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Impact of Bleeding and Myocardial Infarction on Mortality in All-Comer Patients Undergoing Percutaneous Coronary Intervention

2020· article· en· W3081423218 on OpenAlexaff
Hironori Hara, Kuniaki Takahashi, Norihiro Kogame, Mariusz Tomaniak, Laura S.M. Kerkmeijer, Masafumi Ono, Hideyuki Kawashima, Rutao Wang, Chao Gao, Joanna J. Wykrzykowska, Robbert J. de Winter, Franz‐Josef Neumann, Sylvain Planté, Pedro A. Lemos, Scot Garg, Peter Jüni, Pascal Vranckx, Stephan Windecker, Marco Valgimigli, Christian W. Hamm, Philippe Gabríel Steg, Yoshinobu Onuma, Patrick W. Serruys

Bibliographic record

VenueCirculation Cardiovascular Interventions · 2020
Typearticle
Languageen
FieldMedicine
TopicAntiplatelet Therapy and Cardiovascular Diseases
Canadian institutionsSt. Michael's HospitalSouthlake Regional Health Center
Fundersnot available
KeywordsMedicinePercutaneous coronary interventionMyocardial infarctionInternal medicineHazard ratioCardiologyAdverse effectMortality rateStentSurgeryConfidence interval

Abstract

fetched live from OpenAlex

Background: Bleeding and myocardial infarction (MI) after percutaneous coronary intervention are independent risk factors for mortality. This study aimed to investigate the association of all-cause mortality after percutaneous coronary intervention with site-reported bleeding and MI, when considered as individual, repeated, or combined events. Methods: We used the data from the GLOBAL LEADERS trial (GLOBAL LEADERS: A Clinical Study Comparing Two Forms of Anti-Platelet Therapy After Stent Implantation), an all-comers trial of 15 968 patients undergoing percutaneous coronary intervention. Bleeding was defined as Bleeding Academic Research Consortium (BARC) 2, 3, or 5, whereas MI included periprocedural and spontaneous MIs according to the Third Universal Definition. Results: At 2-year follow-up, 1061 and 498 patients (6.64% and 3.12%) experienced bleeding and MI, respectively. Patients with a bleeding event had a 10.8% mortality (hazard ratio [HR], 5.97 [95% CI, 4.76–7.49]; P <0.001), and the mortality of patients with an MI was 10.4% (HR, 5.06 [95% CI, 3.72–6.90]; P <0.001), whereas the overall mortality was 2.99%. Albeit reduced over time, MI and even minor BARC 2 bleeding significantly influenced mortality beyond 1 year after adverse events (HR of MI, 2.32 [95% CI, 1.18–4.55]; P =0.014, and HR of BARC 2 bleeding, 1.79 [95% CI, 1.02–3.15]; P =0.044). The mortality rates in patients with repetitive bleeding, repetitive MI, and both bleeding and MI were 16.1%, 19.2%, and 19.0%, and their HRs for 2-year mortality were 8.58 (95% CI, 5.63–13.09; P <0.001), 5.57 (95% CI, 2.53–12.25; P <0.001), and 6.60 (95% CI, 3.44–12.65; P <0.001), respectively. De-escalation of antiplatelet therapy at the time of BARC 3 bleeding was associated with a lower subsequent bleeding or MI rate, compared with continuation of antiplatelet therapy (HR, 0.32 [95% CI, 0.11–0.92]; P =0.034). Conclusions: The fatal impact of bleeding and MI persisted beyond one year. Additional bleeding or MIs resulted in a poorer prognosis. De-escalation of antiplatelet therapy at the time of BARC 3 bleeding could have a major safety merit. These results emphasize the importance of considering the net clinical benefit including ischemic and bleeding events. Registration: URL: https://www.clinicaltrials.gov . Unique identifier: NCT01813435.

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)0.004
metaresearch head score (Gemma)0.008
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 score0.004
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.057
GPT teacher head0.323
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 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

Citations36
Published2020
Admission routes1
Has abstractyes

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