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Record W2940803790 · doi:10.1016/j.bja.2019.03.022

Chronotropic incompetence and myocardial injury after noncardiac surgery: planned secondary analysis of a prospective observational international cohort study

2019· article· en· W2940803790 on OpenAlexafffund
Tom Abbott, Rupert M. Pearse, W. Scott Beattie, Mandeep Phull, Christian M. Beilstein, Ashok Raj, Michael P. W. Grocott, Brian H. Cuthbertson, Duminda N. Wijeysundera, Gareth L. Ackland

Bibliographic record

VenueBritish Journal of Anaesthesia · 2019
Typearticle
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsSt. Michael's HospitalHealth Sciences CentreSunnybrook Health Science CentreUniversity of Toronto
FundersMedical Research CouncilNational Institute for Health Research Southampton Biomedical Research CentreUniversity of TorontoRoyal College of AnaesthetistsCanadian Institutes of Health ResearchNational Institute for Health and Care ResearchNIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer ResearchBritish Heart FoundationNational Institute on Handicapped Research
KeywordsMedicineChronotropicCardiologyInternal medicineHeart failureOdds ratioHeart rateNatriuretic peptideBlood pressure

Abstract

fetched live from OpenAlex

Background Physiological measures of heart failure are common in surgical patients, despite the absence of a diagnosis. Heart rate (HR) increases during exercise are frequently blunted in heart failure (termed chronotropic incompetence ), which primarily reflects beta-adrenoreceptor dysfunction. We examined whether chronotropic incompetence was associated with myocardial injury after noncardiac surgery. Methods This was a predefined analysis of an international cohort study where participants aged ≥40 yr underwent symptom-limited cardiopulmonary exercise testing before noncardiac surgery. Chronotropic incompetence was defined as the ratio of increase in HR during exercise to age-predicted maximal increase in HR <0.6. The primary outcome was myocardial injury within 3 days after surgery, defined by high-sensitivity troponin assays >99th centile. Explanatory variables were biomarkers for heart failure (ventilatory efficiency slope [minute ventilation/carbon dioxide production] ≥34; peak oxygen consumption ≤14 ml kg −1 min −1 ; HR recovery ≤6 beats min −1 decrease 1 min post-exercise; preoperative N-terminal pro-B-type natriuretic peptide [NT pro-BNP] >300 pg ml −1 ). Myocardial injury was compared in the presence or absence of sympathetic (i.e. chronotropic incompetence) or parasympathetic (i.e. impaired HR recovery after exercise) thresholds indicative of dysfunction. Data are presented as odds ratios (ORs) (95% confidence intervals). Results Chronotropic incompetence occurred in 396/1325 (29.9%) participants; only 16/1325 (1.2%) had a heart failure diagnosis. Myocardial injury was sustained by 162/1325 (12.2%) patients. Raised preoperative NT pro-BNP was more common when chronotropic incompetence was <0.6 (OR: 1.57 [1.11–2.23]; P =0.011). Chronotropic incompetence was not significantly associated with myocardial injury (OR: 1.05 [0.74–1.50]; P =0.78), independent of rate-limiting therapy. HR recovery <12 beats min −1 decrease after exercise was associated with myocardial injury in the presence (OR: 1.62 [1.05–2.51]; P =0.03) or absence (OR: 1.60 [1.06–2.39]; P =0.02) of chronotropic incompetence. Conclusions Chronotropic incompetence is common in surgical patients. In contrast to parasympathetic dysfunction which was associated with myocardial injury, preoperative chronotropic incompetence (suggestive of sympathetic dysfunction) was not associated with postoperative myocardial injury.

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)0.003
metaresearch head score (Gemma)0.003
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 score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.007
GPT teacher head0.243
Teacher spread0.236 · 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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Citations16
Published2019
Admission routes2
Has abstractyes

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