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10 Cardiac alterations after renal transplant; contoversies unravelled by cardiac mri

2017· article· en· W2737540227 on OpenAlexaboutno aff
Manvir Hayer, Anna Price, Shanat Baig, Boyang Liu, John Townend, Charles J. Ferro, Richard P. Steeds, Nicola C. Edwards

Bibliographic record

VenueHeart · 2017
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDialysisCardiologyTransplantationInternal medicineKidney diseasePeritoneal dialysisKidney transplantationPopulationCardiac function curveRenal functionLeft ventricular hypertrophyHeart failureBlood pressure

Abstract

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Background Successful kidney transplantation is associated with reduced cardiovascular (CV) morbidity and mortality compared to patients who remain on dialysis but is higher than in the general population. Longitudinal data reporting changes in uremic cardiomyopathy after renal transplant are conflicting; studies with echo have reported regression of left ventricular (LV) hypertrophy and improved systolic function but have not been replicated using cardiac MRI which is volume independent and does not depend on geometric assumptions. The CV response early after transplant with restoration of normal renal function have not been reported. 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). Method All subjects were prospectively recruited prior to live-donor kidney transplantation. Patients had no history of CV disease or diabetes and underwent cardiac MRI pre-operatively and within eight weeks post-operatively. Stress echocardiography or a myocardial perfusion scan was performed to exclude ischaemic heart disease. Haemodialysis patients were scanned on the day after dialysis, and peritoneal dialysis patients were scanned at their dry weight. Cardiac MRI data were analysed using CVi42 (Calgary, Canada). Results In total 10 patients were studied (male gender 70%, age 45 years [30-60], dialysis 40%). Cardiac MRI data is presented in Table 1. Pre-operative studies demonstrated; median left ventricular mass 82 g/m2 with 6 patients reaching criteria for LV hypertrophy. Increased segmental wall thickness >11 mm in 8 patients. Mean LV ejection fraction (LVEF) 66%±10, only 2 patients had mild LV impairment (LVEF 50%–55%). The mean estimated glomerular filtration rate (eGFR) increased from 11 ml/min/1.73 m 2 to 53 ml/min/1.73 m 2 after transplantation without a change in body weight. Left ventricular and atrial volumes decreased at follow up without a change in LV mass. The reduction in indexed left ventricular diastolic volume (LVEDVi) was associated with an increase in ejection fraction (EF) (r=−0.810, p<0.001), and with an increased MAPSE (r=−0.868, p=0.001). Discussion A reduction in LV volumes acutely after renal transplantation is associated with improved prognostic markers of LV function and atrial size. Patients with ESKD are chronically fluid overloaded even at dry weigh. Cardiac MRI is the method of choice for longitudinal studies in defining the natural history of uremic cardiomyopathy after renal transplantation. Values are expressed as mean±SD or median (interquartile range). P Value <0.05 demonstrates significance in change of variable following transplantation. Abstract 10 Table 1 Cardiac MRI data for the change in left ventricular volumes, mass and function between pre-operative and follow up scan (<8 weeks post-transplant) Pre-operative Post-operative Change P Value LVEDV (ml) 167±79 72±17 −95±66 0.001 LVEDVi (ml/m 2 ) 91±33 72±17 −19±19 0.012 LVESV (ml) 65±41 40±20 −25±29 0.025 LVESV (ml/m 2 ) 33±20 21±9 −12±16 0.044 LV Mass (g) 160 (124 to 189) 164 (113 to 180) −9 (−25 to 9) 0.305 LV Mass Indexed (g/m 2 )

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.629
Threshold uncertainty score0.603

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.012
GPT teacher head0.297
Teacher spread0.285 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
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