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Record W3110300040 · doi:10.1093/ehjci/ehaa946.1675

High-sensitivity troponin I predicts major cardiovascular events after noncardiac surgery

2020· article· en· W3110300040 on OpenAlexafffundabout
Flávia K. Borges, Emmanuelle Duceppe, Diane Heels‐Ansdell, Sandra Ofori, Maura Marcucci, Peter A. Kavsak, Shirley Pettit, Jessica Spence, Emilie P. Belley‐Côté, Yannick LeManach, Michael McGillion, Richard Whitlock, André Lamy, P.J. Devereaux

Bibliographic record

VenueEuropean Heart Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsUniversité de MontréalMcMaster University
FundersCanadian Institutes of Health Research
KeywordsMedicineMyocardial infarctionHazard ratioTroponinHeart failureCardiologyInternal medicineProspective cohort studyTroponin TTroponin ICardiac surgeryIncidence (geometry)Confidence interval

Abstract

fetched live from OpenAlex

Abstract Background Myocardial injury after noncardiac surgery (MINS) is common and is associated with postoperative major cardiac events and 30-day mortality. We have previously established the diagnostic criteria for MINS with the 4th-generation cardiac troponin T assay (TnT) and 5th-generation high-sensitivity TnT assay (hsTnT) based on prognostically relevant thresholds. Little is known about diagnostic criteria for MINS using the high-sensitivity troponin I (hsTnI) assay. Purpose To determine hsTnI thresholds associated with 30-day major cardiac events and death after noncardiac surgery. Methods We performed a nested prospective biobank cohort study of 4545 patients from the VISION Study. Patients were aged ≥45 years and underwent in-patient noncardiac surgery under regional or general anesthesia. Patients had samples collected and frozen preoperatively, and on postoperative days 1, 2 and 3. We measured hsTnI on thawed preoperative and postoperative samples. We used iterative Cox proportional hazard models to determine peak postoperative hsTnI thresholds independently associated with major cardiac events (i.e., composite of death, non-fatal cardiac arrest, congestive heart failure within 30 days and non-fatal myocardial infarction from postoperative days 4–30). Results Major cardiac events occurred in 89/4545 (2.0%) patients. Peak hsTnI values of <75 ng/L, 75 ng/L to <1000 ng/L, and ≥1000 ng/L were associated with an incidence of major cardiac events of 1.2% (95% CI 0.9–1.6), 7.1% (95% CI 4.8–10.5) and 25.9% (95% CI 16.3–38.4), respectively. Compared to peak hsTnI <75 ng/L (reference), hsTnI values 75 ng/L to <1000 ng/L and ≥1000 ng/L were associated with adjusted hazard ratios (aHR) of 4.53 (95% CI 2.75–7.48) and 16.17 (95% CI 8.70–30.07), respectively. No change from preoperative hsTnI to peak postoperative hsTnI significantly improved the model when included on top of the identified thresholds. Incidence of major cardiac events was 31/343 (9%) in patients with postoperative peak hsTnI ≥75 ng/L versus 52/4178 (1%) in patients with postoperative peak hsTnI <75 ng/L (aHR 5.76; 95% CI 3.64–9.11). A postoperative peak hsTnI ≥75 ng/L was associated with increased risk of major cardiac events either in the presence (aHR 9.35; 95% CI 5.28–16.55) or absence (aHR 3.99; 95% CI 2.19–7.25) of clinical features of myocardial injury (e.g., chest pain, ischemic electrocardiography changes). Conclusion A hsTnI elevation within the first 3 days after noncardiac surgery independently predicts major cardiac events at 30 days. A peak postoperative hsTnI ≥75 ng/L was associated with a 6-fold increase in the risk of subsequent major cardiac events at 30 days as compared to peak postoperative hsTnI<75 ng/L. This hsTnI threshold can be used to diagnose MINS. Funding Acknowledgement Type of funding source: Private company. Main funding source(s): Siemens Healthcare Diagnostics Inc. Canadian Institutes of Health Research

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)0.001
metaresearch head score (Gemma)0.002
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.028
GPT teacher head0.241
Teacher spread0.213 · 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".

Quick stats

Citations2
Published2020
Admission routes3
Has abstractyes

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