staff was aware of any unexpected increase in the number of Shigella isolates. Twenty-six signals were investigated by PFGE, which confirmed a close relationship among the isolates for 22 events (84.6%). Seven events were investigated epidemiologically, which revealed links among the patients. Seventeen events were found at the resistance profile level. The system detected events of public health importance: infrequent resistance profiles, long-lasting and/or re-emergent clusters and events important for their duration or size, which were reported to local public health authorities.

Conclusions/significance: The WHONET-SaTScan system may serve as a model for surveillance and can be applied to other pathogens, implemented by other networks, and scaled up to national and international levels for early detection and control of outbreaks.
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http://dx.doi.org/10.1371/journal.pntd.0002521DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3861122PMC
July 2014

Integrated Multilevel Surveillance of the World's Infecting Microbes and Their Resistance to Antimicrobial Agents.

Clin Microbiol Rev 2011 Apr;24(2):281-95

The World Health Organization Collaborating Centre for Surveillance of Antimicrobial Resistance, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.

Microbial surveillance systems have varied in their source of support; type of laboratory reporting (patient care or reference); inclusiveness of reports filed; extent of microbial typing; whether single hospital, multihospital, or multicountry; proportion of total medical centers participating; and types, levels, integration across levels, and automation of analyses performed. These surveillance systems variably support the diagnosis and treatment of patients, local or regional infection control, local or national policies and guidelines, laboratory capacity building, sentinel surveillance, and patient safety. Overall, however, only a small fraction of available data are under any surveillance, and very few data are fully integrated and analyzed. Advancing informatics and genomics can make microbial surveillance far more efficient and effective at preventing infections and improving their outcomes. The world's microbiology laboratories should upload their reports each day to programs that detect events, trends, and epidemics in communities, hospitals, countries, and the world.
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http://dx.doi.org/10.1128/CMR.00021-10DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3122493PMC
April 2011

A framework for global surveillance of antibiotic resistance.

Drug Resist Updat 2011 Apr 8;14(2):79-87. Epub 2011 Apr 8.

University Medical Centre Groningen, Rijksuniversiteit Groningen, The Netherlands.

The foreseen decline in antibiotic effectiveness explains the needs for data to inform the global public health agenda about the magnitude and evolution of antibiotic resistance as a serious threat to human health and development. Opportunistic bacterial pathogens are the cause of the majority of community and hospital-acquired infections worldwide. We provide an inventory of pre-existing regional surveillance programs in the six WHO regions which should form the underpinning for the consolidation of a global network infrastructure and we outline the structural components such as an international network of reference laboratories that need to be put in place to address the void of these crucial data. In addition we suggest to make use of existing Health and Demographic Surveillance Sites (HDSS) to obtain crucial information from communities in resource limited settings at household level in low- and middle-income countries in Asia and Africa. For optimising the use of surveillance data for public health action i.e. priority setting for new drug development, comparative quantification of antibiotic effectiveness at local, national, regional and global level and identification of the action gaps can be helpful.
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http://dx.doi.org/10.1016/j.drup.2011.02.007DOI Listing
April 2011

Automated detection of infectious disease outbreaks in hospitals: a retrospective cohort study.

PLoS Med 2010 Feb 23;7(2):e1000238. Epub 2010 Feb 23.

Division of Infectious Diseases and Health Policy Research Institute, University of California Irvine School of Medicine, Irvine, California, United States of America.

Background: Detection of outbreaks of hospital-acquired infections is often based on simple rules, such as the occurrence of three new cases of a single pathogen in two weeks on the same ward. These rules typically focus on only a few pathogens, and they do not account for the pathogens' underlying prevalence, the normal random variation in rates, and clusters that may occur beyond a single ward, such as those associated with specialty services. Ideally, outbreak detection programs should evaluate many pathogens, using a wide array of data sources.

Methods And Findings: We applied a space-time permutation scan statistic to microbiology data from patients admitted to a 750-bed academic medical center in 2002-2006, using WHONET-SaTScan laboratory information software from the World Health Organization (WHO) Collaborating Centre for Surveillance of Antimicrobial Resistance. We evaluated patients' first isolates for each potential pathogenic species. In order to evaluate hospital-associated infections, only pathogens first isolated >2 d after admission were included. Clusters were sought daily across the entire hospital, as well as in hospital wards, specialty services, and using similar antimicrobial susceptibility profiles. We assessed clusters that had a likelihood of occurring by chance less than once per year. For methicillin-resistant Staphylococcus aureus (MRSA) or vancomycin-resistant enterococci (VRE), WHONET-SaTScan-generated clusters were compared to those previously identified by the Infection Control program, which were based on a rule-based criterion of three occurrences in two weeks in the same ward. Two hospital epidemiologists independently classified each cluster's importance. From 2002 to 2006, WHONET-SaTScan found 59 clusters involving 2-27 patients (median 4). Clusters were identified by antimicrobial resistance profile (41%), wards (29%), service (13%), and hospital-wide assessments (17%). WHONET-SaTScan rapidly detected the two previously known gram-negative pathogen clusters. Compared to rule-based thresholds, WHONET-SaTScan considered only one of 73 previously designated MRSA clusters and 0 of 87 VRE clusters as episodes statistically unlikely to have occurred by chance. WHONET-SaTScan identified six MRSA and four VRE clusters that were previously unknown. Epidemiologists considered more than 95% of the 59 detected clusters to merit consideration, with 27% warranting active investigation or intervention.

Conclusions: Automated statistical software identified hospital clusters that had escaped routine detection. It also classified many previously identified clusters as events likely to occur because of normal random fluctuations. This automated method has the potential to provide valuable real-time guidance both by identifying otherwise unrecognized outbreaks and by preventing the unnecessary implementation of resource-intensive infection control measures that interfere with regular patient care. Please see later in the article for the Editors' Summary.
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http://dx.doi.org/10.1371/journal.pmed.1000238DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2826381PMC
February 2010

Analysis and presentation of cumulative antibiograms: a new consensus guideline from the Clinical and Laboratory Standards Institute.

Clin Infect Dis 2007 Mar 8;44(6):867-73. Epub 2007 Feb 8.

University of California Los Angeles Medical Center, Los Angeles, CA 90095-1713, USA.

It is crucial to monitor emerging trends in resistance at the local level to support clinical decision making, infection-control interventions, and antimicrobial-resistance containment strategies. Monitoring of antimicrobial resistance trends is commonly performed in health care facilities using an annual summary of susceptibility rates, known as a cumulative antibiogram report. The Clinical and Laboratory Standards Institute M39-A2 consensus document, entitled "Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data," provides guidance to clinical laboratories in the preparation of a cumulative antibiogram. The purpose of this review is to describe this document, explain the rationale for some of the recommendations, discuss limitations of its use, and propose new directions for future revisions. The document contains specific recommendations for the collection, storage, analysis, and presentation of data and includes sample templates highlighting the recommendations. Critical issues include the recommended frequency of reporting, the number of isolates to include in a statistic, and a mechanism for eliminating multiple isolates of a given bacterial species obtained from an individual patient.
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http://dx.doi.org/10.1086/511864DOI Listing
March 2007

Integrating Escherichia coli antimicrobial susceptibility data from multiple surveillance programs.

Emerg Infect Dis 2005 Jun;11(6):873-82

Alliance for the Prudent Use of Antibiotics, Boston, Massachusetts 02115, USA.

Collaboration between networks presents opportunities to increase analytical power and cross-validate findings. Multivariate analyses of 2 large, international datasets (MYSTIC and SENTRY) from the Global Advisory on Antibiotic Resistance Data program explored temporal, geographic, and demographic trends in Escherichia coli resistance from 1997 to 2001. Elevated rates of nonsusceptibility were seen in Latin America, southern Europe, and the western Pacific, and lower rates were seen in North America. For most antimicrobial drugs considered, nonsusceptibility was higher in isolates from men, older patients, and intensive care unit patients. Nonsusceptibility to ciprofloxacin was higher in younger patients, rose with time, and was not associated with intensive care unit status. In univariate analyses, estimates of nonsusceptibility from MYSTIC were consistently higher than those from SENTRY, but these differences disappeared in multivariate analyses, which supports the epidemiologic relevance of findings from the 2 programs, despite differences in surveillance strategies.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3367601PMC
http://dx.doi.org/10.3201/eid1106.041160DOI Listing
June 2005

Binary cumulative sums and moving averages in nosocomial infection cluster detection.

Emerg Infect Dis 2002 Dec;8(12):1426-32

Massachusetts General Hospital, Boston, USA.

Clusters of nosocomial infection often occur undetected, at substantial cost to the medical system and individual patients. We evaluated binary cumulative sum (CUSUM) and moving average (MA) control charts for automated detection of nosocomial clusters. We selected two outbreaks with genotyped strains and used resistance as inputs to the control charts. We identified design parameters for the CUSUM and MA (window size, k, alpha, Beta, p(0), p(1)) that detected both outbreaks, then calculated an associated positive predictive value (PPV) and time until detection (TUD) for sensitive charts. For CUSUM, optimal performance (high PPV, low TUD, fully sensitive) was for 0.1 < or = alpha < or = 0.25 and 0.2 < or = Beta < or = 0.25, with p(0) = 0.05, with a mean TUD of 20 (range 8-43) isolates. Mean PPV was 96.5% (relaxed criteria) to 82.6% (strict criteria). MAs had a mean PPV of 88.5% (relaxed criteria) to 46.1% (strict criteria). CUSUM and MA may be useful techniques for automated surveillance of resistant infections.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2737829PMC
http://dx.doi.org/10.3201/eid0812.010514DOI Listing
December 2002