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Year : 2018  |  Volume : 66  |  Issue : 1  |  Page : 110-113

WINROP algorithm for prediction of sight threatening retinopathy of prematurity: Initial experience in Indian preterm infants

1 Department of Vitreo-Retina, Sangam Netralaya, Mohali, Punjab, India
2 Department of Neonatology, Chaitanya Hospital, Chandigarh, India
3 Department of Neonatology, Cosmo Hospital, Mohali, Punjab, India
4 Department of Vitreo-Retina, Advanced Eye Centre, Postgraduate Institute of Medical Education and Research, Chandigarh, India

Correspondence Address:
Dr. Gaurav Sanghi
Department of Vitreo-Retina, Sangam Netralaya, SCO 669, Sector 70, Mohali - 160 071, Punjab
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/ijo.IJO_486_17

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Purpose: To determine the efficacy of the online monitoring tool, WINROP ( in detecting sight-threatening type 1 retinopathy of prematurity (ROP) in Indian preterm infants. Methods: Birth weight, gestational age, and weekly weight measurements of seventy preterm infants (<32 weeks gestation) born between June 2014 and August 2016 were entered into WINROP algorithm. Based on weekly weight gain, WINROP algorithm signaled an alarm to indicate that the infant is at risk for sight-threatening Type 1 ROP. ROP screening was done according to standard guidelines. The negative and positive predictive values were calculated using the sensitivity, specificity, and prevalence of ROP type 1 for the study group. 95% confidence interval (CI) was calculated. Results: Of the seventy infants enrolled in the study, 31 (44.28%) developed Type 1 ROP. WINROP alarm was signaled in 74.28% (52/70) of all infants and 90.32% (28/31) of infants treated for Type 1 ROP. The specificity was 38.46% (15/39). The positive predictive value was 53.84% (95% CI: 39.59–67.53) and negative predictive value was 83.3% (95% CI: 57.73–95.59). Conclusion: This is the first study from India using a weight gain-based algorithm for prediction of ROP. Overall sensitivity of WINROP algorithm in detecting Type 1 ROP was 90.32%. The overall specificity was 38.46%. Population-specific tweaking of algorithm may improve the result and practical utility for ophthalmologists and neonatologists.

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