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Prediction method for onset risk of preeclampsia in early pregnancy

A technology for preeclampsia and early pregnancy, applied in health index calculation, medical automated diagnosis, medical informatics, etc., can solve problems such as the net income and clinical utility of the prediction model have not been evaluated, and achieve superior prediction performance and concise nomogram , the effect of reducing economic and mental burden

Inactive Publication Date: 2021-05-18
THE OBSTETRICS & GYNECOLOGY HOSPITAL OF FUDAN UNIV
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The net benefit and clinical utility of the predictive model was not assessed

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  • Prediction method for onset risk of preeclampsia in early pregnancy
  • Prediction method for onset risk of preeclampsia in early pregnancy
  • Prediction method for onset risk of preeclampsia in early pregnancy

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[0031] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. All other embodiments obtained by persons of ordinary skill in the art based on the embodiments of the present invention belong to the protection scope of the present invention.

[0032] According to an embodiment of the present invention, a method for predicting the risk of preeclampsia in early pregnancy is provided.

[0033] Such as Figure 1-Figure 2 As shown, the method for predicting the risk of preeclampsia in early pregnancy according to an embodiment of the present invention includes the following steps:

[0034] Step S1, pre-obtaining the characteristic information of the sample from the pregnant female subject in Ping An;

[0035] In step S2, the character...

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Abstract

The invention discloses a method for predicting preeclampsia onset risk in early pregnancy, and relates to the technical field of preeclampsia onset risk prediction. The method comprises the following steps: feature information of a body sample of a safe pregnant woman being obtained in advance; performing multi-factor Logistic regression analysis on the obtained feature information of the female subject sample to establish a prediction model, and representing the prediction model by a nomogram; and performing individualized prediction of preeclampsia based on the nomogram. According to the method, the prediction performance is excellent, the column diagram of the prediction model is simple and clear, powerful support can be provided for clinical decision making of clinicians, intervention measures are taken as soon as possible for pregnant women with the high preeclampsia attack risk, very important prenatal and postnatal care significance is achieved, the occurrence risk of intrauterine growth limitation of premature infants and fetuses can be reduced. Economic and mental burdens of society and families are reduced.

Description

technical field [0001] The present invention relates to the technical field of risk prediction of eclampsia, in particular to a method for predicting the risk of preeclampsia in early pregnancy. Background technique [0002] Pre-eclampsia, or eclampsia, is a common obstetric complication caused by the placenta, which endangers 3%-5% of nulliparous pregnancies and infant health and causes 10%-15% of maternal deaths worldwide. Currently, although there are many biomarkers that can be used in clinical predictive models. Soluble FMS-like tyrosine kinase-1, placental growth factor and blood lipids) have some predictive ability, or some predictive models have been established. Most models are neither internally nor externally validated. There are approximately three predictive models in the study population, divided into training and validation sets. However, the three models were not verified by decision curve analysis, and only the area under the ROC curve was used to evaluat...

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Application Information

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IPC IPC(8): G16H50/30G16H50/20
CPCG16H50/30G16H50/20
Inventor 岳朝艳应春妹高蒋平
Owner THE OBSTETRICS & GYNECOLOGY HOSPITAL OF FUDAN UNIV
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