DIGITAL RESOURCES IN PREDICTION OF PREECLAMPSIA AND FETAL GROWTH RESTRICTION

A. V. Ganeeva, Polina L. Kapelyushnik
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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.
预测子痫前期和胎儿生长受限的数字资源
弃权。导言子痫前期和胎儿生长受限是与大产科综合征相关的妊娠并发症,具有相似的发病机制。目前还没有针对这两种病症的抗病原体治疗方法,但有一种预防方法:乙酰水杨酸。这种药物只对高危孕妇有效;因此,目前正在开发各种预测子痫前期和胎儿生长受限的模型,包括数字模型。其目的是帮助选择需要这种预防措施的患者。研究目的本研究旨在根据患者的电子医疗卡创建一个早期预测子痫前期的数字模型。材料和方法。调查涉及 231 名孕妇。根据对全身和局部血液动力学指标的综合检测,开发了一种用于早期预测子痫前期和胎儿生长受限的算法:两次就诊之间血压的高变异性(长期变异性)和子宫动脉血流的高阻力。计算每个孕期血压的长期(诊间)变异性:首先,计算连续就诊时收缩压值的算术平均值;其次,根据结果计算标准偏差。在妊娠 11-13 周时使用多普勒超声技术检查子宫动脉:评估左右子宫动脉的血流电阻率(外周电阻率)。结果与讨论。为实现这一新的预测算法,开发了一个数字模型,即 ECAPP 程序(注册证号:2018660666 - 电子产前记录,根据血压变化和子宫血流阻力评估患先兆子痫的风险)。结论所介绍的信息技术资源具有有效早期预测子痫前期和预测胎儿生长受限的潜力。关键词:子痫前期、胎儿生长受限、预后、数字模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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