Validation of the DIGIROP-birth model in a Chinese cohort

Sizhe Chen,Xiaohe Lu,He Chen,Rong Wu,Songfu Feng,Wenbei Ma,Shaolin Du,Chao Li

doi:10.1186/s12886-021-01952-0

Abstract

BackgroundWe 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.MethodsWe 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.ResultsFour 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.ConclusionsAlthough 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.

Highlights

We aimed to validate the predictive performance of the DIGIROP-Birth model for identifying treatment-requiring retinopathy of prematurity (TR-Retinopathy of prematurity (ROP)) in Chinese preterm infants to evaluate its generalizability across countries and races
The data gathered from less-developed countries are scarce but indispensable. These data indicate that racial variation exists, with Asian infants being at greater risk of ROP than White infants, suggesting a genetic predisposition to ROP in addition to the underlying socioeconomic factors associated with low birth weight, small for gestational age, and preterm birth [20, 21]
A significant difference in sex was observed between the groups (P = 0.046), where the proportion of males was significantly higher in infants with treatment-requiring retinopathy of prematurity (TR-ROP)

Summary

Introduction

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. The data gathered from less-developed countries are scarce but indispensable These data indicate that racial variation exists, with Asian infants being at greater risk of ROP than White infants, suggesting a genetic predisposition to ROP in addition to the underlying socioeconomic factors associated with low birth weight, small for gestational age, and preterm birth [20, 21]. The WINROP model, based on weekly weight gain [6], has been widely validated around the globe It displayed favorable utility in predicting severe ROP in developed countries [14,15,16], but performed less satisfyingly in moderately or less-developed countries such as China, Mexico, and Turkey [17,18,19]. ROP risk prediction models should be validated across different races and countries or regions with various clinical settings

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