To assess the validity of the online WINROP algorithm in two Spanish populations of premature infants. The study population consisted of 502 premature infants born in the San Cecilio University Hospital of Granada and the Regional University Hospital of Málaga in the years 2000-2015. The WINROP algorithm was used to determine an alarm threshold for retinopathy of prematurity (ROP). The results were compared with those obtained from serial examinations of premature infants. The global WINROP algorithm showed a sensitivity of 62%, specificity of 74%, positive predictive value (PPV) of 59%, and negative predictive value (NPV) of 77%. This algorithm showed a greater sensitivity (76%) to identify severe ROP. The WINROP screening algorithm in this study showed moderate sensitivity, so many ROP cases amenable to treatment were not detected. Other criteria should be added to the algorithm to increase the sensitivity.
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http://dx.doi.org/10.1080/14767058.2018.1517325 | DOI Listing |
J Paediatr Child Health
January 2025
Singapore General Hospital Neonatal & Developmental Medicine, Singapore, Singapore.
Aim: To investigate the validity of WINROP use in multi-ethnic population in a tertiary centre in Singapore.
Methods: Birth weight, gestational age, and weekly weight measurements of four hundred two preterm infants (<32 weeks gestation) born between year 2011 and 2019 were entered into WINROP algorithm. Based on their weekly weight gain, WINROP algorithm would signal an alarm if the infant is at risk for type 1 ROP requiring treatment.
Eye (Lond)
June 2024
Ophthalmology Department, Cairo University, Cairo, Egypt.
Background: Retinopathy of prematurity (ROP) is a leading cause of preventable childhood blindness worldwide. Proper screening for ROP can prevent loss of vision. WINROP (weight, insulin-like growth factor 1, neonatal, retinopathy of prematurity) is an online surveillance system based on gestational age, birth weight and weekly weight gain that can predict infants at risk of sight-threatening retinopathy of prematurity.
View Article and Find Full Text PDFActa Clin Croat
April 2023
Division of Neonatology, Department of Gynecology and Obstetrics, Zagreb University Hospital Center, Zagreb, Croatia.
Care of extremely premature infants is in constant need for evaluation and progress. WINROP, a predictive model based on weight gain, has been developed to reduce the number of stressful examinations for retinopathy for prematurity. Validation studies of WINROP emphasize the difference of applicability in neonatal units of various practice.
View Article and Find Full Text PDFJ Ophthalmol
August 2023
Mater Mother's Hospital Brisbane, Raymond Tce, South Brisbane 4101, QLD, Australia.
Purpose: Four weight-gain-based algorithms are compared for the prediction of type 1 ROP in an Australian cohort: the weight, insulin-like growth factor, neonatal retinopathy of prematurity (WINROP) algorithm, the Children's Hospital of Philadelphia Retinopathy of Prematurity (CHOPROP), the Colorado Retinopathy of Prematurity (CO-ROP) algorithm, and the postnatal growth, retinopathy of prematurity (G-ROP) algorithm.
Methods: A four-year retrospective cohort analysis of infants screened for ROP in a tertiary neonatal intensive care unit in Brisbane, Australia. The main outcome measures were sensitivities, specificities, and positive and negative predictive values.
Indian J Ophthalmol
June 2023
College of Medicine, King Saud bin Abdulaziz University for Health Sciences; King Abdullah International Medical Research Center; Department of Pediatrics, Neonatology Division, King Abdulaziz Medical City, Ministry of National Guard Health Affairs, Western Region, Jeddah, Saudi Arabia.
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.
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