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Purpose: Screening guidelines for retinopathy of prematurity (ROP) are updated frequently to help clinicians identify infants at risk of type 1 ROP. This study aims to evaluate the accuracy of three different predictive algorithms—WINROP, ROPScore, and CO?ROP—in detecting ROP in preterm infants in a developing country. Methods: This retrospective study was conducted on 386 preterm infants from two centers between 2015 and 2021. Neonates with gestational age ?30 weeks and/or birth weight ?1500 g who underwent ROP screening were included. Results: One hundred twenty?three neonates (31.9%) developed ROP. The sensitivity to identify type 1 ROP was as follows: WINROP, 100%; ROPScore, 100%; and CO?ROP, 92.3%. The specificity was 28% for WINROP, 1.4% for ROPScore, and 19.3% for CO?ROP. CO?ROP missed two neonates with type 1 ROP. WINROP provided the best performance for type 1 ROP with an area under the curve score at 0.61. Conclusion: The sensitivity was at 100% for WINROP and ROPScore for type 1 ROP; however, specificity was quite low for both algorithms. Highly specific algorithms tailored to our population may serve as a useful adjunctive tool to detect preterm infants at risk of sight?threatening ROP
ABSTRACT
Background: Among the premature infants, WINROP (weight, insulin like growth factor 1, neonatal retinopathy of prematurity), a web- based retinopathy of prematurity (ROP) risk algorithm, uses postnatal weight gain in predicting the risk of severe ROP. This retrospective study assess the sensitivity and specificity of WINROP algorithm to predict proliferative ROP (type 1, type 2).Methods: This was a tertiary hospital based retrospective study conducted in level 3 - NICU from February -November 2018. The data was entered in WINROP website. 45 neonates enrolled in the study, were classified as either alarm given (increased risk of severe ROP) or not given (no risk of severe ROP/ no ROP). Timing of alarm was also noted.Result: 10 neonates (22%) had severe ROP requiring treatment. The mean gestational age was 30 weeks and mean birth weight was 1275 grams. In this study, sensitivity to WINROP online system was found to be 90%, specificity of 48.6%, positive predictive value of 33.3% and negative predictive value of 94.4%. The median time from alarm to treatment was 6 weeks (3-8 weeks).Conclusion: WINROP algorithm has a good sensitivity in detection of treatable ROP.
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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.
ABSTRACT
Objective@#To validate WINROP, a web-based screening tool for retinopathy of prematurity (ROP), in the detection of any-stage ROP or treatment-requiring ROP among Filipino preterm infants screened for ROP from January 2013 to April 2017.@*Methods@#Charts of preterm infants who were screened for ROP at a tertiary hospital from January 2013 to April 2017 were reviewed. Birth date, gestational age, birth weight, and weekly postnatal weight measurements were collected and entered into WINROP. The number of infants that were tagged by WINROP with alarm signals for any-stage ROP or treatment-requiring ROP were noted and compared with actual ROP screening findings. The sensitivity, specificity, positive predictive values (PPV), and negative predictive values (NPV) of the WINROP application in predicting any-stage ROP and treatment-requiring ROP were computed.@*Results@#Charts of 138 preterm infants were included in the study. Sixty-four (64) had a chart diagnosis of anystage ROP and 13 had treatment-requiring ROP. WINROP tagged 77 and 10 preterm infants with any-stage ROP and treatment-requiring ROP, respectively. The sensitivity and specificity rates of WINROP for detecting any-stage ROP were 63.5% (95% CI: 51.5% - 74.2%) and 78.1% (95% CI: 65.7% - 87.1%), respectively. While the sensitivity and specificity rates at identifying treatment-requiring ROP were 76.9% (95% CI: 45.9% - 93.8%) and 46.4% (95% CI: 37.5% - 55.5%), respectively. @*Conclusion@#WINROP is fairly sensitive and specific in predicting any-stage ROP but has fair sensitivity and poor specificity in predicting treatment-requiring ROP. WINROP may aid in ROP prediction, but regular screening of preterm infants at risk for ROP based on current criteria remains to be the standard of care.