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Dynamic Ventilatory Reserve During Incremental Exercise: Reference Values and Clinical Validation in Chronic Obstructive Pulmonary Disease

2023· article· en· W4383314656 on OpenAlexaff
Danilo Cortozi Berton, Franciele Plachi, Matthew D. James, Sandra G. Vincent, Reginald Smyth, Nicolle J. Domnik, Devin B. Phillips, Juan P. de‐Torres, Luíz Eduardo Nery, Denis E. O’Donnell, J. Alberto Neder

Bibliographic record

VenueAnnals of the American Thoracic Society · 2023
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsYork UniversityKingston Health Sciences CentreKingston General HospitalQueen's University
Fundersnot available
KeywordsMedicineCOPDDynamic hyperinflationCardiologyVentilation (architecture)Respiratory minute volumeIncremental exerciseObstructive lung diseaseInternal medicinePhysical therapyLung volumesLungRespiratory systemHeart rate

Abstract

fetched live from OpenAlex

Abstract Rationale Ventilatory demand–capacity imbalance, as inferred based on a low ventilatory reserve, is currently assessed only at peak cardiopulmonary exercise testing (CPET). Peak ventilatory reserve, however, is poorly sensitive to the submaximal, dynamic mechanical ventilatory abnormalities that are key to dyspnea genesis and exercise intolerance. Objectives After establishing sex- and age-corrected norms for dynamic ventilatory reserve at progressively higher work rates, we compared peak and dynamic ventilatory reserve for their ability to expose increased exertional dyspnea and poor exercise tolerance in mild to very severe chronic obstructive pulmonary disease (COPD). Methods We analyzed resting functional and incremental CPET data from 275 controls (130 men, aged 19–85 yr) and 359 Global Initiative for Chronic Obstructive Lung Disease patients with stage 1–4 obstruction (203 men) who were prospectively recruited for previous ethically approved studies in three research centers. In addition to peak and dynamic ventilatory reserve (1 − [ventilation / estimated maximal voluntary ventilation] × 100), operating lung volumes and dyspnea scores (0–10 on the Borg scale) were obtained. Results Dynamic ventilatory reserve was asymmetrically distributed in controls; thus, we calculated its centile distribution at every 20 W. The lower limit of normal (lower than the fifth centile) was consistently lower in women and older subjects. Peak and dynamic ventilatory reserve disagreed significantly in indicating an abnormally low test result in patients: whereas approximately 50% of those with a normal peak ventilatory reserve showed a reduced dynamic ventilatory reserve, the opposite was found in approximately 15% (P < 0.001). Irrespective of peak ventilatory reserve and COPD severity, patients who had a dynamic ventilatory reserve below the lower limit of normal at an isowork rate of 40 W had greater ventilatory requirements, prompting earlier attainment of critically low inspiratory reserve. Consequently, they reported higher dyspnea scores, showing poorer exercise tolerance compared with those with preserved dynamic ventilatory reserve. Conversely, patients with preserved dynamic ventilatory reserve but reduced peak ventilatory reserve reported the lowest dyspnea scores, showing the best exercise tolerance. Conclusions Reduced submaximal dynamic ventilatory reserve, even in the setting of preserved peak ventilatory reserve, is a powerful predictor of exertional dyspnea and exercise intolerance in COPD. This new parameter of ventilatory demand–capacity mismatch may enhance the yield of clinical CPET in the investigation of activity-related breathlessness in individual patients with COPD and other prevalent cardiopulmonary diseases.

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)0.004
metaresearch head score (Gemma)0.013
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 score0.004
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.088
GPT teacher head0.438
Teacher spread0.350 · 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".

Quick stats

Citations11
Published2023
Admission routes1
Has abstractyes

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