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Record W4318691545 · doi:10.1088/1361-6579/acb7c6

Cardio-respiratory interactions in response to lower-body negative pressure

2023· article· en· W4318691545 on OpenAlexaff
Rabie Fadil, Ajay Verma, Farshid Sadeghian, Andrew P. Blaber, Kouhyar Tavakolian

Bibliographic record

VenuePhysiological Measurement · 2023
Typearticle
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsHypovolemiaMedicineBlood pressureHeart rateCardiorespiratory fitnessSupine positionCardiologyAnesthesiaRespiratory ratePresyncopeInternal medicine

Abstract

fetched live from OpenAlex

The relationship between heart rate and blood pressure, as well as cardiorespiratory coupling, play a critical role in maintaining blood pressure and organ perfusion during conditions of blood loss. Traditional vital signs such as blood pressure, breathing rate, and oxygen saturation are poor markers of blood loss, making it difficult for medics to assess the severity of central hypovolemia. Monitoring hemorrhage is further complicated by the fact that some patients have a low tolerance to hemorrhage and would reach the point of cardiovascular collapse in less time than high tolerant individuals. Therefore, this study aimed to investigate the potential of the physiological interaction between heart rate and blood pressure, and cardiorespiratory coupling to track the progression of simulated hemorrhage, as well as distinguish individuals with low tolerance (LT) from the ones with high tolerance (HT) to hypovolemia. Nineteen subjects (age: 28 ± 6 years; height: 170 ± 7 cm; weight: 68 ± 10 kg) underwent a progressive lower body negative pressure (LBNP) protocol in which the participant was supine inside the chamber for 12 min (baseline) before 12 min of chamber decompression at -20, -30, -40, -50 and -60 mmHg followed by a 12 min recovery period. Twelve subjects reached presyncope before or during -60 mmHg LBNP stage and were considered low tolerant (LT, 12 participants), while the ones who completed -60 mmHg were considered high tolerant (HT, 7 participants). Continuous blood pressure (BP), respiration (RSP), and electrocardiogram (ECG) signals were acquired simultaneously during baseline and each LBNP stage. RR interval was calculated using ECG, while systolic blood pressure (SBP), and pulse pressure were derived from BP waveform. Wavelet transform coherence and convergent cross-mapping techniques were employed to study the physiological interdependence and the causal relationship between heart rate, blood pressure, and respiration. The interaction between blood pressure and heart rate in terms of gain, active gain, and fraction time active(SBP↔RR,PP↔RR)to maintain homeostasis was higher in the LT group during baseline, and LBNP simulated mild, moderate, and severe hemorrhage. The significant time of interaction between SBP and RSP, and the causal effect of blood pressure on respiration were higher in the HT group during baseline compared to the LT group. HT participants also had a higher causal effect of respiration on blood pressure(RSP→SBP,RSP→PP)during -30 and -40 mmHg compared to LT. Moreover, the HT group displayed a higher causal drive of respiratory-related changes in heart rate(RSP→RR)and heart rate mediated changes in respirationRR→RSPduring severe simulated hemorrhage (-40 mmHg) compared to the LT group. The calculated metrics to distinguish between individual LT from HT subjects achieved a sensitivity of 58%-83%, an accuracy of 63%-84%, and an area under the ROC curve of 74%-86%, while the overlap of LT individual responses with HT was 0%-33%. These results indicate the potential of cardiorespiratory coupling, and heart rate and blood pressure interaction toward tracking the progression of hemorrhage and distinguishing individuals with low tolerance to hypovolemia from those with high tolerance. Measurements of such interactions could improve clinical outcomes for patients with low tolerance to hypovolemia and therefore reduce morbidity and mortality through early implementation of life-saving interventions.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.984
Threshold uncertainty score0.848

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.001

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 head0.125
GPT teacher head0.343
Teacher spread0.218 · 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 designBench or experimental
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

Citations4
Published2023
Admission routes1
Has abstractyes

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