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Effects of distal radial access vs conventional radial access in patients undergoing coronary angiography and/or intervention: a meta-analysis of randomised trials

2022· article· en· W4306319932 on OpenAlexaff
Francesco Condello, Saahithi Rao, Matteo Maurina, Matteo Sturla, Sanjit S. Jolly, Samir Pancholy, O Bertrand, T. Lefèvre, Gianluigi Condorelli, Giulio Stefanini, Bernhard Reimers, Marco Valgimigli, Giuseppe Ferrante

Bibliographic record

VenueEuropean Heart Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicVascular Procedures and Complications
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicinePercutaneous coronary interventionRandomized controlled trialRadial arteryAcute coronary syndromeInternal medicineMeta-analysisPopulationAngiographyCardiologyClinical endpointCoronary angiographyMyocardial infarctionArtery

Abstract

fetched live from OpenAlex

Abstract Background Recently the distal radial access (DRA) technique has been introduced for coronary angiography and intervention as an alternative to the conventional radial access (RA). Purpose The aim of this study was to provide a quantitative appraisal of the effects of DRA vs conventional RA for coronary angiography with or without intervention. Methods The PubMed, Embase, Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov databases were searched for randomised clinical trials (RCT) comparing DRA versus RA for coronary angiography and/or intervention. Data were pooled by meta-analysis using a random-effects model. The number of patients needed to treat for an additional beneficial outcome (NNTB) and the number needed to treat for an additional harmful outcome (NNTH) were calculated. The primary endpoint was radial artery occlusion (RAO) at the longest available follow-up. Additional secondary outcomes were assessed. Results A total of 13 RCT including 4,901 patients were identified and included in the final analysis. Coronary angiography alone was performed in 3 studies, percutaneous coronary intervention was performed in a variable proportion of patients ranging from 24% to 100% across studies. Study population included patients with chronic coronary syndrome in the large majority, and the proportion of acute coronary syndrome ranged from to 31% to 100% across studies. Compared with RA, DRA was associated with a significant lower risk of RAO, either detected at the longest follow-up (risk ratio [RR]: 0.36; 95% CI: 0.23 to 0.58; p<0.001, NNTB = 24.5), or in-hospital (RR: 0.32; 95% CI: 0.18 to 0.57; p<0.001, NNTB = 21.5), as well as Early discharge after transradial stenting of coronary arteries (EASY) Scale ≥ II hematoma (RR: 0.46; 95% CI: 0.22 to 0.97; p=0.04, NNTB = 66). By contrast, DRA was associated with a higher risk of access site crossover (RR: 3.43; 95% CI: 1.88 to 6.25, p<0.001, NNTH = 10), a longer time for radial artery puncture (standardized mean difference [SMD]: 3.56; 95% CI: 0.96 to 6.16; p<0.001), and sheath insertion (SMD: 0.38; 95% CI: 0.11 to 0.65, p=0.006), and a higher number of puncture attempts (SMD: 0.59, 95% CI: 0.48 to 0.69, p<0.001). A meta-regression analysis showed that increasing age in the RA group was associated with a reduced effect of DRA, compared with RA, on the risk of RAO. No significant effect of other variables such as the prevalence of female sex, diabetes, smoking status, acute coronary syndrome, and percutaneous coronary intervention was found. Conclusions Compared with RA, DRA is associated with lower risks of RAO and EASY ≥ II hematoma, but requires longer time for radial artery puncture and sheath insertion, more puncture attempts and a higher access site crossover. Funding Acknowledgement Type of funding sources: None.

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)0.026
metaresearch head score (Gemma)0.045
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: Empirical · Consensus signal: none
Teacher disagreement score0.026
Threshold uncertainty score0.138

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.045
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0260.059
Bibliometrics0.0050.005
Science and technology studies0.0010.001
Scholarly communication0.0050.002
Open science0.0020.002
Research integrity0.0030.003
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 head0.098
GPT teacher head0.365
Teacher spread0.266 · 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
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
Published2022
Admission routes1
Has abstractyes

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