Технология прогнозирования дородового излития околоплодных вод

Abstract

Aim of study. To create a technology for prediction of preterm discharge of amniotic fl uid based on universally accessible methods of laboratory and instrumental evaluation. Material and methods. A retrospective analysis of 200 birth cases dated 2018-2021 at the premises of obstetric facilities in Chita and Ufa cities featuring patients admitted to the inpatient unit shortly before term labour (1-2 days). In the course of the study, 2 groups were distinguished: Group 1 included 128 female patients with term discharge of amniotic fluid and Group 2 was constituted by 72 female patients with preterm discharge of amniotic fluid. The groups were comparable in age, anthropomorphic parameters and extragenital pathology. On admission, all women underwent general medical examination and ultrasonography. Statistical processing of the results was performed via the IBM SPSS Statistics Version 25.0 soft ware. Results. The technology for prediction of preterm discharge of amniotic fluid was based on multilayer perceptron learning. The structure of the learning neural network included 5 input neurons: body mass index, fundal height, the total bilirubin level, activated partial thromboplastin time and the amniotic fluid index. Th e percentage of incorrect predictions of the neural network totalled 28.5 %. Conclusion. A complex approach based on integration of universally accessible methods for laboratory and instrumental tests shortly before the labour based on a neural network makes it possible to predict possible preterm discharge of amniotic fl uid with an accuracy of up to 75 %. Application of this technology in clinical practice will make it possible not only to perform timely preparation of the parturient canal but also to reduce the frequency of adverse obstetric and perinatal outcomes