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Fractional Flow Reserve and Instantaneous Wave-Free Ratio as Predictors of the Placebo-Controlled Response to Percutaneous Coronary Intervention in Stable Single-Vessel Coronary Artery Disease

2018· article· en· W2804642822 on OpenAlexaboutno aff
Rasha Al‐Lamee, James P. Howard, Matthew Shun‐Shin, David Thompson, Hakim‐Moulay Dehbi, Sayan Sen, Sukhjinder Nijjer, Ricardo Petraco, John R. Davies, Thomas Keeble, Kare Tang, Iqbal Malik, Christopher Cook, Yousif Ahmad, Andrew S.P. Sharp, Robert Gerber, Christopher Baker, Raffi Kaprielian, Suneel Talwar, Ravi Assomull, Graham Cole, Niall Keenan, Gajen Kanaganayagam, Joban Sehmi, Roland Wensel, Frank E. Harrell, Jamil Mayet, Simon Thom, Justin E. Davies, Dárrel P. Francis

Bibliographic record

VenueCirculation · 2018
Typearticle
Languageen
FieldMedicine
TopicCoronary Interventions and Diagnostics
Canadian institutionsnot available
FundersNational Institute for Health and Care Research
KeywordsMedicineFractional flow reserveConventional PCICardiologyPercutaneous coronary interventionCoronary artery diseaseInternal medicinePlaceboAnginaAngioplastyCanadian Cardiovascular SocietySurgeryMyocardial infarctionPathology

Abstract

fetched live from OpenAlex

Background: There are no data on how fractional flow reserve (FFR) and instantaneous wave-free ratio (iFR) are associated with the placebo-controlled efficacy of percutaneous coronary intervention (PCI) in stable single-vessel coronary artery disease. Methods: We report the association between prerandomization invasive physiology within ORBITA (Objective Randomised Blinded Investigation With Optimal Medical Therapy of Angioplasty in Stable Angina), a placebo-controlled trial of patients who have stable angina with angiographically severe single-vessel coronary disease clinically eligible for PCI. Patients underwent prerandomization research FFR and iFR assessment. The operator was blinded to these values. Assessment of response variables, treadmill exercise time, stress echocardiography score, symptom frequency, and angina severity were performed at prerandomization and blinded follow-up. Effects were calculated by analysis of covariance. The ability of FFR and iFR to predict placebo-controlled changes in response variables was tested by using regression modeling. Results: Invasive physiology data were available in 196 patients (103 PCI and 93 placebo). At prerandomization, the majority had Canadian Cardiovascular Society class II or III symptoms (150/196, 76.5%). Mean FFR and iFR were 0.69±0.16 and 0.76±0.22, respectively; 97% had ≥1 positive ischemia tests. The estimated effect of PCI on between-arm prerandomization-adjusted total exercise time was 20.7 s (95% confidence interval [CI], –4.0 to 45.5; P =0.100) with no interaction of FFR ( P interaction =0.318) or iFR ( P interaction =0.523). PCI improved stress echocardiography score more than placebo (1.07 segment units; 95% CI, 0.70–1.44; P <0.00001). The placebo-controlled effect of PCI on stress echocardiography score increased progressively with decreasing FFR ( P interaction <0.00001) and decreasing iFR ( P interaction <0.00001). PCI did not improve angina frequency score significantly more than placebo (odds ratio, 1.64; 95% CI, 0.96–2.80; P =0.072) with no detectable evidence of interaction with FFR ( P interaction =0.849) or iFR ( P interaction =0.783). However, PCI resulted in more patient-reported freedom from angina than placebo (49.5% versus 31.5%; odds ratio, 2.47; 95% CI, 1.30–4.72; P =0.006) but neither FFR ( P interaction =0.693) nor iFR ( P interaction =0.761) modified this effect. Conclusions: In patients with stable angina and severe single-vessel disease, the blinded effect of PCI was more clearly seen by stress echocardiography score and freedom from angina than change in treadmill exercise time. Moreover, the lower the FFR or iFR, the greater the magnitude of stress echocardiographic improvement caused by PCI. Clinical Trial Registration: URL: https://www.clinicaltrials.gov . Unique identifier: NCT02062593.

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)0.002
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 score0.274
Threshold uncertainty score0.619

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0000.000

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 head0.259
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".

Quick stats

Citations112
Published2018
Admission routes1
Has abstractyes

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