A comprehensive predictive method for low fetal fraction in noninvasive prenatal screening

Abstract

BackgroundThe study was a retrospective cohort analysis based on the results of noninvasive prenatal screening (NIPS), complete blood count, thyroxin test and Down's syndrome screening in first or second trimester from 14043 pregnant women. Random forests algorithm was applied to predict the low fetal fraction of cell free DNA (with FF lower than 10th percentile) through individual and laboratory information. Performance of the model was evaluated and compared to prediction using maternal weight.To investigate factors associated with lower FF in the NIPS and to develop a new predictive method for low FF before NIPS. ResultsOf 14043 cases, maternal weight, RBC, HGB and free T3 were significantly negative correlated with FF while gestation age, free T4, PAPP-A, AFP, uE3 and β-hCG were significantly positive correlated with FF. Compared to prediction using maternal weight as isolated parameter, the model has a higher area under curve(AUC) of Receiver Operating Characteristic (ROC) and overall accuracy. ConclusionsThe comprehensive predictive method based on combined multiple factors was more effective than single-factor model in low FF status prediction. This method can provide more information for clinical choice and pre-test quality control of NIPS.