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Updated: Aug 10, 2025
Monitoring Dynamic Growth of Retinal Vessels in Oxygen-Induced Retinopathy Mouse Model
Published on: April 2, 2021
Early prediction of severe retinopathy of prematurity requiring laser treatment using physiological data
Jarinda A Poppe1, Sean P Fitzgibbon2, H Rob Taal3
1Department of Neonatal and Pediatric Intensive Care, Division of Neonatology, Erasmus University Medical Center Sophia Children's Hospital, Rotterdam, the Netherlands. j.poppe@erasmusmc.nl.
Insights
Physiological data in the first month can predict retinopathy of prematurity (ROP) requiring laser treatment in preterm infants. Models including oxygen saturation/inspired oxygen ratio and demographics show promise for early ROP risk stratification.
Area of Science:
- Neonatal Medicine
- Ophthalmology
- Biomedical Engineering
Background:
- Early risk stratification for retinopathy of prematurity (ROP) is crucial for timely intervention and preventing vision impairment in preterm infants.
- Identifying predictive markers from readily available physiological data can optimize screening protocols.
Purpose of the Study:
- To evaluate the predictive capability of physiological data collected during the first postnatal month for identifying preterm infants who will require laser treatment for ROP.
- To compare the performance of models incorporating physiological data versus baseline characteristics alone.
Main Methods:
- A cohort study included preterm infants (gestational age <32 weeks or birth weight <1500g) screened for ROP.
- Tree-based classification models were developed using physiological parameters (e.g., SpO2/FiO2 ratio, heart rate variability) and baseline demographics.
- Models were trained and independently tested to predict the need for laser treatment for ROP.
Main Results:
- Significant differences in hypoxia, hyperoxia, oxygen requirements, and heart rate patterns were observed between infants who did and did not require laser treatment.
- The best predictive model, incorporating the SpO2/FiO2 ratio and baseline demographics, achieved a balanced accuracy of 0.81, sensitivity of 0.73, and specificity of 0.88.
- Model performance was comparable to using baseline characteristics alone.
Conclusions:
- Routinely monitored physiological data in the first postnatal month can predict the development of ROP requiring laser treatment.
- These findings suggest that physiological monitoring offers potential for early ROP prediction and targeted interventions.
- Further validation in larger cohorts is warranted to refine predictive models and clinical utility.
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.
