Evidence map›Paper›PMID 37767525›Full record

ArticleTransplant international : official journal of the European Society for Organ Transplantation2023

A Multi-Step Precision Pathway for Predicting Allograft Survival in Heterogeneous Cohorts of Kidney Transplant Recipients.

Yunwei Zhang, Danny Deng, Samuel Muller, Germaine Wong, Jean Yee Hwa Yang

Abstract read
In one paragraph

Article in Transplant international : official journal of the European Society for Organ Transplantation, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Yunwei ZhangCharles Perkins Centre, The University of Sydney, Sydney, NSW, Australia.
Danny DengCentre for Kidney Research, Kids Research Institute, The Children's Hospital at Westmead, Sydney, NSW, Australia.
Samuel MullerSchool of Mathematics and Statistics, The University of Sydney, Sydney, NSW, Australia.
Germaine WongCentre for Kidney Research, Kids Research Institute, The Children's Hospital at Westmead, Sydney, NSW, Australia.
Jean Yee Hwa YangCharles Perkins Centre, The University of Sydney, Sydney, NSW, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate prediction of allograft survival after kidney transplantation allows early identification of at-risk recipients for adverse outcomes and initiation of preventive interventions to optimize post-transplant care. Many prediction algorithms do not model cohort heterogeneity and may lead to inaccurate assessment of longer-term graft outcomes among minority groups. Using data from a national Australian kidney transplant cohort (2008-2017) as the derivation set, we developed P-Cube, a multi-step precision prediction pathway model for predicting overall graft survival in three ethnic subgroups: European Australians, Asian Australians and Aboriginal and Torres Strait Islander Peoples. The concordance index for the European Australians, Asian Australians, and Aboriginal and Torres Strait Islander Peoples subpopulations were 0.99 (0.98-0.99), 0.93 (0.92-0.94) and 0.92 (0.91-0.93), respectively. Similar findings were observed when validating P-cube using an external dataset [Scientific Registry of Transplant Recipient Registry (2006-2020)]. Six sub-categories of recipients with distinct risk factor profiles were identified. Some factors such as blood group compatibility were considered important across the entire transplant population. Other factors such as human leukocyte antigen (HLA)-DR mismatches were unique to older recipients. The P-cube model identifies allograft survival specific risk factors within a heterogenous population and offers personalized survival predictions in a diverse cohort.

Indexed as

Kidney TransplantationAllograftsAustraliaHumansTransplantation, HomologousTransplant Recipientsallograft survival predictionANZDATAkidney transplantationkidney transplantation graft survivalprediction model

Identifiers

PMID37767525
PMCPMC10520244

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.