Evidence map›Paper›PMID 42283830›Full record

Observational studyPediatric nephrology (Berlin, Germany)2026

Machine learning prediction of childhood nephrotic syndrome outcomes.

Cal H Robinson, Tanay Joshi, Konrad Samsel, Zahra Shakeri, Nowrin Aman, Tonny H M Banh, Josefina Brooke, Valentina Bruno, Vaneet Dhillon, Mackenzie Garner and 9 more

Abstract readObservational Study
PubMed Publisher
In one paragraph

Observational study in Pediatric nephrology (Berlin, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

19 authors.

Cal H RobinsonDivision of Nephrology, The Hospital for Sick Children, Toronto, ON, Canada. cal.robinson@sickkids.ca.ORCID http://orcid.org/0000-0002-2223-0646
Tanay JoshiChild Health Evaluative Sciences, Research Institute, The Hospital for Sick Children, Toronto, ON, Canada.
Konrad SamselInstitute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada.
Zahra ShakeriInstitute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada.
Nowrin AmanChild Health Evaluative Sciences, Research Institute, The Hospital for Sick Children, Toronto, ON, Canada.
Tonny H M BanhChild Health Evaluative Sciences, Research Institute, The Hospital for Sick Children, Toronto, ON, Canada.
Josefina BrookeDivision of Nephrology, The Hospital for Sick Children, Toronto, ON, Canada.
Valentina BrunoDivision of Nephrology, The Hospital for Sick Children, Toronto, ON, Canada.
Vaneet DhillonDivision of Nephrology, The Hospital for Sick Children, Toronto, ON, Canada.
Mackenzie GarnerChild Health Evaluative Sciences, Research Institute, The Hospital for Sick Children, Toronto, ON, Canada.
Christoph LichtDivision of Nephrology, The Hospital for Sick Children, Toronto, ON, Canada.
Ashlene McKayDivision of Nephrology, The Hospital for Sick Children, Toronto, ON, Canada.
Rachel PearlDivision of Nephrology, The Hospital for Sick Children, Toronto, ON, Canada.
Seetha RadhakrishnanDivision of Nephrology, The Hospital for Sick Children, Toronto, ON, Canada.
Keisha RasoolDivision of Nephrology, The Hospital for Sick Children, Toronto, ON, Canada.
Nithiakishna SelvathesanDivision of Nephrology, The Hospital for Sick Children, Toronto, ON, Canada.
Chia Wei TeohDivision of Nephrology, The Hospital for Sick Children, Toronto, ON, Canada.
Jovanka Vasilevska-RistovskaChild Health Evaluative Sciences, Research Institute, The Hospital for Sick Children, Toronto, ON, Canada.
Rulan S ParekhDivision of Nephrology, The Hospital for Sick Children, Toronto, ON, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChildren with steroid-resistant, frequently relapsing, and steroid-dependent nephrotic syndrome experience high disease and treatment-related morbidity. There are few prediction models for childhood nephrotic syndrome outcomes. Our aim was to develop and internally validate outcome prediction models, using machine learning methods.

methodsWe analyzed data from Insight into Nephrotic Syndrome: Investigating Genes, Health, and Therapeutics, a prospective observational childhood nephrotic syndrome cohort. We included children (1-18 years) diagnosed from 1996 to 2023 from the Greater Toronto Area, Canada, with baseline data from within 90 days of diagnosis. Outcomes were frequent relapses or steroid dependence by 1 year, relapse occurrence by 1 year, steroid-sparing medication initiation by 2 years, and initial steroid resistance. We developed and compared the performance of nine different machine learning algorithms for each outcome. Predictors included sociodemographic, clinical, and laboratory features.

resultsWe included 515 children diagnosed with nephrotic syndrome. Of these, 484 (94%) were steroid-sensitive and 31 (6%) were steroid-resistant. Nine machine learning models were developed and optimized by hyperparameter tuning for each outcome. The random forest model had the best performance for predicting frequent relapses or steroid dependence, although its predictive ability was low (AUC 0.60). Machine learning models also had weak predictive ability for relapse occurrence (AUC 0.62; logistic regression with recursive feature elimination), steroid-sparing medication initiation (AUC 0.61; XGBoost), and steroid resistance (AUC 0.64; XGBoost).

conclusionsRoutinely collected sociodemographic, clinical, and laboratory features at nephrotic syndrome diagnosis are weakly predictive of subsequent relapses and treatment response, using machine learning methods. Discovery of novel biomarkers may improve future prediction and proactive treatment.

Indexed as

Machine LearningNephrotic SyndromeAdolescentBoosting Machine Learning AlgorithmsChildChild, PreschoolClassification AlgorithmsDrug ResistanceFemaleHumansInfantMalePrediction AlgorithmsPredictive Learning ModelsPrognosisProspective StudiesSteroidsChildrenMachine learningNephrotic syndromePediatricPrediction model

Identifiers

PMID42283830

What OpenQuestion holds

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Read underepoch 390

Registered trials

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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.