Evidence map›Paper›PMID 40593956›Full record

ArticleScientific reports2025

Personalized prediction model generated with machine learning for kidney function one year after living kidney donation.

Rikako Oki, Toshihio Hirai, Kazuhiro Iwadoh, Yu Kijima, Hiroyuki Hashimoto, Yasunori Nishimura, Taro Banno, Kohei Unagami, Kazuya Omoto, Tomokazu Shimizu and 4 more

Abstract read
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In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

14 authors.

Rikako OkiDepartment of Organ Transplant Medicine, Tokyo Women's Medical University, Shinjuku City, Tokyo, Japan.
Toshihio HiraiDepartment of Urology, Tokyo Women's Medical University, 8-1 Kawadacho, Shinjuku-ku, Tokyo, 162-8666, Japan.
Kazuhiro IwadohDepartment of Transplant Surgery, Mita Hospital, International University of Health and Welfare, Minato City, Japan.
Yu KijimaDepartment of Urology, Tokyo Women's Medical University, 8-1 Kawadacho, Shinjuku-ku, Tokyo, 162-8666, Japan.
Hiroyuki HashimotoDepartment of Radiation Oncology, Tokyo Women's Medical University, Shinjuku City, Tokyo, Japan.
Yasunori NishimuraDepartment of Radiation Oncology, Tokyo Women's Medical University, Shinjuku City, Tokyo, Japan.
Taro BannoDepartment of Urology, Tokyo Women's Medical University, 8-1 Kawadacho, Shinjuku-ku, Tokyo, 162-8666, Japan.
Kohei UnagamiDepartment of Organ Transplant Medicine, Tokyo Women's Medical University, Shinjuku City, Tokyo, Japan.
Kazuya OmotoDepartment of Urology, Tokyo Women's Medical University, 8-1 Kawadacho, Shinjuku-ku, Tokyo, 162-8666, Japan.
Tomokazu ShimizuDepartment of Organ Transplant Medicine, Tokyo Women's Medical University, Shinjuku City, Tokyo, Japan.
Junichi HoshinoDepartment of Nephrology, Tokyo Women's Medical University, Shinjuku City, Tokyo, Japan.
Toshio TakagiDepartment of Urology, Tokyo Women's Medical University, 8-1 Kawadacho, Shinjuku-ku, Tokyo, 162-8666, Japan.
Hideki IshidaDepartment of Organ Transplant Medicine, Tokyo Women's Medical University, Shinjuku City, Tokyo, Japan.
Toshihito HiraiDepartment of Urology, Tokyo Women's Medical University, 8-1 Kawadacho, Shinjuku-ku, Tokyo, 162-8666, Japan. hirai.toshihito@twmu.ac.jp.ORCID http://orcid.org/0000-0001-7309-0658

Funding

Initiative for Realizing Diversity in the Research Environment, Tokyo Women's Medical UniversityTokyo Women's Medical University. A-23001
6 · The paper itself

Abstract

Living kidney donors typically experience approximately a 30% reduction in kidney function after donation, although the degree of reduction varies among individuals. This study aimed to develop a machine learning (ML) model to predict serum creatinine (Cre) levels at one year post-donation using preoperative clinical data, including kidney-, fat-, and muscle-volumetry values from computed tomography. A total of 204 living kidney donors were included. Symbolic regression via genetic programming was employed to create an ML-based Cre prediction model using preoperative clinical variables. Validation was conducted using a 7:3 training-to-test data split. The ML model demonstrated a median absolute error of 0.079 mg/dL for predicting Cre. In the validation cohort, it outperformed conventional methods (which assume post-donation eGFR to be 70% of the preoperative value) with higher R

Indexed as

KidneyKidney TransplantationLiving DonorsMachine LearningNephrectomyAdultCreatinineFemaleGlomerular Filtration RateHumansKidney Function TestsMaleMiddle AgedCreatinineKidney function post-donationKidney transplantationLiving donorMachine learningPrediction

Identifiers

PMID40593956
PMCPMC12215664

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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.