The competing risk approach for prediction of preeclampsia

被引:180
作者
Wright, David [1 ]
Wright, Alan [1 ]
Nicolaides, Kypros H. [2 ]
机构
[1] Univ Exeter, Inst Hlth Res, Exeter, Devon, England
[2] Kings Coll London, Harris Birthright Res Ctr Fetal Med, London, England
关键词
Bayes theorem; biomarker; mean arterial pressure; personalized distribution; placental growth factor; preeclampsia; soluble fms-like tyrosine kinase-1; PLACENTAL GROWTH-FACTOR; PROPOSED CLINICAL MANAGEMENT; 36 WEEKS GESTATION; MATERNAL CHARACTERISTICS; TYROSINE KINASE-1; BIOCHEMICAL MARKERS; ANGIOGENIC FACTORS; SFLT-1/PLGF RATIO; PREGNANCY; TRIMESTERS;
D O I
10.1016/j.ajog.2019.11.1247
中图分类号
R71 [妇产科学];
学科分类号
100211 ;
摘要
The established method of the assessment of the risk for development of preeclampsia is to identify risk factors from maternal demographic characteristics and medical history; in the presence of such factors, the patient is classified as high risk and in their absence as low risk. Although this approach is simple to perform, it has poor performance of the prediction of preeclampsia and does not provide patient-specific risks. This review de- scribes a new approach that allows the estimation of patient-specific risks of delivery with preeclampsia before any specified gestational age by maternal demographic charac- teristics and medical history with biomarkers obtained either individually or in combi- nation at any stage in pregnancy. In the competing risks approach, every woman has a personalized distribution of gestational age at delivery with preeclampsia; whether she experiences preeclampsia or not before a specified gestational age depends on competition between delivery before or after the development of preeclampsia. The personalized distribution comes from the application of Bayes theorem to combine a previous distribution, which is determined from maternal factors, with likelihoods from biomarkers. As new data become available, what were posterior probabilities take the role as the previous probability, and data collected at different stages are combined by repeating the application of Bayes theorem to form a new posterior at each stage, which allows for dynamic prediction of preeclampsia. The competing risk model can be used for precision medicine and risk stratification at different stages of pregnancy. In the first trimester, the model has been applied to identify a high-risk group that would benefit from preventative therapeutic interventions. In the second trimester, the model has been used to stratify the population into high-, intermediate-, and low-risk groups in need of different intensities of subsequent monitoring, thereby minimizing unexpected adverse perinatal events. The competing risks model can also be used in surveillance of women presenting to specialist clinics with signs or symptoms of hypertensive disorders; combination of maternal factors and biomarkers provide patient-specific risks for pre- eclampsia that lead to personalized stratification of the intensity of monitoring, with risks updated on each visit on the basis of biomarker measurements.
引用
收藏
页码:12 / +
页数:19
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