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Record W3107898630 · doi:10.1093/ehjci/ehaa946.1485

One year clinical outcomes of contemporary PCI in patients with chronic coronary syndrome: experience from large scale e-ULTIMASTER registry

2020· article· en· W3107898630 on OpenAlexaboutno aff
Jawed Polad

Bibliographic record

VenueEuropean Heart Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicCoronary Interventions and Diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMyocardial infarctionConventional PCIInternal medicineTarget lesionAnginaRevascularizationCardiologyCoronary artery diseaseClinical endpointDiabetes mellitusPercutaneous coronary interventionCanadian Cardiovascular SocietyUnstable anginaSurgeryClinical trial

Abstract

fetched live from OpenAlex

Abstract Background The 2019 ESC guideline on chronic coronary syndromes (CCS) provided insights on the management of patients with stable angina. Purpose To explore one-year outcomes of CCS patients undergoing percutaneous revascularisation representing daily clinical practice. Methods We investigated CCS patients enrolled in e-Ultimaster registry (NCT02188355), which is a prospective, multicentre, worldwide, all-comers registry enrolled >36, 000 patients treated with a thin strut (80μm) bioresorbable polymer sirolimus-eluting stents (Ultimaster). The primary endpoint was target lesion failure (TLF) at 1 year (defined as a composite of cardiac death, target-vessel related myocardial infarction (TV-MI) and clinically-driven target lesion revascularization (CD-TLR). An independent Clinical Event Committee adjudicated all end-point related events. Results Our analysis included in total 15540 patients presented with stable angina (n=12300, 79.2%) or silent ischemia (n=3240, 20.9%) at enrolment. The mean age of CCS patients was 65.7±10.5 years and 76.1% were male. Regarding comorbidities, 69.9% of CCS patient were hypertensive, 30.9% had diabetes, 62.2% had high cholesterol, and 7.5% had renal impairment. The percentage of current smokers was 15.7%. Among CCS patients, 28.1% reported previous MI while 35.2% had history of PCI and 7.4% of CABG. Among CCS patients, 42.4% were diagnosed with multivessel disease. The average number of lesions identified per patient was 1.8±1.0, and the number of treated lesions was 1.5±0.8. The left main artery was treated in 4.1% of CCS patients, 8.1% for chronic total occlusion, 6.1% for instent restenosis, while 14% of the patients were treated in one or more bifurcation lesions. Approximately 80% of the procedures were preformed via radial access. The rate of TLF at one year was 2.9%, with 0.95% of cardiac death, 0.8% of TV-MI and 1.5% of CD-TLR. Definite or probable stent thrombosis (ST) rate was 0.42%, and any bleeding occurred in 1.8% of the patients. 71.3% of the patients were already on dual antiplatelet therapy (DAPT) before procedure, 93.3% were on DAPT at 3 months follow up, while DAPT was continued in 64.9% of patients at 1 year. At 3 months after index PCI, 91.4% of the treated CCS patients were angina free, 7.6% reported stable angina, and 0.5% unstable angina. The similar result was observed at 1 year, with 90.9% of the patients remaining angina free, 7.5% of patients with stable angina, while only 0.6% reported unstable angina. In a stepwise regression model, we identified risk factors of TLF in CCS patients including age, body mass index, diabetes, renal failure, lesion complexity such as target vessel LM or bifurcation, and number of stents implanted. Conclusions In this large, international all-comers registry, more than 90% of CCS patients treated with PCI remain angina free at one year, with low rate of TLF and ST, adding further evidence to ongoing debate about CCS treatment strategy. Funding Acknowledgement Type of funding source: Private grant(s) and/or Sponsorship. Main funding source(s): Limted study funding by Terumo

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)0.002
metaresearch head score (Gemma)0.006
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 score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.074
GPT teacher head0.332
Teacher spread0.258 · 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
Published2020
Admission routes1
Has abstractyes

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