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Record W2939127216 · doi:10.1101/601542

The Dialysis Procedure Triggers Autonomic Imbalance and Cardiac Arrhythmias: Insights from Continuous 14-day ECG Monitoring

2019· preprint· en· W2939127216 on OpenAlexaff
Nichole M. Rogovoy, Stacey J. Howell, Tiffany Lee, Christopher Hamilton, Erick Andres Perez Alday, Muammar Kabir, Yin Li‐Pershing, Yanwei Zhang, Esther D. Kim, Jessica Fitzpatrick, Jose M. Monroy‐Trujillo, Michelle M. Estrella, Stephen M. Sozio, Bernard G. Jaar, Rulan S. Parekh, Larisa G. Tereshchenko

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2019
Typepreprint
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Heart, Lung, and Blood Institute
KeywordsMedicineCardiologyInternal medicineHeart rate variabilityDialysisVentricular tachycardiaHeart rateEjection fractionHeart failureBlood pressure

Abstract

fetched live from OpenAlex

Abstract Background In end-stage kidney disease the dialytic cycle relates to the rate of sudden cardiac death. We hypothesized that circadian, dialytic cycles, paroxysmal arrhythmias, and cardiovascular risk factors are associated with periodic changes in heart rate and heart rate variability (HRV) in incident dialysis patients. Methods We conducted a prospective ancillary study of the Predictors of Arrhythmic and Cardiovascular Risk in End Stage Renal Disease cohort (n=28; age 54±13 y; 57% men; 96% black; 33% with a history of structural heart disease; left ventricular ejection fraction 70±9%). Continuous ECG monitoring was performed using an ECG patch (Zio Patch, iRhythm) and short-term HRV was measured for three minutes every hour. HRV was measured by root mean square of the successive normal-to-normal intervals (rMSSD), high and low frequency power, Poincaré plot, and sample and Renyi entropy. Results Arrhythmias were detected in 46% (n=13). Non-sustained ventricular tachycardia (VT) was more frequent during dialysis or within 6 hours post-dialysis, as compared to pre-or between-dialysis (63% vs. 37%, P=0.015), whereas supraventricular tachycardia was more frequent pre-/ between-dialysis, as compared to during-/ post-dialysis (84% vs. 16%, P=0.015). In adjusted for cardiovascular disease and its risk factors autoregressive conditional heteroscedasticity panel (ARCH) model, VT events were associated with increased heart rate by 11.2 (95%CI 10.1-12.3) bpm (P<0.0001). During regular dialytic cycle, rMSSD demonstrated significant circadian pattern (Mesor 10.6(0.9-11.2) ms; Amplitude 1.5(1.0-3.1) ms; Peak at 02:01(20:22-03:16) am; P<0.0001), which was abolished on a second day interdialytic extension (adjusted ARCH trend for rMSSD −1.41(−1.67 to −1.15) ms per 24h; P<0.0001). Conclusion Cardiac arrhythmias associate with dialytic phase. Regular dialytic schedule preserves physiological circadian rhythm, but the second day without dialysis is characterized by parasympathetic withdrawal and a steady increase in sympathetic predominance. Subject Terms Arrhythmias, Autonomic Nervous System, Electrocardiology (ECG), Treatment.

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 score0.001
Threshold uncertainty score0.003

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)0.0010.000

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 head0.008
GPT teacher head0.209
Teacher spread0.202 · 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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Citations1
Published2019
Admission routes1
Has abstractyes

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