A multivariate modeling method for the prediction of low fetal fraction before noninvasive prenatal testing

Abstract

Objective: To investigate factors associated with fetal fraction and to develop a new predictive method for low fetal fraction before noninvasive prenatal testing. Methods: The study was a retrospective cohort analysis based on the results of noninvasive prenatal testing, complete blood count, thyroxin test, and Down's syndrome screening during the first or second trimester in 14,043 pregnant women. Random forests algorithm was applied to predict the low fetal fraction status (fetal fraction < 4%) through individual information and laboratory records. The performance of the model was evaluated and compared to predictions using maternal weight. Results: Of 14,043 cases, maternal weight, red blood cell, hemoglobin, and free T3 were significantly negatively correlated with fetal fraction while gestation age, free T4, pregnancy-associated plasma protein-A, alpha-fetoprotein, unconjugated estriol, and β-human chorionic gonadotropin were significantly positively correlated with fetal fraction. Compared to predictions using maternal weight as an isolated parameter, the model had a higher area under the curve of receiver operating characteristic and overall accuracy. Conclusions: The comprehensive predictive method based on combined multiple factors was more effective than a single-factor model in low fetal fraction status prediction. This method can provide more pretest quality control for noninvasive prenatal testing.