Author(s)

ZhiJian Dai^*, LinYv Yang, Kun Li, ZongSheng Wang, YiFeng Liu, JunRan Zhao, Liang Xia

Affiliation(s)

College of Public Health and Health Sciences, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China.

Corresponding Author

ZhiJian Dai

ABSTRACT

This study aims to enhance the accuracy of non-invasive prenatal testing (NIPT) by optimizing detection timing for male fetuses and improving abnormality diagnosis for female fetuses. For male fetuses, a multivariate nonlinear regression model quantified the associations of Y-chromosome fraction with gestational weeks and maternal BMI, revealing quadratic and decreasing trends, respectively. Ordered clustering and a risk-based optimization identified four BMI groups with distinct optimal testing windows, where higher BMI delayed the best timing. Monte Carlo simulations further confirmed sequencing quality as the main error source. For female fetuses, a combined logistic regression–random forest model was developed using standardized Z-scores and quality indicators, with an optimal diagnostic threshold (τ = 0.35). The resulting workflow integrates quality screening, Z-score assessment, probability evaluation, and BMI-specific correction. These models provide clinically interpretable guidance to improve NIPT reliability and prenatal decision-making.

KEYWORDS

NIPT; Nonlinear regression; Ordered clustering; Multi-objective optimization; Female fetal abnormality detection

CITE THIS PAPER

ZhiJian Dai, LinYv Yang, Kun Li, ZongSheng Wang, YiFeng Liu, JunRan Zhao, Liang Xia. Optimization of nipt timing for male fetuses and abnormality detection in female fetuses based on quality-corrected models. Journal of Pharmaceutical and Medical Research. 2025, 7(2): 52-58. DOI: https://doi.org/10.61784/jpmr3046.

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