Research on Dynamic Timing Decision-Making for Non-Invasive Prenatal Testing Based on Risk-Error Co-Optimization

Yanting Liu

doi:10.62051/ijphmr.v6n2.06

Authors

Yanting Liu

DOI:

https://doi.org/10.62051/ijphmr.v6n2.06

Keywords:

Non-invasive prenatal testing, K-means clustering, Risk-error optimization model

Abstract

Addressing the risks faced by high-BMI pregnant women in non-invasive prenatal testing (NIPT)—namely delayed attainment of sufficient fetal cell-free DNA (cfDNA) concentration and missed optimal intervention windows—this study establishes a data-driven system for optimizing testing timepoints. The research first preprocesses raw samples by converting gestational age into continuous numerical variables and develops evaluation metrics encompassing clinical intervention risks and testing accuracy. To address the limitation of static grouping in distinguishing individual differences, the K-means clustering algorithm was employed to reclassify pregnant women into five mutually exclusive clusters based on BMI characteristics. Kaplan-Meier survival analysis was then applied to reveal the temporal evolution of Y chromosome concentration attainment rates across different BMI levels. Building upon this foundation, a nonlinear optimization model was constructed to balance sequencing failure risk and missed therapeutic window risk. By minimizing total risk and sequencing error, optimal detection timepoints were determined for each group. Validation results demonstrated that the optimal detection timepoint for the high-BMI group was significantly delayed compared to the low-BMI group, with a maximum deviation of up to 4 weeks. Finally, sensitivity analysis via Monte Carlo simulation confirmed the model's robustness to sequencing perturbations

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