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Automation in microbiology finally gains pace

The advances and innovations in microbial diagnostic technologies over the last decade are beginning to have a significant impact on the way infectious diseases are diagnosed and managed. In the coming years, an additional cohort of new microbial diagnostics is expected to enter the space.

When it comes to automation, clinical microbiology has for many years lagged behind other laboratory disciplines. Robotics and computer processing revolutionized chemistry and hematology instruments decades ago. Meanwhile, clinical microbiologists continue to open specimen containers by hand and thus growing bacteria using methods familiar to microbiology’s founding fathers from the 19th century.

Early attempts at microbiology automation were foiled not only by the variety of specimens and container types – from stool samples and bone biopsies to tubes of blood – but also by the variety in methods required to identify organisms. Even as mass spectrometry has standardized microbe identification, and blood culture and antimicrobial susceptibility testing have been automated, laboratories still often conduct specimen processing and culture workup manually. Now, however, that may finally be changing.

Total laboratory automation systems currently are available to handle specimens, streak plates, incubate, and digitally image cultures. That is one of the great things about microbiology at the moment. There has been more change and more advances in the last few years than there have been for decades. Additionally, algorithms are available that will determine not only growth/no growth from cultures, but also use segregation software along with chromogenic agars to sort these cultures into user-defined groups of insignificant growth, mixed growth, and those that are of significant counts that would need identifications and susceptibilities performed. The software was shown to decrease the turnaround time for culture results, improve workflow and quality, and allow for cost savings. Artificial intelligence (AI) software eliminates mundane tasks for staff, allowing them to concentrate their efforts on the more difficult cultures and laboratory testing. AI also has a role in assisting in the interpretation of growth from traditional media (non-chromogenic agars). Investigators have shown a reduction of approximately 50 percent in the time necessary to perform specific tasks in the laboratory when using full laboratory automation and AI algorithms. For example, when assessing the tasks of urine screening and reading, picking of colonies for further analysis, and screening of MRSA cultures, it was shown that these three tasks took approximately 17 hours daily before the implementation of full laboratory automation and AI algorithms; after implementation, these same three tasks took approximately nine hours per day.

As microbiology labs continue to face unprecedented challenges, microbiology laboratory automation, AI, and innovative reading algorithms can help to fill these vacancies by relieving workload, decreasing errors, and reducing the repetitive motion injuries associated with specimen processing and culture work up/reporting.

Technological advances
In recent years, the introduction of new technologies has positively impacted both the diagnosis and treatment paradigms for infections. New technologies are in the process of revolutionizing clinical microbiology testing in various settings. These include technologies, such as matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), multiplex molecular diagnostic panels, and innovations, which bring nucleic acid amplification testing to the point of care. The proteomic-based technology, MALDI-TOF MS, has seen wide adoption, particularly among academic hospitals. While this method still requires isolation and culture of pathogens, MALDI-TOF MS allows for the identification of a specific microbe based upon its unique proteomic fingerprint. MALDI-TOF MS has led to significant time and cost savings as correct diagnoses are made more rapidly without the need for additional confirmatory tests. Multiplex molecular diagnostic panels are also being introduced more commonly for a variety of conditions, including sepsis and non-specific syndromes, such as respiratory or gastrointestinal infections. Multiplex assays can combine tests for numerous pathogens and resistance markers in a single panel, which can significantly reduce the time to diagnosis and, in select situations, bypass the need for culture. Furthermore, improvements in engineering and technology have also led to the development of improved POC tests, which are poised to significantly impact the future treatment paradigm for many infectious conditions such as viral respiratory infections and sexually transmitted infections (STIs). Low complexity POC tests allow for non-laboratory personnel (e.g., nurses and physician assistants) to conduct tests at the initial site of care, potentially allowing physicians to administer the therapy at the initial consultation, and eliminating the need for follow-up assessments.

Automation and informatics are critical to helping overcome challenges faced by microbiology labs. Microbiology lab automation technologies can automate time-consuming and error-prone manual tasks in order to increase lab efficiency, helping labs to provide higher quality and more accurate results faster. These technologies can free up precious labor resources so that skilled staff can be allocated to higher skilled and more technical tasks, while the lab processes a higher volume of samples. Lab automation is helping to transform the microbiology lab workflow by automating plate selection, barcoding, inoculation, streaking, transportation, incubation, and imaging. Digital images allow lab technicians and microbiologists to review plates anytime, anywhere, including remote locations, and offer the ability to build algorithms that can automatically provide results without human intervention. Informatics tools also enable seamless connectivity between the laboratory information system (LIS) and other lab instruments. These tools help labs create an integrated workflow with a single-user interface, and provide laboratorians with access to real-time data and analytics. Cloud-based informatics systems also allow data to be aggregated across labs from the same network, helping with performance benchmarking, analysis, and standardization of protocols. Modern informatics systems also ensure that the latest data privacy and cyber security requirements are met, while providing on-demand access to information, reports, and analytics, which can improve lab productivity, reduce errors, and shorten time to results.

These tools have the potential to address many key challenges in the field of infection management by reducing the time to diagnosis and informing earlier therapeutic decisions, which may improve clinical decision making, patient outcomes, workflow, and antimicrobial stewardship. These types of innovations also have the potential to significantly improve both individual patient outcomes and broader public health by facilitating better tracking of pathogens and changes/developments of antibiotic resistance.

The advances and innovations in microbial diagnostic technologies over the last decade are beginning to have a significant impact on the way infectious diseases are diagnosed and managed. In the coming years, an additional cohort of new microbial diagnostics is expected to enter the space. Technologies including advanced genomics, proteomics, and rapid susceptibility tests are expected to cause dramatic changes by tackling some of the most important problems for microbial diagnostics. Additionally, advanced analytic tools, such as AI and machine learning, can enhance the information extracted from the data these technologies collect. For example, the menu of culture-independent nucleic-acid amplification tests and syndromic panels is expanding. These advances will likely favor the deployment of culture-independent reporting of anti-microbial resistance AMR determinants, including the creation of a clear correlation of AMR genotype to anti-microbial susceptibility phenotype/minimum inhibitory concentrations (MIC). Also, automated microscopy is being leveraged for early detection of sepsis by detection of morphological changes in monocytes, indicative of dysregulated immune response, or morphological changes in bacteria, indicative of drug susceptibility.

Next-generation sequencing methods and proteomics (e.g., MALDI-TOF) are expected to impact key diagnostic segments in the future. In contrast to PCR panels, these methods have the potential for hypothesis-free detection of pathogens and host-response markers. NGS-based analysis of pathogens further allows phenotypic prediction, such as detection of AMR determinants, virulence factors, and mobile genetic elements. Also, whole genome sequencing of isolates by next-generation sequencing allows strain typing at nucleotide-level resolution for epidemiological studies and infection control. These methods have tremendous potential in the clinical microbiology lab, opening a novel paradigm for diagnostics.

However, to be deployed clinically and realize this potential, these technologies will need to build on efforts associated with a more established technology that has demonstrated clinical utility. Adoption of these technologies may also require hospitals and payers to place a higher-prioritized infection control than is currently happening, and to support their infection-control centers.

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