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Record W4412849443 · doi:10.1177/20543581251358143

Mortality and Graft Failure With Medical Management Alone Versus Revascularization After Coronary Angiography Among Kidney Transplant Recipients: A Population-Based Study

2025· article· en· W4412849443 on OpenAlexaffabout
Labib Imran Faruque, Robert R. Quinn, Pietro Ravani, Tyrone G. Harrison, Brenda R. Hemmelgarn, Stephen B. Wilton, Alix Clarke, Matthew T. James, Ngan N. Lam

Bibliographic record

VenueCanadian Journal of Kidney Health and Disease · 2025
Typearticle
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsUniversity of AlbertaUniversity of CalgaryUniversity of Toronto
Fundersnot available
KeywordsMedicineRevascularizationCoronary angiographyInternal medicineCardiologyCoronary artery diseaseHeart failureAngiographyPopulationKidney diseaseKidney transplantKidney transplantationIntensive care medicineSurgeryKidneyMyocardial infarction

Abstract

fetched live from OpenAlex

Background: There are limited data on the outcomes following medical management alone versus revascularization (percutaneous coronary intervention [PCI] or coronary artery bypass grafting [CABG]) after coronary angiography in kidney transplant recipients. Objective: The objective was to compare survival and graft loss in kidney transplant recipients treated with medical therapy alone versus coronary revascularization following coronary angiography. Design: We conducted a retrospective, population-based cohort study using linked health care databases. Setting: This study was conducted in Alberta, Canada. Patients: We included adult, kidney-only transplant recipients between January 1997 and March 2015 who survived at least 1-year post-transplant with a functioning graft and had a coronary angiography during follow-up. Measurements: The outcomes were all-cause mortality, death-censored graft failure, death with a functioning graft, and all-cause graft failure. Methods: We ascertained baseline characteristics, covariate information, and outcome data from the Alberta Kidney Disease Network (AKDN) and Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease (APPROACH) databases. We used Cox proportional hazards models to compare mortality and graft loss between recipients treated with medical management versus revascularization (PCI or CABG) following angiography. Results: We identified 142 kidney transplant recipients who received a coronary angiography: 69 (49%) were treated with medical management, and 73 (51%) were treated with revascularization (PCI n = 52, CABG n = 21). The median age was 60 years (interquartile range [IQR] 50-66), 76% were male, the median baseline estimated glomerular filtration rate (eGFR) was 54 mL/min/1.73 m 2 (IQR 41-69), and the median follow-up was 5 years (IQR 2-8). Compared to medical management, recipients treated with revascularization did not have statistically higher risk of all-cause mortality (55% vs 62%; 80 vs 102 events/1000 person-years; adjusted hazard ratio [aHR] 1.32, 95% CI 0.86-2.02; P = .21). There was no significant difference in death-censored graft failure between the two treatment groups (20% vs 22%; 33 vs 40 events/1000 person-years; aHR 1.22, 95% CI 0.58-2.58; P = .60). Limitations: The clinical indications for medical management alone versus revascularization might influence the choice of these interventions. Due to the smaller sample size, we could not present the outcomes by PCI versus CABG. We also did not have complete data on blood pressure, body mass index, or medication usage which might have influenced our outcomes. Conclusions: In kidney transplant recipients undergoing coronary angiography, the rate of mortality was more than double that of graft failure, regardless of post-angiography management of coronary artery disease. The high overall risk for both groups requires further exploration in larger cohorts with longer follow-up.

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 score0.072
Threshold uncertainty score0.144

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
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 head0.012
GPT teacher head0.284
Teacher spread0.272 · 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
Published2025
Admission routes2
Has abstractyes

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