PT - JOURNAL ARTICLE AU - Ahmedhussain, Huda K. AU - Raffa, Lina H. AU - Alosaimif, Amal M. AU - Alessa, Sarah K. AU - Alharbi, Suzan Y. AU - Almarzouki, Hashem AU - AlQurashi, Mansour A. TI - Validation of DIGIROP- Birth and DIGIROP- Screen for the discovery of retinopathy of prematurity requiring treatment in preterm births in Saudi Arabia AID - 10.15537/smj.2025.46.4.20240773 DP - 2025 Apr 01 TA - Saudi Medical Journal PG - 345--351 VI - 46 IP - 4 4099 - http://smj.org.sa/content/46/4/345.short 4100 - http://smj.org.sa/content/46/4/345.full SO - Saudi Med J2025 Apr 01; 46 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.