Diagnostic Accuracy Estimates for COVID-19 Real-Time Polymerase Chain Reaction and Lateral Flow Immunoassay Tests With Bayesian Latent-Class Models

High-Precision Viral Detection Using Electrochemical Kinetic Profiling of Aptamer-Antigen Recognition in Clinical Samples and Machine Learning

High-precision viral detection at point of need with clinical samples plays a pivotal role in the diagnosis of infectious diseases and the control of a global pandemic. However, the complexity of clinical samples that often contain very low viral concentrations makes it a huge challenge to develop simple diagnostic devices that do not require any sample processing and yet are capable of meeting performance metrics such as very high sensitivity and specificity. Herein we describe a new single-pot and single-step electrochemical method that uses real-time kinetic profiling of the interaction between a high-affinity aptamer and an antigen on a viral surface. This method generates many data points per sample, which when combined with machine learning, can deliver highly accurate test results in a short testing time. We demonstrate this concept using both SARS-CoV-2 and Influenza A viruses as model viruses with specifically engineered high-affinity aptamers. Utilizing this technique to diagnose COVID-19 with 37 real human saliva samples results in a sensitivity and specificity of both 100 % (27 true negatives and 10 true positives, with 0 false negative and 0 false positive), which showcases the superb diagnostic precision of this method.

Expression of soluble moloney murine leukemia virus-reverse transcriptase in Escherichia coli BL21 star (DE3) using autoinduction system

Autoinduction systems in Escherichia coli can control the production of proteins without the addition of a particular inducer. In the present study, we optimized the heterologous expression of Moloney Murine Leukemia Virus derived Reverse Transcriptase (MMLV-RT) in E. coli. Among 4 autoinduction media, media Imperial College resulted the highest MMLV-RT overexpression in E. coli BL21 Star (DE3) with incubation time 96 h. The enzyme was produced most optimum in soluble fraction of lysate cells. The MMLV-RT was then purified using the Immobilized Metal Affinity Chromatography method and had specific activity of 629.4 U/mg. The system resulted lower specific activity and longer incubation of the enzyme than a classical Isopropyl ß-D-1-thiogalactopyranoside (IPTG)-induction system. However, the autoinduction resulted higher yield of the enzyme than the conventional induction (27.8%). Techno Economic Analysis revealed that this method could produce MMLV-RT using autoinduction at half the cost of MMLV-RT production by IPTG-induction. Bioprocessing techniques are necessary to conduct to obtain higher quality of MMLV-RT under autoinduction system.

Estimates of sensitivity and specificity of serological tests for SARS-CoV-2 specific antibodies using a Bayesian latent class model approach

OBJECTIVES

Assessing the accuracy of serological tests for SARS-CoV-2 was challenging due to the lack of a gold standard. This study aimed to estimate the accuracy of SARS-CoV-2-specific serological tests using Bayesian latent class models (BLCM) and compare methods with and without a gold standard.

STUDY DESIGN AND SETTING

In this study, we analyzed 356 samples-254 positives, ie, from individuals with a previous SARS-CoV-2 infection diagnosis, and 102 negatives, ie, prepandemic samples-using six different rapid serological tests and one laboratory assay. A BLCM was employed to concurrently estimate the sensitivity and specificity of all serological tests for the immunoglobulin (Ig) M and IgG antibodies specific for SARS-CoV-2. Noninformative priors were used. A sensitivity analysis was conducted considering three methods: 1) reverse transcription-polymerase chain reaction test (RT-PCR) as the gold standard, 2) BLCM with RT-PCR as an imperfect gold standard, and 3) frequentist latent class model (LCM). All analyses used software R version 4.3.0, and BLCM were fitted using package runjags using the software JAGS (Just Another Gibbs Sampler).

RESULTS

The BLCM-derived sensitivity for IgM varied from 10.7% [95% credibility interval (CrI):1.9-24.6] to 96.9% (95% CrI: 91.0-100.0), with specificities ranging from 48.3% (95% CrI: 39.0-57.6) to 98.9% (95% CrI: 96.2-100.0). Sensitivity for IgG varied between 76.9% (95% CrI: 68.2-84.7) and 99.1% (95% CrI: 96.1-100.0), and specificity ranged from 49.9% (95% CrI: 19.4-95.8) to 99.3% (95% CrI: 97.2-100.0). LCM results were comparable to BLCM. Considering the RT-PCR as a gold standard underestimated the tests' sensitivity, particularly for IgM.

CONCLUSION

BLCM-derived results deviated from those using a gold standard, which underestimated the tests' characteristics, particularly sensitivity. Although Bayesian and frequentist LCM approaches yielded comparable results, BLCM had the benefit of enabling credibility interval computation even when sample power is limited.

Detection of COVID-19 features in lung ultrasound images using deep neural networks

BACKGROUND

Deep neural networks (DNNs) to detect COVID-19 features in lung ultrasound B-mode images have primarily relied on either in vivo or simulated images as training data. However, in vivo images suffer from limited access to required manual labeling of thousands of training image examples, and simulated images can suffer from poor generalizability to in vivo images due to domain differences. We address these limitations and identify the best training strategy.

METHODS

We investigated in vivo COVID-19 feature detection with DNNs trained on our carefully simulated datasets (40,000 images), publicly available in vivo datasets (174 images), in vivo datasets curated by our team (958 images), and a combination of simulated and internal or external in vivo datasets. Seven DNN training strategies were tested on in vivo B-mode images from COVID-19 patients.

RESULTS

Here, we show that Dice similarity coefficients (DSCs) between ground truth and DNN predictions are maximized when simulated data are mixed with external in vivo data and tested on internal in vivo data (i.e., 0.482 ± 0.211), compared with using only simulated B-mode image training data (i.e., 0.464 ± 0.230) or only external in vivo B-mode training data (i.e., 0.407 ± 0.177). Additional maximization is achieved when a separate subset of the internal in vivo B-mode images are included in the training dataset, with the greatest maximization of DSC (and minimization of required training time, or epochs) obtained after mixing simulated data with internal and external in vivo data during training, then testing on the held-out subset of the internal in vivo dataset (i.e., 0.735 ± 0.187).

CONCLUSIONS

DNNs trained with simulated and in vivo data are promising alternatives to training with only real or only simulated data when segmenting in vivo COVID-19 lung ultrasound features.

Estimating cutoff values for diagnostic tests to achieve target specificity using extreme value theory

BACKGROUND

Rapidly developing tests for emerging diseases is critical for early disease monitoring. In the early stages of an epidemic, when low prevalences are expected, high specificity tests are desired to avoid numerous false positives. Selecting a cutoff to classify positive and negative test results that has the desired operating characteristics, such as specificity, is challenging for new tests because of limited validation data with known disease status. While there is ample statistical literature on estimating quantiles of a distribution, there is limited evidence on estimating extreme quantiles from limited validation data and the resulting test characteristics in the disease testing context.

METHODS

We propose using extreme value theory to select a cutoff with predetermined specificity by fitting a Pareto distribution to the upper tail of the negative controls. We compared this method to five previously proposed cutoff selection methods in a data analysis and simulation study. We analyzed COVID-19 enzyme linked immunosorbent assay antibody test results from long-term care facilities and skilled nursing staff in Colorado between May and December of 2020.

RESULTS

We found the extreme value approach had minimal bias when targeting a specificity of 0.995. Using the empirical quantile of the negative controls performed well when targeting a specificity of 0.95. The higher target specificity is preferred for overall test accuracy when prevalence is low, whereas the lower target specificity is preferred when prevalence is higher and resulted in less variable prevalence estimation.

DISCUSSION

While commonly used, the normal based methods showed considerable bias compared to the empirical and extreme value theory-based methods.

CONCLUSIONS

When determining disease testing cutoffs from small training data samples, we recommend using the extreme value based-methods when targeting a high specificity and the empirical quantile when targeting a lower specificity.

Estimating the number of probable new SARS-CoV-2 infections among tested subjects from the number of confirmed cases

OBJECTIVES

In most African countries, confirmed COVID-19 case counts underestimate the number of new SARS-CoV-2 infection cases. We propose a multiplying factor to approximate the number of biologically probable new infections from the number of confirmed cases.

METHODS

Each of the first thousand suspect (or alert) cases recorded in South Kivu (DRC) between 29 March and 29 November 2020 underwent a RT-PCR test and an IgM and IgG serology. A latent class model and a Bayesian inference method were used to estimate (i) the incidence proportion of SARS-CoV-2 infection using RT-PCR and IgM test results, (ii) the prevalence using RT-PCR, IgM and IgG test results; and, (iii) the multiplying factor (ratio of the incidence proportion on the proportion of confirmed -RT-PCR+- cases).

RESULTS

Among 933 alert cases with complete data, 218 (23%) were RT-PCR+; 434 (47%) IgM+; 464 (~ 50%) RT-PCR+, IgM+, or both; and 647 (69%) either IgG + or IgM+. The incidence proportion of SARS-CoV-2 infection was estimated at 58% (95% credibility interval: 51.8-64), its prevalence at 72.83% (65.68-77.89), and the multiplying factor at 2.42 (1.95-3.01).

CONCLUSIONS

In monitoring the pandemic dynamics, the number of biologically probable cases is also useful. The multiplying factor helps approximating it.

Determination of the Diagnostic Performance of Laboratory Tests in the Absence of a Perfect Reference Standard: The Case of SARS-CoV-2 Tests

BACKGROUND

Currently, assessing the diagnostic performance of new laboratory tests assumes a perfect reference standard, which is rarely the case. Wrong classifications of the true disease status will inevitably lead to biased estimates of sensitivity and specificity.

OBJECTIVES

Using Bayesian' latent class models (BLCMs), an approach that does not assume a perfect reference standard, we re-analyzed data of a large prospective observational study assessing the diagnostic accuracy of an antigen test for the diagnosis of SARS-CoV-2 infection in clinical practice.

METHODS

A cohort of consecutive patients presenting to a COVID-19 testing facility affiliated with a Swiss University Hospital were recruited (n = 1465). Two real-time PCR tests were conducted in parallel with the Roche/SD Biosensor rapid antigen test on nasopharyngeal swabs. A two-test (PCR and antigen test), three-population BLCM was fitted to the frequencies of paired test results.

RESULTS

Based on the BLCM, the sensitivities of the RT-PCR and the Roche/SD Biosensor rapid antigen test were 98.5% [95% CRI 94.8;100] and 82.7% [95% CRI 66.8;100]. The specificities were 97.7% [96.1;99.7] and 99.9% [95% CRI 99.6;100].

CONCLUSIONS

Applying the BLCM, the diagnostic accuracy of RT-PCR was high but not perfect. In contrast to previous results, the sensitivity of the antigen test was higher. Our results suggest that BLCMs are valuable tools for investigating the diagnostic performance of laboratory tests in the absence of perfect reference standard.

Application and utility of boosting machine learning model based on laboratory test in the differential diagnosis of non-COVID-19 pneumonia and COVID-19

BACKGROUND

Non-Coronavirus disease 2019 (COVID-19) pneumonia and COVID-19 have similar clinical features but last for different periods, and consequently, require different treatment protocols. Therefore, they must be differentially diagnosed. This study uses artificial intelligence (AI) to classify the two forms of pneumonia using mainly laboratory test data.

METHODS

Various AI models are applied, including boosting models known for deftly solving classification problems. In addition, important features that affect the classification prediction performance are identified using the feature importance technique and SHapley Additive exPlanations method. Despite the data imbalance, the developed model exhibits robust performance.

RESULTS

eXtreme gradient boosting, category boosting, and light gradient boosted machine yield an area under the receiver operating characteristic of 0.99 or more, accuracy of 0.96-0.97, and F1-score of 0.96-0.97. In addition, D-dimer, eosinophil, glucose, aspartate aminotransferase, and basophil, which are rather nonspecific laboratory test results, are demonstrated to be important features in differentiating the two disease groups.

CONCLUSIONS

The boosting model, which excels in producing classification models using categorical data, excels in developing classification models using linear numerical data, such as laboratory tests. Finally, the proposed model can be applied in various fields to solve classification problems.

Addressing misclassification bias in vaccine effectiveness studies with an application to Covid-19

Safe and effective vaccines are crucial for the control of Covid-19 and to protect individuals at higher risk of severe disease. The test-negative design is a popular option for evaluating the effectiveness of Covid-19 vaccines. However, the findings could be biased by several factors, including imperfect sensitivity and/or specificity of the test used for diagnosing the SARS-Cov-2 infection. We propose a simple Bayesian modeling approach for estimating vaccine effectiveness that is robust even when the diagnostic test is imperfect. We use simulation studies to demonstrate the robustness of our method to misclassification bias and illustrate the utility of our approach using real-world examples.

Novel Comparative Study for the Detection of COVID-19 Using CT Scan and Chest X-ray Images

The number of coronavirus disease (COVID-19) cases is constantly rising as the pandemic continues, with new variants constantly emerging. Therefore, to prevent the virus from spreading, coronavirus cases must be diagnosed as soon as possible. The COVID-19 pandemic has had a devastating impact on people's health and the economy worldwide. For COVID-19 detection, reverse transcription-polymerase chain reaction testing is the benchmark. However, this test takes a long time and necessitates a lot of laboratory resources. A new trend is emerging to address these limitations regarding the use of machine learning and deep learning techniques for automatic analysis, as these can attain high diagnosis results, especially by using medical imaging techniques. However, a key question arises whether a chest computed tomography scan or chest X-ray can be used for COVID-19 detection. A total of 17,599 images were examined in this work to develop the models used to classify the occurrence of COVID-19 infection, while four different classifiers were studied. These are the convolutional neural network (proposed architecture (named, SCovNet) and Resnet18), support vector machine, and logistic regression. Out of all four models, the proposed SCoVNet architecture reached the best performance with an accuracy of almost 99% and 98% on chest computed tomography scan images and chest X-ray images, respectively.

Detection of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) RNA in the Human Eye

PURPOSE

To determine the presence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in postmortem ocular specimens of patients with severe COVID-19 disease.

PATIENTS AND METHODS

Postmortem conjunctival (28 samples), aqueous humor (30 samples) and vitreous humor (30 samples) specimens were obtained bilaterally from the eyes of 15 deceased COVID-19 patients within one hour of death. The presence of viral RNA was evaluated in samples using Real-time reverse transcriptase-polymerase chain reaction (RT-PCR).

RESULTS

Positive RT-PCR SARS-COV-2 results were found in one conjunctival and 2 vitreous humor samples. All aqueous humor samples tested negative for the presence of SARS-COV-2 RNA. Of note, three positive samples were obtained from three different patients. The overall prevalence of positive RT-PCR ocular samples was 3.4% among all samples and 20% at the patient level.

CONCLUSION

SARS-CoV-2 RNA is detectable in postmortem conjunctival and vitreous humor samples of patients with severe COVID-19.

Comparison of test-negative and syndrome-negative controls in SARS-CoV-2 vaccine effectiveness evaluations for preventing COVID-19 hospitalizations in the United States

BACKGROUND

Test-negative design (TND) studies have produced validated estimates of vaccine effectiveness (VE) for influenza vaccine studies. However, syndrome-negative controls have been proposed for differentiating bias and true estimates in VE evaluations for COVID-19. To understand the use of alternative control groups, we compared characteristics and VE estimates of syndrome-negative and test-negative VE controls.

METHODS

Adults hospitalized at 21 medical centers in 18 states March 11-August 31, 2021 were eligible for analysis. Case patients had symptomatic acute respiratory infection (ARI) and tested positive for SARS-CoV-2. Control groups were test-negative patients with ARI but negative SARS-CoV-2 testing, and syndrome-negative controls were without ARI and negative SARS-CoV-2 testing. Chi square and Wilcoxon rank sum tests were used to detect differences in baseline characteristics. VE against COVID-19 hospitalization was calculated using logistic regression comparing adjusted odds of prior mRNA vaccination between cases hospitalized with COVID-19 and each control group.

RESULTS

5811 adults (2726 cases, 1696 test-negative controls, and 1389 syndrome-negative controls) were included. Control groups differed across characteristics including age, race/ethnicity, employment, previous hospitalizations, medical conditions, and immunosuppression. However, control-group-specific VE estimates were very similar. Among immunocompetent patients aged 18-64 years, VE was 93 % (95 % CI: 90-94) using syndrome-negative controls and 91 % (95 % CI: 88-93) using test-negative controls.

CONCLUSIONS

Despite demographic and clinical differences between control groups, the use of either control group produced similar VE estimates across age groups and immunosuppression status. These findings support the use of test-negative controls and increase confidence in COVID-19 VE estimates produced by test-negative design studies.

Interobserver Variability in CT Severity Scoring System in COVID-19 Positive Patients

Background: Chest CT scans are done in cases of coronavirus disease 2019 (COVID-19)-positive patients to understand the severity of the disease and plan treatment accordingly. Severity is determined according to a 25-point scoring system, however, there could be interobserver variability in using this scoring system thus leading to the different categorization of patients. We tried to look for this interobserver variability and thus find out its reliability.

Methods: The study was retrospective and was done in a designated COVID center. Some 100 patients were involved in the study who tested positive for COVID-19 disease. The research was conducted over six months (January 2021 to June 2021). Images were given to three radiologists with a minimum of 10 years of experience in thoracic imaging working in different setups at different places for interpretation and scoring further and their scores were compared. Before the study, the local ethics committee granted its approval.

Results: There was no significant variability in the interobserver scoring system thus proving its reliability. The standard deviation between different observers was less than three. There was almost perfect agreement amongst all the observers (Fleiss’ K=0.99 [95% confidence interval, CI: 0.995-0.998]). Maximum variations were observed in the moderate class. 

Conclusion: There was minimum inter-observer variability in the 25-point scoring system thus proving its reliability in categorizing patients according to severity. There was no change in the class of the patient according to its severity. A 25-point scoring system hence can be used by clinicians to plan treatment and thus improve a patient's prognosis.

SARS-CoV-2 Infections in Icelandic Children: Close Follow-up of All Confirmed Cases in a Nationwide Study

INTRODUCTION

Children are less likely to acquire SARS-CoV-2 infections than adults and when infected, usually have milder disease. True infection and complication rates are, however, difficult to ascertain. In Iceland, a strict test, trace and isolate policy was maintained from the start of the pandemic and offers more accurate information of the number of truly infected children in a nationwide study.

MATERIAL AND METHODS

All children with positive PCR for SARS-CoV-2 infections from February 28, 2020 to August 31, 2021 were followed up through telephone consultations for at least 14 days and their symptoms were registered. Symptom severity and duration were categorized based on age groups and the source of infection was registered.

RESULTS

A total of 1749 children were infected with SARS-CoV-2 in 3 waves of infections. All waves had similar disease severity whereas the incidence was 5-fold higher in the third wave (3.5 vs. 0.73/1000 children/month). No children had severe symptoms, 81 (4.6%) had moderate symptoms, 1287 (73.9%) had mild and 374 (21.5%) were asymptomatic. Symptoms from upper (n = 839, 48%) and lower respiratory tract (n = 744, 43%) were most common. Median duration of symptoms was 5 days and adolescents had a higher risk of prolonged duration [OR:1.84 (1.39-2.43)]. Nineteen (1.1%) children needed medical attention, but no child was hospitalized. The source of infection was a household member in 65% of cases.

DISCUSSION

During the first 3 waves of the pandemic, SARS-CoV-2 infections in Icelandic children were mild and none were hospitalized. The most common symptoms were respiratory symptoms followed by fever, headache and tiredness. This study helps shed light on true complication rates of children with confirmed SARS-CoV-2 infection.

Diagnostic Approaches For COVID-19: Lessons Learned and the Path Forward

Coronavirus disease 2019 (COVID-19) is a transmitted respiratory disease caused by the infection of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although humankind has experienced several outbreaks of infectious diseases, the COVID-19 pandemic has the highest rate of infection and has had high levels of social and economic repercussions. The current COVID-19 pandemic has highlighted the limitations of existing virological tests, which have failed to be adopted at a rate to properly slow the rapid spread of SARS-CoV-2. Pandemic preparedness has developed as a focus of many governments around the world in the event of a future outbreak. Despite the largely widespread availability of vaccines, the importance of testing has not diminished to monitor the evolution of the virus and the resulting stages of the pandemic. Therefore, developing diagnostic technology that serves as a line of defense has become imperative. In particular, that test should satisfy three criteria to be widely adopted: simplicity, economic feasibility, and accessibility. At the heart of it all, it must enable early diagnosis in the course of infection to reduce spread. However, diagnostic manufacturers need guidance on the optimal characteristics of a virological test to ensure pandemic preparedness and to aid in the effective treatment of viral infections. Nanomaterials are a decisive element in developing COVID-19 diagnostic kits as well as a key contributor to enhance the performance of existing tests. Our objective is to develop a profile of the criteria that should be available in a platform as the target product. In this work, virus detection tests were evaluated from the perspective of the COVID-19 pandemic, and then we generalized the requirements to develop a target product profile for a platform for virus detection.

Real-World Evidence-Current Developments and Perspectives

Real-world evidence (RWE) is increasingly involved in the early benefit assessment of medicinal drugs. It is expected that RWE will help to speed up approval processes comparable to RWE developments in vaccine research during the COVID-19 pandemic. Definitions of RWE are diverse, marking the highly fluid status in this field. So far, RWE comprises information produced from data routinely collected on patient's health status and/or delivery of health care from various sources other than traditional clinical trials. These sources can include electronic health records, claims, patient-generated data including in home-use settings, data from mobile devices, as well as patient, product, and disease registries. The aim of the present update was to review current RWE developments and guidelines, mainly in the U.S. and Europe over the last decade. RWE has already been included in various approval procedures of regulatory authorities, reflecting its actual acceptance and growing importance in evaluating and accelerating new therapies. However, since RWE research is still in a transition process, and since a number of gaps in this field have been explored, more guidance and a consented definition are necessary to increase the implementation of real-world data.

Pooled surveillance testing for asymptomatic SARS-CoV-2 infections at a Veterinary Teaching Hospital College, University of Minnesota, December 2020–April 2021

To evaluate the use of asymptomatic surveillance, we implemented a surveillance program for asymptomatic SARS-CoV-2 infection in a voluntary sample of individuals at the College of Veterinary Medicine at the University of Minnesota. Self-collected anterior nasal samples were tested using real time reverse transcription-polymerase chain reaction (RT-PCR), in a 5:1 pooled testing strategy, twice weekly for 18 weeks. Positive pools were deconvoluted into individual tests, revealing an observed prevalence of 0.07% (3/4,525). Pooled testing allowed for large scale testing with an estimated cost savings of 79.3% and modeling demonstrated this testing strategy prevented up to 2 workplace transmission events, averting up to 4 clinical cases. At the study endpoint, antibody testing revealed 80.7% of participants had detectable vaccine antibody levels while 9.6% of participants had detectable antibodies to natural infection.

Adaptive group testing in a compartmental model of COVID-19. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022;19:11018-11033. [PMID: 36124578 DOI: 10.3934/mbe.2022513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Various measures have been implemented around the world to prevent the spread of SARS-CoV-2. A potential tool to reduce disease transmission is regular mass testing of a high percentage of the population, possibly with pooling (testing a compound of several samples with one single test). We develop a compartmental model to study the applicability of this method and compare different pooling strategies: regular and Dorfman pooling. The model includes isolated compartments as well, from where individuals rejoin the active population after some time delay. We develop a method to optimize Dorfman pooling depending on disease prevalence and establish an adaptive strategy to select variable pool sizes during the course of the epidemic. It is shown that optimizing the pool size can avert a significant number of infections. The adaptive strategy is much more efficient, and may prevent an epidemic outbreak even in situations when a fixed pool size strategy can not.

Estimating the sensitivity and specificity of serum ELISA and pooled and individual fecal PCR for detecting Mycobacterium avium subspecies paratuberculosis in Canadian cow-calf herds using Bayesian latent class models

While Johne's disease (JD) is less common in beef than in dairy herds, consolidation is increasing transmission risk. Estimates of Mycobacterium avium spp. paratuberculosis (MAP) prevalence and test performance in cow-calf herds are needed to inform control programs. Objectives of this study included describing the prevalence of MAP in Canadian cow-calf herds and comparing the relative performance of a serum ELISA, pooled fecal PCR and individual fecal PCR using Bayesian latent class models, and to investigate factors associated with positive MAP tests. Blood and fecal samples (n = 3,171) were collected from 159 Canadian cow-calf herds. All samples were analyzed using serum ELISA and fecal PCR (pools of five samples) and a subset of 913 fecal samples were also tested with individual PCR. Based on latent class analysis, MAP prevalence was higher in eastern compared to western Canada for both animals {East, 3% [95% Credible Interval (CrI) 1–7%]; West, 1% [95% CrI 0.2–2%]} and herds [East, 15% (95% CrI 2–35%); West, 10% (95% CrI 1–26%), based on one or more positive results]. Sensitivity (Se) and specificity (Sp) for animal level individual PCR were 96% (95% CrI 80–100%) and 98% (95% CrI 96–100%), respectively followed by pooled PCR [Se = 54% (95% CrI 36–72%), Sp > 99.9% (95% CrI 99.8–100%)] and ELISA [Se = 36% (95% CrI 22–52%), Sp = 98% (95% CrI 96–99%)]. Based on 20 samples per herd, the herd level Se of ELISA was 79% (95% CrI 47–100%) (at least one positive sample) compared to 43% (95% CrI 14–94%) for pooled PCR. Herd-level Sp was 99% (95% CrI 96–100%) for pooled PCR and 90% (95% CrI 83–100%) for ELISA. Cows from herds with dairy cattle on farm and cows with symptoms of JD in the past 3 years were more likely to be MAP positive. Herds that had animals with JD symptoms in the previous 3 years and those with more breeding females were most likely to test positive for MAP. While serum ELISA can be effective for herd screening, PCR performed better for animal testing. Pooled PCR testing could be a less costly option; however, determining the most cost-effective approach will require further economic analysis.

The Revolution of Lateral Flow Assay in the Field of AMR Detection

The global spread of antimicrobial resistant (AMR) bacteria represents a considerable public health concern, yet their detection and identification of their resistance mechanisms remain challenging. Optimal diagnostic tests should provide rapid results at low cost to enable implementation in any microbiology laboratory. Lateral flow assays (LFA) meet these requirements and have become essential tools to combat AMR. This review presents the versatility of LFA developed for the AMR detection field, with particular attention to those directly triggering β-lactamases, their performances, and specific limitations. It considers how LFA can be modified by detecting not only the enzyme, but also its β-lactamase activity for a broader clinical sensitivity. Moreover, although LFA allow a short time-to-result, they are generally only implemented after fastidious and time-consuming techniques. We present a sample processing device that shortens and simplifies the handling of clinical samples before the use of LFA. Finally, the capacity of LFA to detect amplified genetic determinants of AMR by isothermal PCR will be discussed. LFA are inexpensive, rapid, and efficient tools that are easy to implement in the routine workflow of laboratories as new first-line tests against AMR with bacterial colonies, and in the near future directly with biological media.

Evaluation of diagnostic test procedures for SARS-CoV-2 using latent class models

Polymerase chain reaction (PCR) and antigen tests have been used extensively for screening during the severe acute respiratory syndrome coronavirus 2 pandemics. However, the real‐world sensitivity and specificity of the two testing procedures in the field have not yet been estimated without assuming that the PCR constitutes a gold standard test. We use latent class models to estimate the in situ performance of both tests using data from the Danish national registries. We find that the specificity of both tests is very high (>99.7%), while the sensitivities are 95.7% (95% confidence interval [CI]: 92.8%–98.4%) and 53.8% (95% CI: 49.8%–57.9%) for the PCR and antigen tests, respectively. These findings have implications for the use of confirmatory PCR tests following a positive antigen test result: we estimate that serial testing is counterproductive at higher prevalence levels.

Leishmania exposure in dogs from two endemic countries from New and Old Worlds (Brazil and Portugal): evaluation of three serological tests using Bayesian Latent Class Models

BACKGROUND

Zoonotic leishmaniosis caused by Leishmania infantum is endemic in several countries of the Mediterranean Basin, Latin America, and Asia. Dogs are the main hosts and reservoirs of human infection. Thus, from a One Health perspective, early diagnosis of Leishmania infection in dogs is essential to control the dissemination of the parasite among other dogs and to humans. The aim of this study was to estimate the diagnosis accuracy of three serological tests to detect antibodies to Leishmania in dogs from two endemic settings using Bayesian latent class models (BLCM).

METHODS

A total of 378 dogs from two Portuguese and Brazilian endemic areas of leishmaniosis (194 animals from Portugal and 184 from Brazil) were screened. Detection of anti-Leishmania antibodies was performed using two commercial ELISA (L. infantum IgG-ELISA® and EIE-LVC®) and a rapid immunochromatographic test (DPP-LVC®). Bayesian latent class models were used to estimate Leishmania infection prevalence, together with sensitivities and specificities of the three diagnostic tests, in the two dog populations simultaneously. Predictive values were also calculated. Credibility intervals (CI) were obtained, considering different types of prior information.

RESULTS

A posterior median Leishmania seroprevalence of 13.4% (95% CI 9.0-18.7) and of 21.6% (15.0-28.3) was estimated to the Portuguese and Brazilian dog subpopulations, respectively. The Bayesian analysis indicated that all tests were highly specific (specificity above 90%), and that the DPP-LVC® was more sensitive (96.6%; 83.1-99.9) than both ELISAs in the Portuguese subpopulation, while in the Brazilian subpopulation, EIE-LVC® and L. infantum IgG-ELISA®, had the highest sensitivity (88.2%; 73.7-97.0) and specificity (98.7%; 95.1-99.9), respectively.

CONCLUSIONS

In general, the levels of diagnosis accuracy of the three serological tests to detect Leishmania antibodies assessed by BLCM indicate their utility in canine epidemiological studies. The same approach should be used to assess the performance of these techniques in the clinical management of infected and sick dogs using representative samples from the wide spectrum of clinical situations, namely from subclinical infection to manifest disease. The low positive predictive value of the serological tests used in the current protocol of the Brazilian Ministry of Health suggests that they should not be used individually and may not be sufficient to target reservoir-based control interventions.

Clinical characteristics of patients with asymptomatic and symptomatic COVID-19 admitted to a tertiary referral centre in the Philippines

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Asymptomatic infection is common.

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Bimodal age distribution of coronavirus disease 2019 (COVID-19) was observed at the University of the Philippines–Philippine General Hospital.

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Universal testing impacts infection control measures in resource-limited settings.

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Further blood testing is likely to be unnecessary for mild and asymptomatic cases of COVID-19.

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Symptom-based isolation protocol reduces length of hospitalization.

Objectives

To describe the clinical profile and outcomes of hospitalized patients with coronavirus disease 2019 (COVID-19) across the spectrum of disease severity.

Methods

This retrospective study included adult patients with confirmed COVID-19 infection admitted to a referral hospital. Descriptive statistics, tests for trend, Kaplan–Meier curve and log-rank test were used to compare characteristics and outcomes across disease severity categories.

Results

Of 1500 patients with COVID-19, 14.8% were asymptomatic, 13.5% had mild disease, 36.6% had moderate disease, 12.3% had severe disease and 22.7% had critical disease. Asymptomatic patients were admitted for a concurrent condition or for isolation. Patients aged >60 years, male gender and with co-morbidities had more severe disease. Fever, cough, shortness of breath, malaise, gastrointestinal symptoms and decreased sensorium were more common in patients with severe disease. Bilateral pulmonary infiltrates were common (51.1%), with sicker patients having more abnormal findings. The overall mortality rate was 15.1%. Adopting a symptom-based strategy reduced the length of hospitalization from a median of 13 [interquartile range (IQR) 7–21] days to 9 (IQR 5–14) days.

Conclusion

The clinical profile and outcomes for this cohort of patients with COVID-19 was consistent with published reports. Asymptomatic infection was common, and universal testing may be a valuable strategy in the correct context, given the implications for infection control. A symptom-based strategy was found to reduce the length of hospitalization considerably.

Diagnostic accuracy of three commercially available one step RT-PCR assays for the detection of SARS-CoV-2 in resource limited settings

Background

COVID-19 is an ongoing public health pandemic regardless of the countless efforts made by various actors. Quality diagnostic tests are important for early detection and control. Notably, several commercially available one step RT-PCR based assays have been recommended by the WHO. Yet, their analytic and diagnostic performances have not been well documented in resource-limited settings. Hence, this study aimed to evaluate the diagnostic sensitivities and specificities of three commercially available one step reverse transcriptase-polymerase chain reaction (RT-PCR) assays in Ethiopia in clinical setting.

Methods

A cross-sectional study was conducted from April to June, 2021 on 279 respiratory swabs originating from community surveillance, contact cases and suspect cases. RNA was extracted using manual extraction method. Master-mix preparation, amplification and result interpretation was done as per the respective manufacturer. Agreements between RT-PCRs were analyzed using kappa values. Bayesian latent class models (BLCM) were fitted to obtain reliable estimates of diagnostic sensitivities, specificities of the three assays and prevalence in the absence of a true gold standard.

Results

Among the 279 respiratory samples, 50(18%), 59(21.2%), and 69(24.7%) were tested positive by TIB, Da An, and BGI assays, respectively. Moderate to substantial level of agreement was reported among the three assays with kappa value between 0 .55 and 0.72. Based on the BLCM relatively high specificities (95% CI) of 0.991(0.973–1.000), 0.961(0.930–0.991) and 0.916(0.875–0.952) and considerably lower sensitivities with 0.813(0.658–0.938), 0.836(0.712–0.940) and 0.810(0.687–0.920) for TIB MOLBIOL, Da An and BGI respectively were found.

Conclusions

While all the three RT-PCR assays displayed comparable sensitivities, the specificities of TIB MOLBIOL and Da An were considerably higher than BGI. These results help adjust the apparent prevalence determined by the three RT-PCRs and thus support public health decisions in resource limited settings and consider alternatives as per their prioritization matrix.

Artificial Intelligence and COVID-19 Using Chest CT Scan and Chest X-ray Images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment

OBJECTIVE

To report an overview and update on Artificial Intelligence (AI) and COVID-19 using chest Computed Tomography (CT) scan and chest X-ray images (CXR). Machine Learning and Deep Learning Approaches for Diagnosis and Treatment were identified.

METHODS

Several electronic datasets were analyzed. The search covered the years from January 2019 to June 2021. The inclusion criteria were studied evaluating the use of AI methods in COVID-19 disease reporting performance results in terms of accuracy or precision or area under Receiver Operating Characteristic (ROC) curve (AUC).

RESULTS

Twenty-two studies met the inclusion criteria: 13 papers were based on AI in CXR and 10 based on AI in CT. The summarized mean value of the accuracy and precision of CXR in COVID-19 disease were 93.7% ± 10.0% of standard deviation (range 68.4-99.9%) and 95.7% ± 7.1% of standard deviation (range 83.0-100.0%), respectively. The summarized mean value of the accuracy and specificity of CT in COVID-19 disease were 89.1% ± 7.3% of standard deviation (range 78.0-99.9%) and 94.5 ± 6.4% of standard deviation (range 86.0-100.0%), respectively. No statistically significant difference in summarized accuracy mean value between CXR and CT was observed using the Chi square test (p value > 0.05).

CONCLUSIONS

Summarized accuracy of the selected papers is high but there was an important variability; however, less in CT studies compared to CXR studies. Nonetheless, AI approaches could be used in the identification of disease clusters, monitoring of cases, prediction of the future outbreaks, mortality risk, COVID-19 diagnosis, and disease management.