Evidence map›Paper›PMID 39073700›Full record

ArticleJournal of nephrology2024

Prediction of post-donation renal function using machine learning techniques and conventional regression models in living kidney donors.

Junseok Jeon, Yeejun Song, Jae Yong Yu, Weon Jung, Kyungho Lee, Jung Eun Lee, Wooseong Huh, Won Chul Cha, Hye Ryoun Jang

Abstract read
PubMed Publisher
In one paragraph

Article in Journal of nephrology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

3 citing papers in PubMed.

  1. Article
  2. A Brief Review of Artificial Intelligence in Living Kidney Donation.Transplant international : official journal of the European Society for Organ Transplantation · 2025
    Review
  3. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Junseok Jeon *Division of Nephrology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
Yeejun Song *Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Seoul, South Korea.
Jae Yong YuDepartment of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, South Korea.
Weon JungSmart Health Lab, Research Institute of Future Medicine, Samsung Medical Center, Seoul, South Korea.
Kyungho LeeDivision of Nephrology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
Jung Eun LeeDivision of Nephrology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
Wooseong HuhDivision of Nephrology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
Won Chul ChaDepartment of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Seoul, South Korea. docchaster@gmail.com.
Hye Ryoun JangDivision of Nephrology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea. shinehr@skku.edu.ORCID 0000-0001-9856-6341

Funding

Ministry of Science and ICT, South Korea 22A0302L1-01National Research Foundation of Korea 2019R1A5A2027340National Research Foundation of Korea 2022R1A2B5B01001298
6 · The paper itself

Abstract

backgroundAccurate prediction of renal function following kidney donation and careful selection of living donors are essential for living-kidney donation programs. We aimed to develop a prediction model for post-donation renal function following living kidney donation using machine learning.

methodsThis retrospective cohort study was conducted with 823 living kidney donors between 2009 and 2020. The dataset was randomly split into training (80%) and test sets (20%). The main outcome was the post-donation estimated glomerular filtration rate (eGFR) 12 months after nephrectomy. We compared the performance of machine learning techniques, traditional regression models, and models from previous studies. The best-performing model was selected based on the mean absolute error (MAE) and root mean square error (RMSE).

resultsThe mean age of the participants was 45.2 ± 12.3 years, and 48.4% were males. The mean pre-donation and post-donation eGFRs were 101.3 ± 13.0 and 68.8 ± 12.7 mL/min/1.73 m

conclusionsThe prediction model using XGBoost accurately predicted the post-donation eGFR after living kidney donation. This model can be applied in clinical practice using KDNI, the developed web application.

Indexed as

Glomerular Filtration RateKidneyKidney TransplantationLiving DonorsMachine LearningNephrectomyAdultCreatinineFemaleHumansMaleMiddle AgedPredictive Value of TestsRetrospective StudiesCreatinineLiving kidney donorMachine learningPost-donation renal functionPrediction model

Identifiers

PMID39073700

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.