Clinical microbiology informatics

Working with the Electronic Health Record and Laboratory Information System to Maximize Ordering and Reporting of Molecular Microbiology Results

Molecular microbiology assays have a higher cost of testing compared to traditional methods and need to be utilized appropriately. Results from these assays may also require interpretation and appropriate follow-up. Electronic tools available in the electronic health record and laboratory information system can be deployed both preanalytically and postanalytically to influence ordering behaviors and positively impact diagnostic stewardship. Next generation technologies, such as machine learning and artificial intelligence, have the potential to expand upon the capabilities currently available and warrant additional study and development but also require regulation around their use in health care.

Preliminary reading of antibiogram by microdilution for clinical isolates in urine culture

PURPOSE

We evaluated a modification of automated antibiograms in urine cultures designed to facilitate the early interpretation of minimum inhibitory concentrations (MICs) and accelerate the targeted treatment of urinary tract infections (UTIs), METHODS: A prospective study was conducted of 309 isolates (219 Enterobacteriaceae, 75 Enterococcus spp., and 15 non-fermenting Gram-negative bacilli (NFGNB), and a retrospective study of 9 carbapenemase-producing clinical isolates from urine cultures. Colonies grown on conventional isolation plates were inoculated in MicroScan Walkaway system panels and incubated for 7 h, using a MicroScan AutoScan-4 plate reader for preliminary MIC determination by turbidimetry. Resulting antibiograms were compared with definitive antibiograms obtained after incubation for 17 h.

RESULTS

Preliminary and definitive readings were concordant for 86.7% of Gram-positive cocci isolates (65/75), 61.6% of Enterobacteriaceae (135/219), and 53.3% of NFGNB. The agreement rate was greater than 90% for most antimicrobials against Gram-positive cocci (94.7% or more) and Enterobacteriaceae, (97.2% or more for 10 of 17 antibiotics) except with nitrofurantoin (89%). The agreement rate was 86.7% or more for most antibiotics against NFGNB apart from piperacillin/tazobactam, aztreonam, amikacin, and ciprofloxacin. Gram-negative bacilli showed the highest differences in MIC values between preliminary and definitive readings.

CONCLUSIONS

A preliminary antibiogram reading may be useful in urine cultures to reduce the delay before targeted antibiotherapy, especially against Enterobacteriaceae and Gram-positive cocci, but not in cases of carbapenemase-producing NFGNB. Further local studies are warranted to evaluate the usefulness of this approach in relation to resistance rates.

Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology

Full Laboratory Automation is revolutionizing work habits in an increasing number of clinical microbiology facilities worldwide, generating huge streams of digital images for interpretation. Contextually, deep learning architectures are leading to paradigm shifts in the way computers can assist with difficult visual interpretation tasks in several domains. At the crossroads of these epochal trends, we present a system able to tackle a core task in clinical microbiology, namely the global interpretation of diagnostic bacterial culture plates, including presumptive pathogen identification. This is achieved by decomposing the problem into a hierarchy of complex subtasks and addressing them with a multi-network architecture we call DeepColony. Working on a large stream of clinical data and a complete set of 32 pathogens, the proposed system is capable of effectively assist plate interpretation with a surprising degree of accuracy in the widespread and demanding framework of Urinary Tract Infections. Moreover, thanks to the rich species-related generated information, DeepColony can be used for developing trustworthy clinical decision support services in laboratory automation ecosystems from local to global scale.

The Use of Machine Learning for Image Analysis Artificial Intelligence in Clinical Microbiology

The growing transition to digital microbiology in clinical laboratories creates the opportunity to interpret images using software. Software analysis tools can be designed to use human-curated knowledge and expert rules, but more novel artificial intelligence (AI) approaches such as machine learning (ML) are being integrated into clinical microbiology practice. These image analysis AI (IAAI) tools are beginning to penetrate routine clinical microbiology practice, and their scope and impact on routine clinical microbiology practice will continue to grow. This review separates the IAAI applications into 2 broad classification categories: (i) rare event detection/classification or (ii) score-based/categorical classification. Rare event detection can be used for screening purposes or for final identification of a microbe including microscopic detection of mycobacteria in a primary specimen, detection of bacterial colonies growing on nutrient agar, or detection of parasites in a stool preparation or blood smear. Score-based image analysis can be applied to a scoring system that classifies images in toto as its output interpretation and examples include application of the Nugent score for diagnosing bacterial vaginosis and interpretation of urine cultures. The benefits, challenges, development, and implementation strategies of IAAI tools are explored. In conclusion, IAAI is beginning to impact the routine practice of clinical microbiology, and its use can enhance the efficiency and quality of clinical microbiology practice. Although the future of IAAI is promising, currently IAAI only augments human effort and is not a replacement for human expertise.

Selective and Cascade Reporting of Antimicrobial Susceptibility Testing Results and Its Impact on Antimicrobial Resistance Surveillance-National Healthcare Safety Network, April 2020 to March 2021

Selective or cascade reporting (SR/CR) of antimicrobial susceptibility testing (AST) results is a strategy for antimicrobial stewardship. SR/CR is often achieved by suppressing AST results of secondary drugs in electronic laboratory reports. We assessed the extent of SR/CR and its impact on cumulative antibiograms (CAs) in a large cohort of U.S. hospitals submitting AST data to the CDC's National Healthcare Safety Network (NHSN) through electronic data exchange. The NHSN calls for hospitals to extract AST data from their electronic systems. We analyzed the AST reported for Escherichia coli (blood and urine) and Staphylococcus aureus (blood and lower respiratory tract [LRT]) isolates from April 2020 to March 2021, used AST reporting patterns to assign SR/CR reporting status for hospitals, and compared their CAs. Sensitivity analyses were done to account for those potentially extracted complete data. At least 35% and 41% of the hospitals had AST data that were suppressed in more than 20% blood isolates for E. coli and S. aureus isolates, respectively. At least 63% (blood) and 50% (urine) routinely reported ciprofloxacin or levofloxacin for E. coli isolates; and 60% (blood) and 59% (LRT) routinely reported vancomycin for S. aureus isolates. The distribution of CAs for many agents differed between high SR/CR and low- or non-SR/CR hospitals. Hospitals struggled to obtain complete AST data through electronic data exchange because of data suppression. Use of SR/CR can bias CAs if incomplete data are used. Technical solutions are needed for extracting complete AST results for public health surveillance. IMPORTANCE This study is the first to assess the extent of using selective and/or cascade antimicrobial susceptibility reporting for antimicrobial stewardship among U.S. hospitals and its impact on cumulative antibiograms in the context of electronic data exchange for national antimicrobial resistance surveillance.

Forty Years of Molecular Diagnostics for Infectious Diseases

Nearly 40 years have elapsed since the invention of the PCR, with its extremely sensitive and specific ability to detect nucleic acids via in vitro enzyme-mediated amplification. In turn, more than 2 years have passed since the onset of the coronavirus disease 2019 (COVID-19) pandemic, during which time molecular diagnostics for infectious diseases have assumed a larger global role than ever before. In this context, we review broadly the progression of molecular techniques in clinical microbiology, to their current prominence. Notably, these methods now entail both the detection and quantification of microbial nucleic acids, along with their sequence-based characterization. Overall, we seek to provide a combined perspective on the techniques themselves, as well as how they have come to shape health care at the intersection of technologic innovation, pathophysiologic knowledge, clinical/laboratory logistics, and even financial/regulatory factors.

Development and validation of MicrobEx: an open-source package for microbiology culture concept extraction

Objective

Microbiology culture reports contain critical information for important clinical and public health applications. However, microbiology reports often have complex, semistructured, free-text data that present a barrier for secondary use. Here we present the development and validation of an open-source package designed to ingest free-text microbiology reports, determine whether the culture is positive, and return a list of Systemized Nomenclature of Medicine (SNOMED)-CT mapped bacteria.

Materials and Methods

Our concept extraction Python package, MicrobEx, is built upon a rule-based natural language processing algorithm and was developed using microbiology reports from 2 different electronic health record systems in a large healthcare organization, and then externally validated on the reports of 2 other institutions with manually reviewed results as a benchmark.

Results

MicrobEx achieved F1 scores >0.95 on all classification tasks across 2 independent validation sets with minimal customization. Additionally, MicrobEx matched or surpassed our MetaMap-based benchmark algorithm performance across positive culture classification and species capture classification tasks.

Discussion

Our results suggest that MicrobEx can be used to reliably estimate binary bacterial culture status, extract bacterial species, and map these to SNOMED organism observations when applied to semistructured, free-text microbiology reports from different institutions with relatively low customization.

Conclusion

MicrobEx offers an open-source software solution (available on both GitHub and PyPI) for bacterial culture status estimation and bacterial species extraction from free-text microbiology reports. The package was designed to be reused and adapted to individual institutions as an upstream process for other clinical applications such as: machine learning, clinical decision support, and disease surveillance systems.

Teleclinical Microbiology: An Innovative Approach to Providing Web-Enabled Diagnostic Laboratory Services in Syria

OBJECTIVES

Telemedicine can compensate for the lack of health care specialists in response to protracted humanitarian crises. We sought to assess the usability of a teleclinical microbiology (TCM) program to provide diagnostic services in a hard-to-reach region of Syria.

METHODS

A semimobile station was equipped with conventional micrograph and macrograph digital imaging systems. An electronic platform (Telemicrobiology in Humanitarian Crises, TmHC) was created to facilitate sharing, interpreting, and storing the results. A pilot study was conducted to identify the bacterial species and antimicrobial susceptibility pattern of 74 urinary clinical isolates. An experience survey was conducted to capture the feedback of 8 participants in the program.

RESULTS

The TmHC platform (https://sdh.ngo/tmhc/) enabled systematic transmission of the laboratory records and co-interpretation of the results. The isolates were identified as Escherichia coli (n = 61), Klebsiella pneumoniae (n = 12), and Proteus mirabilis(n = 1). All the isolates were multidrug resistant. The performance of our TCM module was rated 4 (satisfying) and 5 (very satisfying) by 6 and 2 users, respectively. Data security of and cost-effectiveness were the main perceived concerns.

CONCLUSIONS

Although we encountered several context-related obstacles, our TCM program managed to reach a highly vulnerable population of 4 million people confined in the northwest region of Syria.

Microbes, Clinical trials, Drug Discovery, and Vaccine Development: The Current Perspectives

Because of the frequent emergence of novel microbial species and the re-emergence of genetic variants of hitherto known microbes, the global healthcare system, and human health has been thrown into jeopardy. Also, certain microbes that possess the ability to develop multi-drug resistance (MDR) have limited the treatment options in cases of serious infections, and increased hospital and treatment costs, and associated morbidity and mortality. The recent discovery of the novel Coronavirus (n-CoV), the Severe Acute Respiratory Syndrome CoV-2 (SARS-CoV-2) that is causing the CoV Disease-19 (COVID-19) has resulted in severe morbidity and mortality throughout the world affecting normal human lives. The major concern with the current pandemic is the non-availability of specific drugs and an incomplete understanding of the pathobiology of the virus. It is therefore important for pharmaceutical establishments to envisage the discovery of therapeutic interventions and potential vaccines against the novel and MDR microbes. Therefore, this review is attempted to update and explore the current perspectives in microbes, clinical research, drug discovery, and vaccine development to effectively combat the emerging novel and re-emerging genetic variants of microbes.

Artificial Intelligence and Mapping a New Direction in Laboratory Medicine: A Review

BACKGROUND

Modern artificial intelligence (AI) and machine learning (ML) methods are now capable of completing tasks with performance characteristics that are comparable to those of expert human operators. As a result, many areas throughout healthcare are incorporating these technologies, including in vitro diagnostics and, more broadly, laboratory medicine. However, there are limited literature reviews of the landscape, likely future, and challenges of the application of AI/ML in laboratory medicine.

CONTENT

In this review, we begin with a brief introduction to AI and its subfield of ML. The ensuing sections describe ML systems that are currently in clinical laboratory practice or are being proposed for such use in recent literature, ML systems that use laboratory data outside the clinical laboratory, challenges to the adoption of ML, and future opportunities for ML in laboratory medicine.

SUMMARY

AI and ML have and will continue to influence the practice and scope of laboratory medicine dramatically. This has been made possible by advancements in modern computing and the widespread digitization of health information. These technologies are being rapidly developed and described, but in comparison, their implementation thus far has been modest. To spur the implementation of reliable and sophisticated ML-based technologies, we need to establish best practices further and improve our information system and communication infrastructure. The participation of the clinical laboratory community is essential to ensure that laboratory data are sufficiently available and incorporated conscientiously into robust, safe, and clinically effective ML-supported clinical diagnostics.

Leapfrogging laboratories: the promise and pitfalls of high-tech solutions for antimicrobial resistance surveillance in low-income settings

The scope and trajectory of today’s escalating antimicrobial resistance (AMR) crisis is inadequately captured by existing surveillance systems, particularly those of lower income settings. AMR surveillance systems typically collate data from routine culture and susceptibility testing performed in diagnostic bacteriology laboratories to support healthcare. Limited access to high quality culture and susceptibility testing results in the dearth of AMR surveillance data, typical of many parts of the world where the infectious disease burden and antimicrobial need are high. Culture and susceptibility testing by traditional techniques is also slow, which limits its value in infection management. Here, we outline hurdles to effective resistance surveillance in many low-income settings and encourage an open attitude towards new and evolving technologies that, if adopted, could close resistance surveillance gaps. Emerging advancements in point-of-care testing, laboratory detection of resistance through or without culture, and in data handling, have the potential to generate resistance data from previously unrepresented locales while simultaneously supporting healthcare. Among them are microfluidic, nucleic acid amplification technology and next-generation sequencing approaches. Other low tech or as yet unidentified innovations could also rapidly accelerate AMR surveillance. Parallel advances in data handling further promise to significantly improve AMR surveillance, and new frameworks that can capture, collate and use alternate data formats may need to be developed. We outline the promise and limitations of such technologies, their potential to leapfrog surveillance over currently available, conventional technologies in use today and early steps that health systems could take towards preparing to adopt them.

Recent advances in rapid antimicrobial susceptibility testing systems

INTRODUCTION

Until recently antimicrobial susceptibility testing (AST) methods based on the demonstration of phenotypic susceptibility in 16-24 h remained largely unchanged.

AREAS COVERED

Advances in rapid phenotypic and molecular-based AST systems.

EXPERT OPINION

AST has changed over the past decade, with many rapid phenotypic and molecular methods developed to demonstrate phenotypic or genotypic resistance, or biochemical markers of resistance such as β-lactamases associated with carbapenem resistance. Most methods still require isolation of bacteria from specimens before both legacy and newer methods can be used. Bacterial identification by MALDI-TOF mass spectroscopy is now widely used and is often key to the interpretation of rapid AST results. Several PCR arrays are available to detect the most frequent pathogens associated with bloodstream infections and their major antimicrobial resistance genes. Many advances in whole-genome sequencing of bacteria and fungi isolated by culture as well as directly from clinical specimens have been made but are not yet widely available. High cost and limited throughput are the major obstacles to uptake of rapid methods, but targeted use, continued development and decreasing costs are expected to result in more extensive use of these increasingly useful methods.

Laboratory informatics capacity for effective antimicrobial resistance surveillance in resource-limited settings

Antimicrobial resistance (AMR) is a major threat to human health globally. Surveillance is a key activity to determine AMR burden, impacts, and trends and to monitor effects of interventions. Surveillance systems require efficient capture and onward sharing of high-quality laboratory data. Substantial investment is being made to improve laboratory capacity, particularly in low-income and middle-income countries (LMICs) with high disease burdens. However, building capacity for effective laboratory data management remains an under-resourced area, which, unless addressed, will limit progress towards comprehensive AMR surveillance in LMICs. The lack of a fit-for-purpose and open-source laboratory information management system software is of particular concern. In this Personal View, we summarise the technical requirements for microbiology laboratory data management, provide a snapshot of laboratory data management in LMIC laboratories, and describe the key steps required to improve the situation. Without action to improve information technology infrastructure and data management systems in microbiology laboratories, the ongoing efforts to develop capacity for AMR surveillance in LMICs might not realise their full potential.

Performance of the VITEK®2 advanced expert system™ for the validation of antimicrobial susceptibility testing results

Time to reporting antimicrobial susceptibility testing (AST) results to physicians plays an essential role in antibiotic stewardship programs. Expert software has been developed for facilitating the microbiologists' AST review process. The reliability of the VITEK®2 Advanced Expert™ software to effectively alert the microbiologist in detection of atypical and inconsistent AST results was assessed at the Labor Berlin-Charité Vivantes services. The results demonstrated a confidence rate of 99.3% in reporting fully consistent AST results to physicians.

Analysis of trends of post-analytical quality indicators in Clinical Bacteriology laboratory: A pilot study from a tertiary care teaching hospital in Uttarakhand, India

CONTEXT

Implementation of quality management system (QMS) which encompasses various quality indicators (QIs), can serve as a stepping stone for continuous improvement & help in achieving globally accepted quality standards in a diagnostic laboratory.

AIMS

To generate preliminary data on trends of post-analytical QIs in Bacteriology section.

SETTINGS AND DESIGN

A pilot study was conducted at a tertiary care teaching hospital located in Rishikesh, Uttarakhand.

METHODS AND MATERIAL

Data of the following four quality indicators pertaining to aerobic culture and sensitivity testing of various clinical samples received in Clinical Bacteriology laboratory was compiled and retrospectively analysed: (i) Rate of reporting errors; (ii) Rate of re-dos; (iii) Percentage of reports correlating with clinical diagnosis; (iv) Percentage of adherence to safety precautions by employees working in diagnostics.

STATISTICAL ANALYSIS USED

Descriptive statistics like mean and frequency distribution plots.

RESULTS

The mean reporting error rate was 0.12 per 1000 tests. It was consistently low from July 2018 to May 2019, after which an overall increasing trend was observed. The mean rate of re-dos was 2.79 per 1000 tests. An overall decreasing trend was observed with maximum rates during the months of December 2017 and January 2018. On an average only 7.86% of the reports co-related with clinical diagnosis. Almost 100% adherence to safety precautions was observed with the exception of two instances of needle stick injuries (NSIs).

CONCLUSIONS

Commitment of laboratory personnel in adopting, maintaining and analysing QMS data will lead to further strengthening of our existing healthcare system.

Digital microbiology

BACKGROUND

Digitalization and artificial intelligence have an important impact on the way microbiology laboratories will work in the near future. Opportunities and challenges lie ahead to digitalize the microbiological workflows. Making efficient use of big data, machine learning, and artificial intelligence in clinical microbiology requires a profound understanding of data handling aspects.

OBJECTIVE

This review article summarizes the most important concepts of digital microbiology. The article gives microbiologists, clinicians and data scientists a viewpoint and practical examples along the diagnostic process.

SOURCES

We used peer-reviewed literature identified by a PubMed search for digitalization, machine learning, artificial intelligence and microbiology.

CONTENT

We describe the opportunities and challenges of digitalization in microbiological diagnostic processes with various examples. We also provide in this context key aspects of data structure and interoperability, as well as legal aspects. Finally, we outline the way for applications in a modern microbiology laboratory.

IMPLICATIONS

We predict that digitalization and the usage of machine learning will have a profound impact on the daily routine of laboratory staff. Along the analytical process, the most important steps should be identified, where digital technologies can be applied and provide a benefit. The education of all staff involved should be adapted to prepare for the advances in digital microbiology.

Computer Vision and Artificial Intelligence Are Emerging Diagnostic Tools for the Clinical Microbiologist

Artificial intelligence (AI) is increasingly becoming an important component of clinical microbiology informatics. Researchers, microbiologists, laboratorians, and diagnosticians are interested in AI-based testing because these solutions have the potential to improve a test's turnaround time, quality, and cost. A study by Mathison et al. used computer vision AI (B. A. Mathison, J. L. Kohan, J. F. Walker, R. B. Smith, et al., J Clin Microbiol 58:e02053-19, 2020, https://doi.org/10.1128/JCM.02053-19), but additional opportunities for AI applications exist within the clinical microbiology laboratory. Large data sets within clinical microbiology that are amenable to the development of AI diagnostics include genomic information from isolated bacteria, metagenomic microbial findings from primary specimens, mass spectra captured from cultured bacterial isolates, and large digital images, which is the medium that Mathison et al. chose to use. AI in general and computer vision in specific are emerging tools that clinical microbiologists need to study, develop, and implement in order to improve clinical microbiology.

Consolidation of Clinical Microbiology Laboratories and Introduction of Transformative Technologies

Clinical microbiology is experiencing revolutionary advances in the deployment of molecular, genome sequencing-based, and mass spectrometry-driven detection, identification, and characterization assays. Laboratory automation and the linkage of information systems for big(ger) data management, including artificial intelligence (AI) approaches, also are being introduced. The initial optimism associated with these developments has now entered a more reality-driven phase of reflection on the significant challenges, complexities, and health care benefits posed by these innovations. With this in mind, the ongoing process of clinical laboratory consolidation, covering large geographical regions, represents an opportunity for the efficient and cost-effective introduction of new laboratory technologies and improvements in translational research and development. This will further define and generate the mandatory infrastructure used in validation and implementation of newer high-throughput diagnostic approaches. Effective, structured access to large numbers of well-documented biobanked biological materials from networked laboratories will release countless opportunities for clinical and scientific infectious disease research and will generate positive health care impacts. We describe why consolidation of clinical microbiology laboratories will generate quality benefits for many, if not most, aspects of the services separate institutions already provided individually. We also define the important role of innovative and large-scale diagnostic platforms. Such platforms lend themselves particularly well to computational (AI)-driven genomics and bioinformatics applications. These and other diagnostic innovations will allow for better infectious disease detection, surveillance, and prevention with novel translational research and optimized (diagnostic) product and service development opportunities as key results.

Essential role of laboratory physicians in transformation of laboratory practice and management to a value-based patient-centric model

The laboratory is a vital part of the continuum of patient care. In fact, there are few programs in the healthcare system that do not rely on ready access and availability of complex diagnostic laboratory services. The existing transactional model of laboratory "medical practice" will not be able to meet the needs of the healthcare system as it rapidly shifts toward value-based care and precision medicine, which demands that practice be based on total system indicators, clinical effectiveness, and patient outcomes. Laboratory "value" will no longer be focused primarily on internal testing quality and efficiencies but rather on the relative cost of diagnostic testing compared to direct improvement in clinical and system outcomes. The medical laboratory as a "business" focused on operational efficiency and cost-controls must transform to become an essential clinical service that is a tightly integrated equal partner in direct patient care. We would argue that this paradigm shift would not be necessary if laboratory services had remained a "patient-centric" medical practice throughout the last few decades. This review is focused on the essential role of laboratory physicians in transforming laboratory practice and management to a value-based patient-centric model. Value-based practice is necessary not only to meet the challenges of the new precision medicine world order but also to bring about sustainable healthcare service delivery.

In-Vitro Biofilm Formation and Antimicrobial Resistance of Escherichia coli in Diabetic and Nondiabetic Patients

Background

Diabetic patients are more susceptible to urinary tract infection compared to nondiabetic patients, Escherichia coli being the most common uropathogen causing UTI. Unreasonable and incorrect antibiotic prescription for UTI in these patients may induce the development of antibiotic-resistant urinary pathogens resulting in delayed recovery and longer hospitalization. In addition to these, biofilm forming capacity of the pathogen may worsen the problem. The main aim of this cross-sectional study (conducted from March to September 2015) is to detect the biofilm forming capacity of UTI causing micro-organisms and compare the antibiotic resistance pattern of Escherichia coli, the most common cause of UTI, which will help the physician in choosing the best antibiotic.

Method

Total of 1,099 clean-catch mid stream urine (CCMSU) was processed by standard microbiological technique; 182 were from the diabetic group and 917 nondiabetic. Following identification, all isolates were subjected to antibiotic susceptibility testing using modified Kirby-Bauer disc diffusion method. In-vitro biofilm forming capacity of the isolates were detected by Microtitre plate method. The data were analyzed using SPSS software 16.

Result

Urinary tract infection was found to be significantly higher in diabetic patients (42.9%) compared to nondiabetic patients (17.4%) with Escherichia coli as the most common uropathogen in both diabetic and nondiabetic groups. Similarly, UTI was more common in elderly population (29.5%). Imipenem, nitrofurantoin and amikacin were found to be the most effective drug for uropathogenic E. coli in both diabetic and nondiabetic patients, whereas amoxicillin, ciprofloxacin, and cotrimoxazole were least effective. Of the total bacterial isolates, 43.3% showed positive results for in-vitro biofilm production by the Microtitre plate method. A significantly higher resistance rate was observed among biofilm producing E. coli for quinolones, cotrimoxazole, and third generation cephalosporin ceftriaxone. Most of the biofilm producers (79.5%) were found to be MDR (p-value 0.015).

Conclusion

Elderly populations with diabetes are at a higher risk of UTI. Higher biofilm production and resistance to in-use antimicrobial agents in this study render its inefficacy for empirical treatment and point out the importance of biofilm screening to ensure the effective management of infection.

Optimization of Turnaround Time for Group A Streptococcus PCR

The use of some nucleic acid amplification tests (NAATs) for the diagnosis of group A Streptococcus (GAS) pharyngitis allows laboratories to adopt single-tiered testing without reflex culture. However, centralization may delay the delivery of actionable information to the bedside, particularly in the outpatient setting. We describe two novel workflows at our institution and their effect on in-lab turnaround time (TAT) at a tertiary care microbiology lab. Laboratory records were extracted, and relevant data were analyzed after the implementation of qualitative in vitro diagnostic testing for GAS with the Xpert Xpress Strep A assay, performed using the GeneXpert Infinity-48s. Workflow optimization steps studied included: (i) direct specimen submission to the microbiology laboratory via the pneumatic tube system and (ii) autoverification of GAS NAAT results in the laboratory information system. Between April 2018 and October 2018, 2,595 unique specimens were tested for GAS by PCR. Of these, 2,523 were included in the final analysis. Linear regression established that the total in-lab TAT was significantly reduced by direct specimen submission to the microbiology laboratory, autoverification, and processing during the night shift. We describe two workflow optimization methods that reduced the in-lab TAT for GAS NAAT. Although microbiology labs historically use manual processes, the advent of total laboratory automation and the adoption of on-demand NAATs will allow for more streamlined processing of microbiology specimens. It may be beneficial to consider instrument interfacing and specimen processing optimization during the early phases of implementation planning for NAATs in the microbiology laboratory.

Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry for the Rapid Detection of Antimicrobial Resistance Mechanisms and Beyond

Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) has been successfully applied in recent years for first-line identification of pathogens in clinical microbiology because it is simple to use, rapid, and accurate and has economic benefits in hospital management. The range of clinical applications of MALDI-TOF MS for bacterial isolates is increasing constantly, from species identification to the two most promising applications in the near future: detection of antimicrobial resistance and strain typing for epidemiological studies. The aim of this review is to outline the contribution of previous MALDI-TOF MS studies in relation to detection of antimicrobial resistance and to discuss potential future challenges in this field. Three main approaches are ready (or almost ready) for clinical use, including the detection of antibiotic modifications due to the enzymatic activity of bacteria, the detection of antimicrobial resistance by analysis of the peak patterns of bacteria or mass peak profiles, and the detection of resistance by semiquantification of bacterial growth in the presence of a given antibiotic. This review provides an expert guide for MALDI-TOF MS users to new approaches in the field of antimicrobial resistance detection, especially possible applications as a routine diagnostic tool in microbiology laboratories.

Lowering the Barriers to Routine Whole-Genome Sequencing of Bacteria in the Clinical Microbiology Laboratory

Whole-genome sequencing of bacterial isolates is increasingly being used to predict antibacterial susceptibility and resistance. Mason and coauthors describe the phenotypic susceptibility interpretations of more than 1,300 Staphylococcus aureus isolates tested against a dozen antistaphylococcal agents, and they compared these findings to susceptibility predictions made by analyzing whole-genome sequence data (J Clin Microbiol 56:e01815-17, 2018, https://doi.org/10.1128/JCM.01815-17). The genotype-phenotype susceptibility interpretations correlated in 96.3% (2,720/2,825) of resistant findings and 98.8% (11,504/11,639) of susceptible findings. This work by Mason and colleagues is helping to lower the barriers to using whole-genome sequencing of S. aureus in clinical microbiology practice.

Development and evaluation of a novel user interface for reviewing clinical microbiology results

Background

Microbiology laboratory results are complex and cumbersome to review. We sought to develop a new review tool to improve the ease and accuracy of microbiology results review.

Methods

We observed and informally interviewed clinicians to determine areas in which existing microbiology review tools were lacking. We developed a new tool that reorganizes microbiology results by time and organism. We conducted a scenario-based usability evaluation to compare the new tool to existing legacy tools, using a balanced block design.

Results

The average time-on-task decreased from 45.3 min for the legacy tools to 27.1 min for the new tool (P < .0001). Total errors decreased from 41 with the legacy tools to 19 with the new tool (P = .0068). The average Single Ease Question score was 5.65 (out of 7) for the new tool, compared to 3.78 for the legacy tools (P < .0001). The new tool scored 88 ("Excellent") on the System Usability Scale.

Conclusions

The new tool substantially improved efficiency, accuracy, and usability. It was subsequently integrated into the electronic health record and rolled out system-wide. This project provides an example of how clinical and informatics teams can innovative alongside a commercial Electronic Health Record (EHR).

Data Science Priorities for a University Hospital-Based Institute of Infectious Diseases: A Viewpoint

Automation of laboratory tests, bioinformatic analysis of biological sequences, and professional data management are used routinely in a modern university hospital-based infectious diseases institute. This dates back to at least the 1980s. However, the scientific methods of this 21st century are changing with the increased power and speed of computers, with the "big data" revolution having already happened in genomics and environment, and eventually arriving in medical informatics. The research will be increasingly "data driven," and the powerful machine learning methods whose efficiency is demonstrated in daily life will also revolutionize medical research. A university-based institute of infectious diseases must therefore not only gather excellent computer scientists and statisticians (as in the past, and as in any medical discipline), but also fully integrate the biologists and clinicians with these computer scientists, statisticians, and mathematical modelers having a broad culture in machine learning, knowledge representation, and knowledge discovery.

Clinical bacteriology in low-resource settings: today's solutions

Low-resource settings are disproportionately burdened by infectious diseases and antimicrobial resistance. Good quality clinical bacteriology through a well functioning reference laboratory network is necessary for effective resistance control, but low-resource settings face infrastructural, technical, and behavioural challenges in the implementation of clinical bacteriology. In this Personal View, we explore what constitutes successful implementation of clinical bacteriology in low-resource settings and describe a framework for implementation that is suitable for general referral hospitals in low-income and middle-income countries with a moderate infrastructure. Most microbiological techniques and equipment are not developed for the specific needs of such settings. Pending the arrival of a new generation diagnostics for these settings, we suggest focus on improving, adapting, and implementing conventional, culture-based techniques. Priorities in low-resource settings include harmonised, quality assured, and tropicalised equipment, consumables, and techniques, and rationalised bacterial identification and testing for antimicrobial resistance. Diagnostics should be integrated into clinical care and patient management; clinically relevant specimens must be appropriately selected and prioritised. Open-access training materials and information management tools should be developed. Also important is the need for onsite validation and field adoption of diagnostics in low-resource settings, with considerable shortening of the time between development and implementation of diagnostics. We argue that the implementation of clinical bacteriology in low-resource settings improves patient management, provides valuable surveillance for local antibiotic treatment guidelines and national policies, and supports containment of antimicrobial resistance and the prevention and control of hospital-acquired infections.

Addressing the key communication barriers between microbiology laboratories and clinical units: a qualitative study

Background

Many countries are on the brink of establishing antibiotic stewardship programmes in hospitals nationwide. In a previous study we found that communication between microbiology laboratories and clinical units is a barrier to implementing efficient antibiotic stewardship programmes in Norway. We have now addressed the key communication barriers between microbiology laboratories and clinical units from a laboratory point of view.

Methods

Qualitative semi-structured interviews were conducted with 18 employees (managers, doctors and technicians) from six diverse Norwegian microbiological laboratories, representing all four regional health authorities. Interviews were recorded and transcribed verbatim. Thematic analysis was applied, identifying emergent themes, subthemes and corresponding descriptions.

Results

The main barrier to communication is disruption involving specimen logistics, information on request forms, verbal reporting of test results and information transfer between poorly integrated IT systems. Furthermore, communication is challenged by lack of insight into each other's area of expertise and limited provision of laboratory services, leading to prolonged turnaround time, limited advisory services and restricted opening hours.

Conclusions

Communication between microbiology laboratories and clinical units can be improved by a review of testing processes, educational programmes to increase insights into the other's area of expertise, an evaluation of work tasks and expansion of rapid and point-of-care test services. Antibiotic stewardship programmes may serve as a valuable framework to establish these measures.

Hyperspectral image analysis for rapid and accurate discrimination of bacterial infections: A benchmark study

With the rapid diffusion of Full Laboratory Automation systems, Clinical Microbiology is currently experiencing a new digital revolution. The ability to capture and process large amounts of visual data from microbiological specimen processing enables the definition of completely new objectives. These include the direct identification of pathogens growing on culturing plates, with expected improvements in rapid definition of the right treatment for patients affected by bacterial infections. In this framework, the synergies between light spectroscopy and image analysis, offered by hyperspectral imaging, are of prominent interest. This leads us to assess the feasibility of a reliable and rapid discrimination of pathogens through the classification of their spectral signatures extracted from hyperspectral image acquisitions of bacteria colonies growing on blood agar plates. We designed and implemented the whole data acquisition and processing pipeline and performed a comprehensive comparison among 40 combinations of different data preprocessing and classification techniques. High discrimination performance has been achieved also thanks to improved colony segmentation and spectral signature extraction. Experimental results reveal the high accuracy and suitability of the proposed approach, driving the selection of most suitable and scalable classification pipelines and stimulating clinical validations.

Validation and Implementation of Clinical Laboratory Improvements Act-Compliant Whole-Genome Sequencing in the Public Health Microbiology Laboratory

Public health microbiology laboratories (PHLs) are on the cusp of unprecedented improvements in pathogen identification, antibiotic resistance detection, and outbreak investigation by using whole-genome sequencing (WGS). However, considerable challenges remain due to the lack of common standards. Here, we describe the validation of WGS on the Illumina platform for routine use in PHLs according to Clinical Laboratory Improvements Act (CLIA) guidelines for laboratory-developed tests (LDTs). We developed a validation panel comprising 10 Enterobacteriaceae isolates, 5 Gram-positive cocci, 5 Gram-negative nonfermenting species, 9 Mycobacterium tuberculosis isolates, and 5 miscellaneous bacteria. The genome coverage range was 15.71× to 216.4× (average, 79.72×; median, 71.55×); the limit of detection (LOD) for single nucleotide polymorphisms (SNPs) was 60×. The accuracy, reproducibility, and repeatability of base calling were >99.9%. The accuracy of phylogenetic analysis was 100%. The specificity and sensitivity inferred from multilocus sequence typing (MLST) and genome-wide SNP-based phylogenetic assays were 100%. The following objectives were accomplished: (i) the establishment of the performance specifications for WGS applications in PHLs according to CLIA guidelines, (ii) the development of quality assurance and quality control measures, (iii) the development of a reporting format for end users with or without WGS expertise, (iv) the availability of a validation set of microorganisms, and (v) the creation of a modular template for the validation of WGS processes in PHLs. The validation panel, sequencing analytics, and raw sequences could facilitate multilaboratory comparisons of WGS data. Additionally, the WGS performance specifications and modular template are adaptable for the validation of other platforms and reagent kits.

Antimicrobial Stewardship: How the Microbiology Laboratory Can Right the Ship

Antimicrobial stewardship is a bundle of integrated interventions employed to optimize the use of antimicrobials in health care settings. While infectious-disease-trained physicians, with clinical pharmacists, are considered the main leaders of antimicrobial stewardship programs, clinical microbiologists can play a key role in these programs. This review is intended to provide a comprehensive discussion of the different components of antimicrobial stewardship in which microbiology laboratories and clinical microbiologists can make significant contributions, including cumulative antimicrobial susceptibility reports, enhanced culture and susceptibility reports, guidance in the preanalytic phase, rapid diagnostic test availability, provider education, and alert and surveillance systems. In reviewing this material, we emphasize how the rapid, and especially the recent, evolution of clinical microbiology has reinforced the importance of clinical microbiologists' collaboration with antimicrobial stewardship programs.

Clinical and laboratory considerations for the rapid detection of carbapenem-resistant Enterobacteriaceae

Carbapenem resistance among the Enterobacteriaceae has become a significant clinical and public health dilemma. Rapid administration of effective antimicrobials and implementation of supplemental infection control practices is required to both improve patient outcomes and limit the spread of these highly resistant organisms. However, carbapenem-resistant Enterobacteriaceae (CRE)-infected patients are predominantly identified by routine culture methods, which take days to perform. Rapid genomic and phenotypic methods are currently available to accelerate the identification of carbapenemase-producing CRE. Effective use of these technologies is reliant on close collaboration between clinical microbiology, infection prevention, antimicrobial stewardship and infectious diseases specialists. This review discusses the performance characteristics of these technologies to date, and describes strategies for their optimal implementation.

Identification of Low-Level Vancomycin Resistance in Staphylococcus aureus in the Era of Informatics

Vancomycin-intermediateStaphylococcus aureus(VISA) and heteroresistant VISA (hVISA) are pathogens for which accurate antimicrobial susceptibility testing (AST) would rule out standard treatment with vancomycin. Unfortunately, AST for vancomycin is relatively slow and standard methods are unable to reliably detect VISA and hVISA. An article in this issue (C. A. Mather, B. J. Werth, S. Sivagnanam, D. J. SenGupta, and S. M. Butler-Wu, J Clin Microbiol 54:883-890, 2016, doi:http://dx.doi.org/10.1128/JCM.02428-15) describes a rapid whole-cell matrix-assisted laser desorption ionization-time of flight proxy susceptibility method that highlights current innovations and challenges with rapid AST, VISA/hVISA identification, and clinical bioinformatics.

Design of a Tablet Computer App for Facilitation of a Molecular Blood Culture Test in Clinical Microbiology and Preliminary Usability Evaluation

Background

User mobility is an important aspect of the development of clinical information systems for health care professionals. Mobile phones and tablet computers have obtained widespread use by health care professionals, offering an opportunity for supporting the access to patient information through specialized applications (apps) while supporting the mobility of the users. The use of apps for mobile phones and tablet computers may support workflow of complex tasks, for example, molecular-based diagnostic tests in clinical microbiology. Multiplex Blood Culture Test (MuxBCT) is a molecular-based diagnostic test used for rapid identification of pathogens in positive blood cultures. To facilitate the workflow of the MuxBCT, a specialized tablet computer app was developed as an accessory to the diagnostic test. The app aims to reduce the complexity of the test by step-by-step guidance of microscopy and to assist users in reaching an exact bacterial or fungal diagnosis based on blood specimen observations and controls. Additionally, the app allows for entry of test results, and communication thereof to the laboratory information system (LIS).

Objective

The objective of the study was to describe the design considerations of the MuxBCT app and the results of a preliminary usability evaluation.

Methods

The MuxBCT tablet app was developed and set up for use in a clinical microbiology laboratory. A near-live simulation study was conducted in the clinical microbiology laboratory to evaluate the usability of the MuxBCT app. The study was designed to achieve a high degree of realism as participants carried out a scenario representing the context of use for the MuxBCT app. As the MuxBCT was under development, the scenario involved the use of molecular blood culture tests similar to the MuxBCT for identification of microorganisms from positive blood culture samples. The study participants were observed, and their interactions with the app were recorded. After the study, the participants were debriefed to clarify observations.

Results

Four medical laboratory technicians, for example, representative of end users of the app, participated in the clinical simulation study. Using the MuxBCT app, the study participants successfully identified and reported all microorganisms from the positive blood cultures examined. Three of the four participants reported that they found the app useful, while one study participant reported that she would prefer to make notes on paper and later enter them into the LIS.

Conclusions

The preliminary usability evaluation results indicate that use of the MuxBCT tablet app can facilitate the workflow of the MuxBCT diagnostic test.

Comparison of the diagnostic utility of digital pathology systems for telemicrobiology

INTRODUCTION

Telemicrobiology is a growing component of clinical microbiology informatics. However, few studies have been performed to assess the diagnostic utility of telemicroscopy systems in evaluating infectious agents.

OBJECTIVE

Evaluate multiple contemporary digital pathology platforms for use in diagnostic telemicrobiology.

MATERIALS AND METHODS

A mix of thirty cases that included viral, bacterial, fungal, and parasitological findings were evaluated by four experts using ×40 whole slide imaging (WSI) scans, ×83 oil-immersion WSI scans, ×100 oil-immersion WSI scans, digital photomicrographs, and glass slides.

RESULTS

The ×83 WSI, ×100 WSI, and photomicrograph interpretations were not significantly different in quality and accuracy when compared to glass slide interpretations. The ×40 WSI interpretations were of lower quality and were more likely to be incorrect when compared to glass slide interpretations.

CONCLUSIONS

In this study, high magnification, oil-immersion digital pathology platforms are better suited to support telemicrobiology applications and yield interpretations on par with glass slide evaluations.

Review of Telemicrobiology

CONTEXT

Microbiology laboratories are continually pursuing means to improve quality, rapidity, and efficiency of specimen analysis in the face of limited resources. One means by which to achieve these improvements is through the remote analysis of digital images. Telemicrobiology enables the remote interpretation of images of microbiology specimens. To date, the practice of clinical telemicrobiology has not been thoroughly reviewed.

OBJECTIVE

To identify the various methods that can be employed for telemicrobiology, including emerging technologies that may provide value to the clinical laboratory.

DATA SOURCES

Peer-reviewed literature, conference proceedings, meeting presentations, and expert opinions pertaining to telemicrobiology have been evaluated.

CONCLUSIONS

A number of modalities have been employed for telemicroscopy, including static capture techniques, whole slide imaging, video telemicroscopy, mobile devices, and hybrid systems. Telemicrobiology has been successfully implemented for several applications, including routine primary diagnosis, expert teleconsultation, and proficiency testing. Emerging areas of telemicrobiology include digital plate reading of bacterial cultures, mobile health applications, and computer-augmented analysis of digital images. To date, static image capture techniques have been the most widely used modality for telemicrobiology, despite newer technologies being available that may produce better quality interpretations. Telemicrobiology adds value, quality, and efficiency to the clinical microbiology laboratory, and increased adoption of telemicrobiology is anticipated.

Hyperspectral image acquisition and analysis of cultured bacteria for the discrimination of urinary tract infections

Because of their widespread diffusion and impact on human health, early identification of pathogens responsible for urinary tract infections (UTI) is one of the main challenges of clinical microbiology. Currently, bacteria culturing on Chromogenic plates is widely adopted for UTI detection for its readily interpretable visual outcomes. However, the search of alternate solutions can be highly attractive, especially in the rapidly developing context of bacteriology laboratory automation and digitization, as long as they can improve cost-effectiveness or allow early discrimination. In this work, we consider and develop hyperspectral image acquisition and analysis solutions to verify the feasibility of a "virtual chromogenic agar" approach, based on the acquisition of spectral signatures from bacterial colonies growing on blood agar plates, and their interpretation by means of machine learning solutions. We implemented and tested two classification approaches (PCA+SVM and RSIMCA) that evidenced good capability to discriminate among five selected UTI bacteria. For its better performance, robustness and attitude to work with an expanding set of pathogens, we conclude that the RSIMCA-based approach is worth to be further investigated in a clinical usage perspective.

MyPro: A seamless pipeline for automated prokaryotic genome assembly and annotation

MyPro is a software pipeline for high-quality prokaryotic genome assembly and annotation. It was validated on 18 oral streptococcal strains to produce submission-ready, annotated draft genomes. MyPro installed as a virtual machine and supported by updated databases will enable biologists to perform quality prokaryotic genome assembly and annotation with ease.

Emerging commercial molecular tests for the diagnosis of bloodstream infection

Bloodstream infection (BSI) by microorganisms can lead to sepsis. This condition has a high mortality rate, which rises significantly with delays in initiation of appropriate antimicrobial treatment. Current culture methods for diagnosing BSI have long turnaround times and poor clinical sensitivity. While clinicians wait for culture diagnosis, patients are treated empirically, which can result in inappropriate treatment, undesirable side effects and contribute to drug resistance development. Molecular diagnostics assays that target pathogen DNA can identify pathogens and resistance markers within hours. Early diagnosis improves antibiotic stewardship and is associated with favorable clinical outcomes. Nonetheless, limitations of current molecular diagnostic methods are substantial. This article reviews recent commercially available molecular methods that use pathogen DNA to diagnose BSI, either by testing positive blood cultures or directly testing patient blood. We critically assess these tests and their application in clinical microbiology. A view of future directions in BSI diagnosis is also provided.