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Peak Exercise and Post‐Exercise Recovery Oxygen Uptake and Muscle Oxygenation in Patients with Heart Failure and Preserved Ejection Fraction and Healthy Matched Adults

2019· article· en· W3174119270 on OpenAlexaff
Natasha G. Boyes, Janine Eckstein, Stephen Pylypchuk, Dana S. Lahti, Scotty Butcher, Darcy D. Marciniuk, Dalisizwe M.K. Dewa, C. Robert E. Wells, Mark J. Haykowsky, Corey R. Tomczak

Bibliographic record

VenueThe FASEB Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsSaskatchewan Health AuthorityUniversity of Saskatchewan
Fundersnot available
KeywordsCardiologyMedicineInternal medicineOxygenationHeart failureExercise intoleranceHeart failure with preserved ejection fractionEjection fractionVO2 maxPopulationIncremental exerciseHeart rateDeoxygenated HemoglobinAnesthesiaHemoglobinBlood pressure

Abstract

fetched live from OpenAlex

Background Exercise intolerance and muscle dysfunction characterize heart failure with preserved ejection fraction (HFpEF). Exercise intolerance, as measured by low peak oxygen uptake (VO 2 ), and slow post‐exercise VO 2 recovery are predictors of mortality. The relationship between peak exercise and recovery VO 2 and muscle oxygenation is unknown in this population. Purpose We tested the hypothesis that post‐exercise VO 2 recovery would be slower in patients with HFpEF compared to controls, and that slower post‐exercise VO 2 recovery would be related to slower muscle oxygenation recovery in patients with HFpEF. Methods Eight patients with HFpEF and 8 healthy age‐and sex‐matched controls completed a stationary cycling peak exercise test to volition fatigue followed by 5‐min of passive recovery. Pulmonary VO 2 (gas exchange via metabolic cart) and muscle oxygenation (tissue muscle oxygenation index, TOI) and deoxygenated hemoglobin (HHb via near infrared spectroscopy) were sampled continuously during the exercise test and recovery. Breath‐by‐breath VO 2 data were linearly interpolated to 1‐s intervals, and both VO 2 and NIRS data were averaged into 5‐s time bins. VO 2 recovery data were mono‐exponentially curve‐fitted (OriginPro, 2017) to yield a recovery time constant (tau) and amplitude change. TOI and HHb at end‐exercise, end‐recovery, and the amplitude change were calculated as 10‐s averages. Statistical analyses included independent t ‐tests and stepwise multiple regression. Significance was accepted at P <0.05. Results Peak VO 2 (15.8 ± 5.9 vs. 24.6 ± 6.6 mL/kg/min, P = 0.011) and VO 2 recovery amplitude (−10.0 ± 4.9 vs. −15.1 ± 4.2 mL/kg/min, P = 0.041) were significantly lower in patients with HFpEF compared to matched controls. However, there were no differences between groups in VO 2 recovery tau (98 ± 43 vs. 71 ± 16 s, P = 0.122) nor in any TOI or HHb parameter (all P >0.05). Stepwise regression by group to predict peak VO 2 yielded a positive regression using both peak‐exercise HHb and VO 2 recovery amplitude in HFpEF ( R 2 = 0.957, P <0.001) but only VO 2 recovery amplitude in controls ( R 2 = 0.947, P <0.001). The same parameters predicted VO 2 recovery tau in HFpEF ( R 2 = 0.940, P = 0.001) with no significant finding in controls ( P >0.05). Conclusions Slower post‐exercise VO 2 recovery in patients with HFpEF compared to their healthy counterparts was not confirmed with these data, although this may be largely influenced by the elevated variance in the HFpEF group, or the mild‐moderate HFpEF severity. Regression analyses suggest that the level of muscle deoxygenation, i.e. , the level of O 2 extraction, during peak exercise may be a more important contributor to peak VO 2 in patients with HFpEF compared to their healthy counterparts. The latter may suggest a muscle‐based limitation in HFpEF not observed in healthy controls. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.005
GPT teacher head0.208
Teacher spread0.203 · 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
Published2019
Admission routes1
Has abstractyes

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