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Reduced Exercise Tolerance in Long-COVID Patients

2021· article· en· W3159730836 on OpenAlexaff
Rhea Varughese, Grace Y. Lam, Andrew R. Brotto, E. Bok, Eric Wong, A. Dean Befus, Ronald W. Damant, Giovanni Ferrara, Maeve P. Smith, Michael K. Stickland

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineCoronavirus disease 2019 (COVID-19)Internal medicineRespiratory systemCardiologyPulmonary function testingSeverity of illnessPhysical therapy

Abstract

fetched live from OpenAlex

Introduction: Because many patients report long-term symptoms including dyspnea and fatigue after an acute COVID-19 infection, we recently developed a comprehensive follow-up clinic to understand and manage these patients. We conducted cardiopulmonary exercise tests (CPET) to characterize the respiratory responses to exercise as a potential cause of dyspnea in long-COVID patients. Methods: Seven long-COVID patients (mean age 53±4 years, 100% female) and seven age, sex and height-matched healthy controls (mean age 55±10 years) completed a pulmonary function test and an incremental CPET to exhaustion. These seven long-COVID patients were assessed due to persistent dyspnea after recovery from the acute infection;three long-COVID patients required hospitalization during the acute infection. The CPET was performed on average 158±59 days since COVID-19 diagnosis. Arterial saturation (SpO2) and breath-by-breath respiratory data, including ventilatory equivalents for carbon dioxide (VE/VCO2), were collected continuously, while inspiratory capacity (IC), inspiratory reserve volume (IRV), and dyspnea (modified Borg scale) were evaluated throughout exercise. Statistical analyses were performed using unpaired t-tests with a significance level of 0.05. Results: Prior to testing, COVID-19 patients reported resting dyspnea (mean modified Medical Research Council Dyspnea scale 2.1±0.7) and elevated post-COVID functional scale (mean 2.1±1.2), revealing persistent symptoms. Spirometric assessment at rest was within expected normal limits, though a reduction in FEV1 was seen in the COVID-19 patients (88.9±16.6% predicted) compared to matched controls (111.1±13.2% predicted;p = 0.02). Lung volumes and diffusion capacity were similar between both groups. Most notably, COVID-19 patients (19.6±7.4 mL/kg/min) had a reduced VO2peak when compared to controls (29.1±8.3 mL/kg/min, p<0.01). As well, VE/VCO2 at rest and anaerobic threshold were elevated in COVID-19 patients as compared to controls. SpO2 at peak exercise was not different between COVID-19 patients and controls (95±4% vs. 94±3%, p=0.57). At peak exercise, there were no between-group differences in tidal volume, breathing frequency, IC, IRV observed. Patient-reported dyspnea and leg fatigue at peak exercise were also similar between groups. Conclusion: Initial results suggest that long-COVID patients demonstrate reduced exercise tolerance and elevated VE/VCO2;however, SpO2, operating lung volumes and dyspnea responses to exercise are similar to healthy controls, suggesting that there may be a non-pulmonary cause for the observed exercise intolerance. Further investigation is required to understand this limitation and its relationship to symptoms in long-COVID patients.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.291
Teacher spread0.280 · 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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Citations6
Published2021
Admission routes1
Has abstractyes

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