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WINROP algorithm for prediction of sight threatening retinopathy of prematurity: Initial experience in Indian preterm
Gaurav Sanghi1, Anil Narang2, Sunny Narula3
1Department of Vitreo-Retina, Sangam Netralaya, Mohali, Punjab, India.
Insights
The WINROP tool shows high sensitivity (90.32%) in detecting sight-threatening retinopathy of prematurity (ROP) in Indian preterm infants. Further algorithm refinement may enhance its clinical utility for ophthalmologists and neonatologists.
Area of Science:
- Ophthalmology
- Neonatology
- Medical Technology
Background:
- Retinopathy of prematurity (ROP) is a leading cause of blindness in preterm infants.
- Early detection and treatment are crucial for preventing vision loss.
- The WINROP tool utilizes weight gain patterns to predict ROP risk.
Purpose of the Study:
- To evaluate the efficacy of the WINROP online monitoring tool.
- To assess WINROP's ability to detect sight-threatening Type 1 ROP in Indian preterm infants.
Main Methods:
- Seventy preterm infants (<32 weeks gestation) were enrolled.
- Birth weight, gestational age, and weekly weight data were input into the WINROP algorithm.
- Sensitivity, specificity, positive, and negative predictive values were calculated.
Main Results:
- 31 (44.28%) infants developed Type 1 ROP.
- WINROP signaled an alarm in 74.28% of all infants and 90.32% of those with Type 1 ROP.
- The negative predictive value was 83.3%, while specificity was 38.46%.
Conclusions:
- WINROP demonstrated high sensitivity in detecting Type 1 ROP in this Indian cohort.
- The study is the first in India to use a weight gain-based algorithm for ROP prediction.
- Population-specific algorithm adjustments could improve WINROP's clinical utility.
Purpose:
To determine the efficacy of the online monitoring tool, WINROP (https://winrop.com/) 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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