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Record W4376610074 · doi:10.3389/fcvm.2023.1123487

Sutureless vs. rapid-deployment valve: a systemic review and meta-analysis for a direct comparison of intraoperative performance and clinical outcomes

2023· review· en· W4376610074 on OpenAlexaboutno aff
Chenhao Wang, Yi Xie, Hongwei Zhang, Peng Yang, Yu Zhang, Lu Chen, Yu Liu, Haiyue Wang, Zhenyuan Xu, Jia Hu

Bibliographic record

VenueFrontiers in Cardiovascular Medicine · 2023
Typereview
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsnot available
FundersWest China Hospital, Sichuan UniversitySichuan UniversityNational Natural Science Foundation of China
KeywordsMedicineAortic valve replacementCardiopulmonary bypassAortic valveCardiac skeletonMeta-analysisInternal medicineCardiologyRandomized controlled trialIntensive care unitStroke (engine)Surgery

Abstract

fetched live from OpenAlex

Background: Sutureless and rapid-deployment valves are bioprostheses anchoring within the aortic annulus with few sutures, and they act as a hybrid of conventional surgical and transcatheter valves under aortic valve replacement. Considering that the 3F Enable valve is now off-market, the only two sutureless and rapid-deployment valves available on the world marketplace are the Perceval and Intuity valves. However, a direct comparison of the function of these two valves eludes researchers. Purpose: Against this background, we performed this systematic review and meta-analysis comparing the intraoperative performance and early clinical outcomes between the Perceval valve and the Intuity valve under sutureless and rapid-deployment aortic valve replacement. Methods: We systematically searched electronic databases through PubMed/MEDLINE, OvidWeb, Web of Science, and Cochrane Central Register of Controlled Trials (from the establishment of the database to November 17, 2022, without language restriction) for studies comparing the sutureless valve (the Perceval) and the rapid-deployment valve (the Intuity) under aortic valve replacement. Our primary outcomes were early mortality and postoperative transvalvular pressure gradients. The secondary outcomes were defined to include aortic cross-clamp and cardiopulmonary bypass time, paravalvular leak (any paravalvular leak, moderate-to-severe paravalvular leak) after aortic valve replacement, need for pacemaker implantation, postoperative neurological events (stroke), and intensive care unit stay. Results: This meta-analysis included ten non-randomized trials with 3,526 patients enrolled (sutureless group = 1,772 and rapid-deployment group = 1,754). Quality assessments were performed, with the mean scores of the studies reading 6.90 (SD = 0.99) out of 9 according to the Newcastle-Ottawa Scale. Compared with rapid-deployment aortic valve replacement, sutureless aortic valve replacement was associated with higher mean and peak transvalvular pressure gradients postoperatively. In contrast, aortic cross-clamp and cardiopulmonary time were needed less in sutureless aortic valve replacement vs. rapid-deployment aortic valve replacement. There was no evidence of significant publication bias observed by the funnel plot and Egger's test. Conclusions: For postoperative hemodynamics, sutureless aortic valve replacement was associated with increased mean and peak transvalvular pressure gradients compared with rapid-deployment aortic valve replacement. In sharp contrast, sutureless aortic valve replacement significantly reduced the amount of time needed for fixing the aortic cross-clamp and the cardiopulmonary bypass procedure. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42022343884.

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)0.011
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.017
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.026
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0170.042
Bibliometrics0.0060.007
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.119
GPT teacher head0.446
Teacher spread0.328 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations4
Published2023
Admission routes1
Has abstractyes

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