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Medical AI Inquiry: You was designed to assist in medical decision-making based on the provided user questions. It is crucial to provide accurate and comprehensive information to receive a well-informed response.  Labels: OBJECTIVE, METHODS, RESULTS. Meshes: Algorithms, Animals, Birth Weight, Female, Gestational Age, Humans, Infant, Extremely Premature, Infant, Newborn, Male, Pregnancy, Retinopathy of Prematurity, Retrospective Studies, Weight Gain.
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Does wINROP identify severe retinopathy of prematurity at an early stage in a nation-based cohort of extremely preterm infants? Context: To evaluate the ability of a postnatal weight-gain algorithm (WINROP) to identify sight-threatening retinopathy of prematurity (ROP type 1) in a nation-based extremely preterm infant cohort. This study enrolled all 707 live-born extremely preterm (gestational age [GA] <27 weeks) infants, born 2004-2007 in Sweden; the Extremely preterm Infants in Sweden Study (EXPRESS). WINROP analysis was performed retrospectively in 407 of the infants using weekly weight gain to assess the preterm infant's risk of developing ROP type 1 requiring treatment. GA, birthweight (BW), and weekly postnatal weight measurements were entered into WINROP. WINROP signals with an alarm to indicate if the preterm infant is at risk for ROP type 1. In this extremely preterm population, WINROP correctly identified 96% (45/47) of the infants who required treatment for ROP type 1. The median time from alarm to treatment was 9 weeks (range, 4-20 weeks).
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WINROP, an online surveillance system using weekly weight gain, identified extremely preterm infants at risk for ROP type 1 requiring treatment at an early stage and with high sensitivity in a Swedish nation-based cohort. Final Decision: yes