A model for predicting birth defects of the fetus based on risk factors in mothers with a history of premature birth

Abstract

Birth defects (BD) are an important cause of neonatal mortality and can be associated with premature birth. The study aimed to develop a prognostic model for congenital malformations in mothers with a history of preterm delivery, using logistic regression analysis. The study included 665 mothers of children with BD, of which 432 (65%) had a history of preterm delivery (main group), and 233 (35%) had term delivery (control group). Variables examined included pregnancy history, genetic factors, and biochemical markers. Statistical analysis found significant associations between BD and preterm delivery, intrauterine malformations, miscarriages, MTHFR polymorphism, and HLA antigens. The logistic model showed good predictive performance. The area under the ROC curve was 0.769 for pregnancy history, 0.699 for miscarriages, and 0.630 for intrauterine malformations, indicating moderate predictive ability. A statistical relationship was found between BD risk and pregnancy history, intrauterine malformations, miscarriages, and genetic factors. The resulting logistic model may help predict BD risk in mothers with a preterm delivery history.

Keywords

• regression analysis
• fetal anomalies
• statistical analysis
• logistic model
• neonatal mortality