scholarly journals Avoiding COVID-19 Hospital Outbreaks: RT-PCR Swab and Clinical Assessment

Author(s):  
Luca Allievi ◽  
Amedeo Bongarzoni ◽  
Guido Tassinario ◽  
Stefano Carugo

Nasopharyngeal RT-PCR swab test for COVID-19 diagnosis has a high specificity but also a low sensitivity. The high false-negative rate and the overconfidence in negative results sometimes lead to hospital outbreaks. Therefore, we recommend always integrating the clinical assessment in the diagnostic process, mostly after the test, to determine what degree of confidence can be attributed to a negative result.

2020 ◽  
Vol 7 (11) ◽  
Author(s):  
Gwynngelle A Borillo ◽  
Ron M Kagan ◽  
Russell E Baumann ◽  
Boris M Fainstein ◽  
Lamela Umaru ◽  
...  

Abstract Background Nucleic acid amplification testing is a critical tool for addressing the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. Specimen pooling can increase throughput and conserve testing resources but requires validation to ensure that reduced sensitivity does not increase the false-negative rate. We evaluated the performance of a real-time reverse transcription polymerase chain reaction (RT-PCR) test authorized by the US Food and Drug Administration (FDA) for emergency use for pooled testing of upper respiratory specimens. Methods Positive specimens were selected from 3 prevalence groups, 1%–3%, >3%–6%, and >6%–10%. Positive percent agreement (PPA) was assessed by pooling single-positive specimens with 3 negative specimens; performance was assessed using Passing-Bablok regression. Additionally, we assessed the distributions of RT-PCR cycle threshold (Ct) values for 3091 positive specimens. Results PPA was 100% for the 101 pooled specimens. There was a linear relationship between Ct values for pooled and single-tested specimens (r = 0.96–0.99; slope ≈ 1). The mean pooled Ct shifts at 40 cycles were 2.38 and 1.90, respectively, for the N1 and N3 targets. The median Cts for 3091 positive specimens were 25.9 (N1) and 24.7 (N3). The percentage of positive specimens with Cts between 40 and the shifted Ct was 1.42% (N1) and 0.0% (N3). Conclusions Pooled and individual testing of specimens positive for SARS-CoV-2 demonstrated 100% agreement, which demonstrates the viability of pooled specimens for SARS-COV-2 testing using a dual-target RT-PCR system. Pooled specimen testing can help increase testing capacity for SARS-CoV-2 with a low risk of false-negative results.


2021 ◽  
Vol 8 ◽  
Author(s):  
Amir Reza Alizad Rahvar ◽  
Safar Vafadar ◽  
Mehdi Totonchi ◽  
Mehdi Sadeghi

After lifting the COVID-19 lockdown restrictions and opening businesses, screening is essential to prevent the spread of the virus. Group testing could be a promising candidate for screening to save time and resources. However, due to the high false-negative rate (FNR) of the RT-PCR diagnostic test, we should be cautious about using group testing because a group's false-negative result identifies all the individuals in a group as uninfected. Repeating the test is the best solution to reduce the FNR, and repeats should be integrated with the group-testing method to increase the sensitivity of the test. The simplest way is to replicate the test twice for each group (the 2Rgt method). In this paper, we present a new method for group testing (the groupMix method), which integrates two repeats in the test. Then we introduce the 2-stage sequential version of both the groupMix and the 2Rgt methods. We compare these methods analytically regarding the sensitivity and the average number of tests. The tradeoff between the sensitivity and the average number of tests should be considered when choosing the best method for the screening strategy. We applied the groupMix method to screening 263 people and identified 2 infected individuals by performing 98 tests. This method achieved a 63% saving in the number of tests compared to individual testing. Our experimental results show that in COVID-19 screening, the viral load can be low, and the group size should not be more than 6; otherwise, the FNR increases significantly. A web interface of the groupMix method is publicly available for laboratories to implement this method.


1989 ◽  
Vol 75 (2) ◽  
pp. 156-162 ◽  
Author(s):  
Sandro Sulfaro ◽  
Francesco Querin ◽  
Luigi Barzan ◽  
Mario Lutman ◽  
Roberto Comoretto ◽  
...  

Sixty-six whole-organ sectioned laryngopharyngectomy specimens removed for cancer during a seven-year period were uniformly examined to determine the accuracy of preoperative high resolution computerized tomography (CT) for detection of cartilaginous involvement. Our results indicate that CT has a high overall specificity (88.2%) but a low sensitivity (47.1 %); we observed a high false-negative rate (26.5%) and a fairly low false-positive rate (5.9%). Massive cartilage destruction was easily assessed by CT, whereas both small macroscopic and microscopic neoplastic foci of cartilaginous invasion were missed on CT scans. Moreover, false-positive cases were mainly due to proximity of the tumor to the cartilage. Clinical implications of these results are discussed.


2021 ◽  
Author(s):  
Gabriel Sousa Silva Costa ◽  
Anselmo C. Paiva ◽  
Geraldo Braz Júnior ◽  
Marco Melo Ferreira

Even though vaccines are already in use worldwide, the COVID-19 pandemic is far from over, with some countries re-establishing the lockdown state, the virus has taken over 2 million lives until today, being a serious health issue. Although real-time reverse transcription-polymerase chain reaction (RTPCR) is the first tool for COVID-19 diagnosis, its high false-negative rate and low sensitivity might delay accurate diagnosis. Therefore, fast COVID-19 diagnosis and quarantine, combined with effective vaccination plans, is crucial for the pandemic to be over as soon as possible. To that end, we propose an intelligent system to classify computed tomography (CT) of lung images between a normal, pneumonia caused by something other than the coronavirus or pneumonia caused by the coronavirus. This paper aims to evaluate a complete selfattention mechanism with a Transformer network to capture COVID-19 pattern over CT images. This approach has reached the state-of-the-art in multiple NLP problems and just recently is being applied for computer vision tasks. We combine vision transformer and performer (linear attention transformers), and also a modified vision transformer, reaching 96.00% accuracy.


Author(s):  
Rupam Bhattacharyya ◽  
Ritwik Bhaduri ◽  
Ritoban Kundu ◽  
Maxwell Salvatore ◽  
Bhramar Mukherjee

Underreporting of COVID-19 cases and deaths is a hindrance to correctly modeling and monitoring the pandemic. This is primarily due to limited testing, lack of reporting infrastructure and a large number of asymptomatic infections. In addition, diagnostic tests (RT-PCR tests for detecting current infection) and serological antibody tests for IgG (to assess past infections) are imperfect. In particular, the diagnostic tests have a high false negative rate. Epidemiologic models with a latent compartment for unascertained infections like the Susceptible-Exposed-Infected-Removed (SEIR) models can provide predictions for unreported cases and deaths under certain assumptions. Typically, the number of unascertained cases is unobserved and thus we cannot validate these estimates for a real study except for simulation studies. Population-based seroprevalence studies can provide a rough estimate of the total number of infections and help us check epidemiologic model projections. In this paper, we develop a method to account for high false negative rates in RT-PCR in an extension to the classic SEIR model. We apply this method to Delhi, the national capital region of India, with a population of 19.8 million and a COVID-19 hotspot of the country, obtaining estimates of underreporting factor for cases at 34-53 times and that for deaths at 8-13 times. Based on a recently released serological survey for Delhi with an estimated 22.86% seroprevalence, we compute adjusted estimates of the true number of infections reported by the survey (after accounting for misclassification of the antibody test results) which is largely consistent with the model outputs, yielding an underreporting factor for cases from 30-42. Together with the model and the serosurvey, this implies approximately 96-98% cases in Delhi remained unreported and whereas only 109,140 cases were reported on July 10, the true number of infections varied somewhere between 4.4-4.6 million across different estimates. While repeated serological monitoring is resource intensive, model-based adjustments, run with the most up to date data, can provide a viable option to keep track of the unreported cases and deaths and gauge the true extent of transmission of this insidious virus.


2020 ◽  
Author(s):  
Amir Reza Alizad-Rahvar ◽  
Safar Vafadar ◽  
Mehdi Totonchi ◽  
Mehdi Sadeghi

After lifting the COVID-19 lockdown restrictions and opening businesses, screening is essential to prevent the spread of the virus. Group testing could be a promising candidate for screening to save time and resources. However, due to the high false-negative rate (FNR) of the RT-PCR diagnostic test, we should be cautious about using group testing because a group's false-negative result identifies all the individuals in a group as uninfected. Repeating the test is the best solution to reduce the FNR, and repeats should be integrated with the group-testing method to increase the sensitivity of the test. The simplest way is to replicate the test twice for each group (the 2Rgt method). In this paper, we present a new method for group testing (the groupMix method), which integrates two repeats in the test. Then we introduce the adaptive version of both the groupMix and the 2Rgt methods. We compare these methods analytically regarding the sensitivity and the average number of tests. The tradeoff between the sensitivity and the average number of tests should be considered when choosing the best method for the screening strategy. We applied the non-adaptive groupMix method to screening 263 people and identified 2 infected individuals by performing 98 tests. This method achieved a 63% saving in the number of tests compared to individual testing. This method is currently applied to COVID-19 screening in the Clinical Genetic Laboratory at the Royan Institute, Tehran, Iran. Our experimental results show that in COVID-19 screening, the viral load can be low, and the group size should not be more than 6; otherwise, the FNR increases significantly. A web interface of the non-adaptive groupMix method is publicly available for laboratories to implement this method.


2004 ◽  
Vol 50 (11) ◽  
pp. 2141-2147 ◽  
Author(s):  
Mogens Fenger ◽  
Allan Wiik ◽  
Mimi Høier-Madsen ◽  
Jens J Lykkegaard ◽  
Teresa Rozenfeld ◽  
...  

Abstract Background: Antinuclear antibodies (ANAs) are associated with several inflammatory rheumatic diseases. The aim of the present work was to evaluate enzyme immunoassays (EIAs) and compare them with classic immunofluorescent analysis (IFA) for the detection of ANA. Methods: Seven enzyme immunoassays were used in this study. All assays were applied as described by the manufacturers. Three populations were included in the study: (a) a population of patients with well-established autoimmune inflammatory disease (n = 102); (b) a population in which a rheumatic disease was diagnosed up to 5 years after an IFA was performed (n = 164); and (c) a population of consecutive outpatients suspected to have a rheumatic disease (n = 101). The current clinical diagnoses of the patients served as the standard against which performance of the assays was evaluated. Results: In patients with well-established rheumatic disorders, the newly developed EIA in which HEp-2 extracts were included had sensitivities and specificities comparable to or in some instances better than the IFA. The assays without HEp-2 extracts included had significantly lower sensitivities and specificities. In the outpatient population, up to 51% of patients had positive ANA tests that did not correspond to classic ANA-associated disease. However, in the assays in which the HEp-2 extracts were not included, the false-positive rate was <10%. The false-negative rate judged against IFA differed from assay to assay and disease to disease and was mostly <10%. Conclusions: In this study, the sensitivities of EIAs and IFA were largely comparable. However, EIAs without HEp-2 extracts included had a low sensitivity but a high specificity, particularly in nonselected populations. The choice of test is highly dependent on the clinical setting in which the ANA test is to be used and on laboratory policy.


Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 6-7
Author(s):  
Alex Niu ◽  
Bo Ning ◽  
Francisco Socola ◽  
Hana Safah ◽  
Tim Reynolds ◽  
...  

Introduction Patients with hematological malignancies (HM) are uniquely immunocompromised and considered at high risk for COVID-19. However, data regarding the diagnosis, clinical course, treatment, and outcomes of these patients is sparse. In particular, the ability to successfully detect SARS-CoV-2 in patients with HM remains unknown. We have previously reported 2 cases of allogeneic stem cell transplant (SCT) diagnosed with COVID-19 using clustered regularly interspaced short palindromic repeats (CRISPR) technique, following multiple negative nasopharyngeal RT-PCR testing (Niu et al. Bone Marrow Transplantation - Nature). Here we examine 29 patients with a variety of HM with high suspicion for COVID-19 based on clinical presentation, lab results, and imaging, whom were tested with CRISPR and/or RT-PCR based techniques. From 3/31/20 to 7/17/20, 29 patients (age 24 to 82) with a variety of HM (20 lymphoid, 9 myeloid; Table 1), 24 of which presented with an undiagnosed respiratory illness and 5 presented while asymptomatic for testing prior to chemotherapy, were evaluated for COVID-19. While 16 patients tested positive for COVID-19 with guideline-directed nasopharyngeal RT-PCR testing (including the 5 asymptomatic patients), 13 patients tested negative with the same technique. However, based on their clinical history, imaging, and disease course, concern for COVID-19 infection remained in these 13 patients. We then used CRISPR technology available at our institution (Huang et al. Biosensors and Bioelectronics) to test 8 patients who initially tested negative by RT-PCR. Surprisingly, 7 of the 8 patients tested positive for COVID-19 with either a blood sample and/or nasal swab for the SARS-CoV-2 specific N gene and ORF1ab gene. Excluding the patients who were negative by RT-PCR and not tested by CRISPR, the rate of false negativity with RT-PCR testing is significantly elevated at 29% (7/24) in our cohort of HM, which compares unfavorably with the expected false negative rates of RT-PCR techniques. A very high fatality rate was observed with 9 out of the 29 patients (31%) ultimately dying. Fifteen patients were undergoing active chemotherapy, 4 had received an autologous SCT, 6 had received an allogeneic SCT, and 4 were on surveillance. Of the 23 COVID-19 positive patients (by RT-PCR or CRISPR), 8 patients received COVID-19-directed therapy with either hydroxychloroquine/azithromycin, remdesivir, and/or Covid-19 convalescent plasma (CCP) depending on their clinical status, and 4 patients expired. Of the 8 treated patients, 7 improved while 1 patient expired. For the 5 patients who were negative for RT-PCR with no CRISPR completed, 1 patient received hydroxychloroquine/azithromycin proactively due to symptoms and imaging and recovered, while 3 patients expired at outside facilities due to unknown causes. Breakdown of testing and treatment is shown in Fig. 1. The majority of our patients had undergone SCT or were actively on chemotherapy, notably lymphodepleting chemotherapy. Associated with the fact that COVID-19 is known to worsen lymphopenia, our patient's symptoms and immune response to COVID-19 is likely to differ from immunocompetent hosts. This translated into an overall worse outcome as seen by the high mortality with our patients. In our limited dataset, patients presented with a variety of symptoms ranging from asymptomatic to acute respiratory failure. Intriguingly, the 5 asymptomatic patients had lymphoid malignancies and were on chemotherapy. It is thus imperative to establish the diagnosis of COVID-19 quickly, as faster initiation of treatment has been associated with better outcomes. The 8 patients who were diagnosed and treated improved substantially. However, as seen by our dataset, a strikingly high false negative rate was observed. Thus, a high clinical suspicion must guide further workup and therapy in patients with HM who present with an undiagnosed respiratory illness consistent with COVID-19. Patients with HM can have a wide variety of presentations when infected with COVID-19. For this select patient population we must establish an algorithm to diagnose COVID-19 efficiently as we reported a high number of initial false negative COVID-19 tests before the more sensitive CRISPR revealed a positive test. In addition, treatment pathways need to be instituted to not only treat COVID-19 infection, but also provide the best treatment for these patient's underlying HM. Disclosures Safah: Amgen: Honoraria; Verastem: Honoraria; Janssen: Speakers Bureau; Astellas: Speakers Bureau. Saba:Kite: Other: Advisory Board; Pharmacyclics: Other: Advisory Board, Speakers Bureau; AbbVie: Consultancy, Other: Advisory Board, Speakers Bureau; Janssen: Other: Advisory Board, Speakers Bureau; Kyowa Kirin: Other: Advisory Board.


Author(s):  
Jinwei Ai ◽  
Junyan Gong ◽  
Limin Xing ◽  
Renjiao He ◽  
Fangtao Tian ◽  
...  

AbstractBackgroundThe pandemic of coronavirus disease 2019 (COVID-19) has become the first concern in international affairs as the novel coronavirus (SARS-CoV-2) is spreading all over the world at a terrific speed. The accuracy of early diagnosis is critical in the control of the spread of the virus. Although the real-time RT-PCR detection of the virus nucleic acid is the current golden diagnostic standard, it has high false negative rate when only apply single test.ObjectiveSummarize the baseline characteristics and laboratory examination results of hospitalized COVID-19 patients. Analyze the factors that could interfere with the early diagnosis quantitatively to support the timely confirmation of the disease.MethodsAll suspected patients with COVID-19 were included in our study until Feb 9th, 2020. The last day of follow-up was Mar 20th, 2020. Throat swab real-time RT-PCR test was used to confirm SARS-CoV-2 infection. The difference between the epidemiological profile and first laboratory examination results of COVID-19 patients and non-COVID-19 patients were compared and analyzed by multiple logistic regression. Receiver operating characteristic (ROC) curve and area under curve (AUC) were used to assess the potential diagnostic value in factors, which had statistical differences in regression analysis.ResultsIn total, 315 hospitalized patients were included. Among them, 108 were confirmed as COVID-19 patients and 207 were non-COVID-19 patients. Two groups of patients have significance in comparing age, contact history, leukocyte count, lymphocyte count, C-reactive protein, erythrocyte sedimentation rate (p<0.10). Multiple logistic regression analysis showed age, contact history and decreasing lymphocyte count could be used as individual factor that has diagnostic value (p<0.05). The AUC of first RT-PCR test was 0.84 (95% CI 0.73-0.89), AUC of cumulative two times of RT-PCR tests was 0.92 (95% CI 0.88-0.96) and 0.96 (95% CI 0.93-0.99) for cumulative three times of RT-PCR tests. Ninety-six patients showed typical pneumonia radiological features in first CT scan, AUC was 0.74 (95% CI 0.60-0.73). The AUC of patients’ age, contact history with confirmed people and the decreased lymphocytes were 0.66 (95% CI 0.60-0.73), 0.67 (95% CI 0.61-0.73), 0.62 (95% CI 0.56-0.69), respectively. Taking chest CT scan diagnosis together with patients age and decreasing lymphocytes, AUC would be 0.86 (95% CI 0.82-0.90). The age threshold to predict COVID-19 was 41.5 years, with a diagnostic sensitivity of 0.70 (95% CI 0.61-0.79) and a specificity of 0.59 (95% CI 0.52-0.66). Positive and negative likelihood ratios were 1.71 and 0.50, respectively. Threshold of lymphocyte count to diagnose COVID-19 was 1.53×109/L, with a diagnostic sensitivity of 0.82 (95% CI 0.73-0.88) and a specificity of 0.50 (95% CI 0.43-0.57). Positive and negative likelihood ratios were 1.64 and 0.37, respectively.ConclusionSingle RT-PCR test has relatively high false negative rate. When first RT-PCR test show negative result in suspected patients, the chest CT scan, contact history, age and lymphocyte count should be used combinedly to assess the possibility of SARS-CoV-2 infection.


2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Abdulkader Helwan ◽  
Mohammad Khaleel Sallam Ma’aitah ◽  
Hani Hamdan ◽  
Dilber Uzun Ozsahin ◽  
Ozum Tuncyurek

The reverse transcriptase polymerase chain reaction (RT-PCR) is still the routinely used test for the diagnosis of SARS-CoV-2 (COVID-19). However, according to several reports, RT-PCR showed a low sensitivity and multiple tests may be required to rule out false negative results. Recently, chest computed tomography (CT) has been an efficient tool to diagnose COVID-19 as it is directly affecting the lungs. In this paper, we investigate the application of pre-trained models in diagnosing patients who are positive for COVID-19 and differentiating it from normal patients, who tested negative for coronavirus. The study aims to compare the generalization capabilities of deep learning models with two thoracic radiologists in diagnosing COVID-19 chest CT images. A dataset of 3000 images was obtained from the Near East Hospital, Cyprus, and used to train and to test the three employed pre-trained models. In a test set of 250 images used to evaluate the deep neural networks and the radiologists, it was found that deep networks (ResNet-18, ResNet-50, and DenseNet-201) can outperform the radiologists in terms of higher accuracy (97.8%), sensitivity (98.1%), specificity (97.3%), precision (98.4%), and F1-score (198.25%), in classifying COVID-19 images.


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