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Individual Risk Prediction for Sight-Threatening Retinopathy of Prematurity Using Birth Characteristics
Aldina Pivodic1,2, Anna-Lena Hård1, Chatarina Löfqvist1,3
1Department of Ophthalmology, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Insights
This study developed DIGIROP-Birth, an easy-to-use prediction model for retinopathy of prematurity (ROP) treatment using only birth data. It accurately predicts ROP risk in premature infants, improving screening efficiency and potentially preventing blindness.
Area of Science:
- Neonatal ophthalmology
- Predictive modeling in healthcare
- Public health and preventative medicine
Background:
- Retinopathy of prematurity (ROP) is a leading cause of infant blindness, necessitating frequent screening.
- Current screening methods are resource-intensive, with only a small fraction of infants requiring treatment.
- Improved risk stratification is crucial for efficient ROP screening and blindness prevention.
Purpose of the Study:
- To develop and validate an accessible prediction model for ROP treatment using only birth characteristics.
- To establish a continuous hazard function for predicting the need for ROP treatment.
- To enhance early risk stratification for ROP to optimize screening and intervention.
Main Methods:
- Retrospective cohort study analyzing Swedish National Patient Registry data (2007-2018).
- Development of the DIGIROP-Birth model using Poisson regression with time-varying data (postnatal age, gestational age, birth weight, sex).
- Internal and external validation (US, European cohorts) and comparison with four existing ROP prediction models.
Main Results:
- The DIGIROP-Birth model demonstrated high predictive ability across validations (AUCs ranging from 0.87 to 0.94).
- The model's performance was comparable or superior to existing models that require more complex data.
- Postnatal age emerged as a more significant predictor than postmenstrual age for ROP treatment risk.
Conclusions:
- DIGIROP-Birth provides accurate, individualized ROP treatment risk prediction based solely on birth data for infants born at 24-30 weeks' gestational age.
- The model is an accessible online tool, generalizable across different populations and time periods.
- This tool can significantly improve the efficiency of ROP screening and timely intervention, reducing the risk of blindness.
Importance:
To prevent blindness, repeated infant eye examinations are performed to detect severe retinopathy of prematurity (ROP), yet only a small fraction of those screened need treatment. Early individual risk stratification would improve screening timing and efficiency and potentially reduce the risk of blindness.
Objectives:
To create and validate an easy-to-use prediction model using only birth characteristics and to describe a continuous hazard function for ROP treatment.
Design, Setting, And Participants:
In this retrospective cohort study, Swedish National Patient Registry data from infants screened for ROP (born between January 1, 2007, and August 7, 2018) were analyzed with Poisson regression for time-varying data (postnatal age, gestational age [GA], sex, birth weight, and important interactions) to develop an individualized predictive model for ROP treatment (called DIGIROP-Birth [Digital ROP]). The model was validated internally and externally (in US and European cohorts) and compared with 4 published prediction models.
Main Outcomes And Measures:
The study outcome was ROP treatment. The measures were estimated momentary and cumulative risks, hazard ratios with 95% CIs, area under the receiver operating characteristic curve (hereinafter referred to as AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).
Results:
Among 7609 infants (54.6% boys; mean [SD] GA, 28.1 [2.1] weeks; mean [SD] birth weight, 1119 [353] g), 442 (5.8%) were treated for ROP, including 142 (40.1%) treated of 354 born at less than 24 gestational weeks. Irrespective of GA, the risk for receiving ROP treatment increased during postnatal weeks 8 through 12 and decreased thereafter. Validations of DIGIROP-Birth for 24 to 30 weeks' GA showed high predictive ability for the model overall (AUC, 0.90 [95% CI, 0.89-0.92] for internal validation, 0.94 [95% CI, 0.90-0.98] for temporal validation, 0.87 [95% CI, 0.84-0.89] for US external validation, and 0.90 [95% CI, 0.85-0.95] for European external validation) by calendar periods and by race/ethnicity. The sensitivity, specificity, PPV, and NPV were numerically at least as high as those obtained from CHOP-ROP (Children's Hospital of Philadelphia-ROP), OMA-ROP (Omaha-ROP), WINROP (weight, insulinlike growth factor 1, neonatal, ROP), and CO-ROP (Colorado-ROP), models requiring more complex postnatal data.
Conclusions And Relevance:
This study validated an individualized prediction model for infants born at 24 to 30 weeks' GA, enabling early risk prediction of ROP treatment based on birth characteristics data. Postnatal age rather than postmenstrual age was a better predictive variable for the temporal risk of ROP treatment. The model is an accessible online application that appears to be generalizable and to have at least as good test statistics as other models requiring longitudinal neonatal data not always readily available to ophthalmologists.
