ArticleBMJ open ophthalmology2026
External validation and updating of DIGIROP prediction models including parenteral nutrition for treatment-requiring retinopathy of prematurity in a Swedish national cohort.
Article in BMJ open ophthalmology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Prediction of retinopathy of prematurity using machine learning models.Pediatric research · 2026Article
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12 authors.
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Abstract
BACKGROUND/
aimsThis study represents an external validation with model updating of the online DIGIROP-Prescreen and DIGIROP-Screen 2.0 prediction models, incorporating ≥14 days of parenteral nutrition as an additional predictor, to identify infants requiring treatment for retinopathy of prematurity (ROP) in a contemporary Swedish cohort, estimate potential cost savings and compare DIGIROP performance with other models.
methodsInfants born in Sweden, 2021
resultsThe mean gestational age was 27.7 (SD 2.2) weeks, birth weight 1029 (SD 317) g, 689 (45.0%) were girls and 85 (5.6%) infants received ROP treatment. For DIGIROP-Prescreen 2.0, the AUC was 0.89 (95% CI 0.86 to 0.92), sensitivity was 100% (95% CI 95.8% to 100%) and specificity 27.5% (95% CI 25.3% to 29.9%). For DIGIROP-Screen 2.0, the cumulative specificity increased from 27.7% to 67.8% between postnatal weeks 6-14, with a sensitivity of 100%. In total, ∼Int$750 000 could have been saved in screening costs in Sweden during 2021-2023 using DIGIROP models. DIGIROP-Screen at postnatal age 10 weeks had the same sensitivity but higher specificity than G-ROP. WINROP showed lower sensitivity but the highest specificity.
conclusionDIGIROP 2.0 demonstrated high sensitivity and the most robust discrimination for treatment-requiring ROP in a contemporary Swedish cohort, compared with other models, with the potential to reduce unnecessary eye examinations and healthcare costs.
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