Objective: To prospectively validate the WINROP (Weight, Insulin-like growth factor 1, Neonatal, Retinopathy of Prematurity) screening algorithm (www.winrop.com) based on longitudinal measurements of neonatal body weights in predicting the development of severe retinopathy of prematurity (ROP) among preterm infants admitted to the neonatal intensive care unit of a tertiary care center in East Malaysia.

Methods: All premature infants of less than 32 weeks gestational age (GA) were included in this cohort. Their body weight was measured weekly from birth to 36 weeks postmenstrual age and entered into the computer-based surveillance system: WINROP. Infants were then classified by the system into high- or low-risk alarm group. The retinopathy findings were recorded according to Early Treatment for ROP criteria. However, the screening and management of infants were done according to the recommendations of the Continuous Practice Guidelines, Ministry of Health, Malaysia. The team members involved in screening and those recording the findings were kept blinded from each other.

Results: A total of 151 infants with median GA at birth of 30 weeks (interquartile range [IQR] Å} 2.1) and mean birth weight of 1,264 g (standard deviation Å} 271) were analyzed. High-risk alarm was signaled in 85 (56.3%) infants and 9 (6.6%) infants developed type 1 ROP. One infant in the low-risk alarm group developed type 1 ROP requiring laser retinal photocoagulation. The median time lag from the high-risk alarm signal to the development of type 1 ROP was 10.4 (IQR Å} 8.4) weeks.

Conclusion: In this cohort, the WINROP algorithm had a sensitivity of 90%, with negative predictive value of 98.5% (95% confidence interval) for detecting infants with type 1 ROP and was able to predict infants with ROP earlier than their due screening date. This study shows that a modified version of the WINROP algorithm aimed at specific populations may improve the outcome of this technique.