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Record W4409014766 · doi:10.1016/j.resp.2025.104423

Pathophysiological mechanisms of exertional dyspnea in people with cardiopulmonary disease: Recent advances

2025· review· en· W4409014766 on OpenAlexaff
Matthew D. James, Devin B. Phillips, Nicolle J. Domnik, J. Alberto Neder

Bibliographic record

VenueRespiratory Physiology & Neurobiology · 2025
Typereview
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicinePathophysiologyExertional dyspneaDiseaseIntensive care medicineCardiologyInternal medicine

Abstract

fetched live from OpenAlex

Physical activity is a leading trigger of dyspnea in chronic cardiopulmonary diseases. Recently, there has been a renewed interest in uncovering the mechanisms underlying this distressing symptom. We start by articulating a conceptual framework linking cardiorespiratory abnormalities with the central perception of undesirable respiratory sensations during exercise. We specifically emphasize that exertional dyspnea ultimately reflects an imbalance between (high) demand and (low) capacity. As such, the symptom arises in the presence of a heightened inspiratory neural drive – the will to breathe – secondary to a) increased ventilatory output relative to the instantaneous ventilatory capacity ( excessive breathing) and/or b) its impeded translation into the act of breathing due to constraints on tidal volume expansion ( constrained breathing). In patients with chronic obstructive pulmonary disease (COPD), asthma, cystic fibrosis, and interstitial lung disease (ILD), constrained breathing assumes a more dominant role as the disease progresses. Excessive breathing due to heightened wasted ventilation in the physiological dead space is particularly important in the initial stages of COPD, while alveolar hyperventilation has a major contributory role in hypoxemic patients with ILD. Hyperventilation is also a leading driver of dyspnea in heart failure (HF) with reduced ejection fraction (EF), while high physiological dead space is the main underlying mechanism in HF with preserved EF. Similarly, wasted ventilation in poorly perfused lung tissue dominates the scene in pulmonary vascular disease. New artificial intelligence-based approaches to expose the contribution of excessive and constrained breathing may enhance the yield of cardiopulmonary exercise testing in investigating exertional dyspnea in these patients. • Exertional dyspnea reflects high respiratory demand relative to low-capacity • Excessive breathing is secondary to hyperventilation and/or high wasted ventilation • Excessive breathing is the key driver of dyspnea in heart failure and pulmonary vascular disease • Constrained breathing is associated with mechanical restraints to tidal volume expansion • Constrained breathing is the primary mechanism of exertional dyspnea in obstructive and restrictive lung 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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.003
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 head0.021
GPT teacher head0.297
Teacher spread0.276 · 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 designNot applicable
Domainnot available
GenreReview

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
Published2025
Admission routes1
Has abstractyes

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