The diagnosis of periprosthetic joint infection (PJI) is challenging, often requiring multiple diagnostic techniques. The diagnostic performance of the Investigational Use Only BioFire Joint Infection (JI) Panel was compared to 16S rRNA gene-based targeted metagenomic sequencing applied to PJI synovial fluid. While JI Panel sensitivity was excellent for on-panel microorganisms, overall sensitivity was low due to the absence of Staphylococcus epidermidis, a common causative pathogen in PJI, on the panel.

First-hand experiences with an institutional strategy to reduce inappropriate ordering of syndromic gastrointestinal panels will be described. Recent changes to Medicare coverage of these panels and associated implications will also be discussed. Finally, the emerging opportunities for gastrointestinal illness testing will be reviewed.

Use of multiplex panels for the diagnosis of central nervous system (CNS) infections offers comprehensive pathogen detection using minimal sample volume with a rapid turnaround time. Despite clear benefits over traditional testing, the shortcomings of multiplex testing include increased cost, concerning reports of false positive/negative results, and challenges to appropriate implementation. This presentation will address the benefits and limitations of using multiplex molecular panels for the diagnosis of CNS infections.

• Evidence in the literature supporting saliva as a reliable sample type for the detection of respiratory pathogens
• Key considerations for working with this non-traditional sample type 
• Insights gained from the COVID-19 response: self-collection, sample stability, simplified testing methods
• Reasons to incorporate saliva into your laboratory or surveillance systems, particularly in low-resource settings

Central nervous system infections are a medical emergency requiring prompt diagnosis and treatment. Molecular methods are considered the gold standard for the diagnosis of viral CNS diseases; however, currently available tests are limited to a few most common viral pathogens. In this talk, the current state of diagnostics for viral CNS disease will be described and current gaps reviewed and discussed.

Pathogen cell-free nucleic acid exists in both plasma and CSF, allowing for the diagnosis of CNS infections via a variety of modalities. This talk will cover updates in cell-free nucleic acid detection of CNS infections.

Despite advancements in the field of infectious diseases, current countermeasures against emerging pathogens, such as enterovirus D68, a virus associated with acute flaccid myelitis, are still limited.  The use of immunologic and pathogen surveillance can provide information about the immunity and susceptibility of a population to infections. This in turn could help facilitate more rapid development of preventative tools and therapeutics against emerging infectious diseases of pandemic potential.

Herpesviruses can cause a range of central nervous system infections in both immunocompetent and immunocompromised hosts. Diagnostic strategies to accurately detect and interpret contribution to neurological disease can be challenging. Pathways to detect Herpes simplex viruses (HSV-1 and HSV-2) as well as Cytomegalovirus (CMV), varicella-zoster virus (VZV), Epstein-Barr virus (EBV) and human herpesvirus 6 (HHV-6) will be discussed.

Due to advancements in clinical microbiology laboratory automation, artificial intelligence (AI) and image analysis tools are emerging as an important tool in clinical microbiology culture diagnostics. With the convergence of these technologies, laboratories are enabled to use AI to support interpretation of bacterial cultures. However, with the power of AI comes the challenge of translating the nuances of culture interpretation into discrete points for use in image analysis software. This presentation will discuss various uses of AI for bacterial cultures and the challenges in developing algorithms and decision support tools for complicated microbiological cultures.

Urinary tract infections are commonly overdiagnosed, yielding overtreatment and potentially delaying the correct diagnosis and treatment. The use of artificial intelligence shows promise in aiding the laboratory and providers in accurately identifying urinary tract infections.