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Record W3161251041 · doi:10.1097/eja.0000000000001497

Expert consensus on peri-operative myocardial injury screening in noncardiac surgery

2021· letter· en· W3161251041 on OpenAlexaboutno aff
Caroline Anna Sofia Humble, Stefan De Hert, Michelle S. Chew

Bibliographic record

VenueEuropean Journal of Anaesthesiology · 2021
Typeletter
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAsymptomaticGuidelineIntensive care medicinePosition paperPerioperativeMyocardial infarctionMedical emergencySurgeryCardiologyPathology

Abstract

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This Invited Commentary accompanies the following original article: Puelacher C, Pinto BB, Mills NL, et al. Expert consensus on peri-operative myocardial injury screening in noncardiac surgery: A literature review. Eur J Anaesthesiol 2021; 38:600–608. Peri-operative myocardial injury and infarction (PMI) has been subject to intense research within the last 10 years. Studies have shown that PMI occurs in 16 to 18% of patients undergoing noncardiac surgery and is associated with short and long-term mortality.1,2 Importantly, up to 82% of patients are asymptomatic.1,2 Despite the wealth of evidence regarding PMI in noncardiac surgery, we still lack clear directions on who to screen, when to screen, which biomarkers to use, what cut-offs to apply and how to manage patients who have screened positive. In the current issue, Puelacher et al. bring together current evidence and identify knowledge gaps with the aim of providing the clinician with a practical tool for cardiac risk assessment and suggestions for the management of PMI. The specific topics that are addressed include identifying patients for screening, implementing a peri-operative myocardial injury screening programme, interpretation of peri-operative screening tests, knowledge gaps and future research. Regarding the methodology of the article, readers need to keep the following in mind. First, as the authors carefully state, this is a position paper that has not been subject to a formal consensus process (e.g. Delphi). Consequently, the paper should not be considered a formal guideline, despite the fact that all the authors are well respected researchers within the field. The paper should be considered an opinion piece with a proposal for implementation of PMI screening based on the authors’ own experiences with local implementation. Although it is true that current international guidelines3–6 propose systematic peri-operative cardiac troponin screening, the lack of evidence within this field is reflected in the guidelines’ varying degrees of recommendation and their definition of high-risk patients. Second, the writing group constitutes members of an advisory board for Roche Diagnostics, which could raise concerns regarding conflicts of interest. However, the authors explicitly state that the pharmaceutical company was not involved in any part of the writing process. Decision-makers and clinicians need to regard the following caveats before considering implementation of PMI screening. First, current PMI definitions summarised by the authors in Table 2 highlight that no consensus definition of PMI exists and that the definitions lack external validation. An ongoing trial (NCT02573532) aiming to include 20 000 noncardiac surgical patients with increased cardiovascular risk is currently investigating the incidence, pathophysiology and effects of perioperative myocardial injury on long-term outcomes. This study may also validate the definition used in the 2018 study by Puelacher et al.1 That study will compare high-sensitivity cardiac troponins T and I (hs-cTnT and hs-cTnI) for the detection of PMI, which is an interesting aspect, as large studies have primarily investigated hs-cTnT. Emerging evidence in nonsurgical settings suggests significant differences between hs-cTnT and hs-cTnI.7 Second, supported by the Fourth Definition of Myocardial Infarction,7 the authors promote using hs-cTn assays with setting-specific cut-offs. It is plausible that future guidelines also need to consider sex-specific cut-offs. The prognostic and diagnostic accuracy of some hs-cTn assays for myocardial infarction in nonsurgical settings has been reported to increase when employing sex-specific cut-off values. Fourth, the authors advocate the Canadian guideline's4 definition of ‘high-risk’ patients for identifying patients suitable for PMI screening as less strict criteria would be less cost-effective while stricter criteria, as suggested by the European guideline,6 risk missing important events of PMI. There is a need for cost-effectiveness analyses investigating different criteria of high-risk patients to further support the use of a specific definition of ‘high-risk’ patients. Finally, it needs to be re-emphasised that effective interventions to prevent and treat PMI, supported by high-quality randomised controlled trials, are yet to be found. This is one of the main arguments against implementing PMI screening. It is highlighted as a gap in evidence by the authors as well as by the European and Canadian guidelines.4,6 The Canadian guidelines actually recommend the initiation of long-term aspirin and statin (strong recommendation; moderate quality of evidence) in patients suffering from myocardial injury after noncardiac surgery. These data are however based on risk-adjusted observational data. In conclusion, the contribution by Puelacher et al. addresses a clinically relevant issue, but at the same time underlines the current lack of clinically relevant evidence with regard to implementation of biomarker screening in routine peri-operative management. Investigation of the cause of PMI and a more precise quantification of the division of incidence between different types of PMI (e.g. type I and II myocardial infarction) will facilitate designing targeted interventions. Currently, there are at least two ongoing studies investigating the cause of PMI (NCT03438448, NCT03317561).

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.318
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0000.000

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 head0.040
GPT teacher head0.289
Teacher spread0.249 · 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.

Study designNot applicable
Domainnot available
GenreCommentary

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

Citations6
Published2021
Admission routes1
Has abstractyes

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