Evidence map›Paper›PMID 40209838›Full record

ArticleAnnals of epidemiology2025

A comparison of neural networks and regression-based approaches for estimating kidney function in pediatric chronic kidney disease: Practical predictive epidemiology for clinical management of a progressive disease.

Derek K Ng, Ankur Patel, George J Schwartz, Jesse C Seegmiller, Bradley A Warady, Susan L Furth, Christopher Cox, CKiD study investigators

Registry-linked trialAbstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Annals of epidemiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT00327860 (Chronic Kidney Disease in Children Prospective Cohort Study), which is not on this 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.

NCT00327860 recruitingnot on this map

Chronic Kidney Disease in Children Prospective Cohort Study (CKiD)

TypeobservationalSponsorJohns Hopkins Bloomberg School of Public HealthRan2003 to 2028Enrolled1,300ConditionsChronic Kidney Disease
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.

Derek K NgDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. Electronic address: dng@jhu.edu.
Ankur PatelDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
George J SchwartzDepartment of Pediatrics, University of Rochester Medical Center, Rochester, NY, USA.
Jesse C SeegmillerDepartment of Laboratory Medicine and Pathology, School of Medicine, University of Minnesota, Minneapolis, MN, USA.
Bradley A WaradyDivision of Pediatric Nephrology, Children's Mercy Kansas City, Kansas City, MO, USA.
Susan L FurthChildren's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Pediatrics, Division of Nephrology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Christopher CoxDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
CKiD study investigators

Funding

Chronic Kidney Disease in Children Study (C-Kid)U01DK066174 · NIDDK · JOHNS HOPKINS UNIVERSITY · PI SUSAN L. FURTH · 2003 to 2026
$25.1M
CKiD Cardiovascular Testing Travel SupplementU01DK066143 · NIDDK · CHILDREN'S MERCY HOSP (KANSAS CITY, MO) · PI Bradley Alan Warady · 2003 to 2026
$24.2M
The Kidney Disease in Children Data Management and Analysis Center (KIDMAC)U24DK066116 · NIDDK · JOHNS HOPKINS UNIVERSITY · PI Derek K Ng · 2018 to 2026
$12.6M
The Central Biochemistry Laboratory for the Chronic Kidney Disease in Children Cohort (CKiD)U24DK137522 · NIDDK · UNIVERSITY OF MINNESOTA · PI Jesse C Seegmiller · 2023 to 2026
$1.3M
NIDDK NIH HHS U01 DK066143NIDDK NIH HHS U01 DK066174NIDDK NIH HHS U24 DK066116NIDDK NIH HHS U24 DK137522
6 · The paper itself

Abstract

purposeClinical management of pediatric chronic kidney disease requires estimation of glomerular filtration rate (eGFR). Currently, eGFR is determined by two endogenous markers measured in blood: serum creatine (SCr) and cystatin C (CysC). Machine learning methods show promise to potentially improve eGFR, but it is unclear if they can outperform regression-based approaches under clinical constraining requiring real time measurement and only two predictors. We constructed a neural network for eGFR (NNeGFR) and compared it to the clinical standard Under 25 (U25eGFR) equations using the same data for training and validation.

methodsThe U25eGFR data comprised 1683 training and 843 validation observations that included iohexol measured GFR (mGFR), SCr and CysC. Sex-stratified feed forward NNs included the same predictors as U25eGFR (i.e., age, height/SCr, CysC) with additional nonlinear transformations. Performance was evaluated by bias (for calibration), proportions within 10 % and 30 % of mGFR (P

resultsNNeGFR performed comparably to the U25eGFR equations on all metrics. Biases were minimal, slightly favoring U25eGFR. NNeGFR and U25eGFR had similar P

conclusionsNNeGFR performed as well as established equations to estimate GFR. Without additional biomarkers related to kidney function, which are not currently clinically available in real time, NN methods are unlikely to substantially outperform regression derived GFR estimating equations. Implications for translation of these advanced epidemiologic methods to clinical practice are discussed.

Indexed as

Glomerular Filtration RateNeural Networks, ComputerRenal Insufficiency, ChronicAdolescentBiomarkersChildChild, PreschoolCreatineCreatinineCystatin CDisease ProgressionFemaleHumansKidney Function TestsMaleRegression AnalysisBiomarkersCreatineCreatinineCystatin CCKDEGFRGlomerular filtration rateMachine learningMethodologyPediatric nephrology

Identifiers

PMID40209838
PMCPMC12096456

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Registered trials

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.