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Association between completeness of revascularization and cardiovascular outcomes in patients with diabetes and non-ST elevation myocardial infarction: a population-based registry analysis

2024· article· en· W4403847578 on OpenAlexafffundabout
Lucas C. Godoy, Michael E. Farkouh, Peter C. Austin, B Shah, Feng Qiu, Maneesh Sud, José Carlos Nicolau, Stephen E. Fremes, Patrick R. Lawler, Mario Gaudino, Dennis T. Ko

Bibliographic record

VenueEuropean Heart Journal · 2024
Typearticle
Languageen
FieldMedicine
TopicCoronary Interventions and Diagnostics
Canadian institutionsMcGill University Health CentreHealth Sciences CentreSunnybrook Health Science Centre
FundersCanadian Institutes of Health Research
KeywordsMedicineInternal medicineMyocardial infarctionCardiologyRevascularizationDiabetes mellitusPopulationMyocardial revascularization

Abstract

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Abstract Introduction Approximately one-third of the patients presenting with non-ST elevation myocardial infarction (NSTEMI) have diabetes. From these, two-thirds have multivessel coronary disease. The impact of completeness of revascularization in this population remains unclear. Purpose To evaluate the contemporary patterns of coronary revascularization among patients with diabetes and NSTEMI, and the clinical outcomes stratified by anatomic completeness of revascularization. Methods All patients with diabetes and multivessel disease admitted for NSTEMI between April 2009 and March 2020 in Ontario, Canada were included. Patients with previous coronary artery bypass graft surgery (CABG) at any time, percutaneous coronary intervention (PCI) in the previous 90 days, or shock were excluded. Patients were classified in four groups, from the most to the least complete revascularization strategy: CABG, complete revascularization with PCI, incomplete revascularization with PCI, or no revascularization. Outcomes included all-cause death and the composite of all-cause death, myocardial infarction, and stroke. Multivariable Cox regression was used to account for confounding (demographics, comorbidities, left ventricular function, coronary anatomy, laboratory tests, and diabetes treatment). Results We included 14,511 patients (mean age: 68.7±11.5y; 69.6% males). A total of 4,525 (31.2%) patients were treated with CABG, 3,008 (20.7%) with complete PCI, 3,624 (25.0%) with incomplete PCI and 3,354 (23.1%) were not revascularized. Patients with more complex coronary disease were more frequently treated with CABG, while PCI was more common in those with less complex disease (p<0.001; Fig 1). The mean number of grafts in the CABG group was 3.4±1.0. The mean number of stents in the complete and incomplete PCI groups was 2.4±1.3 and 1.7±0.9, respectively. Adjusted 5-y risks of all-cause death following CABG, complete PCI, incomplete PCI and no revascularization were, respectively, 25.9%, 29.8%, 32.2%, and 39.4%. Over a median follow-up of 5.8ys, CABG was associated with reduced all-cause death compared with complete PCI (HR 0.82; 95%CI 0.75 - 0.90), incomplete PCI (HR 0.73; 95%CI 0.67 - 0.80), or no revascularization (HR 0.54; 95%CI 0.50 - 0.58); p<0.001 for all (Fig 2). Adjusted 5-y risks of the composite endpoint following CABG, complete PCI, incomplete PCI and no revascularization were, respectively, 34.0%, 41.5%, 44.6%, and 52.9%. CABG was associated with reduced rates of the composite endpoint compared with complete PCI (HR 0.75; 95%CI 0.69 - 0.81), incomplete PCI (HR 0.67; 95%CI 0.62 - 0.72), or no revascularization (HR 0.50; 95%CI 0.47 - 0.54); p<0.001 for all. Conclusion In this contemporary real-world cohort study, almost a quarter of the patients with diabetes and NSTEMI did not receive any revascularization procedure. Cardiovascular outcomes were incrementally improved with CABG or complete revascularization with PCI, compared to no revascularization.Figure 1Figure 2

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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)0.003
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 score0.063
Threshold uncertainty score0.125

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.013
GPT teacher head0.250
Teacher spread0.237 · 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
Published2024
Admission routes3
Has abstractyes

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