Related Experiment Video
Updated: Aug 24, 2025

Monitoring Dynamic Growth of Retinal Vessels in Oxygen-Induced Retinopathy Mouse Model
Published on: April 2, 2021
Quantification of Early Neonatal Oxygen Exposure as a Risk Factor for Retinopathy of Prematurity Requiring Treatment
Jimmy S Chen1, Jamie E Anderson1, Aaron S Coyner1
1Department of Ophthalmology, Oregon Health and Science University, Portland, Oregon.
Insights
Early oxygen exposure in premature infants is a risk factor for retinopathy of prematurity (ROP). Electronic health record data can predict treatment-requiring ROP (TR-ROP) and aggressive ROP (A-ROP).
Area of Science:
- Neonatal ophthalmology
- Neonatal intensive care
- Medical informatics
Background:
- Retinopathy of prematurity (ROP) is a leading cause of childhood blindness.
- While oxygen monitoring has reduced ROP incidence, its role as a risk factor for severe forms like treatment-requiring ROP (TR-ROP) and aggressive ROP (A-ROP) remains unclear.
- Premature infants require careful oxygen management to prevent ROP.
Purpose of the Study:
- To evaluate early oxygen exposure as a predictive variable for developing TR-ROP and A-ROP.
- To utilize electronic health record (EHR) data for this proof-of-concept study.
- To assess the predictive value of oxygen exposure in infants at risk for ROP.
Main Methods:
- Retrospective cohort study of 244 infants screened for ROP.
- Extraction of oxygen saturation and fraction of inspired oxygen (FiO2) data from EHRs up to 31 weeks postmenstrual age (PMA).
- Random forest models trained with gestational age (GA) and cumulative minimum FiO2 at 30 weeks PMA to predict TR-ROP; ROC analysis for A-ROP.
Main Results:
- Random forest models using GA and cumulative minimum FiO2 showed high predictive performance for TR-ROP (AUC = 0.93 ± 0.06).
- Models using GA alone were not significantly different (AUC = 0.92 ± 0.06).
- Oxygen exposure alone demonstrated predictive capability (AUC = 0.80 ± 0.09), and A-ROP prediction yielded an AUC of 0.92.
Conclusions:
- Early oxygen exposure, extractable from EHR data, is a quantifiable risk factor for TR-ROP and A-ROP.
- EHR data can be leveraged to build risk models for diseases like ROP.
- This approach aids in understanding complex relationships between oxygen exposure and prematurity sequelae.
Purpose:
Retinopathy of prematurity (ROP) is a leading cause of childhood blindness related to oxygen exposure in premature infants. Since oxygen monitoring protocols have reduced the incidence of treatment-requiring ROP (TR-ROP), it remains unclear whether oxygen exposure remains a relevant risk factor for incident TR-ROP and aggressive ROP (A-ROP), a severe, rapidly progressing form of ROP. The purpose of this proof-of-concept study was to use electronic health record (EHR) data to evaluate early oxygen exposure as a predictive variable for developing TR-ROP and A-ROP.
Design:
Retrospective cohort study.
Participants:
Two hundred forty-four infants screened for ROP at a single academic center.
Methods:
For each infant, oxygen saturations and fraction of inspired oxygen (FiO2) were extracted manually from the EHR until 31 weeks postmenstrual age (PMA). Cumulative minimum, maximum, and mean oxygen saturation and FiO2 were calculated on a weekly basis. Random forest models were trained with 5-fold cross-validation using gestational age (GA) and cumulative minimum FiO2 at 30 weeks PMA to identify infants who developed TR-ROP. Secondary receiver operating characteristic (ROC) curve analysis of infants with or without A-ROP was performed without cross-validation because of small numbers.
Main Outcome Measures:
For each model, cross-validation performance for incident TR-ROP was assessed using area under the ROC curve (AUC) and area under the precision-recall curve (AUPRC) scores. For A-ROP, we calculated AUC and evaluated sensitivity and specificity at a high-sensitivity operating point.
Results:
Of the 244 infants included, 33 developed TR-ROP, of which 5 developed A-ROP. For incident TR-ROP, random forest models trained on GA plus cumulative minimum FiO2 (AUC = 0.93 ± 0.06; AUPRC = 0.76 ± 0.08) were not significantly better than models trained on GA alone (AUC = 0.92 ± 0.06 [P = 0.59]; AUPRC = 0.74 ± 0.12 [P = 0.32]). Models using oxygen alone showed an AUC of 0.80 ± 0.09. ROC analysis for A-ROP found an AUC of 0.92 (95% confidence interval, 0.87-0.96).
Conclusions:
Oxygen exposure can be extracted from the EHR and quantified as a risk factor for incident TR-ROP and A-ROP. Extracting quantifiable clinical features from the EHR may be useful for building risk models for multiple diseases and evaluating the complex relationships among oxygen exposure, ROP, and other sequelae of prematurity.
Related Concept Videos
Respiratory Assessment: Purpose and Indications
Objectives and Importance:
The primary goal of respiratory assessment is to evaluate patients at early risk of clinical deterioration. Since respiratory distress often precedes other signs of declining health, breathing patterns and sounds become a...
Treatment for Pulmonary Arterial Hypertension: Oxygen Therapy for Respiratory Failure
Oxygen therapy is vital in increasing and maintaining blood oxygen levels in PAH patients. As a result, it aids in reducing fatigue,...

