Kidney international reports
September 17, 2026 · via Europe PMC
High Prevalence and Limited Perinatal Prediction of Kidney Dysfunction in Adolescents Born Extremely Preterm.
Sanderson KR, Vazquez JE, Flythe JE, +7 more, O'Shea TM.
AI summary — for doctors, generated from the abstract only
Abnormal kidney health was very common in adolescents born extremely preterm, and perinatal factors did not predict it.
What was studied
Cohort analysis of 675 adolescents (mean age 18) from the ELGAN-ECHO cohort at 14 US hospitals, assessing eGFR, blood pressure and microalbuminuria and testing whether perinatal covariates predicted abnormal kidney health.
Key findings
- Abnormal kidney health affected 66% of complete cases and 82% of all classifiable cases, with bounded estimates of 43% to 91%.
- Reduced eGFR was seen in 30.5%, elevated BP in 42% and microalbuminuria in 9%.
- Perinatal predictors performed no better than chance in the test set (AUC 0.48 to 0.55).
Clinical relevance
Pediatric and adult nephrology and primary care should expect frequent kidney and blood pressure abnormalities after extremely preterm birth.
Who should care
Limitations
Only 52% had classifiable kidney status, so prevalence is uncertain, and blood pressure was likely from limited measurements.
Screen kidney function, blood pressure and urine albumin regularly in people born extremely preterm, since birth history cannot identify who is at risk.
This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.
Abstract
Introduction
Extremely preterm birth (< 28 weeks' gestation) increases chronic kidney disease (CKD) risk. However, among those born extremely preterm, the prevalence of abnormal kidney health during adolescence and its perinatal predictors remain poorly defined.
Methods
Adolescents from the Extremely Low Gestational Age Newborn -Environmental influences on Child Health Outcomes (ELGAN-ECHO) cohort (14 US hospitals) were assessed for abnormal kidney health defined as ≥ 1 indicator-chronic kidney disease in children (CKiD) U25 estimated glomerular filtration rate (eGFR) 2, blood pressure (BP) ≥ 120/80 mm Hg, or microalbuminuria > 30 mg/g.Five feature selection methods screened perinatal covariate predictors using a 60 to 40 training-test split. Multiple imputation addressed missing data in sensitivity analyses. Area under the curve (AUC) was the primary performance metric.
Results
Of the 675 adolescents (mean age 18 years), 354 (52%) had a classifiable kidney health status, as follows: 188 with all 3 measurements and 166 with partial measurements showing ≥ 1 abnormality. The prevalence of abnormal kidney health was 66% among complete cases (125/188) and 82% among all determinate cases (291/354). In the full cohort, prevalence ranged from 43% to 91% (bounding estimates accounting for missing data). Individual measurements revealed 30.5% with reduced eGFR, 42% with elevated BP, and 9% with microalbuminuria. Across all methods, perinatal covariates achieved random test-set discrimination (AUC: 0.48-0.55) for abnormal kidney health prediction, compared with training set AUCs of 0.66-0.72.
Conclusion
Abnormal kidney health is highly prevalent after extremely preterm birth, underscoring the importance of protocolized longitudinal kidney health surveillance throughout childhood to enable early identification of CKD and elevated BP in children born extremely preterm. The perinatal factors we examined did not meaningfully predict kidney health abnormalities and variable importance did not translate to individual-level prediction in this cohort.
DOI
10.1016/j.ekir.2026.107086
PMID
42903614
Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.
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