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Record W4393200073 · doi:10.1093/ehjacc/zuae038

How to use natriuretic peptides in non-cardiac surgery

2024· article· en· W4393200073 on OpenAlexaffabout
Emmanuelle Duceppe, Nicholas L. Mills, Christian Mueller, Evangelos Giannitsis, Lori B. Daniels, Kurt Huber, Johannes Mair, Louise Cullen, Ola Hammarsten, Martin Möckel, Konstantin A. Krychtiuk, Kristian Thygesen, Matthias Thielmann, Allan S. Jaffe

Bibliographic record

VenueEuropean Heart Journal Acute Cardiovascular Care · 2024
Typearticle
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsUniversité de MontréalCentre Hospitalier de l’Université de MontréalPopulation Health Research Institute
Fundersnot available
KeywordsMedicineOriginal researchLibrary scienceClassicsHistory

Abstract

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Most adults will undergo surgery during their lifetime, and the need for surgery is growing worldwide.1 It is estimated that >300 million undergo surgery annually, and of those, an estimated 4.2 million die within 30 days.1 Major cardiac complications are amongst the most common complications after non-cardiac surgery and contribute to a third of perioperative deaths.1 Evaluation of cardiovascular risk is therefore a central component of the assessment of patients undergoing non-cardiac surgery. Amongst the tools available for clinicians for risk stratification beyond clinical evaluation are cardiac biomarkers. Natriuretic peptides are the most studied biomarkers for cardiac risk prediction in non-cardiac surgery.2 B-type natriuretic peptide (BNP) is released by cardiomyocytes as a prohormone, which is cleaved into the active BNP hormone and the inactive N-terminal pro-BNP (NT-proBNP) in response to various stimuli affecting the myocardium including mechanical stretch, inflammation, haemodynamic variation, hypoxaemia, and ischaemia.3 Measures of BNP and NT-proBNP reflect the same biological activity, although their laboratory range differs. National guidelines, including the 2022 European Society of Cardiology (ESC) ‘guidelines on cardiovascular assessment and management of patients undergoing non-cardiac surgery’, recommend the measurement of pre-operative BNP/NT-proBNP to inform on the prognosis of patients undergoing intermediate- or high-risk non-cardiac surgery with known cardiovascular disease, risk factors, or symptoms suggestive of cardiovascular disease (Class IIa).4 Several studies have shown that pre-operative BNP/NT-proBNP measurements are associated with post-operative cardiac events and mortality when used alone or in combination with cardiac risk scores or pre-operative cardiac troponin.2 NT-proBNP has also been shown to be better than echocardiography to predict post-operative major cardiac events.3 A BNP threshold of 92 ng/L has been proposed to identify those at increased risk of myocardial infarction and death at 30 days after non-cardiac surgery.5 For NT-proBNP, thresholds of 100, 200, and 1500 ng/L have been identified to predict post-operative cardiac events, with an incidence of 3.0% [95% confidence interval (CI), 2.2–3.8%], 7.9% (95% CI, 6.8–8.9%), and 15.8% (95% CI, 12.8–18.7%) of myocardial infarction and death at 30 days, respectively, for each threshold, compared with 1.7% (95% CI, 1.4–2.1%) in patients with NT-proBNP < 100 ng/L.6 Natriuretic peptides can be used to risk stratify patients before non-cardiac surgery and can also be used to determine the need for further pre-operative cardiac investigation (e.g. echocardiogram) in patients with clinical signs or symptoms (e.g. chest pain, dyspnoea, or peripheral oedema)4 or new changes on pre-operative electrocardiogram (Figure 1). Although increased BNP/NT-proBNP concentrations were first demonstrated in patients with heart failure, they can elevated due to several other cardiovascular conditions that are relevant in the perioperative setting.3 A few of these conditions include poorly controlled hypertension, ischaemic heart disease, and pulmonary hypertension. In most cases, the degree of BNP/NT-proBNP elevation correlates with the severity of the underlying disease. On the other hand, a low value has been shown to have a good negative predictive value, making BNP/NT-proBNP good ‘rule out’ tests when assessing if a patient should undergo further cardiac testing before surgery.3 For example, a patient with chronic obstructive pulmonary disease and shortness of breath but normal BNP/NT-proBNP measurement is unlikely to have undiagnosed heart failure or significant pulmonary hypertension.3 Pre-operative cardiac investigation, including BNP/NT-proBNP and other cardiac tests, should be tailored to the patient’s risk factors, clinical presentation, and known or suspected comorbidities and also take into consideration the surgery-related risk, duration, and urgency. BNP/NT-proBNP measurements in combination with the patient’s risk factors can also be used to inform which patients may benefit from post-operative cardiac troponin surveillance in the first 2–3 days after surgery.1,4 Used in this way, cardiac biomarkers could support both the prevention and early recognition of cardiac complications following non-cardiac surgery and improve outcomes. Aetiology and investigation of elevated natriuretic peptides in non-cardiac surgery. Pre-operative cardiac investigations include an echocardiogram and cardiac stress testing, with the choice of investigation individualized to the patient’s clinical presentation and likely aetiology of B-type natriuretic peptide/N-terminal pro-B-type natriuretic peptide elevation. Further testing may be required in those with new or worsening symptoms (e.g. shortness of breath), physical signs (e.g. heart murmur), new abnormalities on the electrocardiogram, risk factors (e.g. poorly controlled hypertension or diabetes, smoking history, and family history of cardiac disease), and comorbidities (e.g. known cardiac disease, moderate to severe chronic obstructive pulmonary disease, or sleep apnoea) or in those undergoing higher risk surgery (e.g. intra-abdominal, intra-thoracic, or vascular surgery). Other members of the Study Group on Biomarkers of the ESC Association for Acute Cardiovascular Care include: Evangelos Giannitsis, MD1; Lori Daniels, MD2; Kurt Huber, MD3; Johannes Mair, MD;4 Louise Cullen, MD, PhD5; Ola Hammarsten, MD, PhD6; Martin Möckel, MD7; Konstantin Krychtiuk, MD8; Kristian Thygesen, MD9; Matthias Thielmann, MD10; Allan S. Jaffe, MD11 1Department of Cardiology, University Heidelberg, Germany 2Department of Medicine, Sulpizio Cardiovascular Center, University of California, San Diego, La Jolla, CA, USA 3Department of Medicine, Cardiology and Intensive Care Medicine, Wilhelminen Hospital, and Sigmund Freud University, Medical School, Vienna, Austria 4Department of Internal Medicine III—Cardiology and Angiology, Medical University Innsbruck, Austria 5Emergency and Trauma Centre, Royal Brisbane and Women’s Hospital, University of Queensland, Australia 6Department of Clinical Chemistry and Transfusion Medicine, University of Gothenburg, Gothenburg, Sweden 7Division of Emergency Medicine, Charité-Universitätsmedizin Berlin, Germany 8Department of Internal Medicine II, Division of Cardiology, Medical University of Vienna, Austria 9Department of Cardiology, Aarhus University Hospital, Denmark 10Klinik für Thorax- und Kardiovaskuläre Chirurgie, Westdeutsches Herz- und Gefäßzentrum Essen, University of Duisburg-Essen, Germany 11Departments of Cardiology and Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA None declared.

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)0.005
metaresearch head score (Gemma)0.051
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.196
Threshold uncertainty score0.655

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.051
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0050.002
Science and technology studies0.0010.001
Scholarly communication0.0060.008
Open science0.0020.003
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.1960.127

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 head0.027
GPT teacher head0.261
Teacher spread0.234 · 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 designNot applicable
Domainnot available
GenreMethods

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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Citations2
Published2024
Admission routes2
Has abstractyes

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