Post

New research · Ophthalmology
Investigative ophthalmology & visual science · 2d
Cohort studyInvestigative ophthalmology & visual science · 2026

Early Prediction Model for Retinopathy of Prematurity Using Placental and Neonatal Risk Factors.

Salma El Emrani, Frank Doornkamp, Jelle J Goeman … Lotte E van der Meeren
Read paper
OphthalmologyCohort study

Adding placental data to a preterm birth model could cut retinopathy of prematurity eye exams by 25%.

Early Prediction Model for Retinopathy of Prematurity Using Placental and Neonatal Risk Factors.

Salma El Emrani … Lotte E van der Meeren
Investigative ophthalmology & visual science · 2026
Purpose

We hypothesized that high-risk neonates could be identified much earlier if placental and early postnatal risk factors are incorporated, so that this high risk can be considered during neonatal treatment well before retinopathy of prematurity screening begins.

Methods

We included 591 neonates born ≤32 weeks of gestational age (gestational age) and/or birthweight (birthweight) ≤1500 grams.

n = 591 neonates
Results
25%
screens far fewer preemie eyes without missing serious retinopathy of prematurity cases
n = 591 neonates
More results

The PAPROP model had a discriminatory ability between retinopathy of prematurity presence and absence of 0.81 (95% confidence interval [CI] = 0.76-0.86) compared to 0.78 in the reference gestational age&birthweight model.

This model had a sensitivity of 0.97 and specificity of 0.44 in the test set (threshold 10%).

“
Conclusion · 1 of 2

The PAPROP model has a high ability to predict retinopathy of prematurity development at the end of the second postnatal week, has a potential high clinical utility, and is likely cost-effective.

Conclusion · 2 of 2

After further external validation, it may aid in creating a personalized neonatal treatment approach for retinopathy of prematurity prevention in high-risk neonates.

Read paper
0 comments

No comments yet. Be the first.

Related papers

LatestFoundational
AI / Informatics
0·962 for OCT
MerMED-FM was very accurate at diagnosing diseases using eye scans
AI / Informatics
0.98
Artificial intelligence's eyelid-height measurements matched doctors' manual measurements almost perfectly
AI / Informatics
92.1%
combining eye scans and photos correctly told benign from cancerous lesions apart nearly every time
Cohort Study
28.6%
recurred locally in more than 1 in 4 patients over years of follow-up
Cohort Study
-0.395
excision group's post-op eyelid fullness score was lower - greater improvement
Cohort Study
86.7%
of infants probed after 12 months still had unresolved tear duct blockage
Observational
16%
eyelid tissue in rosacea patients showed less of this key repair-signaling protein inside cell nuclei
Cohort Study
25%
about 1 in 4 treated cases had symptoms return after improving