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Record W4200278360 · doi:10.1093/eurheartj/suab139.033

630 Impact of right ventricular dysfunction after mitraclip treatment as a bridge to heart transplantation: insight from the mitrabridge strategy

2021· article· en· W4200278360 on OpenAlexaff
Andrea Munafò, Andrea Scotti, Rodrigo Estévez‐Loureiro, Dabit Arzamendi, Neil Fam, Diego Maffeo, Marianna Adamo, Jf Ooms, Luciano Potena, Anna Sonia Petronio, Carmelo Grasso, Fabien Praz, Claudia Raineri, Gabriele Crimi, Francesco Saia, Cosmo Godino

Bibliographic record

VenueEuropean Heart Journal Supplements · 2021
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsMedicineMitraClipCardiologyInternal medicineHeart transplantationHeart failureMitral regurgitationPercutaneous coronary interventionTransplantationPulmonary arteryVentricular assist deviceDestination therapySurgeryMyocardial infarction

Abstract

fetched live from OpenAlex

Abstract Aims MitraClip treatment has been recently proposed as a ‘bridge strategy’ solution for advanced heart failure (HF) patients with significant functional mitral regurgitation (MR), who are potential candidates or are waiting for cardiac replacement therapy (LVAD or heart transplantation, HTx). In this clinical scenario, left-ventricular-related right ventricular dysfunction (RVD) represents an important prognostic factor. Our study aimed to investigate the possible prognostic implication of RVD in advanced HF patients treated with MitraClip as a bridge to HTx strategy. Methods and results RVD was assessed using the relationship between tricuspid annular peak systolic excursion (TAPSE) and pulmonary artery systolic pressure (PASP). All patients from the MitraBridge registry for whom these two echocardiographic parameters were available, were included in the study. A cut-off value of TAPSE/PASP ratio < 0.36 was used to defined RVD, as previously reported. The primary outcome was a composite Endpoint of all-cause death or rehospitalization for HF at 2-year. For patients who underwent LVAD implantation or HTx, follow-up data were censored at the time of those events. A total of 80 patients were included in the study. The median TAPSE/PASP ratio was 0.35 (25th–75th: 0.27–0.46), with 43 (54%) patients having a TAPSE/PASP ratio < 0.36 (RVD group). The latter had a prevalent MR ischaemic etiology (49% vs. 38%), with a more frequent history of percutaneous coronary intervention (46.5% vs. 22%, P = 0.02). Except for TAPSE (15.7 ± 3.6 mm vs. 19.2 ± 3.7 mm, P = 0.001) and PASP (61 ± 14 mmHg vs. 39.5 ± 9.5 mmHg, P < 0.001), the other echocardiographic characteristics were similar between the two study groups (overall mean left ventricular ejection fraction 26.9 ± 8%, median left ventricular end-diastolic volume index 120.7, 25th–75th: 102.2–146.5 ml/m2). After a median follow-up time of 508 (25th–75th: 160–899) days, elective HTx occurred in 12 patients (7 from the RVD group), while LVAD implantation was performed in 13 patients (7 from the RVD group). The primary outcome occurred in 30 patients (38%) with a 2-year Kaplan–Meier estimate of freedom from the composite endpoint of 41%. At univariate (HR: 1.3; 95% CI: 0.6–2.8, P = 0.451) and multivariate (HR: 1.6; CI: 0.7–3.8, P = 0.249) Cox-regression analysis, TAPSE/PASP ratio < 0.36 was not identified as an independent predictor of primary outcome. Indeed, at follow-up echocardiographic control (median time 252, 25th–75th: 122–365 days), a significant improvement in TAPSE/PASP ratio was observed in the RVD group (baseline median TAPSE/PASP ratio 0.27, 25th–75th: 0.22–0.32 vs. follow-up median TAPSE/PASP ratio 0.37, 25th–75th: 0.28–0.47, P < 0.001). Conclusions In advanced HF patients with functional MR, MitraClip treatment could prevent or ameliorate left-ventricular-related RVD, allowing safe access to HTx or LVAD.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.002
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)0.0040.000

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 head0.029
GPT teacher head0.355
Teacher spread0.327 · 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.

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

Citations0
Published2021
Admission routes1
Has abstractyes

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