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Cardiovascular risk factors and kidney transplantation: a retrospective analysis

2024· article· en· W4403822256 on OpenAlexaff
A. Ardehali, Carolyn Taylor, Jasem Althekrallah, Jessica Gill, Krishnan Ramanathan

Bibliographic record

VenueEuropean Heart Journal · 2024
Typearticle
Languageen
FieldMedicine
TopicOrgan Transplantation Techniques and Outcomes
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineKidney transplantationTransplantationRetrospective cohort studyIntensive care medicineInternal medicineCardiology

Abstract

fetched live from OpenAlex

Abstract Introduction Cardiovascular disease (CVD) poses a significant risk to end-stage kidney disease (ESKD) patients and remains the leading cause of morbidity and mortality among kidney transplant (KT) recipients. With a global rise in ESKD patients dependent on dialysis, transplant programs face growing pressure to consider candidates with a greater burden of CVD for KT despite potential long-term risks. Enhancing pre-KT cardiac risk assessment is crucial to prevent cardiovascular complications post-transplant and meet the demand for KT globally. Purpose High-quality studies evaluating pre-KT cardiovascular risk factors in patients with ESKD are lacking. Our study aims to evaluate the association between pre-KT cardiovascular risk factors and the development of major adverse cardiac events (MACE) post-KT. Methods This retrospective cohort study of ESKD patients at a tertiary center consisted of consecutive patients referred for cardiovascular assessment prior to KT between January 2013 and January 2023. Clinical and demographic data prior to KT and outcomes 1-year post-KT were extracted. Patients who underwent multi-organ transplantation were excluded. The primary endpoint was MACE, defined as all-cause death, myocardial infarction (MI), ischemic stroke, coronary revascularization, heart failure requiring hospitalization, and cardiac resuscitation. Results A total of n=233 patients were referred for cardiac evaluation prior to KT. Of these, n=145 (62%) patients had a KT, n=69 (30%) patients died pre-KT, and n=19 (8%) patients were removed from the transplant program. Of the n=143 KT recipients that met our inclusion criteria, 122 (85%) were free of MACE, while 21 (15%) met the primary endpoint in the year following KT. Patients with MACE were significantly older (mean age = 65 vs. mean age = 59, p < 0.001), and more likely to have coronary artery disease (85.7% vs. 50.8%, p = 0.01), cardiomyopathy (42.9% vs. 17.2%, p = 0.04), smoking history (71.4% vs. 46.7%, p = 0.04), and diabetes (85.7% vs. 49.2%, p = 0.02). Furthermore, deceased donor KT was associated with a greater likelihood of MACE (85.0% vs. 57.5%, p = 0.02) compared to living donor recipients. There were no significant differences with respect to left ventricular ejection fraction, diagnosis of heart failure, or prior MI between MACE and non-MACE groups (p > 0.17 for all). Conclusion Among pre-KT patients with CVD, there is a high mortality rate (30%) prior to KT. Following a successful KT, 15% of patients experienced MACE within 1-year. Our findings underscore the urgency of developing strategies to facilitate timely kidney transplantation, particularly in light of the anticipated rise in demand for kidney transplants. The elevated incidence of major adverse events in the year following a successful KT emphasizes the necessity of collaborative care by cardiologists and nephrologists for these patients, and the need for further studies to improve long-term outcomes.

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)0.003
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.278
Teacher spread0.261 · 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 routes1
Has abstractyes

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