Optimizing the Timing of Male Fetal NIPT Testing: A Model Based on Linear Regression, Survival Analysis, and Monte Carlo Simulation

Abstract

The accuracy of non-invasive prenatal testing (NIPT) is significantly influenced by gestational age and maternal body mass index (BMI), and individual variability makes a single standardized testing time suboptimal for both sensitivity and specificity. In this study, a univariate linear regression model was developed based on fetal Y-chromosome concentration data to examine its association with gestational age and BMI, revealing significant positive correlations for both factors (p < 0.01). Subsequently, K-means clustering combined with survival analysis was employed to stratify pregnant individuals into three BMI groups. Optimal testing times were estimated at 13.71, 15.14, and 19.57 weeks, respectively, using Kaplan-Meier curves, with the Log-rank test confirming significant intergroup differences. Accounting for assay variability, Monte Carlo simulation suggested a delay of 0.5–1 week in testing. Furthermore, incorporating maternal age and parity into a multivariate integrated model revealed that individuals with low BMI and prior delivery history could be tested earlier, whereas those with high BMI and no prior delivery required later testing. After optimization, recommended gestational ages were adjusted later by 0.12–0.15 weeks. Notably, in parous individuals with BMI ranging from 27.7 to 33.8, the testing time increased from 13.86 to 15.26 weeks, achieving a success rate of 84.48%.