Estimate False-Negative RT-PCR Rates for SARS-CoV-2: A Systematic Review and Meta-Analysis

Pecoraro, Valentina; Negro, Antonella; Pirotti, Tommaso; Trenti, Tommaso

doi:10.2139/ssrn.3823218

Cited by 12 publications

(11 citation statements)

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“…While we used RT-PCR as a gold standard, there continues to be debated regarding its accuracy and concerns over false negative, which have been reported to be between 1% and 30% 1,[49][50][51] . False negatives can occur for many reasons ranging from testing that occurs too early or late, poor sample collection, changes in viral shedding, and others.…”

Section: Discussionmentioning

confidence: 99%

Portable Breath-Based Volatile Organic Compound Monitoring for the Detection of COVID-19: Challenges of Emerging Variants

Sharma

Zang

Tabartehfarahani

et al. 2022

Preprint

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Importance Breath analysis has been explored as a noninvasive means to detect COVID19. However, the impact of the emerging variants such as Omicron on the exhaled breath profile and hence the accuracy of breath analysis is unknown. Objective To evaluate the diagnostic accuracies of breath analysis on detecting COVID19 patients in periods where Delta and Omicron were most prevalent. Design, Setting, and Participants A convenience cohort of patients testing positive and negative for COVID19 using reverse transcriptase polymerase chain reaction (RTPCR) were studied and included 167 COVID and nonCOVID patients recruited between April 2021 and May 2022, which covers the period when Delta (and other variants prior to Delta) was the dominant variant (April to December 2021) and when Omicron was the dominant variant (January to May 2022). The breath from those patients were collected and analyzed for volatile organic compounds (VOCs) with a newly developed portable gas chromatography based breath analyzer. Diagnostic patterns and algorithms were developed. Results A total of 205 breath samples were analyzed from 167 COVID and nonCOVID patients. The RTPCR was conducted within 18 hours of the breath analysis to confirm the COVID status of the patients. Among 94 COVID positive samples, 41 samples were collected from the patients in 2021 who were assumed to be infected by the Delta variant (or other variants occurring in 2021) and 53 samples from the patients in 2022 who were assumed to be infected by the Omicron variant (BA.1 and BA.2). Breath analysis using a set of 4 VOC biomarkers was able to distinguish between COVID (Delta and other variants in 2021) and nonCOVID with an overall accuracy of 94.7%. However, the accuracy dropped significantly to 82.1% when the same set of biomarkers were applied to the Omicron variant with and 21 out of 53 COVID positive being misidentified. A new set of 4 VOC biomarkers were found to distinguish the Omicron variant and non-COVID, which yielded an overall accuracy of 90.9%. Breath analysis was also found to be able to distinguish between COVID (for all the variants occurring between April 2021 and May 2022) and nonCOVID with an overall accuracy of 90.2%, and between the Omicron variant and the earlier variants (Delta and other variants occurring in 2021) with an overall accuracy of 91.5%. Conclusions and Relevance Breath analysis of VOCs using point of care gas chromatography may be a promising diagnostic modality for detection of COVID and similar diseases that result in VOC production. However, similar to other diagnostic modalities such as rapid antigen testing, challenges are posed by the dynamic emergence of viral variants. The results of this study warrant additional investment and evaluation on how to overcome these challenges and to exploit breath analysis to improve the diagnosis and care of patients.

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Section: Discussionmentioning

confidence: 99%

Portable Breath-Based Volatile Organic Compound Monitoring for the Detection of COVID-19: Challenges of Emerging Variants

Sharma

Zang

Tabartehfarahani

et al. 2022

Preprint

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“…COVID-19 is normally diagnosed using the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. However, it is known from many researches that the results are sometimes prone to false negatives (Arevalo-Rodriguez et al 2020;Pecoraro et al 2022). It also takes a considerable amount of time to give results (Fu et al 2022).…”

Section: Introductionmentioning

confidence: 99%

Diagnosing COVID-19 using artificial intelligence: a comprehensive review

Khanna

Chadaga

Sampathila

et al. 2022

Netw Model Anal Health Inform Bioinforma

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In early March 2020, the World Health Organization (WHO) proclaimed the novel COVID-19 as a global pandemic. The coronavirus went on to be a life-threatening infection and is still wreaking havoc all around the globe. Though vaccines have been rolled out, a section of the population (the elderly and people with comorbidities) still succumb to this deadly illness. Hence, it is imperative to diagnose this infection early to prevent a potential severe prognosis. This contagious disease is usually diagnosed using a conventional technique called the Reverse Transcription Polymerase Chain Reaction (RT-PCR). However, this procedure leads to a number of wrong and false-negative results. Moreover, it might also not diagnose the newer variants of this mutating virus. Artificial Intelligence has been one of the most widely discussed topics in recent years. It is widely used to tackle various issues across multiple domains in the modern world. In this extensive review, the applications of Artificial Intelligence in the detection of coronavirus using modalities such as CT-Scans, X-rays, Cough sounds, MRIs, ultrasound and clinical markers are explored in depth. This review also provides data enthusiasts and the broader health community with a complete assessment of the current state-of-the-art approaches in diagnosing COVID-19. The key issues and future directions are also provided for upcoming researchers.

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“…Similar findings have been reported in the setting of low viral loads. 5 That concordant positive specimens have higher N gene copies numbers provides a measure of reassurance, as high nasopharyngeal viral loads likely contribute to COVID-19 transmission. However, previous research failed to identify a Ct value cut-off below which transmission does not occur.…”

Section: Discussionmentioning

confidence: 99%

SARS-CoV-2 Viral Load Quantification, Clinical Findings and Outcomes in Children Seeking Emergency Department Care: Prospective Cohort Study

Freedman

Oberding

Kim

et al. 2022

Pediatric Infectious Disease Journal

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We compared the perfomance of SARS-CoV-2 reverse transcriptase real-time polymerase chain reaction (RT-PCR) to droplet digital PCR (ddPCR). 95% and 40% of positive and negative RT-PCR specimens, respectively, were positive on ddPCR yielding sensitivities of 84% (95% CI: 74, 91) and 97% (95% CI: 89, 99), for RT-PCR and ddPCR, respectively. We found that SARS-CoV-2 RT-PCR testing in children has a concerning false-negative rate at lower nucleocapsid gene copy numbers.

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Estimate False-Negative RT-PCR Rates for SARS-CoV-2: A Systematic Review and Meta-Analysis

Cited by 12 publications

References 0 publications

Portable Breath-Based Volatile Organic Compound Monitoring for the Detection of COVID-19: Challenges of Emerging Variants

Portable Breath-Based Volatile Organic Compound Monitoring for the Detection of COVID-19: Challenges of Emerging Variants

Diagnosing COVID-19 using artificial intelligence: a comprehensive review

SARS-CoV-2 Viral Load Quantification, Clinical Findings and Outcomes in Children Seeking Emergency Department Care: Prospective Cohort Study

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