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Record W4397017980 · doi:10.1097/tp.0000000000005055

Are There Sex-based Differences in Excess Risk of Death With Graft Function After Kidney Transplant?

2024· letter· en· W4397017980 on OpenAlexaff
Elizabeth Hendren, Reetinder Kaur, Jagbir Gill

Bibliographic record

VenueTransplantation · 2024
Typeletter
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsKidney transplantKidney transplantationMedicineKidneyFunction (biology)UrologyInternal medicineBiologyGenetics

Abstract

fetched live from OpenAlex

Even though kidney transplantation offers improved mortality compared with dialysis,1 there is still an excess risk of death after transplantation compared with the general population. These deaths may be attributed to graft loss or may occur in recipients with a functioning transplant (commonly caused by infection, malignancy, and cardiovascular disease). Death with graft function (DWGF) represents the largest cause of death for adults in the first year after kidney transplantation.2 The need to explore sex disparities in posttransplant outcomes is critical because it is known that female patients have reduced access to kidney transplantation and a higher rate of graft loss compared with male patients.3 Previously published retrospective cohort analyses have demonstrated higher rates of DWGF for adult male kidney transplant recipients compared with adult female recipients,4 but these analyses do not account for sex-based differences in mortality in the general population.5 To address this, Vinson et al6 have previously used the metric of excess mortality (which benchmarks posttransplant mortality against general population mortality) and have shown that female recipients younger than 45 y and older than 60 y who received a kidney from a male deceased donor had higher excess mortality after kidney transplantation compared with male recipients of the same age. However, whether the excess mortality in women is driven by DWGF, is related to whether the donor is male or female, or is attributed to death or after graft loss remains unknown. In this issue, Vinson et al report their findings of a follow-up retrospective cohort analysis of 3 large datasets (The American Scientific Registry of Transplant Recipients, International Collaborative Transplant Study, and Australia and New Zealand Dialysis and Transplant Registry) to investigate sex differences in the excess risk of DWGF among kidney transplant recipients.7 All recipients of a first deceased donor kidney transplant were categorized into age groups to account for how biologic differences between sexes change with development and aging. The death rate was then compared with publicly available data for the general population for each region in the study to calculate each patient’s expected probability of death. Overall, there was no significant difference in the excess risk of DWGF in women and men at all age groups, except for female recipients 0–12 y of age with male donors (relative excess risk, 1.68; 95% confidence interval, 1.24-2.29). Notably, having a male donor for a female recipient did not associate with an increased excess risk of death in all other age groups, suggesting that the findings in recipients 0–12 y of age in this study should likely not dissuade female patients from receiving a transplant from a male donor. The findings of this study contradict prior analyses4 that have demonstrated a higher risk of DWGF in male recipients and imply that the increased risk of DWGF among men reported in these studies may be attributed to population wide sex-based differences in mortality. Furthermore, these findings suggest that the increased excess risk of posttransplant mortality in women that was previously reported by Vinson et al cannot be attributed to DWGF, suggesting that there may be a higher risk of death among women during or after graft loss. Importantly, death after graft loss was not assessed in this analysis; therefore, it remains unknown if women indeed have a higher risk compared with men. Therefore, findings by Vinson et al point to the need for further evaluation of sex-specific mortality risks after transplant loss, including an evaluation of causes of graft loss and cause-specific death after graft loss. Importantly, the authors appropriately acknowledge that such an analysis may be challenging to do within the constraints of a retrospective analysis because it would risk introducing significant confounding bias.8 Therefore, prospective cohort studies may be indicated in this patient population to further understand sex-based disparities. The use of registry data remains critical in expanding our understanding of transplant care, and Vinson et al should be congratulated on attempting to provide a more comprehensive understanding of this issue across various regions. However, transplant registry analyses are limited by a lack of granular information about rejection, cause of graft failure, and cause of death. Additionally, there may be incomplete capture of death data. These limitations may explain the findings in this study of an increased excess risk of mortality among female recipients 0–12 y of age with male donors, which is a challenging observation to explain. Ultimately, these limitations speak to the need to enhance existing registries and the need for dedicated prospective studies. Although the results of the study are somewhat reassuring, because there is no demonstrated evidence of sex-based disparity in excess DWGF, the implication that sex-based differences in excess mortality after transplantation may be attributed to events related to graft loss is sobering and highlights the urgent need to further our understanding of this issue.

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)0.006
metaresearch head score (Gemma)0.020
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 score0.006
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.021
GPT teacher head0.255
Teacher spread0.234 · 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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