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Record W4411218971 · doi:10.1093/ehjopen/oeaf057

Virtual physiological analysis of non-culprit disease in patients with STEMI and multivessel disease: a substudy of the COMPLETE trial

2025· article· en· W4411218971 on OpenAlex
Gareth Williams, Daniel J. Taylor, Abdulaziz Al Baraikan, Hazel Haley, Mina Ghobrial, Matthew Knight, Kenneth Anigboro, Vignesh Rammohan, Rebecca Gosling, Tom Newman, Mark T Mills, Rod Hose, David A. Wood, John A. Cairns, Chinthanie Ramasundarahettige, Rutaba Khatun, Helen Nguyen, Shamir R Mehta, Robert F. Storey, Julian Gunn, Paul Morris

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEuropean Heart Journal Open · 2025
Typearticle
Languageen
FieldMedicine
TopicCoronary Interventions and Diagnostics
Canadian institutionsHamilton Health SciencesMcMaster UniversityPopulation Health Research InstituteUniversity of British Columbia
FundersNIHR Sheffield Biomedical Research CentreNational Institute for Health and Care ResearchDepartment of Health and Social CareBritish Heart FoundationWellcome Trust
KeywordsCulpritMedicineDiseaseInternal medicineCardiologyMyocardial infarction

Abstract

fetched live from OpenAlex

Abstract Aims In the complete revascularization with multivessel PCI for myocardial infarction (COMPLETE) trial, staged complete revascularization in patients with ST-segment-elevation myocardial infarction (MI) reduced major adverse cardiovascular events compared with culprit-only revascularization. Inclusion was based on angiographic criteria. Objectives We modelled non-culprit virtual fractional flow reserve (vFFR) and investigated interactions between physiological lesion severity and the benefits of complete revascularization in COMPLETE. Methods and results All suitable angiograms from COMPLETE underwent software-based 3-dimensional (3D) arterial reconstruction and analysis of 3D-quantitative coronary angiography (QCA) and vFFR using computational fluid dynamics software. Physiological lesion significance was defined as vFFR ≤0.80 and was compared with operators’ visual angiographic analysis, 2D-QCA and 3D-QCA. vFFR was computed in 635 patients (710 lesions). 302 patients (48%) had ≥1 physiologically significant lesion and 333 (52%) had none. 321 (45%) lesions were physiologically significant and 389 (55%) were not. There was no statistically significant interaction between physiological lesion significance and any of the trial co-primary or key secondary clinical outcomes, or an exploratory outcome of ischaemia-driven revascularization without preceding MI (all interaction P > 0.30). 3D-QCA predicted vFFR significance more accurately than visual and 2D-QCA (concordance 73% vs. 49% vs. 59%, respectively). Conclusion In this virtual physiological substudy of the COMPLETE trial, 52% of patients lacked any physiologically significant lesions and the benefits of complete revascularization appeared to be independent of physiological lesion significance. 3D-QCA was a better predictor of physiological significance than either 2D-QCA or operator visual analysis. Further research is warranted to compare angiography-guided and physiology-guided complete revascularization strategies.

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.

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 score0.002
Threshold uncertainty score0.199

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)0.0000.000

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 head0.046
GPT teacher head0.339
Teacher spread0.293 · 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