Evidence map›Paper›PMID 39813007›Full record

ArticleClinical and experimental nephrology2025

Comparative analysis of kidney function prediction: traditional statistical methods vs. deep learning techniques.

Mizuki Ohashi, Yuya Ishikawa, Satoshi Arai, Tomoharu Nagao, Kaori Kitaoka, Hajime Nagasu, Yuichiro Yano, Naoki Kashihara

Abstract readComparative StudyMulticenter Study
PubMed Publisher
In one paragraph

Article in Clinical and experimental nephrology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

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

8 authors.

Mizuki OhashiShiga University of Medical Science, NCD Epidemiology Research Center, Shiga, Japan.
Yuya IshikawaDepartment of Information Environment, Yokohama National University Graduate School of Environment and Information Sciences, Yokohama, Japan.
Satoshi AraiDepartment of Social Environment and Information, Yokohama National University Graduate School of Environment and Information Sciences, Yokohama, Japan.
Tomoharu NagaoSchool of Environment and Information Sciences, Yokohama National University Graduate, Yokohama National University Institute for Multidisciplinary Sciences, Yokohama, Japan.
Kaori KitaokaShiga University of Medical Science, NCD Epidemiology Research Center, Shiga, Japan.
Hajime NagasuKawasaki Medical School, Department of Nephrology and Hypertension, Kurashiki, Japan.
Yuichiro YanoFaculty of Medicine, Department of General Medicine, Juntendo University, 2-1-1, Hongo, Bunkyo-Ku, Tokyo, 113-8421, Japan. yano.yuichiro@jichi.ac.jp.
Naoki KashiharaKawasaki Medical School, Department of Nephrology and Hypertension, Kurashiki, Japan.

Funding

Japan Society for the Promotion of Science 22K08333
6 · The paper itself

Abstract

backgroundChronic kidney disease (CKD) represents a significant public health challenge, with rates consistently on the rise. Enhancing kidney function prediction could contribute to the early detection, prevention, and management of CKD in clinical practice. We aimed to investigate whether deep learning techniques, especially those suitable for processing missing values, can improve the accuracy of predicting future renal function compared to traditional statistical method, using the Japan Chronic Kidney Disease Database (J-CKD-DB), a nationwide multicenter CKD registry.

methodsFrom the J-CKD-DB-Ex, a prospective longitudinal study within the J-CKD-DB, we selected individuals who had at least two eGFR measurements recorded between 12 and 20 months apart (n = 22,929 CKD patients). We used the multiple linear regression model as a conventional statistical method, and the Feed Forward Neural Network (FFNN) and Gated Recurrent Unit (GRU)-D (decay) models as deep learning techniques. We compared the prediction accuracies of each model for future eGFR based on the existing data using the root mean square error (RMSE).

resultsThe RMSE values were 7.5 for multiple regression analysis, 7.9 for FFNN model, and 7.6 mL/min/1.73 m

conclusionOur result demonstrate the predictive accuracy of future eGFR based on the existing dataset in the J-CKD-DB-Ex. The accuracy was not improved by applying deep learning techniques compared to conventional statistical methods.

Indexed as

Deep LearningGlomerular Filtration RateKidneyRenal Insufficiency, ChronicAgedDatabases, FactualFemaleHumansJapanLinear ModelsLongitudinal StudiesMaleMiddle AgedPredictive Value of TestsProspective StudiesRegistriesChronic kidney diseaseDeep learningEGFRJ-CKD-DB-ExNeural network

Identifiers

PMID39813007

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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.