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Record W2982271158 · doi:10.1093/eurheartj/ehz746.0103

5962Incremental prognostic value of hybrid PET-CT assessed myocardial blood flow, coronary stenosis severity and adverse plaque characteristics

2019· article· en· W2982271158 on OpenAlexaff
Michiel J. Bom, Roel S. Driessen, Pepijn A. van Diemen, Henk Everaars, S P Schumacher, Albert C. van Rossum, Pieter G. Raijmakers, Adriaan A. Lammertsma, Juhani Knuuti, Amir Ahmadi, James K. Min, Jonathon Leipsic, J. Narula, Ibrahim Danad, Paul Knaapen

Bibliographic record

VenueEuropean Heart Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineMaceCoronary artery diseaseStenosisCardiologyMyocardial perfusion imagingRevascularizationInternal medicineRadiologyMyocardial infarctionFractional flow reservePerfusion scanningPositron emission tomographyPerfusionVulnerable plaqueHazard ratioCoronary angiographyPercutaneous coronary interventionConfidence interval

Abstract

fetched live from OpenAlex

Abstract Background Management of patients with suspected coronary artery disease (CAD) can be optimized with prognostic information derived from non-invasive imaging such as positron emission tomography (PET) perfusion imaging and coronary computed tomography angiography (CCTA). The aim of the present study was to determine the incremental prognostic value of combined functional testing using PET perfusion imaging and anatomical testing using CCTA-derived stenosis severity and morphological assessment of CCTA-derived plaque morphology. Methods In this retrospective study, 539 patients referred for hybrid [15O]H2O PET – CCTA imaging because of suspected CAD were investigated. PET perfusion imaging was used to determine hyperemic myocardial blood flow (MBF), whereas CCTA images were evaluated for obstructive stenosis and high-risk plaque morphology. Major adverse coronary events (MACE) included all-cause death, non-fatal myocardial infarction (MI), urgent revascularization and late non-urgent revascularization (i.e. not guided by initial diagnostic work-up with non-invasive imaging). Kaplan Meier analysis and Cox proportional hazard regression were used to evaluate the independent prognostic value of PET-derived MBF, CCTA-derived stenosis and CCTA-derived high-risk plaque. Results During a mean follow-up of 6.8 [4.8–7.9] years, 79 (14.7%) patients experienced MACE, including 23 (4.3%) deaths, 19 (3.5%) MIs, 8 (1.5%) urgent revascularizations and 29 (5.4%) late non-urgent revascularizations. Annualized event rates for normal vs. abnormal results of PET perfusion imaging, CCTA-derived stenosis and high-risk plaque morphology were 1.2% vs 4.1%, 0.6% vs 4.4%, and 1.7% vs 5.6%, respectively (p<0.001 for all). The combined use of these three imaging parameters resulted in excellent long-term risk prediction, with a MACE-free survival of 97% in patients with no positive imaging findings. In contrast, MACE-free survival was only 69% in patients in whom all imaging findings were positive (figure 1). Multivariate Cox proportional hazard regression demonstrated incremental prognostic value of PET perfusion imaging, CCTA-derived stenosis and CCTA-derived high-risk plaques for the occurrence of MACE (p<0.05 for all) MACE-free survival stratified by PET-CT Conclusion PET-derived myocardial blood flow and CCTA-derived stenosis severity and high-risk plaque morphology are independent long-term predictors of adverse cardiac events and provide incremental prognostic value. Combined functional, anatomical and morphological assessment may allow for improved risk stratification in patients with suspected CAD.

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 score0.002
Threshold uncertainty score0.008

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.252
Teacher spread0.238 · 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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