Evidence map›Paper›PMID 41580573›Full record

ArticlePediatric nephrology (Berlin, Germany)2026

Clinical phenotypes and severity stratification in pediatric diethylene glycol poisoning: a latent class analysis of the Gambia acute kidney injury outbreak.

Amadou Barrow, Mustapha Bittaye, Sheikh Omar Bittaye, Sunday Musa Oguche

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Article in Pediatric nephrology (Berlin, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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3 citing papers in PubMed.

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5 · Who and what money

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4 authors.

Amadou BarrowDepartment of Epidemiology, College of Public Health and Health Professions, College of Medicine, University of Florida, Gainesville, FL, USA. abarrow@utg.edu.gm.ORCID http://orcid.org/0000-0002-6006-9355
Mustapha BittayeEdward Francis Small Teaching Hospital, Independence Drive, Banjul, The Gambia.
Sheikh Omar BittayeEdward Francis Small Teaching Hospital, Independence Drive, Banjul, The Gambia.
Sunday Musa OgucheEdward Francis Small Teaching Hospital, Independence Drive, Banjul, The Gambia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBetween June and September 2022, an outbreak of acute kidney injury (AKI) among children in The Gambia caused 66 deaths among 82 cases, linked to medicines contaminated with diethylene glycol (DEG) and ethylene glycol. Clinical heterogeneity in DEG poisoning remains poorly characterized, limiting risk stratification and resource allocation in outbreak settings. We used latent class analysis to identify distinct clinical phenotypes and their predictors.

methodsWe conducted a secondary analysis of a case-cohort study involving 63 AKI cases and 258 controls aged ≤ 8 years. Latent class analysis, a statistical approach that identifies naturally occurring patient subgroups based on symptom patterns, used 13 binary symptom indicators to identify clinical phenotypes via an expectation-maximization algorithm. We compared models with 2-5 classes using the Bayesian Information Criterion and entropy. Multinomial logistic regression examined predictors of phenotype membership.

resultsFour distinct phenotypes were identified: Classic Kidney-Fever (30.2%), Gastrointestinal-Kidney (22.2%), Cardio-Respiratory-Kidney (19.0%), and Mild/Atypical (28.6%). The 4-class model demonstrated excellent classification certainty (entropy = 0.910). The Cardio-Respiratory-Kidney phenotype showed the highest DEG exposure (83.3% vs. 44.4% in Mild/Atypical, p = .015) and was strongly predicted by exposure intensity (adjusted RRR = 6.82, 95% CI [1.45-32.10]) and younger age. Multisystem involvement (≥ 3 organ systems) occurred in 28.6% of cases vs. 0% of controls.

conclusionsDEG-induced AKI manifests as four distinct clinical phenotypes with differing severity and exposure patterns. The severe Cardio-Respiratory-Kidney phenotype represents a critical target for prioritized intervention in resource-constrained settings. Importantly, nearly 30% of cases presented with atypical or minimally symptomatic features, underscoring the need for comprehensive case-finding and close follow-up during outbreak investigations.

Indexed as

Acute Kidney InjuryEthylene GlycolsBayes TheoremCase-Control StudiesChildChild, PreschoolDisease OutbreaksDrug ContaminationFemaleGambiaHumansInfantLatent Class AnalysisMalePhenotypeSeverity of Illness Indexdiethylene glycolEthylene GlycolsAcute kidney injuryClinical phenotypesDiethylene glycol poisoningLatent class analysisPediatric nephrotoxicity

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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.