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Prediction of severe retinopathy of prematurity using the WINROP algorithm in a birth cohort in South East Scotland
  1. Chinthika Piyasena1,
  2. Catherine Dhaliwal2,
  3. Heather Russell3,
  4. Ann Hellstrom4,
  5. Chatarina Löfqvist5,
  6. Ben J Stenson6,
  7. Brian W Fleck7
  1. 1Neonatal Unit, Simpson Centre for Reproductive Health, Royal Infirmary of Edinburgh, Edinburgh, UK
  2. 2Department of Histopathology, Royal Infirmary of Edinburgh, Edinburgh, UK
  3. 3Department of Ophthalmology, Gold Coast Hospital, Southport, Queensland, Australia
  4. 4Department of Pediatric Ophthalmology, Sahlgrenska Academy, The Queen Silvia Children's Hospital, Göteborg, Sweden
  5. 5Department of Ophthalmology, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, Göteborg, Sweden
  6. 6Neonatal Unit, Simpson Centre for Reproductive Health, Royal Infirmary of Edinburgh, Edinburgh, UK
  7. 7Department of Ophthalmology, Princess Alexandra Eye Pavilion, Edinburgh, UK
  1. Correspondence to Dr Chinthika Piyasena, Neonatal Unit, Simpson Centre for Reproductive Health, Royal Infirmary of Edinburgh, 51 Little France Crescent, Edinburgh EH16 4SA, UK; chinthika.piyasena{at}nhs.net

Abstract

Purpose We tested the ability of the ‘Weight, IGF-1, Neonatal Retinopathy of Prematurity (WINROP)’ clinical algorithm to detect preterm infants at risk of severe Retinopathy of Prematurity (ROP) in a birth cohort in the South East of Scotland. In particular, we asked the question: ‘are weekly weight measurements essential when using the WINROP algorithm?’

Study design This was a retrospective cohort study. Anonymised clinical data were uploaded to the online WINROP site, and infants at risk of developing severe ROP were identified. The results using WINROP were compared with the actual ROP screening outcomes. Infants with incomplete weight data were included in the whole group, but were excluded from a subgroup analysis of infants with complete weight data. In addition, data were manipulated to test whether missing weight data points in the early neonatal period would lead to loss of sensitivity of the algorithm.

Results The WINROP algorithm had 73% sensitivity for detecting infants at risk of severe ROP when all infants were included and 87% when the complete weight data subgroup was analysed. Manipulation of data from the complete weight data subgroup demonstrated that one or two missing weight data points in the early postnatal period lead to loss of sensitivity performance by WINROP.

Implications The WINROP program offers a non-invasive method of identifying infants at high risk of severe ROP and also identifying those not at risk. However, for WINROP to function optimally, it has to be used as recommended and designed, namely weekly body weight measurements are required.

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