Validation of DIGIROP- Birth and DIGIROP- Screen for the discovery of retinopathy of prematurity requiring treatment in preterm births in Saudi Arabia.

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.