Prediction model for early accurate detection of preeclampsia

Abstract

The invention discloses a prediction model for early accurate detection of preeclampsia. Through research, it is found that the distribution situation of free DNA in peripheral blood in the region ofa gene transcription start site can reflect the physiological state of pregnant women and fetuses, on the basis that the abundance of free DNA in serum in the region of the gene transcription start site is significantly different between preeclampsia patients and healthy pregnant women, the homogeneity correction is carried out on the abundance of the free DNA, and then by using a machine learningalgorithm and through the optimal combination of different genes, the onset of the preeclampsia can be effectively predicted. Therefore, the screening prediction model of the preeclampsia based on prediction of the free DNA in the peripheral blood and the optimal drone gene combination are constructed, the onset of the preeclampsia can be predicted before preeclampsia clinical symptoms appear, and the model is a relatively noninvasive, economic and convenient method to predict the preeclampsia in the early stage, and has good application prospects in the development of preeclampsia predictionand screening products.

Description

technical field [0001] The invention belongs to the technical field of disease detection products. More specifically, it relates to a predictive model for early and accurate detection of preeclampsia. Background technique [0002] Preeclampsia (preeclampsia), also known as preeclampsia, is a common multisystem disease during pregnancy, with an incidence of about 3-8%. The main features of pre-eclampsia are high blood pressure and proteinuria in pregnant women. Severe cases are accompanied by multiple organ damage or functional failure. In severe cases, pregnant women may suffer from convulsions, coma, and even death, accounting for about 10% of the direct or indirect causes of maternal death. 15%. Although the cause of preeclampsia is not clear, and there is no effective treatment except for termination of pregnancy, early detection and timely intervention can effectively reduce the harm of related complications to pregnant women, so as to effectively prevent pregnant wome...

AI Technical Summary

Problems solved by technology

Benefiting from the discovery of the above high-risk factors, different research groups around the world have reported a series of prediction methods for preeclampsia based on physiological and biochemical indicators, but most of these methods have not been validated and evaluated using independent pregnant data, and a few of them have been validated and evaluated. method, the results are not satisfactory

This may be due to the fact that these physiological and biochemical indicators are affected by various physiological factors, and their correlation with preeclampsia is not strong enough to fully reflect the physiological changes brought about by preeclampsia, resulting in insufficient prediction accuracy

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