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This Is What COVID-19 Survival Looks Like: 129Xe MRI, Oscillometry and Pulmonary Function Measurements

2021· article· en· W3176487952 on OpenAlexaff
Alexander M. Matheson, Marrissa J. McIntosh, Yasal Rajapaksa, Inderdeep Dhaliwal, Michael D. Nicholson, Grace Párraga

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAtomic and Subatomic Physics Research
Canadian institutionsLondon Health Sciences CentreRobarts Clinical TrialsWestern University
Fundersnot available
KeywordsMedicineCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Pulmonary function testingCardiologyInternal medicineIntensive care medicinePathologyDisease

Abstract

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PURPOSE: COVID-19 lung infection has severe consequences that may persist following recovery including impaired oxygen transfer and local and systemic inflammation.1 Although the long-term consequences of COVID-19 are poorly understood, preliminary CT evidence suggests permanent structural damage2 may reduce lung function in some survivors. Oscillometry and multiple breath washout (MBW) may be well suited to detecting small airway abnormalities due to increased sensitivity to heterogeneous small airway and alveolar tissue abnormalities compared to spirometry.3,4 Likewise, inhaled 129Xe MRI provides a unique, quantitative measures of airway and alveolar structure/function through ventilation defect percent (VDP) and the apparent diffusion coefficient (ADC).5 We hypothesized that 129Xe MRI, oscillometry and MBW would reveal abnormal findings in the absence of abnormal pulmonary function test results in COVID-19 survivors. METHODS: Participants with a positive COVID-19 test that were being followed for long-term sequelae provided written informed consent up to three months post-recovery to 129Xe MRI, MBW, oscillometry and spirometry. Ventilation defect percent (VDP) and ADC were calculated using semi-automated segmentation tools.6 Significant differences were determined using paired-sample t-tests. RESULTS: Participants without prior history of chronic respiratory disease (n=8), those with a prior diagnosis of COPD (n=2) and asthma (n=4) were recruited. Mean FEV1 was normal with no post-bronchodilator (post-BD) response (pre-BD=88±19%pred, post-BD=88±20%pred, p=.87), however MBW lung clearance index (LCI) significantly increased (pre-BD=130±46%pred, post-BD=153±47%pred, p=.02). 129Xe MRI VDP was abnormal (pre-BD=7±7%, post-BD=6±5%, p=.46) while ADC (0.052±0.003cm2/s) was similar to values previously evaluated in participants with COPD.7,8 Airway resistance (pre-BD=0.85±0.69, post-BD=0.56±0.72 cmH2O·s/L, p=.010) and reactance (pre-BD=14±13 cmH2O/L, post-BD=9±9 cmH2O/L, p=.036) significantly improved post-BD, were elevated, and similar to previously reported asthma and COPD values.9 No significant post-BD differences were observed between participants with and without obstructive diseases. CONCLUSIONS: Abnormal ADC and VDP in symptomatic participants after COVID-19 recovery were consistent with small airway and alveolar damage. Abnormal oscillometry measurements improved post-bronchodilator, suggesting small airway damage and decreased tissue elastance. An unexpected increase in LCI post-bronchodilator warrants further investigation and may be due to preferential bronchodilator response in certain airways. 129Xe MRI, oscillometry and MBW measurements detect lung function impairment in survivors and may provide useful measures for longitudinal monitoring and treatment response.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.059
GPT teacher head0.314
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2021
Admission routes1
Has abstractyes

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