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Prediction of retinopathy of prematurity using the screening algorithm WINROP in a Saudi cohort of preterm infants
Lina H Raffa1, Sara K Alessa, Aliaa S Alamri
1Department of Ophthalmology, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia. E-mail. lraffa@kau.edu.sa.
Insights
The WINROP algorithm achieved 100% sensitivity in detecting type 1 retinopathy of prematurity (ROP) in Saudi premature infants. However, its specificity was low, suggesting the need for algorithm adjustments for this population.
Area of Science:
- Neonatal Medicine
- Ophthalmology
- Medical Informatics
Background:
- Retinopathy of prematurity (ROP) is a leading cause of vision loss in premature infants.
- Early detection and treatment of type 1 ROP are crucial to prevent severe visual impairment.
- The Weight, Insulin-like Growth Factor I, Neonatal, Retinopathy of Prematurity (WINROP) algorithm is a web-based tool designed for ROP risk assessment.
Purpose of the Study:
- To retrospectively validate the WINROP algorithm for identifying type 1 ROP in a Saudi cohort of premature infants.
- To assess the sensitivity, specificity, and predictive values of the WINROP algorithm in this specific population.
Main Methods:
- Retrospective review of preterm infants (23-32 weeks gestation) born between August 2013 and October 2018.
- Inputting birth weight, gestational age, and weekly weight measurements into the WINROP algorithm.
- Calculating sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).
Main Results:
- 175 infants were included; 13 (7.4%) developed type 1 ROP.
- WINROP triggered an alarm in 70.9% of all infants and 100% of those with type 1 ROP.
- The algorithm demonstrated 100% sensitivity, 31.5% specificity, 10.5% PPV, and 100% NPV.
Conclusions:
- The WINROP algorithm shows high sensitivity (100%) for detecting type 1 ROP in Saudi premature infants, comparable to developed countries.
- The low specificity (31.5%) indicates a high rate of false positives, limiting its practical utility.
- Modifying the WINROP algorithm for the local Saudi population may enhance its specificity and clinical applicability for ophthalmologists and neonatologists.
Objectives:
To validate the web weight gain-based WINROP (weight, insulin-like growth factor I, neonatal, retinopathy of prematurity [ROP]) algorithm retrospectively to identify type 1 ROP in a Saudi cohort of premature infants. Methods: The records of preterm infants (greater than 23 and less than 32 weeks gestation) born between August 2013 and October 2018, were reviewed. Birth weight, gestational age, and weekly weight measurements of the premature infants were entered online. Based on weekly weight gain, the WINROP algorithm alerted clinicians whether infants were at high-risk for vision‑threatening type 1 ROP. Sensitivity, specificity, positive and negative predictive values were calculated. Results: The median gestational age of the infants at birth was 28 weeks, with median birth weight at 1085 g. Of the 175 infants included in the study, 13 (7.4%) developed type 1 ROP. WINROP positive alarm was triggered in 70.9% (124/175) of all infants and 100% (13/13) of those treated for type 1 ROP. The specificity of the algorithm was 31.5%. Positive predictive values was 10.5% and negative was 100%. Conclusion: The general WINROP sensitivity in identifying type 1 ROP was 100% similar to that reported in developed countries; however, its specificity was low at 31.5%. Tweaking of the algorithm based on the population may increase the specificity and promote the practical utility of this non-invasive screening tool for ophthalmologists and neonatologists in this population.
