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Infection clusters can elevate risk of diagnostic target failure for detection of SARS-CoV-2.

ABSTRACT: The C29197T mutation is one of 4 point mutations known to cause N-gene target failure (NGTF) in the Xpert Xpress SARS-CoV-2 and Xpert Omni SARS-CoV-2 assays from Cepheid (Sunnyvale, CA). We describe a high local prevalence in January of 8.5% (CI 4.9-14.2%) for the C29197T mutation, which was over 3-fold higher than the prevalence estimated statewide in California during the same time frame, 2.5% (CI 2.1-2.8%). Using phylogenetic analysis, we discovered that this increase in prevalence was due, at least in part, to a disproportionately large infection cluster of unknown origin. This study emphasizes the importance of sequencing at the local jurisdictional level and demonstrates the impact that regional variation can have when assessing risk due to point mutations that impact clinical test performance. It also reinforces the need for diligent reporting of abnormal test results by clinical laboratories, especially during Emergency Use Authorization (EUA) periods, as additional information is gathered about the target organism and the performance of EUA-authorized tests over time.

SUBMITTER: Lopez D

PROVIDER: S-EPMC8849547 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Infection clusters can elevate risk of diagnostic target failure for detection of SARS-CoV-2.

Lopez Denise D Roberts Jill J Bourgeois Marie M Kootstra Joshua J Minnick Sharon S Black Allison A Mauss Joshua J Flores Nick N

PloS one 20220216 2

The C29197T mutation is one of 4 point mutations known to cause N-gene target failure (NGTF) in the Xpert Xpress SARS-CoV-2 and Xpert Omni SARS-CoV-2 assays from Cepheid (Sunnyvale, CA). We describe a high local prevalence in January of 8.5% (CI 4.9-14.2%) for the C29197T mutation, which was over 3-fold higher than the prevalence estimated statewide in California during the same time frame, 2.5% (CI 2.1-2.8%). Using phylogenetic analysis, we discovered that this increase in prevalence was due, a ...[more]

PMID: 35171960

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Project description:BackgroundThe need for effective public health surveillance systems to track virus spread for targeted interventions was highlighted during the COVID-19 pandemic. It spurred an interest in the use of spatiotemporal clustering and genomic analyses to identify high-risk areas and track the spread of the SARS-CoV-2 virus. However, these two approaches are rarely combined in surveillance systems to complement each one's limitations; spatiotemporal clustering approaches usually consider only one source of virus transmission (i.e., the residential setting) to detect case clusters, while genomic studies require significant resources and processing time that can delay decision-making. Here, we clarify the differences and possible synergies of these two approaches in the context of infectious disease surveillance systems by investigating to what extent geographically-defined clusters are confirmed as transmission clusters based on genome sequences, and how genomic-based analyses can improve the epidemiological investigations associated with spatiotemporal cluster detection.MethodsFor this purpose, we sequenced the SARS-CoV-2 genomes of 172 cases that were part of a collection of spatiotemporal clusters found in a Swiss state (Vaud) during the first epidemic wave. We subsequently examined intra-cluster genetic similarities and spatiotemporal distributions across virus genotypes.ResultsOur results suggest that the congruence between the two approaches might depend on geographic features of the area (rural/urban) and epidemic context (e.g., lockdown). We also identified two potential superspreading events that started from cases in the main urban area of the state, leading to smaller spreading events in neighboring regions, as well as a large spreading in a geographically-isolated area. These superspreading events were characterized by specific mutations assumed to originate from Mulhouse and Milan, respectively. Our analyses propose synergistic benefits of using two complementary approaches in public health surveillance, saving resources and improving surveillance efficiency.

Project description:BackgroundAssigning meaningful probabilities of SARS-CoV-2 infection risk presents a diagnostic challenge across the continuum of care.ObjectiveThe aim of this study was to develop and clinically validate an adaptable, personalized diagnostic model to assist clinicians in ruling in and ruling out COVID-19 in potential patients. We compared the diagnostic performance of probabilistic, graphical, and machine learning models against a previously published benchmark model.MethodsWe integrated patient symptoms and test data using machine learning and Bayesian inference to quantify individual patient risk of SARS-CoV-2 infection. We trained models with 100,000 simulated patient profiles based on 13 symptoms and estimated local prevalence, imaging, and molecular diagnostic performance from published reports. We tested these models with consecutive patients who presented with a COVID-19-compatible illness at the University of California San Diego Medical Center over the course of 14 days starting in March 2020.ResultsWe included 55 consecutive patients with fever (n=43, 78%) or cough (n=42, 77%) presenting for ambulatory (n=11, 20%) or hospital care (n=44, 80%). In total, 51% (n=28) were female and 49% (n=27) were aged <60 years. Common comorbidities included diabetes (n=12, 22%), hypertension (n=15, 27%), cancer (n=9, 16%), and cardiovascular disease (n=7, 13%). Of these, 69% (n=38) were confirmed via reverse transcription-polymerase chain reaction (RT-PCR) to be positive for SARS-CoV-2 infection, and 20% (n=11) had repeated negative nucleic acid testing and an alternate diagnosis. Bayesian inference network, distance metric learning, and ensemble models discriminated between patients with SARS-CoV-2 infection and alternate diagnoses with sensitivities of 81.6%-84.2%, specificities of 58.8%-70.6%, and accuracies of 61.4%-71.8%. After integrating imaging and laboratory test statistics with the predictions of the Bayesian inference network, changes in diagnostic uncertainty at each step in the simulated clinical evaluation process were highly sensitive to location, symptom, and diagnostic test choices.ConclusionsDecision support models that incorporate symptoms and available test results can help providers diagnose SARS-CoV-2 infection in real-world settings.

Dataset Information

Infection clusters can elevate risk of diagnostic target failure for detection of SARS-CoV-2.

Publications

Infection clusters can elevate risk of diagnostic target failure for detection of SARS-CoV-2.

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