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Validation of the DIGIROP-birth model in a Chinese cohort
Sizhe Chen1, Rong Wu1, He Chen1,2
1Department of Ophthalmology, Zhujiang Hospital, Southern Medical University, No.253 Gongyedadao Middle Road, Guangzhou, 510282, Guangdong, China.
Insights
The DIGIROP-Birth model showed lower predictive performance for treatment-requiring retinopathy of prematurity (TR-ROP) in Chinese infants. Modifying the model with postnatal factors improved its accuracy, especially for very preterm infants.
Area of Science:
- Neonatal Ophthalmology
- Medical Informatics
- Predictive Modeling
Background:
- Retinopathy of prematurity (ROP) is a significant cause of visual impairment in preterm infants.
- Early identification of treatment-requiring ROP (TR-ROP) is crucial for timely intervention and preventing vision loss.
- The DIGIROP-Birth model was developed to predict TR-ROP but requires validation in diverse populations.
Purpose of the Study:
- To validate the predictive performance of the DIGIROP-Birth model for TR-ROP in Chinese preterm infants.
- To assess the generalizability of the DIGIROP-Birth model across different countries and ethnicities.
- To explore potential modifications to improve the model's accuracy in a Chinese cohort.
Main Methods:
- Retrospective review of medical records of preterm infants screened for ROP.
- Assessment of the DIGIROP-Birth model's predictive performance using ROC curve analysis.
- Calculation of AUC, sensitivity, specificity, and predictive values for TR-ROP detection.
Main Results:
- The DIGIROP-Birth model showed suboptimal performance in Chinese infants (AUC = 0.634).
- Initial sensitivity for TR-ROP was 51.6%, increasing to 95.7% after incorporating postnatal risk factors.
- The model demonstrated higher sensitivity in infants with gestational age < 28 weeks (92.3%) and birth weight < 1000 g (87.0%).
Conclusions:
- The DIGIROP-Birth model's predictive performance in China was less satisfactory compared to developed countries.
- Modification with postnatal risk factors significantly enhances the model's efficacy for TR-ROP prediction.
- The adjusted model shows potential effectiveness for very preterm infants and those with extremely low birth weight.
Background:
We aimed to validate the predictive performance of the DIGIROP-Birth model for identifying treatment-requiring retinopathy of prematurity (TR-ROP) in Chinese preterm infants to evaluate its generalizability across countries and races.
Methods:
We retrospectively reviewed the medical records of preterm infants who were screened for retinopathy of prematurity (ROP) in a single Chinese hospital between June 2015 and August 2020. The predictive performance of the model for TR-ROP was assessed through the construction of a receiver-operating characteristic (ROC) curve and calculating the areas under the ROC curve (AUC), sensitivity, specificity, and positive and negative predictive values.
Results:
Four hundred and forty-two infants (mean (SD) gestational age = 28.8 (1.3) weeks; mean (SD) birth weight = 1237.0 (236.9) g; 64.7% males) were included in the study. Analyses showed that the DIGIROP-Birth model demonstrated less satisfactory performance than previously reported in identifying infants with TR-ROP, with an area under the receiver-operating characteristic curve of 0.634 (95% confidence interval = 0.564-0.705). With a cutoff value of 0.0084, the DIGIROP-Birth model showed a sensitivity of 48/93 (51.6%), which increased to 89/93 (95.7%) after modification with the addition of postnatal risk factors. In infants with a gestational age < 28 weeks or birth weight < 1000 g, the DIGIROP-Birth model exhibited sensitivities of 36/39 (92.3%) and 20/23 (87.0%), respectively.
Conclusions:
Although the predictive performance was less satisfactory in China than in developed countries, modification of the DIGIROP-Birth model with postnatal risk factors shows promise in improving its efficacy for TR-ROP. The model may also be effective in infants with a younger gestational age or with an extremely low birth weight.
