Preliminary exploration of prediction model and nomograph drawing of Doppler peak systolic velocity ratio, peak systolic velocity 1 and peak systolic velocity 2 of ophthalmic artery in pregnant women for small for gestational age infants

Objective To explore the predictive value of peak systolic velocity (PSV) ratio, PSV1 and PSV2 of ophthalmic artery Doppler in pregnant women for small for gestational age (SGA) infants and to construct a nomogram prediction model. Methods A total of 201 pregnant women who visited our hospital from March 2022 to June 2024 were selected as the research subjects, and their clinical data and ophthalmic artery Doppler parameters were collected. The data were randomly divided into a training set ( n = 295) and a verification set ( n = 126) in a 7:3 ratio. The independent risk factors for SGA were screened by univariate and multivariate Logistic regression analysis, and the nomogram model was constructed. The model calibration degree, prediction efficiency and clinical value were evaluated. Results The incidence of SGA in the training and validation sets was 25.42 and 25.31%, respectively. Multivariate Logistic regression analysis showed that PSV ratio of ophthalmic artery, PSV1, PSV2, Pulse index 1 (PI1), PI2, and gestational week were the independent risk factors for SGA (all p < 0.05). The C-index of the nomogram model in the training set and the verification set was 0.858 and 0.835, respectively, the area under the ROC curve (AUC) was 0.858 (95% CI : 0.804–0.912) and 0.835 (95% CI : 0.734–0.936), and the sensitivity and specificity were 0.887, 0.747 and 0.636 and 0.833, respectively. The calibration curve showed good agreement between the predicted and actual values, respectively, which was good by the Hosmer-Lemeshow test. The analysis of decision curve showed that the model had high clinical application value in the range of threshold probability 0.10–0.80. Conclusion The nomogram model based on PSV ratio of ophthalmic artery Doppler, PSV1, and PSV2 has good prediction performance for SGA, and provides a new tool for early clinical recognition of high-risk pregnant women with SGA. However, the accuracy and applicability of the model still need to be further verified in the multi-center large sample study.