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Microbiology Investigation Criteria for Reporting Objectively (MICRO): a framework for the reporting and interpretation of clinical microbiology data

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Abstract

Background

There is a pressing need to understand better the extent and distribution of antimicrobial resistance on a global scale, to inform development of effective interventions. Collation of datasets for meta-analysis, mathematical modelling and temporo-spatial analysis is hampered by the considerable variability in clinical sampling, variable quality in laboratory practice and inconsistencies in antimicrobial susceptibility testing and reporting.

Methods

The Microbiology Investigation Criteria for Reporting Objectively (MICRO) checklist was developed by an international working group of clinical and laboratory microbiologists, infectious disease physicians, epidemiologists and mathematical modellers.

Results

In keeping with the STROBE checklist, but applicable to all study designs, MICRO defines items to be included in reports of studies involving human clinical microbiology data. It provides a concise and comprehensive reference for clinicians, researchers, reviewers and journals working on, critically appraising, and publishing clinical microbiology datasets.

Conclusions

Implementation of the MICRO checklist will enhance the quality and scientific reporting of clinical microbiology data, increasing data utility and comparability to improve surveillance, grade data quality, facilitate meta-analyses and inform policy and interventions from local to global levels.

Background

There is a global drive to combat the growing problem of antimicrobial resistance (AMR) [1, 2]. To better understand the extent of the situation, a key activity is generation and analysis of high-quality surveillance data. A specific goal of the World Health Organization (WHO) and various funding agencies is to improve AMR surveillance in low- and middle-income countries (LMICs) [3]. There has been also a concerted effort to maximise reporting and analysis of available human clinical microbiology data [4]. However, the utility of many existing AMR datasets is hampered by considerable variability in clinical sampling and laboratory practices [5], along with readily demonstrable inconsistencies in antimicrobial susceptibility testing (AST) data and reporting [6, 7]. These issues result in difficulties in data interpretation and significantly limit inter-study comparability [8]. Examples of methodological and reporting issues and problems that can arise are highlighted in Table 1.

Table 1 Examples of frequently occurring problems in the generation and reporting of clinical antimicrobial resistance data

To ensure that technically accurate and comparable microbiology laboratory results are produced by clinical diagnostic laboratories, various organisations and documents provide guidance on quality management (recently reviewed in [9]), antimicrobial susceptibility testing procedures (e.g. Clinical and Laboratory Standards Institute (CLSI) and European Committee on Antimicrobial Susceptibility Testing (EUCAST) guidelines) and reporting of antimicrobial susceptibility data [10, 11]. Formal accreditation of laboratory quality management by national and/or international organisations (e.g. International Standards Organisation (ISO)) is not yet feasible for laboratories in all LMICs. However, use of standard operating procedures and internal quality controls (to ensure assays are performed reliably and yield intended results) plus, if possible, participation in an external quality assurance scheme (to periodically monitor accuracy and to compare performance with other laboratories) can ensure that such laboratories perform to international quality standards [12].

Previous statements using the STROBE model (Strengthening the Reporting of Observational Studies in Epidemiology) [13] relating to infectious diseases have already been issued, including STROBE-NI (neonatal infections [14]), STROME-ID (molecular testing [15]) and STROBE-AMS (antimicrobial stewardship [16]). In particular, STROBE-NI provides some guidance on reporting of microbiological methods (checklist items 4.6–4.8) but is not comprehensive and may not be sufficiently visible to those working on non-neonatal infections. As yet there are no general recommendations for good scientific reporting of clinical microbiology methodology and results, an area that this paper aims to address.

Methods

Aims and use of MICRO

The proposed Microbiology Investigation Criteria for Reporting Objectively (MICRO) framework described herein is a checklist of items to be included in reports of studies involving human clinical microbiology data, originating from any region of the world, in countries of all income levels. It provides a concise and comprehensive reference for clinicians, researchers, reviewers and journals working on, critically appraising, and publishing clinical microbiology datasets. It is intended to apply to the reporting of microbiology results in any clinical study, not only observational studies, and thus, the term STROBE has not been incorporated into the name. Implementation of this checklist aims to enhance the scientific reporting of clinical microbiology data, increasing data utility to improve surveillance, grade data quality, facilitate meta-analyses and inform policy and interventions from local to global levels.

Development of MICRO

The MICRO framework has been developed by a working group of clinical and laboratory microbiologists, infectious disease physicians, epidemiologists and mathematical modellers working in the UK and various LMICs, using an adaptation of recommended methodology [17] (Table 2). The need for a guideline was mooted during informal meetings as a result of discussions around the highly variable clarity and quality of clinical microbiology and/or AMR data in manuscripts submitted for peer-review. All pre-meeting steps were open and non-anonymised: discussions occurred by teleconference and documents were iterated and circulated electronically to the group.

Table 2 A summary of the MICRO framework development steps (derived from [17])

Review of published microbiology datasets from LMICs in South and South East Asia

Following on from identification of reporting problems from reviews of microbiology data from Africa [5, 8], published microbiology datasets from South and South East Asia were extracted for the ongoing AMR component of the Infectious Diseases Data Observatory-led systematic review, ‘Mapping the aetiology of non-malarial febrile illness globally in malaria-endemic regions’ (PROSPERO registration CRD42016049281). Details of the review search strategy are available at [18]. The review database was accessed on 17 June 2018, and all 177 available datasets were assessed to determine whether the following laboratory quality variables were reported (summarised in Additional file 1):

  • Laboratory EQA participation

  • AST methodology: scheme and version/year

  • Inclusion of internal quality control information for AST testing

We further assessed each dataset to determine whether any technically inconsistent AST results were reported for the following WHO Global Antimicrobial Resistance Surveillance System (GLASS) priority pathogens: Klebsiella pneumoniae, Salmonella spp., S. aureus and S. pneumoniae. These four pathogens were selected on the basis of being important AMR organisms globally, covering both Gram-negative and Gram-positive species, with a range of demonstrable reporting problems resulting from deviations from international guidelines. The intention was to provide illustrative examples of problems frequently encountered, with potentially important consequences, rather than identify the entire range. Data extraction was performed by one author (PS), and another author (PT) verified potential reporting deviations by re-review of the relevant source manuscripts.

Laboratory quality data could be assessed for 112 studies. None of the studies included details of EQA programme participation. Four fifths (93/112; 83%) provided details of an AST guideline: CLSI in almost all cases. The year or version number was not recorded in 12/93 (13%). Use of QC organisms was documented in only 24/112 studies (21%); 14 of these mentioned specific American Type Culture Collection (ATCC) strains.

Examples of deviations from accepted AST reporting practice were detected for all organisms assessed:

  • Staphylococcus aureus. Forty studies reported beta-lactam (penicillin, cephalosporin and/or carbapenem) susceptibility data for S. aureus. Of these, only 15 (38%) specifically included oxacillin and/or cefoxitin results. Several studies reported discordant results for 3rd generation cephalosporins and carbapenems, which would be expected to be more or less identical given the common resistance mechanism. Of note, six studies included susceptibility data for ceftazidime, an anti-pseudomonal third-generation cephalosporin with limited anti-Gram-positive activity which would not normally be tested against S. aureus: two reported susceptible isolates despite the absence of CLSI breakpoints.

  • Streptococcus pneumoniae. Two studies reported testing gentamicin and identification of susceptible isolates despite there being no breakpoints defined by either EUCAST or CLSI for this ‘bug-drug’ combination. Minimum inhibitory concentration (MIC) determination is required for confirmation of reduced susceptibility to penicillin among S. pneumoniae isolates by both CLSI and EUCAST criteria. However, of the seven studies reporting non-susceptible pneumococci, two reported this phenotype based on oxacillin disk diffusion testing alone and one confirmed penicillin MIC in only a subset of oxacillin non-susceptible isolates.

  • Klebsiella pneumoniae. Isolates were reported to be ampicillin susceptible (5–62% of isolates tested) in 5/11 (45%) studies reporting the species, despite almost universal intrinsic resistance globally and CLSI guidance to report all isolates as resistant [19].

  • Salmonella spp. Despite absence of in vivo activity, and a specific CLSI warning against reporting, 24/76 (32%) studies reporting Salmonella spp. included results for gentamicin. All but one study reported susceptible isolates, ranging from 33 to 100% of isolates tested.

Checklist development

Group discussions took place on five occasions between June 2017 and July 2018, where reporting issues, and potential checklist items, were discussed by members of the working group. The issues identified include all aspects pertaining to the unambiguous reporting of clinical microbiology data, including terminology, clinical context and sampling, organism identification and nomenclature, AST methodology, AMR definitions, handling of duplicate isolates and quality assurance (Table 3). This issue list was circulated to a wider group of clinical microbiologists, infectious diseases’ physicians, biomedical scientists (laboratory microbiologists), laboratory managers, epidemiologists and mathematical modellers (i.e. the authors of this manuscript) in advance of a meeting held in Bangkok, September 2018, where the final checklist was agreed. At the time of this meeting, the group members were all working at, or were associated with, clinical and/or research institutions in Asia (Cambodia, Indonesia, Laos, Myanmar, Nepal and Vietnam) or the UK.

Table 3 An overview of the MICRO checklist

Results

The MICRO checklist covers important aspects of reporting of clinical microbiology data. It is expected that it will be used in conjunction with an appropriate overall study reporting statement (e.g. STROBE). Items 1–13 cover key aspects of study methodology whilst 14–20 focus on result reporting (Table 4). Core items, i.e. those that would be expected to be included in every circumstance, are indicated by an asterisk. Non-core items might be appropriately described in the manuscript Additional file 1.

Table 4 The MICRO framework: a checklist of items that should be addressed in reports of studies involving human clinical microbiology data

Discussion

The use of the MICRO checklist will result in the clinical microbiology and AST data from studies being reported in a considerably more consistent manner. In addition to its utility during preparation and peer review of study manuscripts, this checklist will be also useful to researchers when planning new studies. It will increase data quality and will reduce the publication of uninterpretable results. Data harmonisation and opportunities for sharing will be promoted. Report clarity will be improved for non-specialist readers and the prospects for meaningful comparisons between studies will be increased. Indeed, an important use of the framework would be to permit quality grading of datasets for inclusion into meta-analyses. We envisage that there might be five categories based purely on the laboratory data (Table 5), although the quality criteria could be modified depending on the intentions of the meta-analysis.

Table 5 An example of quality grading criteria based on the laboratory components of MICRO

The major limitations of this work are that we did not perform an exhaustive literature review nor carry out a formal Delphi survey to inform the checklist items. However, the reporting errors sought in the literature review for South and South East Asia were the same as those identified previously in datasets from Africa [5, 8]. Common themes arose early and agreement was reached by repeated review with the final workshop allowing consensus. The final checklist contains items that should be readily available for a quality-assured clinical microbiology laboratory service. Thus, we feel confident that the major issues are included. The checklist will be piloted within the University of Oxford Tropical Network, and we will engage actively with the EQUATOR (Enhancing the QUAlity and Transparency Of health Research) Network and relevant professional organisations to promote its use more widely. User comments will be sought following publication and implementation of the checklist. In particular, feedback from users in non-Asian settings will be valuable. It is expected that revision will be required in time. We expect that technological development will result in significant expansion of guidance on reporting of molecular-only organism identification and AST results.

Conclusions

In summary, given the threats to human health from AMR globally, there is a pressing need to capture and model existing infection data whilst new surveillance initiatives mature sufficiently. The MICRO checklist provides a consistent and comprehensive reporting framework to ensure that interpretation and meta-analyses of such datasets are meaningful.

Abbreviations

AMR:

Antimicrobial resistance

AST:

Antimicrobial susceptibility testing

ATCC:

American Type Culture Collection

CAI:

Community-acquired infection

CLSI:

Clinical and Laboratory Standards Institute

CSF:

Cerebrospinal fluid

EQA:

External quality assurance

EQUATOR:

Enhancing the QUAlity and Transparency Of health Research

EUCAST:

European Committee on Antimicrobial Susceptibility Testing

GLASS:

Global Antimicrobial Resistance Surveillance System

HAI:

Hospital-acquired infection

ID:

Identification

ISO:

International Standards Organisation

LMIC:

Low- and middle-income country

MDR:

Multi-drug resistance

MIC:

Minimum inhibitory concentration

MRSA:

Methicillin-resistant Staphylococcus aureus

NTS:

Non-typhoidal Salmonella

QC:

Quality control

STROBE:

Strengthening the Reporting of Observational Studies in Epidemiology

STROBE-AMS:

Strengthening the Reporting of Observational Studies in Epidemiology for Antimicrobial Stewardship

STROBE-NI:

Strengthening the Reporting of Observational Studies in Epidemiology for Newborn Infection

STROME-ID:

Strengthening the Reporting of Molecular Epidemiology for Infectious Diseases

WHO:

World Health Organization

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Acknowledgements

Not applicable.

Funding

The MORU Tropical Health Network is core funded by Wellcome (grant number 106698/Z/14/Z). The funding body had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

Availability of data and materials

Not applicable (all necessary data provided in the manuscript and Additional file 1).

Author information

PT, AFL and EAA conceived the work. PS and PT conducted the published microbiology data review. All authors contributed to the development of the checklist. AFL and PT prepared the first draft of the manuscript. All authors read and approved the final manuscript.

Correspondence to Paul Turner.

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Additional file

Additional file 1:

Details of microbiology datasets from South and South East Asia included in the review. For each study, the antimicrobial susceptibility guideline details are summarised along with deviations from expected reporting for four bug-drug combinations: Klebsiella pneumoniae-ampicillin; Salmonella sp.-gentamicin; Staphylococcus aureus-beta-lactams; and Streptococcus pneumoniae-penicillin. The Staphylococcus aureus-beta-lactams combination includes any penicillin, cephalosporin or carbapenem apart from penicillinase-labile drugs (benzylpenicillin, phenoxymethylpenicillin, amoxicillin, ampicillin). (PDF 489 kb)

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Keywords

  • Antimicrobial
  • Susceptibility
  • Resistance
  • Microbiology
  • Reporting
  • Quality