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

Abstract

Background Early 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. Methods In 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. Results In 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 SpO2/FiO2 ratio and baseline demographics (including gestational age and birth weight). Conclusions Routinely 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 SpO2/FiO2 ratio and baseline characteristics. Physiological data have potential to play a significant role for future ROP prediction and provide opportunities for early interventions to protect infants from abnormal retinal development.

The Finnegan Score for Neonatal Opioid Withdrawal Revisited With Routine Electronic Data: Retrospective Study

“…Thus, we applied specific periods (t–1 and t–2; Figures1 2 ) and identified an individual baseline of vital parameters to compile data with varying temporal resolution. Of note, temporal cross-correlation of vital parameters might have resulted in an improvement in predictive values on the severity of FS, as previously shown in other cohorts of preterm infants with sepsis, necrotizing enterocolitis, or retinopathy [ 38 42 43 ].…”

“…To date, our data did not offer sufficient temporal resolution to cross-correlate vital parameter data strings. However, based on the most the recent study by Poppe et al [ 38 ], we suggest that continuously logged electronic data sampled preferentially at 1 Hz should be obtained. For model development, such high-frequency data may also be obtained during prospective trials.…”

Mechanical Ventilation, Retinal Avascularity and Rate of Vascularisation: A Triad of Predictors for Retinopathy of Prematurity Treatment

“…Other factors that have been studied in this context include fluctuations in oxygenation, high levels of oxygen concentration and prolonged mechanical ventilation [3]. Some predictive models include the latter variable [4]. Weight gain has been used in various algorithms, presenting high sensitivity, to predict severe ROP, thus reducing the number of examinations needed [5].…”