scholarly journals Testing for coronavirus (SARS-CoV-2) infection in populations with low infection prevalence: the largely ignored problem of false positives and the value of repeat testing

2020 ◽  
Author(s):  
Cathie Sudlow ◽  
Peter Diggle ◽  
Oliver Warlow ◽  
David Seymour ◽  
Ben Gordon ◽  
...  
Keyword(s):  
False Positives ◽  
Infection Prevalence ◽  
Test Accuracy ◽  
Single Test ◽  
False Negatives ◽  
Repeat Testing ◽  
Confirmatory Testing ◽  
Test Specificity ◽  
Low Prevalence ◽  
The Impact

Background: Calls are increasing for widespread SARS-CoV-2 infection testing of people from populations with a very low prevalence of infection. We quantified the impact of less than perfect diagnostic test accuracy on populations, and on individuals, in low prevalence settings, focusing on false positives and the role of confirmatory testing. Methods: We developed a simple, interactive tool to assess the impact of different combinations of test sensitivity, specificity and infection prevalence in a notional population of 100,000. We derived numbers of true positives, true negatives, false positives and false negatives, positive predictive value (PPV, the percentage of test positives that are true positives) and overall test accuracy for three testing strategies: (1) single test for all; (2) add repeat testing in test positives; (3) add further repeat testing in those with discrepant results. We also assessed the impact on test results for individuals having one, two or three tests under these three strategies. Results: With sensitivity of 80%, infection prevalence of 1 in 2,000, and specificity 99.9% on all tests, PPV in the tested population of 100,000 will be only 29% with one test, increasing to >99.5% (100% when rounded to the nearest %) with repeat testing in strategies 2 or 3. More realistically, if specificity is 95% for the first and 99.9% for subsequent tests, single test PPV will be only 1%, increasing to 86% with repeat testing in strategy 2, or 79% with strategy 3 (albeit with 6 fewer false negatives than strategy 2). In the whole population, or in particular individuals, PPV increases as infection becomes more common in the population but falls to unacceptably low levels with lower test specificity. Conclusion: To avoid multiple unnecessary restrictions on whole populations, and in particular individuals, from widespread population testing for SARS-CoV-2, the crucial roles of extremely high test specificity and of confirmatory testing must be fully appreciated and incorporated into policy decisions.

scholarly journals The Impact of Increasing Prevalence, False Omissions, and Diagnostic Uncertainty on Coronavirus Disease 2019 (COVID-19) Test Performance

2021 ◽  
Author(s):  
Gerald J. Kost
Keyword(s):  
Test Performance ◽  
Influenza A ◽  
Geometric Mean ◽  
False Positives ◽  
Test Quality ◽  
False Negatives ◽  
Predictive Values ◽  
Sensitivity Specificity ◽  
Tier 3 ◽  
The Impact

ABSTRACT Context. Coronavirus disease 2019 (COVID-19) test performance depends on predictive values in settings of increasing disease prevalence. Geospatially distributed diagnostics with minimal uncertainty facilitate efficient point-of-need strategies. Objectives. To use original mathematics to interpret COVID-19 test metrics; assess Food and Drug Administration Emergency Use Authorizations and Health Canada targets; compare predictive values for multiplex, antigen, polymerase chain reaction kit, point-of-care antibody, and home tests; enhance test performance; and improve decision-making. Design. PubMed/newsprint generated articles documenting prevalence. Mathematica and open access software helped perform recursive calculations, graph multivariate relationships, and visualize performance by comparing predictive value geometric mean-squared patterns. Results. Tiered sensitivity/specificity comprise: T1) 90%, 95%; T2) 95%, 97.5%; and T3) 100%, ≥99%. Tier 1 false negatives exceed true negatives at >90.5% prevalence; false positives exceeded true positives at <5.3% prevalence. High sensitivity/specificity tests reduce false negatives and false positives yielding superior predictive values. Recursive testing improves predictive values. Visual logistics facilitate test comparisons. Antigen test quality falls off as prevalence increases. Multiplex severe acute respiratory syndrome (SARS)-CoV-2)*Influenza A/B*Respiratory-Syncytial Virus (RSV) testing performs reasonably well compared to Tier 3. Tier 3 performance with a Tier 2 confidence band lower limit will generate excellent performance and reliability. Conclusions. The overriding principle is select the best combined performance and reliability pattern for the prevalence bracket. Some public health professionals recommend repetitive testing to compensate for low sensitivity. More logically, improved COVID-19 assays with less uncertainty conserve resources. Multiplex differentiation of COVID-19 from Influenza A/B-RSV represents an effective strategy if seasonal flu surges next year.

No Way That’s My Drug Screen

2021 ◽  
pp. 147-156
Author(s):  
Daniel M. Doleys ◽  
Nicholas D. Doleys
Keyword(s):  
Decision Making ◽  
Chronic Pain ◽  
Sensitivity And Specificity ◽  
Point Of Care ◽  
Standard Of Care ◽  
False Positives ◽  
Drug Screen ◽  
False Negatives ◽  
Confirmatory Testing ◽  
Urine Drug

In the process of treating patients with for chronic pain with opioid type medications, the use of urine drug screens (UDS) is considered the standard of care. The frequency with which a UDS is obtained varies across different guidelines and states/medical boards. It is often associated with dosage, risk for aberrant drug behavior assessment, and ongoing compliance. Most clinicians will obtain a UDS two to four times per year, unless the circumstances require otherwise. In general, the point-of care UDS lacks the sensitivity and specificity of confirmatory testing. The prescribing clinician should (i) be familiar with various types of testing, (ii) create a relationship the testing lab performing the confirmatory testing, and (iii) acquire basic interpretation skills. Clinical decisions should be postponed pending the results of confirmatory testing. False positives, and false negatives, do occur. It behooves the clinician to have “all their ducks in row” before confronting the patient and to accurately document the consultation and decision-making process. In some instances, discontinuation of therapy may be necessary and appropriate. Other cases may be subject to remediation.

scholarly journals Evaluation of Second-Level Inference in fMRI Analysis

2016 ◽  
Vol 2016 ◽  
pp. 1-22 ◽  
Author(s):  
Sanne P. Roels ◽  
Tom Loeys ◽  
Beatrijs Moerkerke
Keyword(s):  
Error Rate ◽  
Cluster Size ◽  
Multiple Testing ◽  
Real Data ◽  
False Positives ◽  
Familywise Error Rate ◽  
False Negatives ◽  
Step Procedure ◽  
Fmri Analysis ◽  
The Impact

We investigate the impact of decisions in the second-level (i.e., over subjects) inferential process in functional magnetic resonance imaging on (1) the balance between false positives and false negatives and on (2) the data-analytical stability, both proxies for the reproducibility of results. Second-level analysis based on a mass univariate approach typically consists of 3 phases. First, one proceeds via a general linear model for a test image that consists of pooled information from different subjects. We evaluate models that take into account first-level (within-subjects) variability and models that do not take into account this variability. Second, one proceeds via inference based on parametrical assumptions or via permutation-based inference. Third, we evaluate 3 commonly used procedures to address the multiple testing problem: familywise error rate correction, False Discovery Rate (FDR) correction, and a two-step procedure with minimal cluster size. Based on a simulation study and real data we find that the two-step procedure with minimal cluster size results in most stable results, followed by the familywise error rate correction. The FDR results in most variable results, for both permutation-based inference and parametrical inference. Modeling the subject-specific variability yields a better balance between false positives and false negatives when using parametric inference.

scholarly journals Public’s understanding of swab test results for SARS-CoV-2: an online behavioural experiment during the April 2020 lockdown

BMJ Open ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. e043925
Author(s):  
Stefania Pighin ◽  
Katya Tentori
Keyword(s):  
Diagnostic Accuracy ◽  
Well Being ◽  
False Positives ◽  
Perceived Usefulness ◽  
Infection Prevalence ◽  
Test Results ◽  
Short Term ◽  
False Negatives ◽  
Predictive Values ◽  
Behavioural Experiment

ObjectiveAlthough widespread testing for SARS-CoV-2 is in place, little is known about how well the public understands these results. We aimed to provide a comprehensive overview of the general public’s grasp of the accuracy and significance of the results of the swab test.DesignWeb-based behavioural experiment.SettingItaly during the April 2020 lockdown.Participants566 Italian residents.Main outcome measuresParticipants’ estimates of the SARS-CoV-2 prevalence; the predictive and diagnostic accuracy of the test; the behavioural impact of (positive vs negative) test results; the perceived usefulness of a short-term repetition of the test following positive or negative results; and rankings of causes for false positives and false negatives.ResultsMost participants considered the swab test useful (89.6%) and provided predictive values consistent with their estimates of test diagnostic accuracy and infection prevalence (67.0%). Participants acknowledged the effects of symptomatic status and geographical location on prevalence (all p<0.001) but failed to take this information into account when estimating the positive or negative predictive value. Overall, test specificity was underestimated (91.5%, 95% CI 90.2% to 92.8%); test sensitivity was overestimated (89.7%, 95% CI 88.3% to 91.0%). Positive results were evaluated as more informative than negative ones (91.6, 95% CI 90.2 to 93.1 and 41.0, 95% CI 37.9 to 44.0, respectively, p<0.001); a short-term repetition of the test was considered more useful after a positive than a negative result (62.7, 95% CI 59.6 to 65.7 and 47.2, 95% CI 44.4 to 50.0, respectively, p=0.013). Human error and technical characteristics were assessed as more likely to be the causes of false positives (p<0.001); the level of the viral load as the cause of false negatives (p<0.001).ConclusionsWhile some aspects of the swab for SARS-CoV-2 are well grasped, others are not and may have a strong bearing on the general public’s health and well-being. The obtained findings provide policymakers with a detailed picture that can guide the design and implementation of interventions for improving efficient communication with the general public as well as adherence to precautionary behaviour.

scholarly journals The Impact of COVID-19 Pandemic and Lockdown Restrictions on Cardiac Implantable Device Recipients with Remote Monitoring

2021 ◽  
Vol 10 (23) ◽  
pp. 5626
Author(s):  
Igor Diemberger ◽  
Alessandro Vicentini ◽  
Giuseppe Cattafi ◽  
Matteo Ziacchi ◽  
Saverio Iacopino ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Negative Impact ◽  
Control Period ◽  
Cardiac Resynchronization ◽  
Infection Prevalence ◽  
Healthcare Organizations ◽  
Significant Drop ◽  
Low Prevalence ◽  
The Impact

From 2020, many countries have adopted several restrictions to limit the COVID-19 pandemic. The forced containment impacted on healthcare organizations and the everyday life of patients with heart disease. We prospectively analyzed data recorded from implantable defibrillators and/or cardiac resynchronization devices of Italian patients during the lockdown (LDP), post-lockdown period (PLDP) and a control period (CP) of the previous year. We analyzed device data of the period 9 March 2019–31 May 2020 of remotely monitored patients from 34 Italian centers. Patients were also categorized according to areas with high/low infection prevalence. Among 696 patients, we observed a significant drop in median activity in LDP as compared to CP that significantly increased in the PLDP, but well below CP (all p < 0.0001). The median day heart rate and heart rate variability showed a similar trend. This behavior was associated during LDP with a significant increase in the burden of atrial arrhythmias (p = 0.0150 versus CP) and of ventricular arrhythmias [6.6 vs. 1.5 per 100 patient-weeks in CP; p = 0.0026]; the latter decreased in PLDP [0.3 per 100 patient-weeks; p = 0.0035 vs. LDP]. No modifications were recorded in thoracic fluid levels. The high/low prevalence of COVID-19 infection had no significant impact. We found an increase in the arrhythmic burden in LDP coupled with a decrease in physical activity and heart rate variability, without significant modifications of transthoracic impedance, independent from COVID-19 infection prevalence. These findings suggest a negative impact of the COVID-19 pandemic, probably related to lockdown restrictions.

Sky Segmentation for Enhanced Depth Reconstruction and Bokeh Rendering with Efficient Architectures

2020 ◽  
Vol 2020 (14) ◽  
pp. 378-1-378-7
Author(s):  
Tyler Nuanes ◽  
Matt Elsey ◽  
Radek Grzeszczuk ◽  
John Paul Shen
Keyword(s):  
Real Time ◽  
Mobile Device ◽  
Computational Cost ◽  
False Positives ◽  
Compact Model ◽  
High Quality ◽  
False Negatives ◽  
Trade Off ◽  
Depth Reconstruction ◽  
Binary Classifiers

We present a high-quality sky segmentation model for depth refinement and investigate residual architecture performance to inform optimally shrinking the network. We describe a model that runs in near real-time on mobile device, present a new, highquality dataset, and detail a unique weighing to trade off false positives and false negatives in binary classifiers. We show how the optimizations improve bokeh rendering by correcting stereo depth misprediction in sky regions. We detail techniques used to preserve edges, reject false positives, and ensure generalization to the diversity of sky scenes. Finally, we present a compact model and compare performance of four popular residual architectures (ShuffleNet, MobileNetV2, Resnet-101, and Resnet-34-like) at constant computational cost.

Automatic Extraction of Acronyms from Text

2020 ◽  
Author(s):  
Stuart Yeates
Keyword(s):  
Digital Library ◽  
False Positives ◽  
Automatic Extraction ◽  
False Negatives ◽  
Library Research ◽  
Communications Theory ◽  
Textual Content

A brief introduction to acronyms is given and motivation for extracting them in a digital library environment is discussed. A technique for extracting acronyms is given with an analysis of the results. The technique is found to have a low number of false negatives and a high number of false positives. Introduction Digital library research seeks to build tools to enable access of content, while making as few as possible assumptions about the content, since assumptions limit the range of applicability of the tools. Generally, the broader the assumptions the more widely applicable the tools. For example, keyword based indexing [5] is based on communications theory and applies to all natural human textual languages (allowances for differences in character sets and similar localisation issues not withstanding) . The algorithm described in this paper makes much stronger assumptions about the content. It assumes textual content that contains acronyms, an assumption which is known to hold for...

scholarly journals Fast radio burst detection in the presence of coloured noise

2021 ◽  
Vol 503 (4) ◽  
pp. 5223-5231
Author(s):  
C F Zhang ◽  
J W Xu ◽  
Y P Men ◽  
X H Deng ◽  
Heng Xu ◽  
...  
Keyword(s):  
False Alarm ◽  
Radio Burst ◽  
Pulse Width ◽  
False Alarm Probability ◽  
False Positives ◽  
Parameter Distribution ◽  
Correlated Noise ◽  
Width Distribution ◽  
Fast Radio Burst ◽  
The Impact

ABSTRACT In this paper, we investigate the impact of correlated noise on fast radio burst (FRB) searching. We found that (1) the correlated noise significantly increases the false alarm probability; (2) the signal-to-noise ratios (S/N) of the false positives become higher; (3) the correlated noise also affects the pulse width distribution of false positives, and there will be more false positives with wider pulse width. We use 55-h observation for M82 galaxy carried out at Nanshan 26m radio telescope to demonstrate the application of the correlated noise modelling. The number of candidates and parameter distribution of the false positives can be reproduced with the modelling of correlated noise. We will also discuss a low S/N candidate detected in the observation, for which we demonstrate the method to evaluate the false alarm probability in the presence of correlated noise. Possible origins of the candidate are discussed, where two possible pictures, an M82-harboured giant pulse and a cosmological FRB, are both compatible with the observation.

scholarly journals Classification of multiwavelength transients with Machine Learning

2020 ◽  
Author(s):  
K Sooknunan ◽  
M Lochner ◽  
Bruce A Bassett ◽  
H V Peiris ◽  
R Fender ◽  
...  
Keyword(s):  
Machine Learning ◽  
Small Sample ◽  
Light Curves ◽  
Machine Learning Techniques ◽  
Optical Data ◽  
Test Time ◽  
Test Accuracy ◽  
Training Set ◽  
The Impact

Abstract With the advent of powerful telescopes such as the Square Kilometer Array and the Vera C. Rubin Observatory, we are entering an era of multiwavelength transient astronomy that will lead to a dramatic increase in data volume. Machine learning techniques are well suited to address this data challenge and rapidly classify newly detected transients. We present a multiwavelength classification algorithm consisting of three steps: (1) interpolation and augmentation of the data using Gaussian processes; (2) feature extraction using wavelets; (3) classification with random forests. Augmentation provides improved performance at test time by balancing the classes and adding diversity into the training set. In the first application of machine learning to the classification of real radio transient data, we apply our technique to the Green Bank Interferometer and other radio light curves. We find we are able to accurately classify most of the eleven classes of radio variables and transients after just eight hours of observations, achieving an overall test accuracy of 78%. We fully investigate the impact of the small sample size of 82 publicly available light curves and use data augmentation techniques to mitigate the effect. We also show that on a significantly larger simulated representative training set that the algorithm achieves an overall accuracy of 97%, illustrating that the method is likely to provide excellent performance on future surveys. Finally, we demonstrate the effectiveness of simultaneous multiwavelength observations by showing how incorporating just one optical data point into the analysis improves the accuracy of the worst performing class by 19%.

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