How Local SARS-CoV-2 Prevalence Shapes Pulmonary Function Testing Laboratory Protocols and Practices During the COVID-19 Pandemic

Saunders, Matthew J.; Haynes, Jeffrey M; McCormack, Meredith C.; Stanojevic, Sanja; Kaminsky, David A.

doi:10.1016/j.chest.2021.05.011

Cited by 8 publications

(6 citation statements)

References 4 publications

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“…The coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has severely restricted spirometry tests, which are the primary tool used for COPD case-nding and severity evaluation, due to concerns over droplet and aerosol generation (7,8). As such, multiple respiratory societies have recommended postponing or limiting spirometry during the COVID-19 pandemic, to prevent virus transmission (9,10). Prior studies have also reported that COPD can either be misdiagnosed or missed entirely when using spirometry alone (4,5,11).…”

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confidence: 99%

Detection and staging of chronic obstructive pulmonary disease using a computed tomography–based weakly supervised deep learning approach

et al. 2022

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BackgroundChronic obstructive pulmonary disease (COPD) remains underdiagnosed globally. The coronavirus disease 2019 pandemic has also severely restricted spirometry, the primary tool used for COPD diagnosis and severity evaluation, due to concerns of virus transmission. Computed tomography (CT)-based deep learning (DL) approaches have been suggested as a cost-effective alternative for COPD identi cation within smokers. The present study aims to develop weakly supervised DL models that utilize CT image data for the automated detection and staging of spirometry-de ned COPD among natural population. MethodsA large, highly heterogenous dataset was established comprising 1393 participants recruited from outpatient, inpatient and physical examination center settings of 4 large public hospitals in China. CT scans, spirometry data, demographic data, and clinical information of each participant were collected for the purpose of model development and evaluation. An attention-based multi-instance learning (MIL) model for COPD detection was trained using CT scans from 837 participants and evaluated using a test set comprised of data from 278 non-overlapping participants. External validation of the COPD detection was performed with 620 low-dose CT (LDCT) scans acquired from the National Lung Screening Trial (NLST) cohort. A multi-channel 3D residual network was further developed to categorize GOLD stages among con rmed COPD patients and evaluated using 5-fold cross validation. Spirometry tests were used to diagnose COPD, with stages de ned according to the GOLD criteria. ResultsThe attention-based MIL model used for COPD detection achieved an area under the receiver operating characteristic curve (AUC) of 0.934 on the test set and 0.866 on the LDCT subset acquired from NLST.The model exhibited high generalizability across distinct scanning devices and slice thicknesses, with an AUC above 0.90. The multi-channel 3D residual network was able to correctly grade 76.4% of COPD patients in the test set (423/553) using the GOLD scale, with a Cohen's weighted Kappa of 0.619 for the assessment of GOLD categorization . ConclusionThe proposed chest CT-DL approach can automatically identify spirometry-de ned COPD and categorize patients according to the GOLD scale, with clinically acceptable performance. As such, this approach may be a powerful novel tool for COPD diagnosis and staging at the population level. BackgroundChronic obstructive pulmonary disease (COPD) is a worldwide public health challenge, due to its high prevalence and long-term effects on related disabilities and mortality (1, 2). The accurate diagnosis of

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Detection and staging of chronic obstructive pulmonary disease using a computed tomography–based weakly supervised deep learning approach

et al. 2022

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“…However multiple studies have shown that patients produce more aerosols through simple breathing, talking, and coughing than from many aerosol generating procedures (AGPs) [29][30][31][32]. A survey of pulmonary function testing laboratories in the USA reported high adherence with the recommendations from the ATS and ERS and the majority considering LFTs as AGP's in line with recent evidence [33]. Almost 75% of laboratories were fully operational again demonstrating the resilience and adaptability necessary to cope with the ever-changing demands of safely providing LFT services during a pandemic.…”

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International consensus on lung function testing during the COVID-19 pandemic and beyond

et al. 2021

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COVID-19 has negatively affected the delivery of respiratory diagnostic services across the world due to the potential risk of disease transmission during lung function testing. Community prevalence, reoccurrence of COVID-19 surges, and the emergence of different variants of the SARS-CoV-2 virus have impeded attempts to restore services. Finding consensus on how to deliver safe lung function services for both patients attending and for staff performing the tests are of paramount importance.This international statement presents the consensus opinion of 23 experts in the field of lung function and respiratory physiology balanced with evidence from the reviewed literature. It describes a robust roadmap for restoration and continuity of lung function testing services during the COVID-19 pandemic and beyond.Important strategies presented in this consensus statement relate to the patient journey when attending for lung function tests. We discuss appointment preparation, operational and environmental issues, testing room requirements including mitigation strategies for transmission risk, requirement for improved ventilation, maintaining physical distance, and use of personal protection equipment. We also provide consensus opinion on precautions relating to specific tests, filters, management of special patient groups, and alternative options to testing in hospitals.The pandemic has highlighted how vulnerable lung function services are and forces us to re-think how long term mitigation strategies can protect our services during this and any possible future pandemic. This statement aspires to address the safety concerns that exist and provide strategies to make lung function tests and the testing environment safer when tests are required.

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“…Regarding PFT lab performance and adherence to guidelines, a survey of 132 laboratories conducted between August and October of 2020 found that nearly all labs required adequate PPE for their technologist, including the use of N95 masks for at least some procedures ( 37 ). Likewise, nearly all labs used in-line filters, used proper recommended room and equipment cleaning and 83% provided time for air exchange.…”

Section: Introductionmentioning

confidence: 99%

What Have We Learned About Transmission of Coronavirus Disease-2019

Kaminsky

Husnain

Khemasuwan

2023

Clinics in Chest Medicine

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How Local SARS-CoV-2 Prevalence Shapes Pulmonary Function Testing Laboratory Protocols and Practices During the COVID-19 Pandemic

Cited by 8 publications

References 4 publications

Detection and staging of chronic obstructive pulmonary disease using a computed tomography–based weakly supervised deep learning approach

Detection and staging of chronic obstructive pulmonary disease using a computed tomography–based weakly supervised deep learning approach

International consensus on lung function testing during the COVID-19 pandemic and beyond

What Have We Learned About Transmission of Coronavirus Disease-2019

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