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Integrating Scalable Genome Sequencing Into Microbiology Laboratories for Routine Antimicrobial Resistance Surveillance.

ABSTRACT: Antimicrobial resistance (AMR) is considered a global threat, and novel drug discovery needs to be complemented with systematic and standardized epidemiological surveillance. Surveillance data are currently generated using phenotypic characterization. However, due to poor scalability, this approach does little for true epidemiological investigations. There is a strong case for whole-genome sequencing (WGS) to enhance the phenotypic data. To establish global AMR surveillance using WGS, we developed a laboratory implementation approach that we applied within the NIHR Global Health Research Unit (GHRU) on Genomic Surveillance of Antimicrobial Resistance. In this paper, we outline the laboratory implementation at 4 units: Colombia, India, Nigeria, and the Philippines. The journey to embedding WGS capacity was split into 4 phases: Assessment, Assembly, Optimization, and Reassessment. We show that on-boarding WGS capabilities can greatly enhance the real-time processing power within regional and national AMR surveillance initiatives, despite the high initial investment in laboratory infrastructure and maintenance. Countries looking to introduce WGS as a surveillance tool could begin by sequencing select Global Antimicrobial Resistance Surveillance System (GLASS) priority pathogens that can demonstrate the standardization and impact genome sequencing has in tackling AMR.

SUBMITTER: Kekre M

PROVIDER: S-EPMC8634525 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundAntimicrobial resistance (AMR) is now recognized as a major threat to public health, and surveillance of AMR is essential for successful containment. In 2000, Japan Nosocomial Infections Surveillance (JANIS) Clinical Laboratory (CL) division has been launched as a voluntary AMR surveillance funded by the Ministry of Health, Labour and Welfare and managed by the National Institute of Infectious Diseases. In this study, we aimed to propose a model of sustainable national AMR surveillance which provides not only national AMR surveillance reports but also benchmarking reports to each hospital to facilitate infection control practices.MethodsJANIS CL division collects comprehensive specimen-based data complies with JANIS data format from participating hospitals each month. It had targeted only blood and cerebrospinal fluid samples but was expanded to all types of specimens in 2007 at revision of JANIS. The JANIS system interprets the antimicrobial susceptibility according to the same criteria and conducts removal of duplicates to allow accurate comparison between hospitals. Monthly feedback reports are created automatically within 48 h, while quarterly and annual reports are generated after data validation.ResultsAt the beginning, 468 hospitals were enrolled in the JANIS CL division, but the number of hospitals that submitted data decreased to 210 (45%) in 2006. After surveillance revision in 2007, annual recruitment of hospitals was initiated and as of 2015, 1475 hospitals participated, and 1461 (99%) of them submitted data throughout the year. Nationwide surveillance data collected over the past decade revealed that the prevalence of methicillin-resistant Staphylococcus aureus has decreased since 2008, and that its prevalence is higher in the western part of Japan, where the number of hospitals per capita is higher than in the eastern part.ConclusionsJANIS CL division serves a model of sustainable national AMR surveillance system. Comprehensive data for all specimens promotes understanding of the sampling frequency and prevalence of AMR. As a well-established system for providing rich information to guide action both locally and nationally, JANIS may also be utilized for sharing AMR data globally.

Project description:BackgroundRoutine microbiology results are a valuable source of antimicrobial resistance (AMR) surveillance data in low- and middle-income countries (LMICs) as well as in high-income countries. Different approaches and strategies are used to generate AMR surveillance data.ObjectivesWe aimed to review strategies for AMR surveillance using routine microbiology results in LMICs and to highlight areas that need support to generate high-quality AMR data.SourcesWe searched PubMed for papers that used routine microbiology to describe the epidemiology of AMR and drug-resistant infections in LMICs. We also included papers that, from our perspective, were critical in highlighting the biases and challenges or employed specific strategies to overcome these in reporting AMR surveillance in LMICs.ContentTopics covered included strategies of identifying AMR cases (including case-finding based on isolates from routine diagnostic specimens and case-based surveillance of clinical syndromes), of collecting data (including cohort, point-prevalence survey, and case-control), of sampling AMR cases (including lot quality assurance surveys), and of processing and analysing data for AMR surveillance in LMICs.ImplicationsThe various AMR surveillance strategies warrant a thorough understanding of their limitations and potential biases to ensure maximum utilization and interpretation of local routine microbiology data across time and space. For instance, surveillance using case-finding based on results from clinical diagnostic specimens is relatively easy to implement and sustain in LMIC settings, but the estimates of incidence and proportion of AMR is at risk of biases due to underuse of microbiology. Case-based surveillance of clinical syndromes generates informative statistics that can be translated to clinical practices but needs financial and technical support as well as locally tailored trainings to sustain. Innovative AMR surveillance strategies that can easily be implemented and sustained with minimal costs will be useful for improving AMR data availability and quality in LMICs.

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Integrating Scalable Genome Sequencing Into Microbiology Laboratories for Routine Antimicrobial Resistance Surveillance.

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