Maternal Lipid Profile as Predictor for Mother and Fetus Outcome-an Artificial Neural Network Approach.

PURPOSE

The study aims to predict mother and fetus outcome based on the mother's lipid profile in the second and third trimester of pregnancy.

MATERIAL AND METHOD

Blood and urinary samples were taken from 135 mothers that were prospectively monitored during the hole pregnancy. Total cholesterol (TC), triglycerides (TG), low-density lipoprotein-cholesterol (LDL-C), high-density lipoprotein-cholesterol (HDL-C), together with other parameters, were used as predictors in a multilayer perceptron (MLP) artificial neural network (ANN). Small for gestational age (SGA) was used to assess the fetal outcome, while Gestational diabetes mellitus (GDM) and, Hypertensive disorders in pregnancy (HDP) to assess the mother's outcome.

RESULTS

SGA prediction rate was 0.637±0.022 for the second trimester and 0.632±0.017 for the third trimester. GDM prediction rate was 0.897±0.006 for the second trimester and 0.632±0.017 for the third trimester. HDP prediction rate was 0.620±0.046 for the second trimester and 0.775±0.030 for the third trimester. When used with other parameters (hemoglobin, thrombocytes, uric acid, GOT, GPT, the presence of proteinuria, urea, and creatinine) the prediction rates raised, going over 90% for the GDM.

CONCLUSIONS

Though individual lipid parameters do not statistically correlate with the output variables the use of ANN generated prediction rates raging from 60% to 90%. The lipid profile from the third trimesters seems to be a better prediction for both fetus and mother outcome.