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Record W3108519884 · doi:10.1093/ehjci/ehaa946.1452

Improved long-term survival with coronary artery bypass graft surgery compared to percutaneous coronary intervention in diabetics with multivessel disease

2020· article· en· W3108519884 on OpenAlexaffabout
Derrick Y. Tam, Christoffer Dharma, Rodolfo V. Rocha, Michael E. Farkouh, Husam Abdel‐Qadir, Lufan Sun, Mario Gaudino, Harindra C. Wijeysundera, Peter C. Austin, Jacob A. Udell, Stephen E. Fremes, D.S Lee

Bibliographic record

VenueEuropean Heart Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicCoronary Interventions and Diagnostics
Canadian institutionsOttawa Heart InstituteWomen's College HospitalToronto General HospitalHealth Sciences CentreInstitute for Clinical Evaluative SciencesSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineConventional PCIPercutaneous coronary interventionCardiologyInternal medicineMyocardial infarctionCoronary artery diseaseRevascularizationTIMIDiabetes mellitusStroke (engine)Propensity score matchingCoronary artery bypass surgeryArterySurgery

Abstract

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Abstract Background While randomized clinical trials have demonstrated the superiority of coronary artery bypass grafting (CABG) over percutaneous coronary intervention (PCI) in patients with diabetes and multivessel coronary artery disease (CAD), there remains a paucity of observational evidence comparing these two modalities. Methods Clinical and administrative databases for Canada's most populous province, Ontario, were linked to obtain records of all patients with angiographic evidence of multivessel CAD (defined as: 2-vessel and 3-vessel disease) treated with either isolated CABG or PCI from October 2008 to March 2017. Left main disease was excluded in the primary analysis. Baseline characteristics of patients undergoing CABG and PCI were compared and 1:1 propensity score matching was performed to account for baseline differences. 30-day mortality was compared in the matched groups. Late mortality and the composite of major cardiovascular and cerebrovascular events (MACCE, consisting of stroke, myocardial infarction (MI), repeat revascularization, and death) were compared between the matched groups using a stratified log rank test and Cox-proportional hazards model. The individual non-fatal components of MACCE were compared using the Fine-Gray model that accounted for death as a competing risk. A secondary analysis that included patients with left main disease was also performed for the outcome of late mortality. A sensitivity analysis that excluded patients with acute coronary syndrome was also conducted for late mortality. Results A total of 9,395 and 4,016 patients underwent CABG and PCI respectively. Prior to matching, CABG patients were younger (65.7 vs 68.5 years, p<0.001), more likely male (78% vs 73%, p<0.001) and with more severe CAD. Propensity score matching based on 24 baseline covariates yielded 3,782 well-balanced pairs. There was no difference in early mortality between CABG and PCI (2.3% vs 2.5%, p=0.65). The rate of all-cause mortality over 8-years was significantly higher with PCI compared to CABG (Figure- HR: 1.35, 95% CI: 1.23–1.50). The cumulative incidence of MI (HR 1.91, 95% CI: 1.66–2.20) and need for repeat revascularization (HR: 4.06, 95% CI: 3.54–4.66) were significantly higher with PCI over 8 years. There was no difference in late stroke between PCI and CABG (stroke (HR: 0.85, 95% CI: 0.68–1.07). Overall MACCE was higher in PCI compared to CABG (HR: 1.94, 95% CI: 1.80–2.09). In our secondary analysis that included patients with left main disease, findings were robust and late mortality remained higher with PCI compared to CABG (HR: 1.42, 95% CI: 1.30–1.54). In a sensitivity analysis where patients with acute coronary syndrome at the time of presentation were excluded, late mortality remained higher with PCI (HR: 1.30, 95% CI: 1.12–1.49) in 2,028 matched pairs. Conclusions In patients with multivessel CAD and diabetes we observed improved long-term mortality and freedom from MACCE at 8-years with CABG compared to PCI. Figure 1 Funding Acknowledgement Type of funding source: Foundation. Main funding source(s): Canadian Institutes of Health Research

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)0.001
metaresearch head score (Gemma)0.002
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 score0.030
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.043
GPT teacher head0.288
Teacher spread0.245 · 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 routes2
Has abstractyes

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