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Record W2618671163 · doi:10.1093/ndt/gfx146.sp319

SP319DEFINING CARDIAC FUNCTION EARLY AFTER RENAL TRANSPLANTATION: CHALLENGES UNRAVELLED BY CARDIAC MAGNETIC RESONANCE IMAGING

2017· article· en· W2618671163 on OpenAlexaboutno aff
Manvir Hayer, Anna Price, John Townend, Charles J. Ferro, Richard P. Steeds, Nicola C. Edwards

Bibliographic record

VenueNephrology Dialysis Transplantation · 2017
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMagnetic resonance imagingCardiac magnetic resonanceTransplantationCardiac function curveCardiac magnetic resonance imagingCardiologyCardiac imagingInternal medicineRadiologyHeart failure

Abstract

fetched live from OpenAlex

INTRODUCTION AND AIMS: Successful kidney transplantation is associated with reduced cardiovascular (CV) mortality compared to patients who remain on dialysis, but this risk is still high compared to the general population. Longitudinal data reporting changes in uremic cardiomyopathy after renal transplantation are conflicting; studies with echocardiography have reported regression of left ventricular (LV) hypertrophy and improved systolic function. However, these findings have not been replicated using cardiac magnetic resonance imaging (CMR), which is volume independent and does not depend on geometric assumptions. The CV response early after transplantation with restoration of normal renal function has not been reported on CMR. The aim of this study was to assess changes in LV structure and function before and acutely (<8 weeks) after renal transplantation in patients with end-stage kidney disease (ESKD). METHODS: All subjects were prospectively recruited prior to live-donor kidney transplantation. Patients had no history of CV disease or diabetes and underwent CMR pre-operatively and within eight weeks post-operatively. Ischaemic heart disease was excluded with stress echocardiography or a myocardial perfusion scan. Haemodialysis patients were scanned at dry weight or on the day after dialysis, and peritoneal dialysis patients were scanned at their dry weight. CMR data were analysed using CVi42 (Calgary, Canada). RESULTS: In total 10 patients were studied (male gender 70%, age 45 years [30-60], dialysis 50%, dialysis vintage 15±9 months). CMR data is presented in table 1. Pre-operative studies demonstrated; median left ventricular mass 82g/m2 with 2 patients reaching criteria for LV hypertrophy; increased segmental wall thickness >11mm in 8 patients; mean LV ejection fraction (LVEF) 66±10%, with only 2 patients reaching criteria for mild LV impairment (LVEF 50-55%). The mean eGFR increased after transplantation without a change in body weight (11ml/min/1.73m2 vs. 53ml/min/1.73m2). Left ventricular and left atrial (LA) volumes decreased at follow up without a change in LV mass. The reduction in indexed left ventricular diastolic volume was associated with an increase in ejection fraction (r=-0.810, p<0.001), and with an increased mitral annular plane excursion (long axis systolic function; r=-0.868, p=0.001). CONCLUSIONS: A reduction in LV volumes early after renal transplantation is associated with improved prognostic markers of LV function and left atrial size. Echo assessment of LV structure and function is limited by the significant changes in fluid status, with CMR being the method of choice for defining the natural history of uraemic cardiomyopathy in longitudinal studies after renal transplantation. SP319 Table 1. CMR data before and <8 weeks post kidney transplantation

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)0.001
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 score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.259
Teacher spread0.248 · 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
Published2017
Admission routes1
Has abstractyes

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