CLINICAL DECISION SUPPORT SYSTEM FOR PREDICTING THE RISK OF PRETERM LABOUR IN WOMEN WITH UNDIFFERENTIATED CONNECTIVE TISSUE DYSPLASIA

Background. Preterm labour is an unresolved problem in modern obstetrics. It is important to identify risk groups among women of reproductive age and develop methods for early prevention. Objective. Development of a clinical decision support system for predicting the risk of preterm labour in patients with undifferentiated connective tissue dysplasia and evaluation of its effectiveness. Material and methods. 159 women of reproductive age with undifferentiated connective tissue dysplasia were examined. To assess the impact of undifferentiated connective tissue dysplasia on the likelihood of preterm labour, the characteristics of the menstrual cycle, the course of pregnancy, the concentration of magnesium in the blood serum and erythrocytes and the concentration of nitric oxide in the blood plasma were studied. Binary logistic multiple regression was used to predict the risk of preterm labour. The results of the obtained equation were adapted for practical application using the full version of Bayes’ theorem. Results. A method for assessing the risk of preterm labour in women with undifferentiated connective tissue dysplasia has been developed, with 100% sensitivity, 94.02% specificity, and 94.4% accuracy. Conclusions. Personalized preconception care under the risk assessment control has led to a full-term labour, improved perinatal outcomes and effective cost savings.