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Record W2163503047 · doi:10.1503/cmaj.141349

Prospective validation of a 1-hour algorithm to rule-out and rule-in acute myocardial infarction using a high-sensitivity cardiac troponin T assay

2015· article· en· W2163503047 on OpenAlexvenueno aff
Tobias Reichlin, Raphael Twerenbold, Karin Wildi, María Rubini Giménez, Nathalie Bergsma, Philip Haaf, Sophie Druey, Christian Puelacher, Berit Moehring, Michael Freese, Claudia Stelzig, Lian Krivoshei, Petra Hillinger, Thomas Herrmann, Philip Kreutzinger, Milos Radosavac, Zoraida Moreno Weidmann, Kateryna Pershyna, Ursina Honegger, Max Wagener, Thierry Vuillomenet, Isabel Campodarve, Roland Bingisser, Òscar Miró, Katharina Rentsch, Stefano Bassetti, Stefan Osswald, Christian Mueller

Bibliographic record

VenueCanadian Medical Association Journal · 2015
Typearticle
Languageen
FieldMedicine
TopicAcute Myocardial Infarction Research
Canadian institutionsnot available
FundersAlereSchweizerische HerzstiftungUniversitätsspital BaselAstraZenecaSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsMedicineMyocardial infarctionAlgorithmInternal medicineCardiologyTroponinClinical prediction ruleConfidence intervalEmergency departmentAcute coronary syndromePredictive value of testsProspective cohort studyPositive predicative valueMyocardial infarction diagnosisPredictive valueMathematics

Abstract

fetched live from OpenAlex

BACKGROUND: We aimed to prospectively validate a novel 1-hour algorithm using high-sensitivity cardiac troponin T measurement for early rule-out and rule-in of acute myocardial infarction (MI). METHODS: In a multicentre study, we enrolled 1320 patients presenting to the emergency department with suspected acute MI. The high-sensitivity cardiac troponin T 1-hour algorithm, incorporating baseline values as well as absolute changes within the first hour, was validated against the final diagnosis. The final diagnosis was then adjudicated by 2 independent cardiologists using all available information, including coronary angiography, echocardiography, follow-up data and serial measurements of high-sensitivity cardiac troponin T levels. RESULTS: Acute MI was the final diagnosis in 17.3% of patients. With application of the high-sensitivity cardiac troponin T 1-hour algorithm, 786 (59.5%) patients were classified as "rule-out," 216 (16.4%) were classified as "rule-in" and 318 (24.1%) were classified to the "observational zone." The sensitivity and the negative predictive value for acute MI in the rule-out zone were 99.6% (95% confidence interval [CI] 97.6%-99.9%) and 99.9% (95% CI 99.3%-100%), respectively. The specificity and the positive predictive value for acute MI in the rule-in zone were 95.7% (95% CI 94.3%-96.8%) and 78.2% (95% CI 72.1%-83.6%), respectively. The 1-hour algorithm provided higher negative and positive predictive values than the standard interpretation of highsensitivity cardiac troponin T using a single cut-off level (both p < 0.05). Cumulative 30-day mortality was 0.0%, 1.6% and 1.9% in patients classified in the rule-out, observational and rule-in groups, respectively (p = 0.001). INTERPRETATION: This rapid strategy incorporating high-sensitivity cardiac troponin T baseline values and absolute changes within the first hour substantially accelerated the management of suspected acute MI by allowing safe rule-out as well as accurate rule-in of acute MI in 3 out of 4 patients. TRIAL REGISTRATION: ClinicalTrials.gov, NCT00470587.

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)0.013
metaresearch head score (Gemma)0.029
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 score0.013
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.029
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.017
GPT teacher head0.299
Teacher spread0.283 · 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

Citations227
Published2015
Admission routes1
Has abstractyes

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