Machine learning and artificial intelligence for the diagnosis of infectious diseases in immunocompromised patients.

PURPOSE OF REVIEW: Immunocompromised patients are at high risk for infection. During the coronavirus disease (COVID-19) pandemic, immunocompromised patients exhibited increased odds of intensive care unit admission and death. Early pathogen identification is essential to mitigating infection related risk in immunocompromised patients. Artificial intelligence (AI) and machine learning (ML) have tremendous appeal to address unmet diagnostic needs. These AI/ML tools often rely on the wealth of data found in healthcare to enhance our ability to identify clinically significant patterns of disease. To this end, our review provides an overview of the current AI/ML landscape as it applies to infectious disease testing with emphasis on immunocompromised patients. RECENT FINDINGS: Examples include AI/ML for predicting sepsis in high risk burn patients. Likewise, ML is utilized to analyze complex host-response proteomic data to predict respiratory infections including COVID-19. These same approaches have also been applied for pathogen identification of bacteria, viruses, and hard to detect fungal microbes. Future uses of AI/ML may include integration of predictive analytics in point-of-care (POC) testing and data fusion applications. SUMMARY: Immunocompromised patients are at high risk for infections. AI/ML is transforming infectious disease testing and has great potential to address challenges encountered in the immune compromised population.

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.

Predominant SARS-CoV-2 variant impacts accuracy when screening for infection using exhaled breath vapor.

BACKGROUND: New technologies with novel and ambitious approaches are being developed to diagnose or screen for SARS-CoV-2, including breath tests. The US FDA approved the first breath test for COVID-19 under emergency use authorization in April 2022. Most breath-based assays measure volatile metabolites exhaled by persons to identify a host response to infection. We hypothesized that the breathprint of COVID-19 fluctuated after Omicron became the primary variant of transmission over the Delta variant. METHODS: We collected breath samples from 142 persons with and without a confirmed COVID-19 infection during the Delta and Omicron waves. Breath samples were analyzed by gas chromatography-mass spectrometry. RESULTS: Here we show that based on 63 exhaled compounds, a general COVID-19 model had an accuracy of 0.73 ± 0.06, which improved to 0.82 ± 0.12 when modeling only the Delta wave, and 0.84 ± 0.06 for the Omicron wave. The specificity improved for the Delta and Omicron models (0.79 ± 0.21 and 0.74 ± 0.12, respectively) relative to the general model (0.61 ± 0.13). CONCLUSIONS: We report that the volatile signature of COVID-19 in breath differs between the Delta-predominant and Omicron-predominant variant waves, and accuracies improve when samples from these waves are modeled separately rather than as one universal approach. Our findings have important implications for groups developing breath-based assays for COVID-19 and other respiratory pathogens, as the host response to infection may significantly differ depending on variants or subtypes.

In recent decades, scientists have found we exhale thousands of compounds that reveal much about our health, including whether we are sick with COVID-19. Our team asked whether the breath profile of someone infected with the Delta variant of COVID-19 would match the breath profile caused by the Omicron variant­a version of the virus that is more transmissible. We analyzed breath samples from 142 people, some sick with either the Delta or Omicron variant of COVID-19, and others who were negative for COVID-19. Our results indicate that the Delta variant altered the contents of our breath in a different way than the Omicron variant, and breath-based tests improved when optimized to detect only one of the variants. These findings might impact the design of future breath-based tests for COVID-19.

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.

Identification of Endogenous Peptides in Nasal Swab Transport Media used in MALDI-TOF-MS Based COVID-19 Screening.

Mass spectrometry (MS) based diagnostic detection of 2019 novel coronavirus infectious disease (COVID-19) has been postulated to be a useful alternative to classical PCR based diagnostics. These MS based approaches have the potential to be both rapid and sensitive and can be done on-site without requiring a dedicated laboratory or depending on constrained supply chains (i.e., reagents and consumables). Matrix-assisted laser desorption ionization (MALDI)-time-of-flight (TOF) MS has a long and established history of microorganism detection and systemic disease assessment. Previously, we have shown that automated machine learning (ML) enhanced MALDI-TOF-MS screening of nasal swabs can be both sensitive and specific for COVID-19 detection. The underlying molecules responsible for this detection are generally unknown nor are they required for this automated ML platform to detect COVID-19. However, the identification of these molecules is important for understanding both the mechanism of detection and potentially the biology of the underlying infection. Here, we used nanoscale liquid chromatography tandem MS to identify endogenous peptides found in nasal swab saline transport media to identify peptides in the same the mass over charge (m/z) values observed by the MALDI-TOF-MS method. With our peptidomics workflow, we demonstrate that we can identify endogenous peptides and endogenous protease cut sites. Further, we show that SARS-CoV-2 viral peptides were not readily detected and are highly unlikely to be responsible for the accuracy of MALDI based SARS-CoV-2 diagnostics. Further analysis with more samples will be needed to validate our findings, but the methodology proves to be promising.

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.

SARS-CoV-2 infection induces germinal center responses with robust stimulation of CD4 T follicular helper cells in rhesus macaques

CD4 T follicular helper (T fh ) cells are important for the generation of long-lasting and specific humoral protection against viral infections. The degree to which SARS-CoV-2 infection generates T fh cells and stimulates the germinal center response is an important question as we investigate vaccine options for the current pandemic. Here we report that, following infection with SARS-CoV-2, adult rhesus macaques exhibited transient accumulation of activated, proliferating T fh cells in their peripheral blood on a transitory basis. The CD4 helper cell responses were skewed predominantly toward a T h 1 response in blood, lung, and lymph nodes, reflective of the interferon-rich cytokine environment following infection. We also observed the generation of germinal center T fh cells specific for the SARS-CoV-2 spike (S) and nucleocapsid (N) proteins, and a corresponding early appearance of antiviral serum IgG antibodies but delayed or absent IgA antibodies. Our data suggest that a vaccine promoting Th1-type Tfh responses that target the S protein may lead to protective immunity.

Novel application of automated machine learning with MALDI-TOF-MS for rapid high-throughput screening of COVID-19: a proof of concept.

The 2019 novel coronavirus infectious disease (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has created an unsustainable need for molecular diagnostic testing. Molecular approaches such as reverse transcription (RT) polymerase chain reaction (PCR) offers highly sensitive and specific means to detect SARS-CoV-2 RNA, however, despite it being the accepted "gold standard", molecular platforms often require a tradeoff between speed versus throughput. Matrix assisted laser desorption ionization (MALDI)-time of flight (TOF)-mass spectrometry (MS) has been proposed as a potential solution for COVID-19 testing and finding a balance between analytical performance, speed, and throughput, without relying on impacted supply chains. Combined with machine learning (ML), this MALDI-TOF-MS approach could overcome logistical barriers encountered by current testing paradigms. We evaluated the analytical performance of an ML-enhanced MALDI-TOF-MS method for screening COVID-19. Residual nasal swab samples from adult volunteers were used for testing and compared against RT-PCR. Two optimized ML models were identified, exhibiting accuracy of 98.3%, positive percent agreement (PPA) of 100%, negative percent agreement (NPA) of 96%, and accuracy of 96.6%, PPA of 98.5%, and NPA of 94% respectively. Machine learning enhanced MALDI-TOF-MS for COVID-19 testing exhibited performance comparable to existing commercial SARS-CoV-2 tests.

Nasopharyngeal SARS-CoV-2 viral loads in young children do not differ significantly from those in older children and adults.

The role of children in the spread of the SARS-CoV-2 coronavirus has become a matter of urgent debate as societies in the US and abroad consider how to safely reopen schools. Small studies have suggested higher viral loads in young children. Here we present a multicenter investigation on over five thousand SARS-CoV-2 cases confirmed by real-time reverse transcription (RT) PCR assay. Notably, we found no discernable difference in amount of viral nucleic acid among young children and adults.

SARS-CoV-2 induces robust germinal center CD4 T follicular helper cell responses in rhesus macaques.

CD4 T follicular helper (Tfh) cells are important for the generation of durable and specific humoral protection against viral infections. The degree to which SARS-CoV-2 infection generates Tfh cells and stimulates the germinal center (GC) response is an important question as we investigate vaccine induced immunity against COVID-19. Here, we report that SARS-CoV-2 infection in rhesus macaques, either infused with convalescent plasma, normal plasma, or receiving no infusion, resulted in transient accumulation of pro-inflammatory monocytes and proliferating Tfh cells with a Th1 profile in peripheral blood. CD4 helper cell responses skewed predominantly toward a Th1 response in blood, lung, and lymph nodes. SARS-CoV-2 Infection induced GC Tfh cells specific for the SARS-CoV-2 spike and nucleocapsid proteins, and a corresponding early appearance of antiviral serum IgG antibodies. Collectively, the data show induction of GC responses in a rhesus model of mild COVID-19.

Clinical Performance of the Point-of-Care cobas Liat for Detection of SARS-CoV-2 in 20 Minutes: a Multicenter Study.

Highly accurate testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at the point of care (POC) is an unmet diagnostic need in emergency care and time-sensitive outpatient care settings. Reverse transcription-PCR (RT-PCR) technology is the gold standard for SARS-CoV-2 diagnostics. We performed a multisite U.S. study comparing the clinical performance of the first U.S. Food and Drug Administration (FDA)-authorized POC RT-PCR for detection of SARS-CoV-2 in 20 min, the cobas Liat SARS-CoV-2 and influenza A/B nucleic acid test, to the most widely used RT-PCR laboratory test, the cobas 68/8800 SARS-CoV-2 test. Clinical nasopharyngeal swab specimens from 444 patients with 357 evaluable specimens at five U.S. clinical laboratories were enrolled from 21 September 2020 to 23 October 2020. The overall agreement between the Liat and 68/8800 systems for SARS-CoV-2 diagnostics was 98.6% (352/357). Using Liat, positive percent agreement for SARS-CoV-2 was 100% (162/162) and the negative percent agreement was 97.4% (190/195). The Liat is an RT-PCR POC test that provides highly accurate SARS-CoV-2 results in 20 min with performance equivalent to that of high-throughput laboratory molecular testing. Rapid RT-PCR testing at the POC can enable more timely infection control and individual care decisions for coronavirus disease 2019.

SARS-CoV-2 infection induces germinal center responses with robust stimulation of CD4 T follicular helper cells in rhesus macaques.

CD4 T follicular helper (T fh ) cells are important for the generation of long-lasting and specific humoral protection against viral infections. The degree to which SARS-CoV-2 infection generates T fh cells and stimulates the germinal center response is an important question as we investigate vaccine options for the current pandemic. Here we report that, following infection with SARS-CoV-2, adult rhesus macaques exhibited transient accumulation of activated, proliferating T fh cells in their peripheral blood on a transitory basis. The CD4 helper cell responses were skewed predominantly toward a T h 1 response in blood, lung, and lymph nodes, reflective of the interferon-rich cytokine environment following infection. We also observed the generation of germinal center T fh cells specific for the SARS-CoV-2 spike (S) and nucleocapsid (N) proteins, and a corresponding early appearance of antiviral serum IgG antibodies but delayed or absent IgA antibodies. Our data suggest that a vaccine promoting Th1-type Tfh responses that target the S protein may lead to protective immunity.