Evidence map›Paper›PMID 41252350›Full record

ArticleNeonatology2026

Machine Learning Risk Prediction for Treated Retinopathy of Prematurity in Infants.

Henry P Foote, Yanchen J Ou, Suchir Bhatt, Matthew M Engelhard, Leonid Bederman, Matthew M Laughon, Kanecia O Zimmerman, Rishikesan Kamaleswaran, Rachel G Greenberg, Veeral N Tolia and 3 more

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Neonatology, 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

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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

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5 · Who and what money

Authors and funding

13 authors.

Henry P FooteDepartment of Pediatrics, Duke University, Durham, North Carolina, USA.
Yanchen J OuDepartment of Computer Science, Duke University, Durham, North Carolina, USA.
Suchir BhattDepartment of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USA.
Matthew M EngelhardDepartment of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USA.
Leonid BedermanDepartment of Pediatrics, Emory University, Atlanta, Georgia, USA.
Matthew M LaughonDepartment of Pediatrics, School of Medicine, University of North Carolina Chapel Hill, Chapel Hill, North Carolina, USA.
Kanecia O ZimmermanDepartment of Pediatrics, Duke University, Durham, North Carolina, USA.
Rishikesan KamaleswaranDepartment of Surgery, Duke University Medical Center, Durham, North Carolina, USA.
Rachel G GreenbergDepartment of Pediatrics, Duke University, Durham, North Carolina, USA.
Veeral N ToliaPediatrix Center for Research, Quality, Education and Safety, Sunrise, Florida, USA.
Christoph P HornikDepartment of Pediatrics, Duke University, Durham, North Carolina, USA.
Ricardo HenaoDepartment of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USA.
Karan R KumarDepartment of Pediatrics, Duke University, Durham, North Carolina, USA, karan.kumar@duke.edu.

Funding

Sepsis Physiomarkers for Appropriate Risk Knowledge of monitored patients in the ICU (SPARK-ICU)R01GM139967 · NIGMS · EMORY UNIVERSITY · PI KAMALESWARAN, RISHIKESAN · 2021 to 2025
$2.8M
Duke-UNC Collaborative Pediatric Clinical Pharmacology Postdoctoral Training ProgramT32HD104576 · NICHD · DUKE UNIVERSITY · PI BROUWER, KIM L.R., GONZALEZ, DANIEL · 2021 to 2025
$962k
Machine Learning Methods to Develop and Deploy Real-Time Risk Surveillance for Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder from the Electronic Health RecordK01MH127309 · NIMH · DUKE UNIVERSITY · PI Matthew Morrow Engelhard · 2022 to 2026
$842k
Mentoring in Pediatric Cardiovascular Pharmacology and Early Phase TrialsK24HL173596 · NHLBI · DUKE UNIVERSITY · PI Christoph Hornik · 2024 to 2026
$363k
NHLBI NIH HHS K24 HL173596NICHD NIH HHS T32 HD104576NIGMS NIH HHS R01 GM139967NIMH NIH HHS K01 MH127309
6 · The paper itself

Abstract

introductionRetinopathy of prematurity (ROP) is a leading cause of childhood blindness. However, current screening guidelines may be overly broad, necessitating better models to detect high-risk infants.

methodsFrom a multicenter cohort of 103,701 infants (3,301 [3.2%] treated for ROP) discharged from 298 neonatal intensive care units from 2006 to 2017 with birth weight ≤1,500 grams or gestational age ≤30 weeks, we used clinically relevant variables to develop machine learning (ML) models at 2-week intervals from postnatal day 14 to 98 to stratify infants by ROP treatment timing. We assessed model performance by concordance index, area under the receiver operating characteristic curve (AUROC), and average precision (AP), validated performance in a cohort of 25,105 infants across 231 sites from 2018 to 2020, and compared model performance to a logistic regression (LR) model.

resultsIn the validation cohort, the day 28 ML model outperformed the LR model by AUROC (0.916 [0.905-0.926] vs. 0.903 [0.892-0.914]; p < 0.001) and AP (0.190 [0.167-0.217] vs. 0.160 [0.140-0.183]; p < 0.001). Using the ML model at a 100% sensitivity threshold would have negative predictive value of >99.9% and could reduce the number of infants needing screening by 14% compared to current guidelines.

conclusionML models can effectively predict the need for ROP treatment and stratify infants by risk, potentially reducing unneeded screening. Future work is needed to translate model-based ROP predictions to the clinical setting.

Indexed as

Machine LearningRetinopathy of PrematurityFemaleGestational AgeHumansInfantInfant, NewbornInfant, PrematureInfant, Very Low Birth WeightIntensive Care Units, NeonatalLogistic ModelsMalePredictive Value of TestsRisk AssessmentRisk FactorsROC CurveMachine learningPrediction modelPrematurityRetinopathy of prematurityVery low birth weight

Identifiers

PMID41252350
PMCPMC13107400

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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.