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Record W2914116542 · doi:10.1161/circ.133.suppl_1.p041

Abstract P041: Impaired Autonomic Control of Heart Rate at Rest and Slower Post-exercise Heart Rate Kinetics in Children with Congenital Heart Defects

2016· article· en· W2914116542 on OpenAlexaff
Elizabeth Hogeweide, Stephanie Fusnik, Josie T.J. Fries, Mark J. Haykowsky, Michael K. Stickland, Shonah Runalls, Ashok Kakadekar, Scott Pharis, Charissa Pockett, Marta Erlandson, Corey R. Tomczak

Bibliographic record

VenueCirculation · 2016
Typearticle
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsUniversity of AlbertaUniversity of Saskatchewan
Fundersnot available
KeywordsMedicineInternal medicineCardiologyTricuspid atresiaHeart rateTetralogy of FallotGreat arteriesDouble outlet right ventricleSupine positionPulmonary atresiaCoarctation of the aortaHeart rate variabilityVentricleCardiomyopathyHeart diseaseHeart failureAortaBlood pressure

Abstract

fetched live from OpenAlex

Introduction: A slow recovery in heart rate (HR) following exercise is due to autonomic dysfunction and is associated with increased cardiovascular event risk. Children with congenital heart defects (CHD) display autonomic dysfunction as shown by a reduction in HR variability (HRV). Whether children with CHD have impairment in post-exercise HR recovery is unknown. Hypothesis: We tested the hypothesis that children with CHD would have reduced HRV at rest and slower HR recovery kinetics following 6-minute walk testing (6MWT) compared to healthy controls. Methods: Twenty-five children with CHD (11±2 years; males=14; females=11) and 21 age- and sex-matched controls (11±3 years; males=10; females=11) were studied. CHD diagnoses included Tetralogy of Fallot (n=7), pulmonary or aortic stenosis (n=3), hypoplastic left or right heart syndrome (n=5), Ebstien’s anomaly (n=1), atrial or ventricular septal defect (n=3), transposition of the great arteries (n=1), double inlet left ventricle (n=1), tricuspid atresia (n=1), coarctation of the aorta (n=2), single ventricle (n=1), and dilated cardiomyopathy (n=1). HRV was determined following 10 min supine rest using a 5 min surface ECG recorded epoch. Post-exercise HR kinetics were determined over a 4-min period following the 6MWT using telemetry-based HR. Mono-exponential modeling was used to derive a HR recovery time constant, tau (time to reach 63% change). Analyses were completed using unpaired t -tests with P < 0.05 being significant. Data are mean ± SD. Results: Children with CHD had a lower 6MWT distance (513±75 vs. 599±81 m; P < 0.001) and lower average exercise HR (122±15 vs. 139±18 beats/min; P = 0.001) compared to controls. Time domain HRV parameters revealed a reduction in the standard deviation of normal R-R intervals (55.9±40.4 vs. 92.1±24.5 ms), the root mean square of successive R-R interval differences (57.4±53.0 vs. 101.1±40.0 ms), and the percentage of consecutive normal R-R intervals that differ by more than 50 ms (24.6±28.7 vs. 53.2±14.0 %) in children with CHD vs. controls, respectively (all P < 0.01). Power spectral HRV analyses revealed no difference in low frequency (LF) power (30±16 vs. 25±16 %; P > 0.05), but significant differences in high frequency (HF) power (34±19 vs. 49±14 %) and the LF/HF ratio (1.4±1.5 vs. 0.6±0.3 %) in children with CHD vs. controls, respectively (all P < 0.05). Post-exercise HR kinetics were slower in children with CHD (tau = 34±15 s) compared to controls (tau = 24±8 s; P = 0.01), indicating a longer recovery time for HR. Conclusions: Children with CHD have autonomic dysfunction as measured by a reduction in HRV and greater LF/HF ratio. Slower HR kinetics in children with CHD may be due to a reduction in parasympathetic activation during the post-exercise recovery period. Our findings suggest that autonomic dysfunction in children with CHD functionally alters HR control during or immediately following exercise.

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 score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.009
GPT teacher head0.236
Teacher spread0.226 · 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
Published2016
Admission routes1
Has abstractyes

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