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Record W2951429854 · doi:10.1148/radiol.2019182264

Prognostic Value and Risk Continuum of Noninvasive Fractional Flow Reserve Derived from Coronary CT Angiography

2019· article· en· W2951429854 on OpenAlexaff
Abdul Rahman Ihdayhid, Bjarne Linde Nørgaard, Sara Gaur, Jonathan Leipsic, Nitesh Nerlekar, Kazuhiro Osawa, Toru Miyoshi, Jesper Møller Jensen, Takeshi Kimura, Hiroki Shiomi, Andrejs Ērglis, Sanda Jēgere, Keith G. Oldroyd, Hans Erik Bøtker, Sujith Seneviratne, Stephan Achenbach, Brian Ko

Bibliographic record

VenueRadiology · 2019
Typearticle
Languageen
FieldMedicine
TopicCoronary Interventions and Diagnostics
Canadian institutionsUniversity of British Columbia
FundersMedical Research CouncilNational Health and Medical Research CouncilNational Heart Foundation of Australia
KeywordsMedicineFractional flow reserveCoronary angiographyCardiologyAngiographyValue (mathematics)RadiologyInternal medicineMyocardial infarctionStatistics

Abstract

fetched live from OpenAlex

Background Coronary CT angiography with noninvasive fractional flow reserve (FFR) predicts lesion-specific ischemia when compared with invasive FFR. The longer term prognostic value of CT-derived FFR (FFRCT) is unknown. Purpose To determine the prognostic value of FFRCT when compared with coronary CT angiography and describe the relationship of the numeric value of FFRCT with outcomes. Materials and Methods This prospective subanalysis of the NXT study (Clinicaltrials.gov: NCT01757678) evaluated participants suspected of having stable coronary artery disease who were referred for invasive angiography and who underwent FFR, coronary CT angiography, and FFRCT. The incidence of the composite primary end point of death, myocardial infarction, and any revascularization and the composite secondary end point of major adverse cardiac events (MACE: cardiac death, myocardial infarction, unplanned revascularization) were compared for an FFRCT of 0.8 or less versus stenosis of 50% or greater on coronary CT angiograms, with treating physicians blinded to the FFRCT result. Results Long-term outcomes were obtained in 206 individuals (age, 64 years ± 9.5), including 64% men. At median follow-up of 4.7 years, there were no cardiac deaths or myocardial infarctions in participants with normal FFRCT. The incidence of the primary end point was more frequent in participants with positive FFRCT compared with clinically significant stenosis at coronary CT angiography (73.4% [80 of 109] vs 48.7% [91 of 187], respectively; P < .001), with the majority of outcomes being planned revascularization. Corresponding hazard ratios (HRs) were 9.2 (95% confidence interval [CI]: 5.1, 17; P < .001) for FFRCT and 5.9 (95% CI: 1.5, 24; P = .01) for coronary CT angiography. FFRCT was a superior predictor compared with coronary CT angiography for primary end point (C-index FFRCT, 0.76 vs coronary CT angiography, 0.54; P < .001) and MACE (FFRCT, 0.71 vs coronary CT angiography, 0.52; P = .001). Frequency of MACE was higher in participants with positive FFRCT compared with coronary CT angiography (15.6% [17 of 109] vs 10.2% [19 of 187], respectively; P = .02), driven by unplanned revascularization. MACE HR was 5.5 (95% CI: 1.6, 19; P = .006) for FFRCT and 2.0 (95% CI: 0.3, 14; P = .46) for coronary CT angiography. Each 0.05-unit FFRCT reduction was independently associated with greater incidence of primary end point (HR, 1.7; 95% CI: 1.4, 1.9; P < .001) and MACE (HR, 1.4; 95% CI: 1.1, 1.8; P < .001). Conclusion In stable patients referred for invasive angiography, a CT-derived fractional flow reserve (FFRCT) value of 0.8 or less was a predictor of long-term outcomes driven by planned and unplanned revascularization and was superior to clinically significant stenosis on coronary CT angiograms. Additionally, the numeric value of FFRCT was an independent predictor of outcomes. Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Dennie and Rubens in this issue.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.003
Threshold uncertainty score0.837

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.010
GPT teacher head0.251
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 teacher head, 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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Citations137
Published2019
Admission routes1
Has abstractyes

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