A statistical framework to estimate diagnostic test performance for COVID-19.
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ABSTRACT:
SUBMITTER: Symons R
PROVIDER: S-EPMC7577695 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:AimsTo validate the diagnostic accuracy of the Augurix SARS-CoV-2 IgM/IgG rapid immunoassay diagnostic test (RDT) for COVID-19.MethodsIn this unmatched 1:1 case-control study, blood samples from 46 real-time RT-PCR-confirmed SARS-CoV-2 hospitalized cases and 45 healthy donors (negative controls) were studied. Diagnostic accuracy of the IgG RDT was assessed against both an in-house recombinant spike-expressing immunofluorescence assay (rIFA), as an established reference method (primary endpoint), and the Euroimmun SARS-CoV-2 IgG enzyme-linked immunosorbent assays (ELISA) (secondary endpoint).ResultsCOVID-19 patients were more likely to be male (61% vs 20%; P = .0001) and older (median 66 vs 47 years old; P < .001) than controls. Whole blood IgG-RDT results showed 86% and 93% overall Kendall concordance with rIFA and IgG ELISA, respectively. IgG RDT performances were similar between plasma and whole blood. Overall, RDT sensitivity was 88% (95% confidence interval [95%CI]: 70-96), specificity 98% (95%CI: 90-100), PPV 97% (95%CI: 80-100) and NPV 94% (95%CI: 84-98). The IgG-RDT carried out from 0 to 6 days, 7 to 14 days and > 14 days after the SARS-CoV-2 RT-PCR test displayed 30%, 73% and 100% positivity rates in the COVID-19 group, respectively. When considering samples taken >14 days after RT-PCR diagnosis, NPV was 100% (95%CI:90-100), and PPV was 100% (95%CI:72-100).ConclusionsThe Augurix IgG-RDT done in whole blood displays a high diagnostic accuracy for SARS-CoV-2 IgG in high COVID-19 prevalence settings, where its use could be considered in the absence of routine diagnostic serology facilities.
| S-EPMC7404380 | biostudies-literature
Project description:BackgroundSince the beginning of the COVID-19 pandemic, researchers and health authorities have sought to identify the different parameters that drive its local transmission cycles to make better decisions regarding prevention and control measures. Different modeling approaches have been proposed in an attempt to predict the behavior of these local cycles.ObjectiveThis paper presents a framework to characterize the different variables that drive the local, or epidemic, cycles of the COVID-19 pandemic, in order to provide a set of relatively simple, yet efficient, statistical tools to be used by local health authorities to support decision making.MethodsVirtually closed cycles were compared to cycles in progress from different locations that present similar patterns in the figures that describe them. With the aim to compare populations of different sizes at different periods of time and locations, the cycles were normalized, allowing an analysis based on the core behavior of the numerical series. A model for the reproduction number was derived from the experimental data, and its performance was presented, including the effect of subnotification (ie, underreporting). A variation of the logistic model was used together with an innovative inventory model to calculate the actual number of infected persons, analyze the incubation period, and determine the actual onset of local epidemic cycles.ResultsThe similarities among cycles were demonstrated. A pattern between the cycles studied, which took on a triangular shape, was identified and used to make predictions about the duration of future cycles. Analyses on effective reproduction number (Rt) and subnotification effects for Germany, Italy, and Sweden were presented to show the performance of the framework introduced here. After comparing data from the three countries, it was possible to determine the probable dates of the actual onset of the epidemic cycles for each country, the typical duration of the incubation period for the disease, and the total number of infected persons during each cycle. In general terms, a probable average incubation time of 5 days was found, and the method used here was able to estimate the end of the cycles up to 34 days in advance, while demonstrating that the impact of the subnotification level (ie, error) on the effective reproduction number was <5%.ConclusionsIt was demonstrated that, with relatively simple mathematical tools, it is possible to obtain a reliable understanding of the behavior of COVID-19 local epidemic cycles, by introducing an integrated framework for identifying cycle patterns and calculating the variables that drive it, namely: the Rt, the subnotification effects on estimations, the most probable actual cycles start dates, the total number of infected, and the most likely incubation period for SARS-CoV-2.
| S-EPMC8078446 | biostudies-literature
Project description:BackgroundEgypt was among the first 10 countries in Africa that experienced COVID-19 cases. The sudden surge in the number of cases is overwhelming the capacity of the national healthcare system, particularly in developing countries. Central to the containment of the ongoing pandemic is the availability of rapid and accurate diagnostic tests that could pinpoint patients at early disease stages. In the current study, we aimed to (1) Evaluate the diagnostic performance of the rapid antigen test (RAT) "Standard™ Q COVID-19 Ag" against reverse transcriptase quantitative real-time PCR (RT-qPCR) in eighty-three swabs collected from COVID-19 suspected individuals showing various demographic features, clinical and radiological findings. (2) Test whether measuring laboratory parameters in participant's blood would enhance the predictive accuracy of RAT. (3) Identify the most important features that determine the results of both RAT and RT-qPCR.MethodsDiagnostic measurements (e.g. sensitivity, specificity, etc.) and receiver operating characteristic curve were used to assess the clinical performance of "Standard™ Q COVID-19 Ag". We used the support vector machine (SVM) model to investigate whether measuring laboratory indices would enhance the accuracy of RAT. Moreover, a random forest classification model was used to determine the most important determinants of the results of RAT and RT-qPCR for COVID-19 diagnosis.ResultsThe sensitivity, specificity, and accuracy of RAT were 78.2, 64.2, and 75.9%, respectively. Samples with high viral load and those that were collected within one-week post-symptoms showed the highest sensitivity and accuracy. The SVM modeling showed that measuring laboratory indices did not enhance the predictive accuracy of RAT.Conclusion"Standard™ Q COVID-19 Ag" should not be used alone for COVID-19 diagnosis due to its low diagnostic performance relative to the RT-qPCR. RAT is best used at the early disease stage and in patients with high viral load.
| S-EPMC8192262 | biostudies-literature
Project description:The WHO-named Coronavirus Disease 2019 (COVID-19) infection had become a pandemic within a short time period since it was detected in Wuhan. The outbreak required the screening of millions of samples daily and overwhelmed diagnostic laboratories worldwide. During this pandemic, the handling of patient specimens according to the universal guidelines was extremely difficult as the WHO, CDC and ECDC required cold chain compliance during transport and storage of the swab samples. The aim of this study was to compare the effects of two different storage conditions on the COVID-19 real-time PCR assay on 30 positive nasopharyngeal and/or oropharyngeal samples stored at both ambient temperature (22 ± 2 °C) and +4 °C. The results revealed that all the samples stored at ambient temperature remain PCR positive for at least six days without any false-negative result. In conclusion, transporting and storing these types of swab samples at ambient temperature for six days under resource-limited conditions during the COVID-19 pandemics are acceptable.
| S-EPMC8673954 | biostudies-literature
Project description:Timely diagnostic testing for active SARS-CoV-2 viral infections is key to controlling the spread of the virus and preventing severe disease. A central public health challenge is defining test allocation strategies with limited resources. In this paper, we provide a mathematical framework for defining an optimal strategy for allocating viral diagnostic tests. The framework accounts for imperfect test results, selective testing in certain high-risk patient populations, practical constraints in terms of budget and/or total number of available tests, and the purpose of testing. Our method is not only useful for detecting infections, but can also be used for long-time surveillance to detect new outbreaks. In our proposed approach, tests can be allocated across population strata defined by symptom severity and other patient characteristics, allowing the test allocation plan to prioritize higher risk patient populations. We illustrate our framework using historical data from the initial wave of the COVID-19 outbreak in New York City. We extend our proposed method to address the challenge of allocating two different types of diagnostic tests with different costs and accuracy, for example, the RT-PCR and the rapid antigen test (RAT), under budget constraints. We show how this latter framework can be useful to reopening of college campuses where university administrators are challenged with finite resources for community surveillance. We provide a R Shiny web application allowing users to explore test allocation strategies across a variety of pandemic scenarios. This work can serve as a useful tool for guiding public health decision-making at a community level and adapting testing plans to different stages of an epidemic. The conceptual framework has broader relevance beyond the current COVID-19 pandemic.
| S-EPMC8661762 | biostudies-literature
Project description:Timely and accurate laboratory testing is essential for managing the global COVID-19 pandemic. Reverse transcription polymerase chain reaction remains the gold-standard for SARS-CoV-2 diagnosis, but several practical issues limit the test's use. Immunoassays have been indicated as an alternative for individual and mass testing.ObjectivesTo access the performance of 12 serological tests for COVID-19 diagnosis.MethodsWe conducted a blind evaluation of six lateral-flow immunoassays (LFIAs) and six enzyme-linked immunosorbent assays (ELISAs) commercially available in Brazil for detecting anti-SARS-CoV-2 antibodies.ResultsConsidering patients with seven or more days of symptoms, the sensitivity ranged from 59.5% to 83.1% for LFIAs and from 50.7% to 92.6% for ELISAs. For both methods, the sensitivity increased with clinical severity and days of symptoms. The agreement among LFIAs performed with digital blood and serum was moderate. Specificity was, in general, higher for LFIAs than for ELISAs. Infectious diseases prevalent in the tropics, such as HIV, leishmaniasis, arboviruses, and malaria, represent conditions with the potential to cause false-positive results with these tests, which significantly compromises their specificity.ConclusionThe performance of immunoassays was only moderate, affected by the duration and clinical severity of the disease. Absence of discriminatory power between IgM/IgA and IgG has also been demonstrated, which prevents the use of acute-phase antibodies for decisions on social isolation.
| S-EPMC7544564 | biostudies-literature
Project description:IntroductionTo efficiently monitor the COVID-19 pandemic for surveillance purposes, reliable serological rapid diagnostic tests (RDTs) are desirable for settings where well-established high-throughput bench-top solutions are not available. Here, we have evaluated such an RDT.MethodsWe have assessed the Xiamen AmonMed Biotechnology COVID-19 IgM/IgG test kit (Colloidal gold) and the EUROIMMUN benchtop assay with serum samples from patients with polymerase chain reaction (PCR)-confirmed COVID-19 disease. Samples from patients with Epstein-Barr-virus (EBV) infection and blood donors were used for specificity testing.ResultsFor the colloid gold rapid test and the EUROIMMUN assay, the study indicated overall sensitivity of 15.2% and 67.4%, respectively, while specificity of 99.0% and 97.9% with the blood donor sera, as well as 100% and 96.8% with the EBV-patients, were observed, respectively. An association of the time period between positive PCR results and serum acquisition with serological test positivity could be observed for the immunologlobulin G subclass of the EUROIMMUN assay only.ConclusionsIn spite of acceptable specificity of the assessed RDT, the detected poor sensitivity leaves room for improvement. The test results remain difficult to interpret and therefore the RDT can currently not be recommended for routine diagnostic or surveillance use.
| S-EPMC7592516 | biostudies-literature
Project description:The lethal novel coronavirus disease 2019 (COVID-19) pandemic is affecting the health of the global population severely, and a huge number of people may have to be screened in the future. There is a need for effective and reliable systems that perform automatic detection and mass screening of COVID-19 as a quick alternative diagnostic option to control its spread. A robust deep learning-based system is proposed to detect the COVID-19 using chest X-ray images. Infected patient's chest X-ray images reveal numerous opacities (denser, confluent, and more profuse) in comparison to healthy lungs images which are used by a deep learning algorithm to generate a model to facilitate an accurate diagnostics for multi-class classification (COVID vs. normal vs. bacterial pneumonia vs. viral pneumonia) and binary classification (COVID-19 vs. non-COVID). COVID-19 positive images have been used for training and model performance assessment from several hospitals of India and also from countries like Australia, Belgium, Canada, China, Egypt, Germany, Iran, Israel, Italy, Korea, Spain, Taiwan, USA, and Vietnam. The data were divided into training, validation and test sets. The average test accuracy of 97.11 ± 2.71% was achieved for multi-class (COVID vs. normal vs. pneumonia) and 99.81% for binary classification (COVID-19 vs. non-COVID). The proposed model performs rapid disease detection in 0.137 s per image in a system equipped with a GPU and can reduce the workload of radiologists by classifying thousands of images on a single click to generate a probabilistic report in real-time.
| S-EPMC7837255 | biostudies-literature
Project description: Not available
| S-BSST563 | biostudies-other
Project description:AimsThis meta-analysis aimed to assess the prognostic value of fasting hyperglycemia in patients with COVID-19.MethodsA systematic literature search on PubMed, Embase, and Scopus were performed up until February 18, 2021. Fasting hyperglycemia was defined as fasting plasma glucose level above the reference value. The outcome of interest was poor outcome, which was a composite of mortality and severe COVID-19. The effect estimate was in odds ratio (OR).ResultsThere were 9045 patients from 12 studies included in this systematic review and meta-analysis. The prevalence of fasting hyperglycemia was 29%. The incidence of poor outcome was 15%. Fasting hyperglycemia was associated with poor outcome in COVID-19 (OR 4.72 [3.32, 6.72], p < 0.001; I2: 69.8%, p < 0.001). Subgroup analysis in patients without prior history of diabetes showed that fasting hyperglycemia was associated with poor outcome in COVID-19 (OR 3.387 [2.433, 4.714], p < 0.001; I2: 0, p = 0.90). Fasting hyperglycemia has a sensitivity of 0.57 [0.45, 0.68], specificity of 0.78 [0.70, 0.84], PLR of 2.6 [2.0, 3.3], NLR of 0.55 [0.44, 0.69], DOR of 5 [3, 7], and AUC of 0.74 [0.70, 0.78] for predicting poor outcome. In this pooled analysis, fasting hyperglycemia has a 32% post-test probability for poor outcome, and absence of fasting hyperglycemia confers to a 9% post-test probability. Meta-regression and subgroup analysis showed that the sensitivity and specificity varies by chronic kidney disease but not by age, male (gender), hypertension, and chronic kidney disease.ConclusionFasting hyperglycemia was associated with mortality in COVID-19 patients, with or without diabetes.ProsperoCRD42021237997.
| S-EPMC8019489 | biostudies-literature