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Do Clinical Cardiopulmonary Exercise Tests Permit Exercise Threshold Identification In Patients Referred To Cardiac Rehabilitation?

2023· article· en· W4387062159 on OpenAlexaff
Randi R. Keltz, Tim Hartley, A. Huitema, Robert S. McKelvie, Neville Suskin, Daniel A. Keir

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsSt Joseph's Health CareWestern University
Fundersnot available
KeywordsRespiratory compensationMedicineVentilatory thresholdCardiologyInternal medicineHeart rateExercise prescriptionVO2 maxAnaerobic exerciseAerobic exerciseCoronary artery diseaseRehabilitationLactate thresholdPhysical therapyExercise intensityBlood pressureBlood lactate

Abstract

fetched live from OpenAlex

In cardiac rehabilitation (CR), exercise training intensity is traditionally prescribed as fixed ranges of percent peak V̇O2 (V̇O2peak) or heart rate (HRpeak) determined from a cardiopulmonary exercise test (CPET). However, there is a growing call to shift aerobic exercise prescription framework from this range-based approach to an individualized threshold-based model that includes identification of the estimated lactate threshold (θLT) and respiratory compensation point (RCP). PURPOSE: To quantify the proportion of patients whose clinical CPET data permit identification of θLT and RCP and to characterize the variability at which these thresholds occur. METHODS: We retrospectively analyzed breath-by-breath CPET data of 1102 patients (age: 65 ± 12 yrs; 306 females; 71% with coronary artery disease; 55% Bruce protocol) referred to St. Joseph’s CR and Secondary Prevention program. V̇O2peak was computed as the highest 20-s rolling average, expressed in absolute and relative terms. θLT and RCP were visually identified and reported in absolute V̇O2, normalized to V̇O2peak (%V̇O2peak) and as percent HRpeak (%HRpeak). Patients were grouped according to the presence or absence of thresholds: Group 0 = neither θLT nor RCP, Group 1 = θLT but not RCP, and Group 2 = both θLT and RCP. RESULTS: Mean V̇O2peak for the sample was 1523 ± 627 mL·min-1 (range: 315-3789 mL·min-1) or 18.0 ± 6.5 mL·kg-1·min-1 (5.2-46.5 mL·kg-1·min-1) and HRpeak was 123 ± 24 bpm (52-207 bpm). There were 556 patients (50%; 212 females) in Group 0, 196 patients (18%; 51 females) in Group 1, and 350 patients (32%; 43 females) in Group 2. In Group 1, mean θLT was 1240 ± 410 mL·min-1 (580-2560 mL·min-1), 75 ± 8%V̇O2peak (52-92%V̇O2peak), or 84 ± 6%HRpeak (64-96%HRpeak). In Group 2, mean θLT was 1390 ± 360 mL·min-1 (640-2430 mL·min-1), 70 ± 8%V̇O2peak (41-88%V̇O2peak), or 78 ± 7%HRpeak (52-96%HRpeak), and RCP was 1680 ± 440 mL·min-1 (730-3090 mL·min-1), 84 ± 7%V̇O2peak (54-99%V̇O2peak) or 87 ± 6%HRpeak (59-99%HRpeak). Compared to Group 1, θLT in Group 2 occurred at a higher V̇O2 but lower %V̇O2peak and %HRpeak (p < 0.05). CONCLUSION: Only 32% of clinical CPET data exhibited both θLT and RCP despite individualized protocol selection. Contemporary protocols may not provide sufficient data for exercise threshold manifestation. Supported by NSERC Grant RGPIN202103980.

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)0.001
metaresearch head score (Gemma)0.010
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 score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.034
GPT teacher head0.386
Teacher spread0.352 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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