Exploring new predictors for hypertensive disorders of pregnancy

Abstract

The best performing predicting Bayesian algorithm for preeclampsia, endorsed by FIGO, identifies high-risk women at first trimester screening who benefits of a closer monitoring and possibly preventive measures. Unfortunately, the most frequent late term and term preeclampsia are less efficiently predicted. This algorithm is based on statistical assumptions at odds with the physiopathology: preeclampsia is a disease and not a syndrome, as we know it is, and the contingent time-based criteria according to which all pregnancies if not terminated by nature should develop this “disease”.

In addition to this, we know that gestational hypertension might cause in fifty percent of cases severe outcome, comparable to preeclampsia. The very definition of preeclampsia as proteinuric hypertension is now extended to hypertension associated with other end-organ damage, including fetal growth restriction (FGR), this latter condition proceeding, in early onset cases, hypertension. Predicting phenotypes of hypertensive Disorders of pregnancy (HDP) could better help clinical practice.

This study reports exploratory observations in women resulted at high and low risk at first trimester screening followed up at second and third trimester, to term. The co-variates interrogated were sFlt1/PlGF ratio, the uterine arteries PI, the systemic vascular resistances (SVR), maternal total body water and visceral fat.

Women were classified as HDP-AGA, HDP-FGR, normotensive-FGR and uneventful pregnancies (controls). We performed a longitudinal Bayesian multivariate mixed-effects model corrected both for pre-gestational BMI and trimester of analysis.

The sFlt-1/PlGF ratio and SVR confirmed their significant difference in HDP-AGA, in normotensive FGR, and HDP-FGR along the three trimesters from controls, but with different strength along the three trimesters.

The bioimpedance analysis of total body water and visceral fat confirmed the association of these co-factors with women who will develop HDP-AGA.

The strength of longitudinal changes observed, even on a limited number of cases, provide evidence that Bayesian algorithms applied at screening tests at different gestational ages, should be based on co-variates significantly associated either with HDP-FGR or with HDP-AGA provided that the main causative co-factors involved are adopted by predictive models aimed at these distinct diseases.