Risk factors for the time to development of retinopathy of prematurity in premature infants in Iran: a machine learning approach

Abstract

Background: Retinopathy of prematurity (ROP), is a preventable leading cause of blindness in infants and is a condition in which the immature retina experiences abnormal blood vessel growth. The development of ROP is multifactorial; nevertheless, the risk factors are controversial. This study aimed to identify risk factors of time to development of ROP in Iran.

Methods: This historical cohort study utilized data from the hospital records of all newborns referred to the ROP department of Farabi Hospital (from 2017 to 2021) and the NICU records of infants referred from Mahdieh Hospital to Farabi Hospital. Preterm infants with birth weight (BW) ≤ 2000 g or gestational age (GA) < 34 wk, as well as selected infants with an unstable clinical course, as determined by their pediatricians or neonatologists, with BW > 2000 g or GA ≥ 34 wk. The outcome variable was the time to development of ROP (in weeks). Random survival forest was used to analyze the data.

Results: A total of 338 cases, including 676 eyes, were evaluated. The mean GA and BW of the study group were 31.59 ± 2.39 weeks and 1656.72 ± 453.80 g, respectively. According to the criteria of minimal depth and variable importance, the most significant predictors of the time to development of ROP were duration of ventilation, GA, duration of oxygen supplementation, bilirubin levels, duration of antibiotic administration, duration of Total Parenteral Nutrition (TPN), mother age, birth order, number of surfactant administration, and on time screening. The concordance index for predicting survival of the fitted model was 0.878.

Conclusion: Our findings indicated that the duration of ventilation, GA, duration of oxygen supplementation, bilirubin levels, duration of antibiotic administration, duration of TPN, mother age, birth order, number of surfactant administrations, and on time screening are potential risk factors of prognosis of ROP. The associations between identified risk factors were mostly nonlinear. Therefore, it is recommended to consider the nature of these relationships in managing treatment and designing early interventions.

Keywords: Machine learning; Maternal risk factors; Neonatal risk factor; Random survival forest; Retinopathy of prematurity ROP.

Fig. 1

Kaplan–Meier estimate of the survival distribution function for 676 eyes related to 338 infants with 95% confidence intervals indicating time to development of Retinopathy of prematurity (ROP)

Fig. 2

One sample tree of the 1000 trees of the created RSF for the Retinopathy of prematurity (ROP) data

Fig. 3

Minimal depth plot of the selected risk factors of developing Retinopathy of prematurity (ROP); smaller values of minimal depth indicate a more important variable

Fig. 4

Variable importance (VIMP) plot of the selected risk factors of developing Retinopathy of prematurity (ROP); smaller values of minimal depth indicate a more important variable

Fig. 5

Partial 3-month predicted survival for selected influential covariates on time to developing Retinopathy of prematurity (ROP). Dashed red lines indicates ± 2 standard error bars

Fig. 6

Random survival forest estimated 3-months survival as a function of birth weight, gestational age, and bilirubin. Smoothed curves are loess curves of the estimated survival

Fig. 7

Survival curves for the two identified risk groups based on selected variables in the RSF analysis