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Record W2981827319 · doi:10.1093/eurheartj/ehz746.0916

P6319CT derived fractional flow reserve (FFRct) for functional coronary artery evaluation in the follow-up of patients after heart transplantation

2019· article· en· W2981827319 on OpenAlexaff
Ricardo P.J. Budde, Fay M. A. Nous, Alina A. Constantinescu, Koen Nieman, Lynne Koweek, Jonathon Leipsic, Olivier C. Manintveld

Bibliographic record

VenueEuropean Heart Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicTransplantation: Methods and Outcomes
Canadian institutionsProvidence Health Care Research Institute
Fundersnot available
KeywordsMedicineFractional flow reserveStenosisHeart transplantationCardiologyCoronary artery diseaseMaceInternal medicineTransplantationRadiologyCoronary angiographyPercutaneous coronary interventionMyocardial infarction

Abstract

fetched live from OpenAlex

Abstract Background Cardiac allograft vasculopathy (CAV) remains a leading cause of morbidity and mortality after heart transplantation. Annual screening is recommended to improve risk stratification and early treatment of CAV and is often performed with invasive coronary angiography (ICA). Coronary computed tomography angiography (CCTA) with CCTA-derived fractional flow reserve (FFRct) might be a non-invasive alternative to ICA for the surveillance of CAV providing both anatomical and functional information. Purpose To describe our initial results with CCTA and FFRct for detection of CAV in a cohort of heart transplant patients. Methods Heart transplant patients who underwent CCTA with FFRct as part of routine annual assessment for CAV were enrolled in a prospective registry from February 2018 to February 2019 in a single center. The most recently known CAV score (0–3) based on invasive angio and single photon emission computed tomography (SPECT) before CCTA was recorded. CCTA image quality was scored as non-diagnostic, moderate, good or excellent. FFRct analysis was performed off-site by a commercial company. For each coronary stenosis >30%, an FFRCTvalue distal to the stenosis was measured. For the RCA, LAD and CX without a stenosis, the FFRct value in the most distal location in the vessel was recorded. CAV classification was rescored based on CCTA. Demographics, additional diagnostic tests, and treatment plans were evaluated including major adverse events (MACE) during 90-day follow-up. Results 65 patients (56 (39–65) years (median/ 25th–75thpercentile), 40% women) that were 11 (7–16) years after transplantation were included. The most recent CAV score was 0 in 52 patients (80%) and 1 or 2 in 13 patients. CCTA image quality was good or excellent in 59 (91%) patients. CCTA reclassified CAV scores in 32 (49%) patients to 33 patients with CAV 0, 18 patients with CAV 1, 9 patients with CAV 2 and 5 patients with CAV 3. In 17 patients (26%) at least one stenosis with FFRct ≤0.80 was detected including 11 patients with single vessel disease, 5 with two-vessel disease and one with three-vessel disease. In the 48 patients without a focal stenosis, mean distal FFRct values were 0.88 (0.86–0.91), 0.87 (0.85–0.90) and 0.90 (0.86–0.91) at less than 10, 10–15 or more than 15 years after transplantation, respectively (p=0.457). Additional tests were performed in 10 (15%) patients (1 SPECT and 10 invasive coronary angiographies), which resulted in revascularization by PCI in 6 (9%) patients. No MACE occurred during 90-day follow-up. Conclusion CCTA with FFRct can be successfully performed in heart transplant patients, detects patients with significant coronary stenosis and CCTA leads to substantial reclassification of CAV grades. Acknowledgement/Funding FFRct analysis was performed as part of the ADVANCE registry which is supported by Heartflow Inc.

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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.072
GPT teacher head0.336
Teacher spread0.263 · 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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