ЦИФРОВЫЕ РЕСУРСЫ В ПРЕДИКЦИИ ПРЕЭКЛАМПСИИ И ЗАДЕРЖКИ РОСТА ПЛОДА

Abstract

Abstact. Introduction. Preeclampsia and fetal growth restriction are pregnancy complications related to Great Obstetrical Syndromes and having a similar pathogenesis. No antipathogenetic therapy methods have been found for both pathologies, but there is a means of prevention: Acetylsalicylic acid. The drug is effective only in high-risk pregnant women; therefore, various models are being developed to predict preeclampsia and fetal growth restriction, including digital models. They aim to help select patients who need that prophylaxis. Aim. This study is aiming to create a digital model for the early prediction of preeclampsia, based on the patient’s electronic medical card. Materials and Methods. The investigation involved 231 pregnant women. An algorithm was developed for the early prediction of preeclampsia and fetal growth restriction, based on detecting a combination of systemic and local hemodynamic markers: High variability of blood pressure between visits (long-term variability) and high resistance of blood flow in uterine arteries. Long-term (intervisit) variability of blood pressure is calculated for each trimester: First, the arithmetic mean is calculated among the values of systolic blood pressure at successive visits; second, the standard deviation is computed based on the result. Uterine arteries are checked using Doppler ultrasound technology at 11-13 weeks of gestation: Resistivity of blood flow (peripheral resistivity) in the right and left uterine arteries is assessed. Results and Discussion. For this new prediction algorithm, a digital model was developed, the ECAPP program (registration certificate No. 2018660666 – Electronic Prenatal Record with assessing the risk of developing preeclampsia based on blood pressure variability and uterine blood flow resistance). Conclusions. The IT resource presented has a potential for the effective early prediction of preeclampsia, as well as for predicting fetal growth restriction. Keywords: preeclampsia, fetal growth restriction, prognosis, digital model.