Innovations in infectious disease testing: Leveraging COVID-19 pandemic technologies for the future.

Innovations in infectious disease testing have improved our abilities to detect and understand the microbial world. The 2019 novel coronavirus infectious disease (COVID-19) pandemic introduced new innovations including non-prescription "over the counter" infectious disease tests, mass spectrometry-based detection of COVID-19 host response, and the implementation of artificial intelligence (AI) and machine learning (ML) to identify individuals infected by the severe acute respiratory syndrome - coronavirus - 2 (SARS-CoV-2). As the world recovers from the COVID-19 pandemic; these innovative solutions will give rise to a new era of infectious disease tests extending beyond the detection of SARS-CoV-2. To this end, the purpose of this review is to summarize current trends in infectious disease testing and discuss innovative applications specifically in the areas of POC testing, MS, molecular diagnostics, sample types, and AI/ML.

Evaluation of pharmacist guided intervention using procalcitonin and respiratory virus testing.

INTRODUCTION: Acute respiratory infections make up a sizable percentage of emergency department (ED) visits and many result in antibiotics being prescribed. Procalcitonin (PCT) has been found to reduce antibiotic use in both outpatient and critical care settings, yet remains underused in the ED. This study aimed to evaluate whether point of care molecular influenza and Respiratory Syncytial Virus (RSV) testing, PCT, and a pharmacist driven educational intervention in aggregate optimizes antibiotic and antiviral prescribing in the ED setting. METHODS: A randomized trial of the Cobas Liat Flu/RSV Assay, procalcitonin, and the use of pharmacist-led education in patients 0-50 years of age being seen in the ED for Influenza Like Illness (ILI) or acute respiratory illness. The study enrolled 200 ED patients between March 2018 and April 2022. RESULTS: There was little difference in antibiotic or antiviral prescribing between the intervention and control groups in this study (39%-32% = 7.0%, 95% CI: -6.2, 20.2, P = 0.30). However, a post-hoc analysis of the use of procalcitonin showed results were used as indicated in the ED (P = 0.001). CONCLUSION: PCT can be used in both adult and pediatric populations to help guide the decision of whether to treat with antibiotics in the ED setting. Pharmacist guided education may not be a driving factor.

Comparative performance of COVID-19 serology testing.

Background: The 2019 novel coronavirus infectious disease (COVID-19) pandemic resulted in a surge of assays aimed at detecting severe acute respiratory syndrome (SARS) - coronavirus (CoV) - 2 infection and prior exposure. Although both molecular and antigen testing have clearly defined uses, the utility of serology remains uncertain and is presently not recommended for assessing immunity. Methods: We conducted a pragmatic, observational study evaluating four commercially available emergency use authorized laboratory-based COVID-19 serology assays (Assays A-D). Remnant samples from hospitalized, and non-hospitalized SARS-CoV-2 PCR positive patients, as well as vaccinated and unvaccinated individuals were collected and tested. Positive percent agreement (PPA) and negative percent agreement (NPA) were calculated. Antibody concentrations were compared across the platforms and populations. Results: A total of 588 remnant samples derived from 500 patients were tested. PPA at 5-12 weeks post-PCR positive results for Assays A-D was 98.3, 97.4, 99.2, and 95.8% respectively. NPA was 100% across all platforms. Mean antibody concentrations at 2-4 weeks post-PCR positive result were significantly higher in hospitalized versus non-hospitalized patients, respectively, for Assay A (131.8 [101.7] vs. 95.6 [100.3] AU/mL, P < 0.001), B (61.7 [62.4] vs. 38.1 [40.5] AU/mL, P < 0.001), and C (157.6 [105.3] vs. 133.3 [100.7] AU/mL, P < 0.001). For individuals receiving two vaccine doses mean antibody concentrations were respectively 169.6 (104.4), 27.3 (50.8), 189.6 (120.9), 21.19 (13.1) AU/mL for Assays A-D. Conclusions: Overall, PPA and NPA differed across the four assays. Assays A and C produced higher PPA and NPA and detected larger concentrations of antibodies following vaccination.

Assessment of a COVID-19 vaccination protocol for unhoused patients in the emergency department.

BACKGROUND: We aimed to evaluate the feasibility of implementing an emergency department (ED)-based Coronavirus Disease of 2019 (COVID-19) vaccination protocol in a population of unhoused patients. METHODS: On June 10, 2021, a best practice alert (BPA) was implemented that fired when an ED provider opened the charts of unhoused patients and prompted the provider to order COVID-19 vaccination for eligible patients. We downloaded electronic medical record data of patients who received a COVID-19 vaccine in the ED between June 10, 2021 and August 26, 2021. The outcomes of interest were the number of unhoused, and the total number of patients vaccinated for COVID-19 during the study period. Data were described with simple descriptive statistics. RESULTS: There were 25,871 patient encounters in 19,992 unique patients (mean 1.3 visits/patient) in the emergency department during the study period. There were 1,474 (6% of total ED population) visits in 1,085 unique patients who were unhoused (mean 1.4 visits/patient). The BPA fired in 1,046 unhoused patient encounters (71% of PEH encounters) and was accepted in 79 (8%). Forty-three unhoused patients were vaccinated as a result of the BPA (4% of BPA fires) and 18 unhoused patients were vaccinated without BPA prompting. An additional 76 domiciled patients were vaccinated in the ED. CONCLUSIONS: Implementing an ED-based COVID-19 vaccination program is feasible, however, only a small number of patients underwent COVID-19 vaccination. Further studies are needed to explore the utility of using the ED as a setting for COVID-19 vaccination.

Detection of Viral Infection and Bacterial Coinfection and Superinfection in Coronavirus Disease 2019 Patients Presenting to the Emergency Department Using the 29-mRNA Host Response Classifier IMX-BVN-3: A Multicenter Study.

Background: Identification of bacterial coinfection in patients with coronavirus disease 2019 (COVID-19) facilitates appropriate initiation or withholding of antibiotics. The Inflammatix Bacterial Viral Noninfected (IMX-BVN) classifier determines the likelihood of bacterial and viral infections. In a multicenter study, we investigated whether IMX-BVN version 3 (IMX-BVN-3) identifies patients with COVID-19 and bacterial coinfections or superinfections. Methods: Patients with polymerase chain reaction-confirmed COVID-19 were enrolled in Berlin, Germany; Basel, Switzerland; and Cleveland, Ohio upon emergency department or hospital admission. PAXgene Blood RNA was extracted and 29 host mRNAs were quantified. IMX-BVN-3 categorized patients into very unlikely, unlikely, possible, and very likely bacterial and viral interpretation bands. IMX-BVN-3 results were compared with clinically adjudicated infection status. Results: IMX-BVN-3 categorized 102 of 111 (91.9%) COVID-19 patients into very likely or possible, 7 (6.3%) into unlikely, and 2 (1.8%) into very unlikely viral bands. Approximately 94% of patients had IMX-BVN-3 unlikely or very unlikely bacterial results. Among 7 (6.3%) patients with possible (n = 4) or very likely (n = 3) bacterial results, 6 (85.7%) had clinically adjudicated bacterial coinfection or superinfection. Overall, 19 of 111 subjects for whom adjudication was performed had a bacterial infection; 7 of these showed a very likely or likely bacterial result in IMX-BVN-3. Conclusions: IMX-BVN-3 identified COVID-19 patients as virally infected and identified bacterial coinfections and superinfections. Future studies will determine whether a point-of-care version of the classifier may improve the management of COVID-19 patients, including appropriate antibiotic use.

Detection of viral infection and bacterial co- and superinfection in COVID-19 patients presenting to the emergency department using the 29-mRNA host response classifier BVN-3 – a multicenter study

Background Identification of bacterial coinfection in COVID-19 patients facilitates appropriate initiation or withholding of antibiotics. The IMX-BVN classifier determines the likelihood of bacterial and viral infections. In a multicenter study, we investigated whether IMX-BVN-3 identifies patients with COVID-19 and bacterial co- or superinfections. Methods PCR-confirmed COVID-19 patients were enrolled in Berlin (Germany), Basel (Switzerland), and Cleveland (both US) upon ED or hospital admission. PAXgene Blood RNA was extracted, and 29 host mRNAs were quantified. BVN-3 categorized patients into Very unlikely, Unlikely, Possible, and Very likely bacterial and viral interpretation bands. BVN-3 results were compared with clinically adjudicated infection status. Results BVN-3 categorized 102 (91.9%) of 111 COVID-19 patients into Very likely or Possible viral bands, 7 (6.3%) into Unlikely, and 2 (1.8%) into Very unlikely viral bands. 93.7% of patients had BVN-3 Unlikely or Very unlikely bacterial results. Among 7 (6.3%) patients with Possible (4) or Very likely (3) bacterial results, 6 (85.7%) had clinically adjudicated bacterial co- or superinfection. Overall, 19 of 111 subjects for whom adjudication was performed had a bacterial infection;7 of these showed a Very likely or Likely bacterial result in IMX-BVN-3. Conclusions BVN-3 identified COVID patients as virally infected and identified bacterial co- and superinfections. Future studies will determine whether a POC version of the classifier may improve the management of COVID-19 patients including appropriate antibiotic use.

Comparative performance of two automated machine learning platforms for COVID-19 detection by MALDI-TOF-MS.

The 2019 novel coronavirus infectious disease (COVID-19) pandemic has resulted in an unsustainable need for diagnostic tests. Currently, molecular tests are the accepted standard for the detection of SARS-CoV-2. Mass spectrometry (MS) enhanced by machine learning (ML) has recently been postulated to serve as a rapid, high-throughput, and low-cost alternative to molecular methods. Automated ML is a novel approach that could move mass spectrometry techniques beyond the confines of traditional laboratory settings. However, it remains unknown how different automated ML platforms perform for COVID-19 MS analysis. To this end, the goal of our study is to compare algorithms produced by two commercial automated ML platforms (Platforms A and B). Our study consisted of MS data derived from 361 subjects with molecular confirmation of COVID-19 status including SARS-CoV-2 variants. The top optimized ML model with respect to positive percent agreement (PPA) within Platforms A and B exhibited an accuracy of 94.9%, PPA of 100%, negative percent agreement (NPA) of 93%, and an accuracy of 91.8%, PPA of 100%, and NPA of 89%, respectively. These results illustrate the MS method's robustness against SARS-CoV-2 variants and highlight similarities and differences in automated ML platforms in producing optimal predictive algorithms for a given dataset.

Prospective Validation of a Rapid Host Gene Expression Test to Discriminate Bacterial From Viral Respiratory Infection.

Importance: Bacterial and viral causes of acute respiratory illness (ARI) are difficult to clinically distinguish, resulting in the inappropriate use of antibacterial therapy. The use of a host gene expression-based test that is able to discriminate bacterial from viral infection in less than 1 hour may improve care and antimicrobial stewardship. Objective: To validate the host response bacterial/viral (HR-B/V) test and assess its ability to accurately differentiate bacterial from viral infection among patients with ARI. Design, Setting, and Participants: This prospective multicenter diagnostic study enrolled 755 children and adults with febrile ARI of 7 or fewer days' duration from 10 US emergency departments. Participants were enrolled from October 3, 2014, to September 1, 2019, followed by additional enrollment of patients with COVID-19 from March 20 to December 3, 2020. Clinical adjudication of enrolled participants identified 616 individuals as having bacterial or viral infection. The primary analysis cohort included 334 participants with high-confidence reference adjudications (based on adjudicator concordance and the presence of an identified pathogen confirmed by microbiological testing). A secondary analysis of the entire cohort of 616 participants included cases with low-confidence reference adjudications (based on adjudicator discordance or the absence of an identified pathogen in microbiological testing). Thirty-three participants with COVID-19 were included post hoc. Interventions: The HR-B/V test quantified the expression of 45 host messenger RNAs in approximately 45 minutes to derive a probability of bacterial infection. Main Outcomes and Measures: Performance characteristics for the HR-B/V test compared with clinical adjudication were reported as either bacterial or viral infection or categorized into 4 likelihood groups (viral very likely [probability score <0.19], viral likely [probability score of 0.19-0.40], bacterial likely [probability score of 0.41-0.73], and bacterial very likely [probability score >0.73]) and compared with procalcitonin measurement. Results: Among 755 enrolled participants, the median age was 26 years (IQR, 16-52 years); 360 participants (47.7%) were female, and 395 (52.3%) were male. A total of 13 participants (1.7%) were American Indian, 13 (1.7%) were Asian, 368 (48.7%) were Black, 131 (17.4%) were Hispanic, 3 (0.4%) were Native Hawaiian or Pacific Islander, 297 (39.3%) were White, and 60 (7.9%) were of unspecified race and/or ethnicity. In the primary analysis involving 334 participants, the HR-B/V test had sensitivity of 89.8% (95% CI, 77.8%-96.2%), specificity of 82.1% (95% CI, 77.4%-86.6%), and a negative predictive value (NPV) of 97.9% (95% CI, 95.3%-99.1%) for bacterial infection. In comparison, the sensitivity of procalcitonin measurement was 28.6% (95% CI, 16.2%-40.9%; P < .001), the specificity was 87.0% (95% CI, 82.7%-90.7%; P = .006), and the NPV was 87.6% (95% CI, 85.5%-89.5%; P < .001). When stratified into likelihood groups, the HR-B/V test had an NPV of 98.9% (95% CI, 96.1%-100%) for bacterial infection in the viral very likely group and a positive predictive value of 63.4% (95% CI, 47.2%-77.9%) for bacterial infection in the bacterial very likely group. The HR-B/V test correctly identified 30 of 33 participants (90.9%) with acute COVID-19 as having a viral infection. Conclusions and Relevance: In this study, the HR-B/V test accurately discriminated bacterial from viral infection among patients with febrile ARI and was superior to procalcitonin measurement. The findings suggest that an accurate point-of-need host response test with high NPV may offer an opportunity to improve antibiotic stewardship and patient outcomes.

Use of an Asymptomatic COVID-19 Testing Protocol in a Pediatric Emergency Department.

BACKGROUND: High rates of asymptomatic infections with COVID-19 have been reported. OBJECTIVE: We aimed to describe an asymptomatic COVID-19 testing protocol in a pediatric emergency department (ED). METHODS: This was a retrospective cohort study of pediatric patients (younger than 18 years) who were tested for COVID-19 via the asymptomatic testing protocol at a single urban pediatric ED between May 2020 and January 2021. This included all pediatric patients undergoing admission, urgent procedures, and psychiatric facility placement. The primary outcome was the percentage of positive COVID-19 tests. COVID-19 testing was performed via real-time polymerase chain reaction RNA assay testing. County-level COVID-19 data were used to estimate local daily COVID-19 cases/100,000 individuals (from all ages). Data were described with simple descriptive statistics. RESULTS: There were 1459 children tested for COVID-19 under the asymptomatic protocol. Mean ± standard deviation age was 8.2 ± 5.8 years. Two tests were inconclusive and 29 (2.0%; 95% confidence interval [CI] 1.3-2.8%) were positive. Of the 29 positive cases, 14 (48%; 95% CI 29-67%) had abnormal vital signs or signs and symptoms of COVID-19, on retrospective review. A total of 15 truly asymptomatic infections were identified. On the days that asymptomatic cases were identified, the lowest average daily community rate was 7.67 cases/100,000 individuals. CONCLUSIONS: Asymptomatic COVID-19 positivity rates in the pediatric ED were low when the average daily community rate was fewer than 7.5 cases/100,000 individuals. In the current pandemic, ED clinicians should assess for signs and symptoms of COVID-19, even when children present to the ED with unrelated chief symptoms.

Evolving Applications of Artificial Intelligence and Machine Learning in Infectious Diseases Testing.

BACKGROUND: Artificial intelligence (AI) and machine learning (ML) are poised to transform infectious disease testing. Uniquely, infectious disease testing is technologically diverse spaces in laboratory medicine, where multiple platforms and approaches may be required to support clinical decision-making. Despite advances in laboratory informatics, the vast array of infectious disease data is constrained by human analytical limitations. Machine learning can exploit multiple data streams, including but not limited to laboratory information and overcome human limitations to provide physicians with predictive and actionable results. As a quickly evolving area of computer science, laboratory professionals should become aware of AI/ML applications for infectious disease testing as more platforms are become commercially available. CONTENT: In this review we: (a) define both AI/ML, (b) provide an overview of common ML approaches used in laboratory medicine, (c) describe the current AI/ML landscape as it relates infectious disease testing, and (d) discuss the future evolution AI/ML for infectious disease testing in both laboratory and point-of-care applications. SUMMARY: The review provides an important educational overview of AI/ML technique in the context of infectious disease testing. This includes supervised ML approaches, which are frequently used in laboratory medicine applications including infectious diseases, such as COVID-19, sepsis, hepatitis, malaria, meningitis, Lyme disease, and tuberculosis. We also apply the concept of "data fusion" describing the future of laboratory testing where multiple data streams are integrated by AI/ML to provide actionable clinical knowledge.

Point-of-care COVID-19 testing in the emergency department: current status and future prospects.

INTRODUCTION: This expert review outlines current and future point-of-care technologies for the diagnosis of the SARS-CoV-2 virus, which is responsible for causing coronavirus disease COVID-19 in the emergency department. COVID-19 first emerged in late 2019 and is responsible for a range of presentations from minor upper respiratory tract symptoms to severe pneumonia and multisystem organ failure. Among the technologies available include the gold standard of molecular point-of-care tests as well as antigen detection tests. AREAS COVERED: We discuss point-of-care molecular tests including multiplex, targeted, and single plex panels as well as various antigen testing methodologies in terms of availability and performance characteristics. In addition, we focus on current testing best practices and considerations for point-of-care testing in the emergency department based on a search of the literature available in PubMed to date and a review of FDA and CDC guidance. EXPERT OPINION: While there have been many advances in SARS-CoV-2 point-of-care testing, there remain challenges to implementation in the emergency department setting. A paradigm shift is needed to improve diagnosis and clinical outcomes.

COVID-19 and beyond: Lessons learned from emergency department HIV screening for population-based screening in healthcare settings.

Emergency departments (EDs) have played a major role in the science and practice of HIV population screening. After decades of experience, EDs have demonstrated the capacity to provide testing and linkage to care to large volumes of patients, particularly those who do not otherwise engage the healthcare system. Efforts to expand ED HIV screening in the United States have been accelerated by a collaborative national network of emergency physicians and other stakeholders called EMTIDE (Emergency Medicine Transmissible Infectious Diseases and Epidemics). As the COVID-19 pandemic evolves, EDs nationwide are being tasked with diagnosing and managing COVID-19 in a myriad of capacities, adopting varied approaches based in part on know-how, local disease trends, and the supply chain. The objective of this article is to broadly summarize the lessons learned from decades of ED HIV screening and provide guidance for many analogous issues and challenges in population screening for COVID-19. Over time, and with the accumulated experience from other epidemics, ED screening should develop into an overarching discipline in which the disease in question may vary, but the efficiency of response is increased by prior knowledge and understanding.