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False positive results of treadmill exercise ECG - we should minimize the unnecessary referrals to invasive coronary angiography

2013· article· en· W2315678134 on OpenAlexaboutno aff
Túlio Sérvio José da Silva, Rúben Ramos, Pedro Rio, Carlos Barbosa, P. Pinho, J. Labandeiro, Marta Afonso Nogueira, G Portugal, Rui Cruz Ferreira

Bibliographic record

VenueEuropean Heart Journal · 2013
Typearticle
Languageen
FieldMedicine
TopicECG Monitoring and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCardiologyInternal medicineCoronary artery diseaseEjection fractionFramingham Risk ScoreAnginaLogistic regressionCanadian Cardiovascular SocietyPre- and post-test probabilityMyocardial infarctionDiseaseHeart failure

Abstract

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Purpose: False positive (FP) results are common in treadmill exercise ECG testing (TMT) performed for obstructive coronary artery disease (OCAD) screening. It is important to discriminate whether an ischemic response in TMT is a FP result, to avoid unnecessary referrals for invasive coronary angiography (ICA). We determined the clinical factors that were independently associated with a TMT FP result. Methods: We analyzed a cohort of patients referred for ICA for stable coronary artery disease diagnosis, based on clinical judgment and a TMT positive for ischemia, in a single tertiary-care center (2006-2011). Traditional and nontraditional coronary artery disease risk factors, modified Framingham risk score, symptoms, pre-test (TMT) probability of OCAD, left ventricular ejection fraction and ICA results were assessed. OCAD was defined as any luminal narrowing ≥70%, or ≥50% for the left main artery. A FP TMT result was defined as an ischemic response (2006 TMT European Society of Cardiology guidelines) and no OCAD. The predictors of a FP result were determined by the chi-square, exact Fisher and t-student tests when appropriate, and multivariate analysis (logistic regression). The discriminatory power for a FP result, of a model based on those predictive factors, was assessed by the area under the ROC curve (AUC) analysis. Results: 1243 patients were included: 65.2±9.9 years, 63.0% male, mean 10-year Framingham risk 17.8%; 51.1% typical angina and 20.1% atypical angina; 65.4% high pre-test probability of OCAD; 4.3% depressed left ventricular ejection fraction (<55%). Globally, 51.6% of TMT were FP results. The factors independently associated with a FP TMT were: absence of severe angina (OR 22.0, 95% CI 5.3-91.2), presence of atypical angina (OR 17.3, 95% CI 9.4-31.6), absence of angina (OR 3.8, 95% CI 2.9-5.2), female gender (OR 2.4, 95% CI 1.8-3.3), non-smoking (OR 2.1, 95% CI 1.4-3.0), absence of diabetes (OR 1.5, 95% CI 1.1-2.0) and younger age (OR 1.1, 95% CI 1.0-1.1), (all p<0.05). A model considering these factors together had good discriminatory power for predicting a FP result: AUC 0.80, 95% CI 0.78-0.83. Conclusions: Half of positive TMT were FP in a population of patients referred for ICA following clinical judgment and a TMT positive for ischemia. When analyzed together, the absence of angina/severe angina, the presence of atypical angina, female gender, non-smoking, absence of diabetes and younger age have good power for discriminating a FP result. These parameters should be given more relevance in order to avoid some unnecessary referrals for ICA.

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)0.002
metaresearch head score (Gemma)0.012
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: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.077
GPT teacher head0.318
Teacher spread0.241 · 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
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".

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Citations2
Published2013
Admission routes1
Has abstractyes

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