Prediction of severe retinopathy of prematurity using the weight gain, insulin-like growth factor 1, and neonatal retinopathy of prematurity algorithm in a Japanese population of preterm infants

• Published in: Japanese journal of ophthalmology Vol. 64; no. 2; pp. 223 - 227
• Main Authors: Ueda, Kaori, Miki, Akiko, Nakai, Shunichiro, Yanagisawa, Suiho, Nomura, Koji, Nakamura, Makoto
• Format: Journal Article
• Language: English
• Published: Tokyo Springer Japan 01.03.2020; Springer Nature B.V
• Subjects: Algorithms; Birth Weight; Clinical Investigation; Female; Follow-Up Studies; Gestation; Gestational Age; Growth factors; Humans; Incidence; Infant, Newborn; Infant, Premature; Infants; Insulin; Insulin-Like Growth Factor I - metabolism; Insulin-like growth factors; Japan - epidemiology; Male; Medical records; Medicine; Medicine & Public Health; Neonates; Newborn babies; Ophthalmology; Premature babies; Reproducibility of Results; Retina; Retina - pathology; Retinopathy; Retinopathy of Prematurity - blood; Retinopathy of Prematurity - diagnosis; Retinopathy of Prematurity - epidemiology; Retrospective Studies; Risk Assessment - methods; Risk Factors; Severity of Illness Index; Weight; Japan; Premature infants; WINROP; Retinopathy of prematurity
• ISSN: 0021-5155; 1613-2246; 1613-2246
• DOI: 10.1007/s10384-019-00709-z

Purpose To retrospectively investigate the sensitivity and specificity of weight gain, insulin-like growth factor 1, and neonatal retinopathy of prematurity (WINROP) algorithm for the prediction of severe retinopathy of prematurity (ROP) in a Japanese population of preterm infants. The WINROP algorithm is a tool based on postnatal weight gain. Study design Retrospective cohort study. Methods The medical records of preterm infants born between January 2011 and March 2017 were retrospectively reviewed. Infants born after 33 weeks of gestation were excluded based on the indications of the WINROP algorithm. Postnatal weight was recorded weekly on the WINROP system until postmenstrual week 36. The sensitivity and specificity of the WINROP algorithm were analyzed. Results In total, 278 infants were included in this study. Based on the WINROP algorithm 110 of these infants were predicted to be at low risk for developing severe ROP and 105 did not develop severe ROP. Based on the WINROP algorithm 168 infants were predicted to be at high risk for developing severe ROP　and 27 developed severe ROP. Thus, the sensitivity of the WINROP algorithm was 84.4% and the specificity 42.7%. Conclusion The WINROP algorithm could be used for preterm infants (gestational age of <28 weeks) without a complicated hospital course. Modification of the algorithm will improve its sensitivity and specificity for the Japanese population.