ArticleEye (London, England)2022
DIGIROP efficacy for detecting treatment-requiring retinopathy of prematurity in a Portuguese cohort.
Article in Eye (London, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 7 citations in OpenAlex.
- Early Prediction Model for Retinopathy of Prematurity Using Placental and Neonatal Risk Factors.Investigative ophthalmology & visual science · 2026Article
- External validation and updating of DIGIROP prediction models including parenteral nutrition for treatment-requiring retinopathy of prematurity in a Swedish national cohort.BMJ open ophthalmology · 2026Article
- Predicting the risk of treatment-requiring retinopathy of prematurity in preterm infants in Greece. External validation of DIGIROP prognostic models.Acta ophthalmologica · 2025Article
- Validation of DIGIROP- Birth and DIGIROP- Screen for the discovery of retinopathy of prematurity requiring treatment in preterm births in Saudi Arabia.Saudi medical journal · 2025Article
- Validation of WINROP algorithm as screening tool of retinopathy of prematurity among Egyptian preterm neonates.Eye (London, England) · 2024Article
- Validation of DIGIROP models and decision support tool for prediction of treatment for retinopathy of prematurity on a contemporary Swedish cohort.The British journal of ophthalmology · 2023Article
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Authors and funding
7 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND/
objectivesTo determine the efficacy of the DIGIROP model in detecting treatment-requiring retinopathy of prematurity (TR-ROP) in a Portuguese cohort. SUBJECTS/
methodsMulticentre, retrospective cohort study of all consecutive preterm infants who underwent ROP screening from April 2012 to May 2019 in two neonatal units. Gestational age (GA), birth weight (BW) and sex were inserted in the DIGIROP platform. The optimal cut-off point to achieve 100% sensitivity was calculated. Area under the receiver operating characteristic curve (AUC) was calculated.
resultsOf the 431 infants who underwent ROP screening, 257 were eligible for DIGIROP analysis and 174 infants were excluded for having a GA outside the range 24-30 weeks imposed by the DIGIROP algorithm. Median GA was 29 weeks (range 24-30) and BW was 1060 g (range 408-2080). Twenty-tree infants (8.9%) developed TR-ROP. The highest risk obtained for TR-ROP was 0.5404 (95% CI 0.4343-0.6616) with a median achieved risk of 0.0938 (range 0.0016-0.5404). The optimal cut-off point to achieve 100% sensitivity on TR-ROP was 0.0016. The number of infants receiving ROP examinations would have been reduced from 257 to 187 infants (-27.2%) if the model was applied.
conclusionsIn our cohort, of 257 infants, the optimal cut-off point to achieve 100% sensitivity for TR-ROP was 0.0016 with moderate accuracy in the AUC (0.70). The number of infants requiring screening would have decreased 27.2% if the model was applied. It is essential that algorithms continue to be tested in different populations, especially in cohorts that include both younger and older GA infants.
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