Early prediction of severe retinopathy of prematurity requiring laser treatment using physiological data.

backgroundEarly risk stratification for developing retinopathy of prematurity (ROP) is essential for tailoring screening strategies and preventing abnormal retinal development. This study aims to examine the ability of physiological data during the first postnatal month to distinguish preterm infants with and without ROP requiring laser treatment.

methodsIn this cohort study, preterm infants with a gestational age <32 weeks and/or birth weight <1500 g, who were screened for ROP were included. Differences in the physiological data between the laser and non-laser group were identified, and tree-based classification models were trained and independently tested to predict ROP requiring laser treatment.

resultsIn total, 208 preterm infants were included in the analysis of whom 30 infants (14%) required laser treatment. Significant differences were identified in the level of hypoxia and hyperoxia, oxygen requirement, and skewness of heart rate. The best model had a balanced accuracy of 0.81 (0.72-0.87), a sensitivity of 0.73 (0.64-0.81), and a specificity of 0.88 (0.80-0.93) and included the SpO

conclusionsRoutinely monitored physiological data from preterm infants in the first postnatal month are already predictive of later development of ROP requiring laser treatment, although validation is required in larger cohorts. IMPACT: Routinely monitored physiological data from the first postnatal month are predictive of later development of ROP requiring laser treatment, although model performance was not significantly better than baseline characteristics (gestational age, birth weight, sex, multiple birth, prenatal glucocorticosteroids, route of delivery, and Apgar scores) alone. A balanced accuracy of 0.81 (0.72-0.87), a sensitivity of 0.73 (0.64-0.81), and a specificity of 0.88 (0.80-0.93) was achieved with a model including the SpO