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The competing risk approach for prediction of preeclampsia

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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 describes a new approach that allows the estimation of patient-specific risks of delivery with preeclampsia before any specified gestational age by maternal demographic characteristics and medical history with biomarkers obtained either individually or in combination 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 preeclampsia that lead to personalized stratification of the intensity of monitoring, with risks updated on each visit on the basis of biomarker measurements.

Section snippets

Prediction by risk scoring systems

The established method of assessing 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.4,5 In the United Kingdom, according to guidelines by the National Institute for Health and Clinical Excellence, women should be considered to be at high risk of the development of preeclampsia if they have any 1 high-risk

Clinical Implementation of the Competing Risks Approach

The competing risk model can be used for precision medicine and risk stratification at different stages of pregnancy. The objective of screening in the first trimester is the identification of a high-risk group that would benefit from preventative therapeutic interventions. The objective of screening in the second and third trimesters is the identification of a high-risk group that would benefit from close monitoring for early diagnosis of preeclampsia, thereby minimizing unexpected adverse

Validation

The competing risk model for use in first-trimester screening has been validated prospectively in 2 studies.40 In these studies, risks were produced, blinded to outcome with the use of a prespecified algorithm in 25,226 pregnancies, which included 712 pregnancies with preeclampsia of which 201 pregnancies were delivered at <37 weeks gestation and 84 pregnancies were delivered at <34 weeks gestation. Performance was assessed by (1) the ability of the model to discriminate between the

Conclusions

The defining features of our approach to prediction of preeclampsia are the use of a time-to-event model for the gestational age at delivery with preeclampsia and the application of Bayes theorem to update the personalized distribution of gestational age at delivery with preeclampsia. Treating delivery with preeclampsia as an event in time allows the prediction of preeclampsia before different gestational ages to be accommodated into the same model; it is a natural way of allowing for

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    This work was supported by grants from the Fetal Medicine Foundation (Charity No: 1037116).

    The Fetal Medicine Foundation had no involvement in the study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.

    The authors report no conflict of interest.

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