Development of a non-invasive diagnostic model for severe Retinopathy of Prematurity integrating clinical and platelet data

To construct a non-invasive and convenient early diagnostic model by integrating multidimensional clinical data and platelet (PLT) indices, and to explore the predictive value of PLT for severe Retinopathy of Prematurity (ROP). A study included premature infants admitted to our hospital from January 2020 to September 2025. According to the results of fundus screening, subjects were divided into the ROP group (n = 190) and the normal control group (n = 142). The ROP group was further categorized into mild (n = 110) and severe (n = 15) subgroups based on treatment requirements, which included platelet data corresponding to a postmenstrual age (PMA) of 30 weeks. Clinical data on parental factors, neonatal factors, and treatment factors were collected, along with PLT results from birth to PMA of 40 weeks. Lasso regression was used to select predictive variables, and a nomogram was constructed using multivariate logistic regression, with the model's discrimination and calibration verified. Lasso regression identified gestational age, in vitro fertilization, maternal age, and PLT as core predictive factors. The Area Under the Curve (AUC) of the Nomogram in the training and validation sets was 0.80 (95%CI: 0.74-0.85) and 0.80 (95%CI: 0.71-0.89) respectively. The PLT levels at PMA of 30 weeks in the severe ROP group were significantly lower than those in the mild group (160 × 10^9/L vs. 254 × 10^9/L, p = 0.048), with the AUC of the Nomogram based on PLT combined with clinical indicators reaching 0.86 (95%CI: 0.76-0.96) for severe ROP. The ROP prediction model in this study can assist in the non-invasive identification of high-risk infants for ROP, particularly demonstrating high predictive efficacy for severe ROP.