BACKGROUND: Retinopathy of prematurity (ROP) is a disorder of the preterm newborn characterized by neurovascular disruption in the immature retina that may cause visual impairment and blindness. OBJECTIVE: To develop a clinical screening tool for early postnatal prediction of ROP in preterm newborns based on risk information available within the first 48 h of postnatal life. METHODS: Using data submitted to the Vermont Oxford Network (VON) between 1995 and 2015, we created logistic regression models based on infants born <28 completed weeks gestational age. We developed a model with 60% of the data and identified birth weight, gestational age, respiratory distress syndrome, non-Hispanic ethnicity, and multiple gestation as predictors of ROP. We tested the model in the remaining 40%, performed tenfold cross-validation, and tested the score in ELGAN study data. RESULTS: Of the 1,052 newborns in the VON database, 627 recorded an ROP status. Forty percent had no ROP, 40% had mild ROP (stages 1 and 2), and 20% had severe ROP (stages 3-5). We created a weighted score to predict any ROP based on the multivariable regression model. A cutoff score of 5 had the best sensitivity (95%, 95% CI 93-97), while maintaining a strong positive predictive value (63%, 95% CI 57-68). When applied to the ELGAN data, sensitivity was lower (72%, 95% CI 69-75), but PPV was higher (80%, 95% CI 77-83). CONCLUSIONS: STEP-ROP is a promising screening tool. It is easy to calculate, does not rely on extensive postnatal data collection, and can be calculated early after birth. Early ROP screening may help physicians limit patient exposure to additional risk factors, and may be useful for risk stratification in clinical trials aimed at reducing ROP.
BACKGROUND:Retinopathy of prematurity (ROP) is a disorder of the preterm newborn characterized by neurovascular disruption in the immature retina that may cause visual impairment and blindness. OBJECTIVE: To develop a clinical screening tool for early postnatal prediction of ROP in preterm newborns based on risk information available within the first 48 h of postnatal life. METHODS: Using data submitted to the Vermont Oxford Network (VON) between 1995 and 2015, we created logistic regression models based on infants born <28 completed weeks gestational age. We developed a model with 60% of the data and identified birth weight, gestational age, respiratory distress syndrome, non-Hispanic ethnicity, and multiple gestation as predictors of ROP. We tested the model in the remaining 40%, performed tenfold cross-validation, and tested the score in ELGAN study data. RESULTS: Of the 1,052 newborns in the VON database, 627 recorded an ROP status. Forty percent had no ROP, 40% had mild ROP (stages 1 and 2), and 20% had severe ROP (stages 3-5). We created a weighted score to predict any ROP based on the multivariable regression model. A cutoff score of 5 had the best sensitivity (95%, 95% CI 93-97), while maintaining a strong positive predictive value (63%, 95% CI 57-68). When applied to the ELGAN data, sensitivity was lower (72%, 95% CI 69-75), but PPV was higher (80%, 95% CI 77-83). CONCLUSIONS: STEP-ROP is a promising screening tool. It is easy to calculate, does not rely on extensive postnatal data collection, and can be calculated early after birth. Early ROP screening may help physicians limit patient exposure to additional risk factors, and may be useful for risk stratification in clinical trials aimed at reducing ROP.
Authors: Hannes Hudalla; Thomas Bruckner; Johannes Pöschl; Thomas Strowitzki; Ruben-J Kuon Journal: Arch Gynecol Obstet Date: 2020-02-28 Impact factor: 2.344
Authors: Sang Jin Kim; Alexander D Port; Ryan Swan; J Peter Campbell; R V Paul Chan; Michael F Chiang Journal: Surv Ophthalmol Date: 2018-04-19 Impact factor: 6.048