RT Journal Article
SR Electronic
T1 Validation of DIGIROP- Birth and DIGIROP- Screen for the discovery of retinopathy of prematurity requiring treatment in preterm births in Saudi Arabia
JF Saudi Medical Journal
JO Saudi Med J
FD Prince Sultan Military Medical City
SP 345
OP 351
DO 10.15537/smj.2025.46.4.20240773
VO 46
IS 4
A1 Ahmedhussain, Huda K.
A1 Raffa, Lina H.
A1 Alosaimif, Amal M.
A1 Alessa, Sarah K.
A1 Alharbi, Suzan Y.
A1 Almarzouki, Hashem
A1 AlQurashi, Mansour A.
YR 2025
UL http://smj.org.sa/content/46/4/345.abstract
AB Objectives: To validate 2 DIGIROP prediction models for retinopathy of prematurity (ROP) type 1 and compare them to other weight-based algorithms in a premature Saudi Arabian infant cohort.Methods: Preterm infants of 24-30 weeks’ gestational age (GA) or body weight (BW) of ≤1500g who were admitted to the neonatal units of 2 Jeddah tertiary centers between January 2015 and September 2021 were included (N=363). The DIGIROP-Birth employed the birth GA, gender, birth weight, and age at ROP onset as predictors. The area under the receiver operating characteristic curve (AUC) with 95% confidence interval, specificity, and sensitivity were projected. The DIGIROP-Screen risk of risk were identified at 6-14 weeks postnatal age (PNA).Results: The mean GA was 27.94±1.6 weeks and the mean BW was 1068.2±269.2 g. The DIGIROP-Birth had a sensitivity of 93.8%; specificity of 48.9%; AUC of 0.70; and accuracy of 52.9%. For DIGIROP-Screen, the AUC for models spanning PNA 6-14 weeks varied from 0.68-0.83, and sensitivity varied from 73.3-96.8%. The DIGIROP-Birth and DIGIROP-Screen showed the highest accuracy and AUC value in comparison to other ROP prediction models.Conclusion: The 2 models demonstrated high predictive capacity for type 1 ROP risk assessment in this cohort. The potential of these tools for identifying high-risk infants and avoiding standard ROP screening in low-risk infants needs to be verified through large-scale studies.