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Record W4399676983 · doi:10.1093/eurjpc/zwae175.200

Effects of high-intensity interval training, moderate continuous training or usual care on ventilatory efficiency parameters in patients with heart failure with preserved ejection fraction

2024· article· en· W4399676983 on OpenAlexaff
Stephan Mueller, Ephraim B. Winzer, Andreas B. Gevaert, D. Dumitrescu, Piergiuseppe Agostoni, Isabel Fegers‐Wustrow, Mark J. Haykowsky, Paul Beckers, Frank Edelmann, Volker Adams, Burkert Pieske, E Van Craenenbroeck, Martin Halle

Bibliographic record

VenueEuropean Journal of Preventive Cardiology · 2024
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineVentilatory thresholdCardiologyHeart failureEjection fractionInternal medicineInterval trainingRespiratory minute volumeVO2 maxHigh-intensity interval trainingHeart failure with preserved ejection fractionVentilation (architecture)Heart ratePhysical therapyIntensity (physics)Respiratory systemBlood pressure

Abstract

fetched live from OpenAlex

Abstract Background In patients with heart failure with preserved ejection fraction (HFpEF), ventilatory inefficiency is associated with worse prognosis, and may indicate co-existing pulmonary hypertension (PH) or a higher risk of developing PH. While different exercise training modes [e.g., moderate continuous training (MCT), high-intensity interval training (HIIT)] have been shown to improve peak oxygen uptake in HFpEF over 3-6 months, the effects on ventilatory efficiency are largely unknown. Purpose To investigate the effects of HIIT, MCT or usual care (UC) over 12 months on ventilatory efficiency in HFpEF. Methods In the OptimEx-Clin trial, 180 stable patients with HFpEF were randomly assigned to 12 months of HIIT (3×38 min/week with 4×4 min at 80-90% heart rate reserve [HRR]), MCT (5×40 min/week at 35-50% HRR) or UC (one-time advice on physical activity). Ventilatory efficiency parameters were assessed during symptom-limited cardiopulmonary exercise testing on a bicycle ergometer at baseline and follow-up. Ventilation to carbon dioxide production (V̇E/V̇CO2) slope and y-intercept were calculated between one minute of cycling and the second ventilatory threshold. The nadir of the ventilatory equivalent for CO2 (EqCO2) was defined as the lowest 60-second average during exercise. PetCO2 values were calculated as the lowest 60-second average at rest and the highest 60-second average during exercise. Statistical analyses were performed using dependent t-tests for within-group changes from baseline to 12 months, and analysis of variance and independent t-tests to compare the changes between groups. Analyses were done in R Statistical Software with α=0.05 and without adjusting for multiple testing. Results Among 180 randomized patients, 138 who had available CPET data both at baseline and 12-month follow-up (66% female; mean age, 70 years) were included in this secondary analysis. In the HIIT group, all investigated ventilatory efficiency parameters (V̇E/V̇CO2 slope, V̇E/V̇CO2 slope y-intercept, EqCO2 nadir, PetCO2 at rest and during exercise) significantly worsened from baseline to 12 months (P<0.05), while none were significantly altered following MCT or UC (Tab. 1). Group comparisons revealed significant differences between HIIT and MCT for change in V̇E/V̇CO2 slope [mean difference, +2.6 (95% CI, 0.8 to 4.5), global P=0.04)], V̇E/V̇CO2 slope y-intercept [-1.4 (95% CI, -2.5 to -0.3); global P=0.02)] and PetCO2 at rest [-2.0 mmHg (-3.5 to -0.5); global P=0.001)]. Moreover, change in PetCO2 at rest was also significantly different between HIIT and UC [-2.7 mmHg (-4.1 to -1.2)]. Conclusions In patients with HFpEF, ventilatory efficiency significantly worsened after 12 months of HIIT, which could be indicative of worsened heart failure prognosis and/or a shift towards PH – a frequent sequela of HFpEF.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.260
Threshold uncertainty score0.517

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.247
Teacher spread0.230 · 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 teacher head, 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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Citations1
Published2024
Admission routes1
Has abstractyes

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