Update of European Society of Clinical Microbiology and Infectious Diseases coronavirus disease 2019 guidelines: diagnostic testing for severe acute respiratory syndrome coronavirus 2.

SCOPE: Since the onset of COVID-19, several assays have been deployed for the diagnosis of SARS-CoV-2. The European Society of Clinical Microbiology and Infectious Diseases (ESCMID) published the first set of guidelines on SARS-CoV-2 in vitro diagnosis in February 2022. Because the COVID-19 landscape is rapidly evolving, the relevant ESCMID guidelines panel releases an update of the previously published recommendations on diagnostic testing for SARS-CoV-2. This update aims to delineate the best diagnostic approach for SARS-CoV-2 in different populations based on current evidence. METHODS: An ESCMID COVID-19 guidelines task force was established by the ESCMID Executive Committee. A small group was established, half appointed by the chair, and the remaining selected with an open call. The panel met virtually once a week. For all decisions, a simple majority vote was used. A list of clinical questions using the population, intervention, comparison, and outcome (PICO) format was developed at the beginning of the process. For each PICO, 2 panel members performed a literature search focusing on systematic reviews with a third panellist involved in case of inconsistent results. The panel reassessed the PICOs previously defined as priority in the first set of guidelines and decided to address 49 PICO questions, because 6 of them were discarded as outdated/non-clinically relevant. The 'Grading of Recommendations Assessment, Development and Evaluation (GRADE)-adoption, adaptation, and de novo development of recommendations (ADOLOPMENT)' evidence-to-decision framework was used to produce the guidelines. QUESTIONS ADDRESSED BY THE GUIDELINES AND RECOMMENDATIONS: After literature search, we updated 16 PICO questions; these PICOs address the use of antigen-based assays among symptomatic and asymptomatic patients with different ages, COVID-19 severity status or risk for severe COVID-19, time since the onset of symptoms/contact with an infectious case, and finally, types of biomaterials used.

Duration of viral infectiousness and correlation with symptoms and diagnostic testing in non-hospitalized adults during acute SARS-CoV-2 infection: A longitudinal cohort study.

BACKGROUND: Guidelines for SARS-CoV-2 have relied on limited data on duration of viral infectiousness and correlation with COVID-19 symptoms and diagnostic testing. METHODS: We enrolled ambulatory adults with acute SARS-CoV-2 infection and performed serial measurements of COVID-19 symptoms, nasal swab viral RNA, nucleocapsid (N) and spike (S) antigens, and replication-competent SARS-CoV-2 by viral growth in culture. We determined average time from symptom onset to a first negative test result and estimated risk of infectiousness, as defined by positive viral growth in culture. RESULTS: Among 95 adults, median [interquartile range] time from symptom onset to first negative test result was 9 [5] days, 13 [6] days, 11 [4] days, and >19 days for S antigen, N antigen, culture growth, and viral RNA by RT-PCR, respectively. Beyond two weeks, virus growth and N antigen titers were rarely positive, while viral RNA remained detectable among half (26/51) of participants tested 21-30 days after symptom onset. Between 6-10 days from symptom onset, N antigen was strongly associated with culture positivity (relative risk=7.61, 95% CI: 3.01-19.22), whereas neither viral RNA nor symptoms were associated with culture positivity. During the 14 days following symptom onset, the presence of N antigen remained strongly associated (adjusted relative risk=7.66, 95% CI: 3.96-14.82) with culture positivity, regardless of COVID-19 symptoms. CONCLUSIONS: Most adults have replication-competent SARS-CoV-2 for 10-14 after symptom onset. N antigen testing is a strong predictor of viral infectiousness and may be a more suitable biomarker, rather than absence of symptoms or viral RNA, to discontinue isolation within two weeks from symptom onset.

Modeling in higher dimensions to improve diagnostic testing accuracy: Theory and examples for multiplex saliva-based SARS-CoV-2 antibody assays.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has emphasized the importance and challenges of correctly interpreting antibody test results. Identification of positive and negative samples requires a classification strategy with low error rates, which is hard to achieve when the corresponding measurement values overlap. Additional uncertainty arises when classification schemes fail to account for complicated structure in data. We address these problems through a mathematical framework that combines high dimensional data modeling and optimal decision theory. Specifically, we show that appropriately increasing the dimension of data better separates positive and negative populations and reveals nuanced structure that can be described in terms of mathematical models. We combine these models with optimal decision theory to yield a classification scheme that better separates positive and negative samples relative to traditional methods such as confidence intervals (CIs) and receiver operating characteristics. We validate the usefulness of this approach in the context of a multiplex salivary SARS-CoV-2 immunoglobulin G assay dataset. This example illustrates how our analysis: (i) improves the assay accuracy, (e.g. lowers classification errors by up to 42% compared to CI methods); (ii) reduces the number of indeterminate samples when an inconclusive class is permissible, (e.g. by 40% compared to the original analysis of the example multiplex dataset) and (iii) decreases the number of antigens needed to classify samples. Our work showcases the power of mathematical modeling in diagnostic classification and highlights a method that can be adopted broadly in public health and clinical settings.

Wild-type SARS-CoV-2 neutralizing immunity decreases across variants and over time but correlates well with diagnostic testing.

Importance: The degree of immune protection against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) variants provided by infection versus vaccination with wild-type virus remains unresolved, which could influence future vaccine strategies. The gold-standard for assessing immune protection is viral neutralization; however, few studies involve a large-scale analysis of viral neutralization against the Omicron variant by sera from individuals infected with wild-type virus. Objectives: 1) To define the degree to which infection versus vaccination with wild-type SARS-CoV-2 induced neutralizing antibodies against Delta and Omicron variants.2) To determine whether clinically available data, such as infection/vaccination timing or antibody status, can predict variant neutralization. Methods: We examined a longitudinal cohort of 653 subjects with sera collected three times at 3-to-6-month intervals from April 2020 to June 2021. Individuals were categorized according to SARS-CoV-2 infection and vaccination status. Spike and nucleocapsid antibodies were detected via ADVIA Centaur® (Siemens) and Elecsys® (Roche) assays, respectively. The Healgen Scientific® lateral flow assay was used to detect IgG and IgM spike antibody responses. Pseudoviral neutralization assays were performed on all samples using human ACE2 receptor-expressing HEK-293T cells infected with SARS-CoV-2 spike protein pseudotyped lentiviral particles for wild-type (WT), B.1.617.2 (Delta), and B.1.1.529 (Omicron) variants. Results: Vaccination after infection led to the highest neutralization titers at all timepoints for all variants. Neutralization was also more durable in the setting of prior infection versus vaccination alone. Spike antibody clinical testing effectively predicted neutralization for wild-type and Delta. However, nucleocapsid antibody presence was the best independent predictor of Omicron neutralization. Neutralization of Omicron was lower than neutralization of either wild-type or Delta virus across all groups and timepoints, with significant activity only present in patients that were first infected and later immunized. Conclusions: Participants having both infection and vaccination with wild-type virus had the highest neutralizing antibody levels against all variants and had persistence of activity. Neutralization of WT and Delta virus correlated with spike antibody levels against wild-type and Delta variants, but Omicron neutralization was better correlated with evidence of prior infection. These data help explain why 'breakthrough' Omicron infections occurred in previously vaccinated individuals and suggest better protection is observed in those with both vaccination and previous infection. This study also supports the concept of future SARS-CoV-2 Omicron-specific vaccine boosters.

SARS-CoV-2 diagnostic testing rates determine the sensitivity of genomic surveillance programs.

The first step in SARS-CoV-2 genomic surveillance is testing to identify people who are infected. However, global testing rates are falling as we emerge from the acute health emergency and remain low in many low- and middle-income countries (mean = 27 tests per 100,000 people per day). We simulated COVID-19 epidemics in a prototypical low- and middle-income country to investigate how testing rates, sampling strategies and sequencing proportions jointly impact surveillance outcomes, and showed that low testing rates and spatiotemporal biases delay time to detection of new variants by weeks to months and can lead to unreliable estimates of variant prevalence, even when the proportion of samples sequenced is increased. Accordingly, investments in wider access to diagnostics to support testing rates of approximately 100 tests per 100,000 people per day could enable more timely detection of new variants and reliable estimates of variant prevalence. The performance of global SARS-CoV-2 genomic surveillance programs is fundamentally limited by access to diagnostic testing.

Surviving Sepsis Campaign: Guidelines on the Management of Critically Ill Adults with Coronavirus Disease 2019 (COVID-19).

BACKGROUND: The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of a rapidly spreading illness, Coronavirus Disease 2019 (COVID-19), affecting thousands of people around the world. Urgent guidance for clinicians caring for the sickest of these patients is needed. METHODS: We formed a panel of 36 experts from 12 countries. All panel members completed the World Health Organization conflict of interest disclosure form. The panel proposed 53 questions that are relevant to the management of COVID-19 in the ICU. We searched the literature for direct and indirect evidence on the management of COVID-19 in critically ill patients in the ICU. We identified relevant and recent systematic reviews on most questions relating to supportive care. We assessed the certainty in the evidence using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach, then generated recommendations based on the balance between benefit and harm, resource and cost implications, equity, and feasibility. Recommendations were either strong or weak, or in the form of best practice recommendations. RESULTS: The Surviving Sepsis Campaign COVID-19 panel issued 54 statements, of which four are best practice statements, nine are strong recommendations, and 35 are weak recommendations. No recommendation was provided for six questions. The topics were: 1) infection control, 2) laboratory diagnosis and specimens, 3) hemodynamic support, 4) ventilatory support, and 5) COVID-19 therapy. CONCLUSION: The Surviving Sepsis Campaign COVID-19 panel issued several recommendations to help support healthcare workers caring for critically ill ICU patients with COVID-19. When available, we will provide new evidence in further releases of these guidelines.

Telemedicine-based approach to caring for patients with inherited retinal diseases: patient satisfaction and diagnostic testing completion rates.

BACKGROUND: During the COVID-19 pandemic, the Emory Ophthalmic Genetics Service (EOGS) adopted a hybrid telehealth-based care model, with patients undergoing a tailored panel of ancillary tests in addition to a video telehealth encounter with the EOGS physician. This study evaluates patient satisfaction with this model and effectiveness of these encounters in producing a clinical and genetic diagnosis. MATERIALS AND METHODS: A trained interviewer administered a 14-question validated patient satisfaction survey to eligible subjects between October 2020 and April 2021. A mean "favorability index" for patient satisfaction was calculated (maximum score = 5). Rates of ancillary testing, completion of genetic testing, and diagnostic accuracy were also assessed, and compared to results from a control group of EOGS patients that underwent in-person visits. RESULTS: Twenty-one of 33 eligible patients completed the survey. Nine (42.9%) were female, with mean (± SD) age 51.3 ± 13.6 years. The control group was comprised of 49 subjects, predominantly female (71.4%), with mean age 51.5 ± 15.2 years. The mean (range) favorability index was 4.3 (3.1-5.0). Rates of ancillary testing were lower for the telemedicine group vs. the control group: 38.1% vs. 85.7% (p < .001) for electrophysiology; 42.9% vs. 71.4% (p = .03) for perimetry; and 81.0% vs. 95.9% (p = .04) for fundus imaging. Two (11.1%) and 1 (2.8%) (p = .21) subjects in the telehealth and control groups, respectively, did not complete recommended genetic testing. The clinical diagnosis was compatible with the genetic diagnosis in all subjects. CONCLUSIONS: Our results suggest high patient satisfaction and diagnostic accuracy with a hybrid telemedicine-based approach for IRD care, despite lower rates of ancillary testing and no in-person examination.

Health Consumer Engagement, Enablement, and Empowerment in Smartphone-Enabled Home-Based Diagnostic Testing for Viral Infections: Mixed Methods Study.

BACKGROUND: Health consumers are increasingly taking a more substantial role in decision-making and self-care regarding their health. A range of digital technologies is available for laypeople to find, share, and generate health-related information that supports their health care processes. There is also innovation and interest in home testing enabled by smartphone technology (smartphone-supported home testing [smart HT]). However, few studies have focused on the process from initial engagement to acting on the test results, which involves multiple decisions. OBJECTIVE: This study aimed to identify and model the key factors leading to health consumers' engagement and enablement associated with smart HT. We also explored multiple levels of health care choices resulting from health consumer empowerment and activation from smart HT use. Understanding the factors and choices associated with engagement, enablement, empowerment, and activation helps both research and practice to support the intended and optimal use of smart HT. METHODS: This study reports the findings from 2 phases of a more extensive pilot study of smart HT for viral infection. In these 2 phases, we used mixed methods (semistructured interviews and surveys) to shed light on the situated complexities of health consumers making autonomous decisions to engage with, perform, and act on smart HT, supporting the diagnostic aspects of their health care. Interview (n=31) and survey (n=282) participants underwent smart HT testing for influenza in earlier pilot phases. The survey also extended the viral infection context to include questions related to potential smart HT use for SARS-CoV-2 diagnosis. RESULTS: Our resulting model revealed the smart HT engagement and enablement factors, as well as choices resulting from empowerment and activation. The model included factors leading to engagement, specifically various intrinsic and extrinsic influences. Moreover, the model included various enablement factors, including the quality of smart HT and the personal capacity to perform smart HT. The model also explores various choices resulting from empowerment and activation from the perspectives of various stakeholders (public vs private) and concerning different levels of impact (personal vs distant). CONCLUSIONS: The findings provide insight into the nuanced and complex ways health consumers make decisions to engage with and perform smart HT and how they may react to positive results in terms of public-private and personal-distant dimensions. Moreover, the study illuminates the role that providers and smart HT sources can play to better support digitally engaged health consumers in the smart HT decision process.

Implementation of a Rural Community Diagnostic Testing Strategy for SARS-CoV-2 in Upstate South Carolina.

By developing a partnership amongst a public university lab, local city government officials and community healthcare providers, we established a drive-through COVID-19 testing site aiming to improve access to SARS-CoV-2 testing in rural Upstate South Carolina. We collected information on symptoms and known exposures of individuals seeking testing to determine the number of pre- or asymptomatic individuals. We completed 71,102 SARS-CoV-2 tests in the community between December 2020-December 2021 and reported 91.49% of results within 24 h. We successfully identified 5,244 positive tests; 73.36% of these tests originated from individuals who did not report symptoms. Finally, we identified high transmission levels during two major surges and compared test positivity rates of the local and regional communities. Importantly, the local community had significantly lower test positivity rates than the regional community throughout 2021 (p < 0.001). While both communities reached peak case load and test positivity near the same time, the local community returned to moderate transmission as indicated by positivity 4 weeks before the regional community. Our university lab facilitated easy testing with fast turnaround times, which encouraged voluntary testing and helped identify a large number of non-symptomatic cases. Finding the balance of simplicity, accessibility, and community trust was vital to the success of our widespread community testing program for SARS-CoV-2.

ESCMID COVID-19 guidelines: diagnostic testing for SARS-CoV-2.

SCOPE: The objective of these guidelines is to identify the most appropriate diagnostic test and/or diagnostic approach for SARS-CoV-2. The recommendations are intended to provide guidance to clinicians, clinical microbiologists, other health care personnel, and decision makers. METHODS: An ESCMID COVID-19 guidelines task force was established by the ESCMID Executive Committee. A small group was established, half appointed by the chair and the remaining selected with an open call. Each panel met virtually once a week. For all decisions, a simple majority vote was used. A list of clinical questions using the PICO (population, intervention, comparison, outcome) format was developed at the beginning of the process. For each PICO, two panel members performed a literature search focusing on systematic reviews, with a third panellist involved in case of inconsistent results. Quality of evidence assessment was based on the GRADE-ADOLOPMENT (Grading of Recommendations Assessment, Development and Evaluation - adoption, adaptation, and de novo development of recommendations) approach. RECOMMENDATIONS: A total of 43 PICO questions were selected that involve the following types of populations: (a) patients with signs and symptoms of COVID-19; (b) travellers, healthcare workers, and other individuals at risk for exposure to SARS-CoV-2; (c) asymptomatic individuals, and (d) close contacts of patients infected with SARS-CoV-2. The type of diagnostic test (commercial rapid nucleic acid amplification tests and rapid antigen detection), biomaterial, time since onset of symptoms/contact with an infectious case, age, disease severity, and risk of developing severe disease are also taken into consideration.

CRISPR Approaches for the Diagnosis of Human Diseases.

CRISPR/Cas is a prokaryotic self-defense system, widely known for its use as a gene-editing tool. Because of their high specificity to detect DNA and RNA sequences, different CRISPR systems have been adapted for nucleic acid detection. CRISPR detection technologies differ highly among them, since they are based on four of the six major subtypes of CRISPR systems. In just 5 years, the CRISPR diagnostic field has rapidly expanded, growing from a set of specific molecular biology discoveries to multiple FDA-authorized COVID-19 tests and the establishment of several companies. CRISPR-based detection methods are coupled with pre-existing preamplification and readout technologies, achieving sensitivity and reproducibility comparable to the current gold standard nucleic acid detection methods. Moreover, they are very versatile, can be easily implemented to detect emerging pathogens and new clinically relevant mutations, and offer multiplexing capability. The advantages of the CRISPR-based diagnostic approaches are a short sample-to-answer time and no requirement of laboratory settings; they are also much more affordable than current nucleic acid detection procedures. In this review, we summarize the applications and development trends of the CRISPR/Cas13 system in the identification of particular pathogens and mutations and discuss the challenges and future prospects of CRISPR-based diagnostic platforms in biomedicine.

Retrospective Detection of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in Symptomatic Patients Prior to Widespread Diagnostic Testing in Southern California.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused one of the worst pandemics in recent history. Few reports have revealed that SARS-CoV-2 was spreading in the United States as early as the end of January. In this study, we aimed to determine if SARS-CoV-2 had been circulating in the Los Angeles (LA) area at a time when access to diagnostic testing for coronavirus disease 2019 (COVID-19) was severely limited. METHODS: We used a pooling strategy to look for SARS-CoV-2 in remnant respiratory samples submitted for regular respiratory pathogen testing from symptomatic patients from November 2019 to early March 2020. We then performed sequencing on the positive samples. RESULTS: We detected SARS-CoV-2 in 7 specimens from 6 patients, dating back to mid-January. The earliest positive patient, with a sample collected on January 13, 2020 had no relevant travel history but did have a sibling with similar symptoms. Sequencing of these SARS-CoV-2 genomes revealed that the virus was introduced into the LA area from both domestic and international sources as early as January. CONCLUSIONS: We present strong evidence of community spread of SARS-CoV-2 in the LA area well before widespread diagnostic testing was being performed in early 2020. These genomic data demonstrate that SARS-CoV-2 was being introduced into Los Angeles County from both international and domestic sources in January 2020.

Using social media to crowdsource collection of urine samples during a national pandemic.

The COVID-19 pandemic and subsequent lockdown had a substantial impact on normal research operations. Researchers needed to adapt their methods to engage at-home participants. One method is crowdsourcing, in which researchers use social media to recruit participants, gather data, and collect samples. We utilized this method to develop a diagnostic test for Interstitial Cystitis/Bladder Pain Syndrome (IC/BPS). Participants were recruited via posts on popular social-media platforms, and enrolled via a website. Participants received and returned a mail kit containing bladder symptom surveys and a urine sample cup containing room-temperature preservative. Using this method, we collected 1254 IC/BPS and control samples in 3 months from all 50 United States. Our data demonstrate that crowdsourcing is a viable alternative to traditional research, with the ability to reach a broad patient population rapidly. Crowdsourcing is a powerful tool for at-home participation in research, particularly during the lockdown caused by the COVID-19 pandemic.

Constructing tongue coating recognition model using deep transfer learning to assist syndrome diagnosis and its potential in noninvasive ethnopharmacological evaluation.

ETHNOPHARMACOLOGICAL RELEVANCE: Tongue coating has been used as an effective signature of health in traditional Chinese medicine (TCM). The level of greasy coating closely relates to the strength of dampness or pathogenic qi in TCM theory. Previous empirical studies and our systematic review have shown the relation between greasy coating and various diseases, including gastroenteropathy, coronary heart disease, and coronavirus disease 2019 (COVID-19). However, the objective and intelligent greasy coating and related diseases recognition methods are still lacking. The construction of the artificial intelligent tongue recognition models may provide important syndrome diagnosis and efficacy evaluation methods, and contribute to the understanding of ethnopharmacological mechanisms based on TCM theory. AIM OF THE STUDY: The present study aimed to develop an artificial intelligent model for greasy tongue coating recognition and explore its application in COVID-19. MATERIALS AND METHODS: Herein, we developed greasy tongue coating recognition networks (GreasyCoatNet) using convolutional neural network technique and a relatively large (N = 1486) set of tongue images from standard devices. Tests were performed using both cross-validation procedures and a new dataset (N = 50) captured by common cameras. Besides, the accuracy and time efficiency comparisons between the GreasyCoatNet and doctors were also conducted. Finally, the model was transferred to recognize the greasy coating level of COVID-19. RESULTS: The overall accuracy in 3-level greasy coating classification with cross-validation was 88.8% and accuracy on new dataset was 82.0%, indicating that GreasyCoatNet can obtain robust greasy coating estimates from diverse datasets. In addition, we conducted user study to confirm that our GreasyCoatNet outperforms TCM practitioners, yet only consuming roughly 1% of doctors' examination time. Critically, we demonstrated that GreasyCoatNet, along with transfer learning, can construct more proper classifier of COVID-19, compared to directly training classifier on patient versus control datasets. We, therefore, derived a disease-specific deep learning network by finetuning the generic GreasyCoatNet. CONCLUSIONS: Our framework may provide an important research paradigm for differentiating tongue characteristics, diagnosing TCM syndrome, tracking disease progression, and evaluating intervention efficacy, exhibiting its unique potential in clinical applications.

Coronavirus Disease Case Definitions, Diagnostic Testing Criteria, and Surveillance in 25 Countries with Highest Reported Case Counts.

We compared case definitions for suspected, probable, and confirmed coronavirus disease (COVID-19), as well as diagnostic testing criteria, used in the 25 countries with the highest reported case counts as of October 1, 2020. Of the identified countries, 56% followed World Health Organization (WHO) recommendations for using a combination of clinical and epidemiologic criteria as part of the suspected case definition. A total of 75% of identified countries followed WHO recommendations on using clinical, epidemiologic, and diagnostic criteria for probable cases; 72% followed WHO recommendations to use PCR testing to confirm COVID-19. Finally, 64% of countries used testing eligibility criteria at least as permissive as WHO. We observed marked heterogeneity in testing eligibility requirements and in how countries define a COVID-19 case. This heterogeneity affects the ability to compare case counts, transmission, and vaccine effectiveness, as well as estimates derived from case surveillance data across countries.