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This chapter should be cited as follows:
Moungmaithong S, Shen L, et al, Glob. libr. women's med.,
ISSN: 1756-2228; DOI 10.3843/GLOWM.416333

The Continuous Textbook of Women’s Medicine SeriesObstetrics Module

Volume 2

Health and risk in pregnancy and childbirth

Volume Editors: Professor Claudia Hanson, Karolinska Institutet, Sweden
Dr Nicola Vousden, King’s College, London, UK


Risk Factors and Predictors for Pre-eclampsia

First published: July 2022

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“I value screening so that I can appropriately contextualize my risk and plan accordingly. Not for anything, but with other children at home, knowing at 12 weeks that I am higher risk for complications would give me much better lead time to find a childcare provider and to budget for it even if I wound up not ultimately needing more advanced care.”

Pre‑eclampsia survivor


The randomized trial of aspirin versus placebo for the prevention of preterm pre-eclampsia (ASPRE) has confirmed that pre-eclampsia is predictable and preventable. The administration of low-dose aspirin initiated before 16 weeks’ gestation significantly reduces the rate of preterm pre-eclampsia. Therefore, it is important to identify pregnant women at risk of developing pre-eclampsia during the first trimester of pregnancy, thus allowing timely therapeutic intervention. Currently, there is no single predictor of pre-eclampsia among women at either low or increased risk of pre-eclampsia that is ready for introduction into clinical practice. However, the prediction of pre-eclampsia could be achieved by multivariable approaches that combine clinical, ultrasound and laboratory predictors. The most effective screening algorithm available is the Fetal Medicine Foundation (FMF) combined test that integrates maternal “a priori” risk based on maternal characteristics and obstetric history with “triple test”, which consists of mean arterial pressure (MAP), uterine artery pulsatility index (UTPI), and serum placental growth factor (PLGF). However, it should be stated that very few of the informative data have been derived from populations of women who bear the greatest burden of experiencing complications of pre-eclampsia, namely women in low- and middle-income countries (LMICs).


In our opinion, this area of research and clinical practice has been confused by several factors, of which we emphasize three.

First, and of particular relevance for colleagues in LMICs, there is the need to identify women who are at increased risk for any placenta‑derived antenatal complication, whether pre‑eclampsia, gestational hypertension, or fetal growth restriction (FGR). Clinically, what matters is to identify those women who would most benefit from careful surveillance during their pregnancy, ideally using the model of accelerating antenatal visits (every 4 weeks until 27 weeks, every 2 weeks between 28 and 35 weeks, and weekly from 36 weeks) that has become the standard of care throughout high-income communities, and prevention with the use of low‑dose aspirin and calcium supplementation (see Chapter 6). The pattern of accelerating antenatal surveillance was developed to identify women with pre‑eclampsia, so that they could be delivered before complications arose. However, no explicit rationale was offered for the timing or clinical content of visits. A major limitation of this surveillance program is that it can only detect pre-eclampsia at a late stage of presentation, which does not allow prevention of this disorder. WHO's four‑visit antenatal care (ANC) (Focused ANC: FANC) model was launched previously to reduce the number of antenatal visits especially for LMICs. This model failed to show benefit on maternal and perinatal outcomes when subjected to a randomized controlled trial.1,2 Although the revised 2016 WHO ANC guidelines increased the number of ANC visits to eight,3 this model may still miss the increased maternal and perinatal risks associated with late-onset/term pre‑eclampsia, which commonly presents between 36 weeks’ gestational and delivery. The ASPRE trial has demonstrated the effectiveness of the first trimester “screen and prevent” protocol. In the screen-positive group, administration of 150 mg aspirin daily compared with placebo has yielded a 62% reduction in the rate of preterm pre-eclampsia.4,5 To emphasize the importance of first-trimester pre-eclampsia screening and prevention, the concept of “inverted pyramid” of ANC has been introduced. An “arrow model” for the ANC of pre-eclampsia has also been proposed with the aim to improve the detection of late-onset pre-eclampsia.4

Second, there has been the conflation of all forms of pre‑eclampsia (whether of primarily placental or maternal origin) into a single diagnosis; we now recognize that, other than the commonality of the presence of a placenta, the pathways to disease vary widely between placental and maternal disease.6 Pre-eclampsia could generally be subclassified according to the time of delivery as early-onset (with delivery at <34 weeks’ gestation), preterm (with delivery <37 weeks’ gestation), late-onset (with delivery at ≥34 weeks’ gestation), and term pre-eclampsia (with delivery ≥37 weeks’ gestation).7,8,9,10 This is because early-onset/preterm pre-eclampsia is more likely to be associated with placental insufficiency than late onset/term pre-eclampsia, with the former being associated with more serious impact on pregnancy outcomes. Late-onset/term pre-eclampsia is considered to be maternal disease, with reduced severity of maternal and neonatal complications.11,12,13,14,15 Furthermore, evidence has suggested maternal cardiovascular and renal interactions with the placenta, introducing the theoretical concept that cardiorenal syndrome is an intrinsic part of the pathophysiology of pre-eclampsia with specific differences in hemodynamic dysfunctions between the placental and maternal types of pre-eclampsia.16 The same issue arises for FGR. Many, even most, pregnancies in which either the fetal abdominal circumference or estimated fetal weight drops below the 10th centile by ultrasound are not complicated (other than by resulting investigations and interventions) – rather, the fetus is constitutionally small.17

Third, how can we be certain that the underlying pathways to pre‑eclampsia are shared by women in HICs (who usually have a prolonged coitarche‑to‑pregnancy interval, often using non‑barrier contraception, and are increasingly often over 30 years old at the first ongoing pregnancy and overweight or obese) and women in LMICs (who are often young and anemic, bear a burden of chronic infection, and conceive within months of first intercourse)? It may be that screening biomarkers that are shown to be effective in HICs in ongoing research are not as effective in LMICs – this is a research priority mentioned below. Conversely, reverse innovation of screening biomarkers that are effective in LMICs may not have clinical utility in HICs.

In the following sections, the risk factors, and predictors of pre‑eclampsia are discussed in detail.


Risk factors are any attributes or exposures that increase the chances for an individual to develop a disease.18 Risk factors for pre‑eclampsia include a wide array of conditions that reflect the complexity of the disease process and their strengths of association are quantified using risk ratios (RRs) or odds ratios (ORs).19 These can be summarized in six categories: (1) familial factors, (2) demographic factors, (3) medical and obstetric factors, (4) current pregnancy‑associated factors, (5) paternal factors, and (6) miscellaneous factors. The maternal risk factors of pre‑eclampsia are shown in Table 1.


Maternal risk factors in the first trimester for pre-eclampsia according to professional organizations. Modified from Chaemsaithong P et al. (2020).203

ACOG 2018 (USA)288

NICE 2019 (UK)289

SOGC 2014 (Canada)185

High-risk factors

High-risk factors

High-risk factors

  • Prior PE
  • Chronic HT
  • Autoimmune disease (SLE/APS)
  • Pre-existing DM
  • Renal disease
  • Multifetal gestation
  • Prior PE
  • Chronic HT
  • Autoimmune disease (SLE/APS)
  • Pre-existing DM
  • Chronic kidney disease
  • Prior PE
  • APS
  • Pre-existing DM
  • Renal disease or proteinuria
  • Chronic HT or booking DBP ≥90 mm Hg
  • Multifetal gestation

Moderate risk factors

Moderate risk factors

Moderate risk factors

  • Nulliparity
  • Age ≥35 y
  • BMI >30 kg/m2
  • Family history of PE (mother or sister)
  • Personal history of SGA or previous adverse pregnancy outcome, Interpregnancy interval >10 y)
  • Sociodemographic characteristics (African American race or low socioeconomic status)
  • Nulliparity
  • Age ≥40 y
  • Interpregnancy interval >10 y
  • BMI at first visit ≥35 kg/m2
  • Family history of PE
  • Multifetal gestation
  • Age ≥40 y
  • Family history of PE (mother or sister)
  • Family history of early-onset cardiovascular disease
  • Lower maternal birthweight or preterm delivery
  • Heritable thrombophilia
  • Non-smoking
  • Increased pre-pregnancy triglycerides
  • Previous miscarriage of <10 wk with same partner
  • Cocaine and methamphetamine use
  • Booking SBP ≥130 mm Hg or DBP of ≥90 mm Hg
  • Vaginal bleeding in early pregnancy
  • Gestational trophoblastic disease
  • Abnormal PAPP-A or free beta-hCG
  • Overweight/obesity
  • Nulliparity/new partner/ART

SOMANZ 2014 (Australia)188

ISSHP 2018290

WHO 2011190

Risk factors

High-risk factors

Risk factors

  • Nulliparity
  • Multifetal gestation
  • Prior PE
  • Family history of PE
  • Overweight
  • Obesity (BMI ≥30 kg/m)2
  • Age ≥40 y
  • SBP >130 mm Hg or DBP >80 mm Hg before 20 wk
  • Autoimmune disease/APS
  • Pre-existing DM
  • Underlying renal disease
  • Interpregnancy interval >10 y
  • Prior PE
  • Chronic HT
  • Pre-existing DM
  • BMI >30 kg/m2
  • Autoimmune disease (SLE/APS)
  • Multifetal gestation
  • Renal disease
  • Prior PE
  • Pre-existing DM
  • Chronic HT
  • Renal disease
  • Autoimmune disease
  • Nulliparity
  • Adolescent pregnancy
  • Obesity
  • Multifetal gestation

Moderate risk factors

  • Advanced maternal age >35 y
  • Family history of PE
  • Short duration of sexual relationship (<6 m) before the pregnancy
  • Primiparity
  • Primipaternity
  • Interpregnancy interval of >5 y)
  • Connective tissue disorder

ACOG, American College of Obstetricians and Gynecologists; ISSHP, International Society for the Study of Hypertension in Pregnancy; NICE, National Institute for Health and Care Excellence; SOGC, Society of Obstetricians and Gynaecologists of Canada; SOMANZ, Society of Obstetric Medicine of Australia and New Zealand; WHO, World Health Organization; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; SLE, systemic lupus erythematosus; APS, antiphospholipid syndrome; HT, hypertension; DM, diabetes mellitus; ART, assisted reproductive technologies; hCG, human chorionic gonadotropin; PAPP-A, pregnancy-associated plasma protein A; PE, pre-eclampsia; SGA, small-for-gestational-age.

Familial factors

Pre‑eclampsia is a complex disorder. For some women, the condition is seen to be inherited in a familial pattern.20,21 The placenta plays a central role in the pathogenesis of pre‑eclampsia, thus implying that both maternally and paternally derived fetal genes may play a role in the development of the disease.20 Pre‑eclampsia complicating any of a given woman’s pregnancies is a significant risk factor for pre‑eclampsia complicating her daughters’ pregnancies.22 Chesley and Cooper reported that for those women who experienced pre‑eclampsia, the rate of disease was higher in sisters (37%), daughters (26%), and grand‑daughters (16%) when compared with daughters‑in‑law (6%).23,24 A review suggests that those with a family history of pre‑eclampsia are at an increased risk for this disorder with RR 2.90 (95% confidence interval [CI], 1.70–4.93).25 A large population‑based study reported a significantly higher risk of pre‑eclampsia in sisters diagnosed with pre‑eclampsia (RR, 2.6; 95% CI, 1.8–3.6).26 This risk increased further with the severity of disease (i.e., 2+ proteinuria) (RR, 3.7; 95% CI, 2.5–5.5).26 A recent case-control study revealed that women whose mothers had pre-eclampsia had 3.38 (95% CI, 2.89–3.96) higher odds than those who did not, and having an affected sister increased pre-eclampsia odds by 2.43 (95% CI, 2.02–2.93). The effect of having both mother and sister affected with pre-eclampsia was even stronger with OR 4.17 (95% CI, 2.60–6.69).27

Further, a large Danish study reported that a history of early‑ or intermediate‑onset pre‑eclampsia in the mother or sister increased the risk of the similar form of pre‑eclampsia by at least 150% compared with an absence of such family history. For those women with a history of late‑onset pre‑eclampsia, this risk increased by 73%.28 In addition, a paternal familial component has been suggested; the partners of men who were the product of a pregnancy complicated by pre‑eclampsia were, themselves, more likely to develop pre‑eclampsia than women whose partners were born of normotensive pregnancies.29 Women with a maternal and/or paternal history of hypertension, diabetes mellitus, or cardiovascular disease had a statistically significant increased risk to develop pre‑eclampsia.27,30,31

Demographic factors


Advanced maternal age (≥35 years old) has been associated with risk of pre‑eclampsia and the risk further increases when maternal age is greater than 40 years old.32 Several studies have reported that advanced maternal age increases the risk of developing pre-eclampsia by 1.2 to three-fold.32,33,34,35,36,37,38 A study evaluated the association between the maternal age and risk according to the severity pre‑eclampsia using multivariate logistic regression analysis, adjusting for confounders, the risk for late-onset pre‑eclampsia was shown to increase by 4% for every year over the age of 32 years.39 Maternal age was not shown to increase the risk of early-onset pre‑eclampsia.39 In a large retrospective cohort study of 76,158 singleton pregnancies, maternal age of 35–39 years or >40 years increased the risk of having pre‑eclampsia by 19% (adjusted OR, 1.19; 95% CI, 1.05–1.35) and 49% (adjusted OR, 1.49; 95% CI, 1.22–1.82), respectively.32 Among women who were diagnosed with pre-eclampsia, the rates of preterm delivery, small for gestational age (SGA), neonatal asphyxia, and neonatal intensive care unit (NICU) admission were higher in women with advanced maternal age, compared with those under 35 years old.38 On the other hand, women <19 years of age were at high risk for eclampsia, but not a diagnosis of pre‑eclampsia – probably related to underdiagnosis of pre‑eclampsia in populations of women without full antenatal surveillance.40


The association between certain maternal ethnic groups and pre‑eclampsia has been extensively described in the literature. Women belonging to Afro‑Caribbean or South Asian ethnicity have been shown to be at higher risk of pre-eclampsia and SGA when compared with Caucasians.41,42,43,44,45,46,47,48 The risk of pre‑eclampsia is also higher in women of South Asian ethnicity than in those of non-Hispanic white (adjusted OR, 1.3; 95% CI, 1.2–1.4).49 African‑American women with severe pre‑eclampsia demonstrate higher blood pressures and require more antihypertensive treatment, while Caucasian women have a higher incidence of HELLP (hemolysis, elevated liver enzymes, and low platelet) syndrome.50 Higher rates of pre-eclampsia have also been reported in the American Indian and Alaskan Native populations compared with non-Hispanic white women (OR, 1.17; 95% CI, 1.06–1.29).51,52 In contrast, women of East Asian ethnicity have a significantly lower risk of pre‑eclampsia, compared to Caucasian women.49 A retrospective cohort study including 54,458 pregnancies examining the risk of developing pre‑eclampsia has reported that East Asian women of Chinese descent have a significantly lower risk, compared to Caucasian women (adjusted OR, 0.8; 95% CI, 0.7–0.8).49 A large UK-based inner city prospective observational cohort study of more than 79,000 singleton pregnancies reported that the risk of pre‑eclampsia was significantly higher in women of Afro-Caribbean and South Asian ethnicities, compared to Caucasian women. This increase in risk remained significant after adjusting for other confounding factors for pre‑eclampsia. In fact, after chronic hypertension, the Afro-Caribbean ethnicity was the second highest risk factor associated with risk for developing pre‑eclampsia with an OR 2.60 (95% CI, 2.32–2.92).42 Race or ethnicity may be a surrogate for several environmental factors including lifestyle, healthcare system, as well as genetic susceptibility to pre‑eclampsia.48,49,50,51,52,53,54,55

Medical or obstetric factors

Maternal birth weight

Women with low birth weight (<2,500 g) have been shown to double the risk of experiencing pre‑eclampsia (OR, 2.3; 95% CI, 1.0–5.3) when compared with women who weighed 2,500–2,999 g at birth.32 Further, the risk increased four‑fold for those women who weighed <2,500 g at birth and were overweight as adults.56 A Danish cohort study reported that there was an increased frequency of pre‑eclampsia in women who were born prematurely and were SGA.57

Stature and pre‑pregnancy body mass index

A large population‑based study reported that short stature of women (£164 cm/5’5”) predisposed them to an increased risk of severe pre‑eclampsia.58 Women who are overweight or obese are known to be at increased risk for pre‑eclampsia.59,60 Extensive evidence has reported that BMI ≥30 kg/m2 increases the risk of pre-eclampsia by two‑ to five‑fold.61,62,63 A systematic review and meta-analysis including 22 studies in LMICs, in which pre-pregnancy or early pregnancy maternal obesity is associated with an increased risk of subsequent development of pre‑eclampsia by four-fold.64 Low-grade inflammation associated with obesity may be responsible for endothelial dysfunction and placental ischemia by immune-mediated mechanism, which in turn leads to production of inflammatory mediators and thus resulting in an exaggerated maternal inflammatory response, as presented in pre-eclampsia.65

Pre‑existing medical conditions

There are certain medical conditions that predispose a woman, when pregnant, for developing pre‑eclampsia. These include type I diabetes mellitus (DM), pre-existing chronic hypertension (CHT), renal disease, autoimmune diseases, such as systemic lupus erythematosus (SLE), and anti-phospholipid syndrome (APS). Pre‑existing DM (type 1 and type 2) is associated with two‑ to four‑fold increased risk of pre‑eclampsia.26,62,66,67 In addition, pre‑existing DM may be a significant contributor to new‑onset late‑postpartum pre‑eclampsia.68 Lecarpentier et al. reported that 23% of women with CHT were at risk of superimposed pre‑eclampsia. MAP ≥95 mmHg was a good predictor of this risk.69 A systematic review by Bartsch et al. reported that the RR of superimposed pre‑eclampsia in women with CHT was five‑fold higher than those without.62 Adverse neonatal outcomes such as preterm delivery (<37 weeks of gestation), low birth weight, and perinatal death in this group of women were three‑to‑four times as likely.70 Women with both CHT and pre‑existing DM are eight times more likely to be diagnosed with pre‑eclampsia when compared with women without either condition.71

Pre‑eclampsia may occur frequently in pregnant women with chronic kidney disease, lupus nephropathy, as well as diabetic nephropathy.72 For women with diabetes, proteinuria of either 190–499 mg/day or ≥+2 on urine dipstick at booking is associated with a significantly higher risk of pre‑eclampsia.73,74 A meta‑analysis of 74 studies evaluating hyperlipidemia and risk of pre‑eclampsia reported that pre-eclampsia was associated with elevated total cholesterol, non-high-density lipoprotein cholesterol (non-HDL-C), and triglyceride levels, regardless of gestational age at the time of blood sampling, and with lower levels of high-density lipoprotein cholesterol (HDL-C) in the third trimester. A marginal association was found with low-density lipoprotein cholesterol (LDL-C) levels. This meta-analysis suggested that women who developed pre-eclampsia had elevated levels of total cholesterol, non-HDL-C, and triglycerides during all trimesters of pregnancy, as well as lower levels of HDL-C during the third trimester.75


There is conflicting evidence regarding the association of genetic thrombophilias and pre-eclampsia. Some meta‑analyses have concluded that factor V Leiden single nucleotide polymorphism (SNP) and prothrombin G20210A SNP are associated with an increased risk of pre‑eclampsia and no association has been found between methylene tetrahydrofolate reductase (MTHFR) SNP and prothrombin SNP and risk of pre‑eclampsia.76,77 In contrast, some other studies and meta-analyses have demonstrated that there is no significant association between carriers of factor V Leiden or prothrombin G20210A and pre-eclampsia, pregnancy loss, SGA (<10th percentile), and placental abruption.78,79,80,81,82

Antiphospholipid syndrome is a systemic autoimmune disorder with raised titers of antiphospholipid antibodies and is characterized by arterial and venous thrombosis, and adverse pregnancy outcomes.83 A meta‑analysis of 28 studies has reported that the risk of pre‑eclampsia is two times higher in women who test positive for lupus anticoagulant and anticardiolipin antibodies (OR, 2.34; 95% CI, 1.18–4.64 and OR, 1.52; 95% CI, 1.05–2.20, respectively).84 However, this association is only reported in case-control, and not in cohort studies.84

While we recognize that this is a very controversial area, in our opinion, thrombophilia screening is not recommended specifically for investigation of previous pre‑eclampsia or other placental complications, with the exception of testing for antiphospholipid antibodies if the woman meets the clinical criteria for the diagnosis.85,86

Parity, interval between pregnancies, and change of partner

Pre‑eclampsia is recognized to complicate a woman’s first pregnancy more commonly by three-fold.20,87,88 A large population‑based study and a meta-analysis have indicated the association between nulliparity with a high risk of pre-eclampsia compared with parous women (RR, 2.1; 95% CI, 1.9–2.4).89,90 A systematic review including 26 studies demonstrated that this elevated risk for pre-eclampsia remained even after adjusting for other risk factors, such as maternal age, race, and BMI and the summary adjusted OR was 2.71 (95% CI, 1.96–3.74).88 In addition, a population‑based cohort study has reported that nulliparity significantly increases the risk of late‑onset pre‑eclampsia.12 While women who have previously conceived, and even after a miscarriage, appear to have some degree of protection against pre‑eclampsia.91,92 This can be explained by the immune maladaptation hypothesis, which states that the fetal-placental unit contains paternal antigens that provoke an abnormal maternal immune response in pre‑eclampsia.93 This hypothesis is further strengthened by the fact that multiparity without prior history of pre‑eclampsia appears to provide a protective effect for pre‑eclampsia and in the case when there is a change of partner, this protective effect is lost.94

A systematic review of five cross-sectional studies and one case-control study including about 1 million pregnant women has revealed that an inter-pregnancy interval >5 years is associated with 60–80% increase in risk of pre‑eclampsia.95 As mentioned above, the risk of pre‑eclampsia is generally lower in the second pregnancy if conceived with the same partner, after adjustment for the presence or absence of a change of partner and maternal age, the odds for pre‑eclampsia for each 1‑year increase in the inter-pregnancy interval are increased by 12% (OR, 1.12; 95% CI, 1.11–1.13).96 A large retrospective study reported that compared with mothers with inter-pregnancy intervals of 12–23 months, mothers with intervals less than 12 months and more than 72 months had increased odds of pre-eclampsia compared to those with inter-pregnancy intervals of 12–23 months.97 It was observed that the longer the interval, the higher the risk of developing pre-eclampsia (72–83 months: adjusted OR, 1.1; 95% CI, 1.02–1.18, 84–95 months: adjusted OR, 1.15; 95% CI, 1.06–1.24, 96–107 months: adjusted OR, 1.18; 95% CI, 1.09–1.27).97 Another population-based cohort study also suggested that a longer inter-pregnancy interval was associated with increased risk of pre-eclampsia compared to 18–23 months (RR, 1.29; 95% CI, 1.18–1.42 for 60–119 months and RR, 1.30; 95% CI, 1.10–1.53 for intervals ≥120 months).98 It is presumed that the rationale for the association between short inter-pregnancy interval and pre-eclampsia are factors related to socioeconomic status, postpartum stress, malnutrition, and inadequate access to healthcare services.99 The reasons for increased pre-eclampsia risk in women with longer inter-pregnancy intervals may attribute to advanced maternal age, infertility, and underlying maternal medical conditions.100,101

Previous miscarriages

Analysis of data obtained from the Norwegian Mother and Child Cohort Study suggested that there may be an increased risk of pre‑eclampsia for women with recurrent miscarriages (adjusted OR, 1.51; 95% CI, 0.80–2.83), although this was not statistically significant.102 Similar findings were reported from a Canadian study where history of prior abortion had no effect on risk of pre‑eclampsia.103 However, for women who had recurrent spontaneous abortions and infertility treatment, a three‑fold increased risk of pre‑eclampsia was seen compared with controls.102

Previous pre‑eclampsia

Women with a history of pre‑eclampsia in a previous pregnancy have an increased risk of pre‑eclampsia in the current pregnancy compared with parous women with no previous pre‑eclampsia. Results from observational studies have suggested that prior history of pre‑eclampsia increases the risk of recurrence in subsequent pregnancies with RRs ranging from seven to ten times higher in a second pregnancy.87,104,105,106 This association was particularly strong for early‑onset, moderate, and severe disease.90 In women with prior pre‑eclampsia, greater risk is associated with earlier gestational age at delivery. A population-based, nested case-control study consisting of pre-eclamptic cases (n = 323) and healthy controls (n = 650) demonstrated that women with pre-eclampsia in a previous pregnancy had a strongly increased risk of pre-eclampsia in the current pregnancy compared to parous women with no previous pre-eclampsia (OR, 21.5; 95% CI, 9.8–47.2). The association was extremely strong (OR, 42.4; 95% CI, 11.9–151.6) for early-onset disease.90 Mostello et al. reported that the risk of recurrent pre‑eclampsia was 12% for those who previously delivered at term and increased to 40% for those who delivered before 28 weeks of gestation.107 In another prospective cohort study, the risk was 14.7% in the second pregnancy for those who had had pre-eclampsia in their first pregnancy and 31.9% for those who had had pre-eclampsia in the previous two pregnancies.108

Previous pregnancy with gestational hypertension

Pre‑eclampsia in a previous pregnancy may "recur" in a subsequent pregnancy as gestational hypertension, just as gestational hypertension in a previous pregnancy may recur as pre‑eclampsia in a subsequent pregnancy. Women with a history of pre‑eclampsia have similar rates of either pre‑eclampsia (median 15%) or gestational hypertension (median 22%) in a subsequent pregnancy. In contrast, most women with a history of gestational hypertension who experience a subsequent hypertensive pregnancy will experience gestational hypertension again (median of 21%, range 8–47%); far fewer will experience their recurrence as pre‑eclampsia (median of 4%, range 1–6%) (four studies, 1,311 women).109,110,111,112,113 The gestational age at which gestational hypertension has developed in the previous pregnancy does not seem to affect whether the hypertensive disorder of pregnancy in the next pregnancy is gestational hypertension or pre‑eclampsia. However, previous early-onset hypertension has been shown to increase the recurrence risk for both gestational hypertension and pre-eclampsia in the subsequent pregnancy.109,110,114

Current pregnancy‑associated factors

Multiple pregnancy

Multiple gestations are a risk factor for pre‑eclampsia.113,115 In a study comparing singletons with dichorionic (DC) and monochorionic (MC) twin pregnancies, the incidence of pre‑eclampsia in singletons was 2.3% (2,162/93,297), in DC twin pregnancies was 8.1% (145/1,789) and in MC twin pregnancies was 6.0% (26/430). Compared with singletons, the RR of total pre‑eclampsia was 3.5 for DC twins and 2.6 for MC twins, the RR of preterm pre‑eclampsia was 8.7 for DC twins and 9.1 for MC twins. In the Cox proportional hazards' regression model, the hazard ratios for DC and MC twin pregnancies relative to singleton pregnancies were 14 and 23, respectively. The authors concluded that the RR of preterm pre‑eclampsia between DC and MC twins was similar and substantially higher when compared with singleton pregnancies.116 A prospective observational study including 43 MC and 36 DC twin pregnancies revealed significantly higher concentration of soluble fms-like tyrosine kinase-1 (sFlt-1) in DC in comparison to MC twin pregnancies in both the first and third trimesters. PLGF and soluble endoglin (sEng) levels did not differ between MC and DC gestation in both study periods. The findings suggested that sFlt-1 level was related to twin gestation chorionicity, while PLGF expression was not.117

Fetal gender

A Norwegian cohort study reported that pre‑eclampsia occurred more often in a pregnancy with a male fetus for those who delivered at 40 weeks or later. For preterm births (gestational weeks 25–36), the proportion of female offspring in pregnancies complicated by pre‑eclampsia was considerably higher than that of males.118 A recent meta-analysis also suggests that a pregnancy with a male fetus is more likely to be associated with term pre-eclampsia, gestational hypertension, total pre-eclampsia, eclampsia, and placental abruption, except for preterm pre-eclampsia, which is associated with a pregnancy with a female fetus.119 Despite the preponderance of male fetuses in women with pre‑eclampsia, no fetal sex‑related differences have been found in perinatal outcomes (stillbirth, perinatal, or neonatal mortality) in such women.120

Use of assisted reproductive technology

Several studies have reported that the use of assisted reproductive technology (ART) doubles the risk of pre‑eclampsia. However, different types of ART carry different risks of developing pre-eclampsia.121,122,123,124 A systematic review reported that ART especially in vitro fertilization (IVF) was associated with higher risk of gestational hypertension and pre‑eclampsia when compared with non‑ART pregnancies.121 Results from the CoNARTaS cohort study reported that hypertensive disorders occurred in 5.9% of singleton and 12.6% of twin ART pregnancies compared with 4.7% of singleton and 10.4% of twin pregnancies in spontaneously conceived pregnancies.125 A large-scale study reported that the adjusted odds of developing pre-eclampsia were increased in women after hyperestrogenic ovarian stimulation, no matter the ART type.126 In contrast, the use of nonhyperestrogenic ovarian stimulation drugs was not associated with an increased risk of pre-eclampsia.126 This may relate to impaired placentation and reduced uteroplacental circulation as well as decreased number of uterine spiral arteries with vascular invasion caused by high estrogen levels during implantation.126,127,128 Moreover, women conceiving by intrauterine insemination (especially by donor sperm) and by donor ovum are at a higher risk of pre-eclampsia.129,130,131,132,133,134 Evidence from IVF pregnancies with ovum donation suggests that there are altered extravillous trophoblast and immunological changes in decidua basalis, which may impede the modification of the spiral arteries.135 A systematic review of 19 studies, including more than 86,000 pregnancies, supports these findings by demonstrating that the risk of pre-eclampsia is higher in oocyte donation IVF cycles, compared to the other methods of ART and spontaneous conception.136


A nested case-control study from the UK reported that antibiotic prescriptions (included as a proxy for acute infection) (OR, 1.28; 95% CI, 1.14–1.44) and urinary tract infection (UTI) (OR, 1.22; 95% CI, 1.03–1.45) in pregnancy were associated with an increased risk of pre‑eclampsia after controlling for confounders such as maternal age, pre‑existing renal disease, DM and multiple gestation.137 A meta‑analysis of 40 studies reported that women with a UTI and those with periodontal disease were more likely to develop pre‑eclampsia than women without these infections. There was no association between the other maternal infections such as chlamydia, malaria, treated or untreated HIV, and group B streptococcal colonization and risk of pre‑eclampsia.138,139

Paternal factors

Paternal age

Epidemiological studies suggest that the risk for pre‑eclampsia doubles if the woman has a partner aged >45 years,140,141 perhaps as a result of spermatozoa being damaged owing to genetic mutations that occur with aging or to environmental factors such as exposure to radiation and heat.59

Primipaternity and sperm exposure

A landmark study by Robillard et al. in 1994 showed that conception within the first 4 months of sexual cohabitation of the couple presented a major risk (40–50% incidence) for hypertension to complicate a pregnancy.142 However, this risk declined significantly for women after at least 1 year of sexual cohabitation before conception.142 A study by Olayemi et al. reported that there was a 4% decrease in the risk of developing hypertension for every month increase in cohabitation.143 This risk was not statistically significant for pre‑eclampsia.143 Repeated intercourse with the same partner leads to maternal mucosal tolerance to paternal antigens, which may be mediated by seminal vesicle‑derived transforming growth factor b (TGFb).140 The exposure to seminal fluid through the vagina is inversely correlated with the risk of pre-eclampsia occurrence, whereas the oral exposure to seminal fluid has no effect on disease development.144,145

Paternal medical history

The data for paternal history of cardiovascular disease and risk of pre‑eclampsia have been conflicting. In a case-control study, Rigo et al. reported that early‑onset chronic hypertension and early‑onset myocardial infarction in the father were associated with a three‑fold increased risk of pre‑eclampsia after controlling for confounders.146 However, the population‑based HUNT study reported that there was no association between the hypertensive disorders of pregnancy and paternal cardiovascular risk factors such as BMI, blood pressure, and lipid profile.147

Molar pregnancy

Molar pregnancy is associated with very early-onset pre-eclampsia. Koga et al. noted that circulating levels of sFlt-1 in molar pregnancies were two-fold to three-fold higher than those in gestationally aged-matched controls. This association may be due to excess production of anti-angiogenic factors, in particular sFlt-1, by trophoblastic tissue.148,149 Complete hydatidiform moles are mostly androgenetic with diploid cells (both sets of chromosomes are paternally derived) presenting a 46, XX or 46, XY karyotype and are associated with a high incidence of severe early-onset pre-eclampsia.150 This implies that the etiology of pre-eclampsia may be due to the participation of paternally expressed genes. The over-representation of the paternal chromosome complement may be responsible for the association between partial hydatidiform moles with paternal origin of extra chromosomal load and an increased risk of pre-eclampsia as well.150

Miscellaneous factors


Cigarette smoking is known to have adverse effects on all organ systems. However, a meta-analysis of nine cohort studies has shown that smokers of <10 cigarettes/day and ≥10 cigarettes/day have 23% and 33% reduction on the risk of pre‑eclampsia, respectively.151 A systematic review of 48 epidemiological studies has reported that smoking during pregnancy approximately halves the risk of pre‑eclampsia.152 This protective effect has been consistently seen irrespective of parity and severity of disease.152 Data from the National Swedish Birth Register showed that smoking in two pregnancies halved the risk of pre‑eclampsia, compared with the risk borne by women who did not smoke in either pregnancy.153 Also, a subsequent systematic review and meta-analysis including 1.8 million pregnant women demonstrated similar findings.154 The pathophysiology of this relationship is not well established. However, it is proposed that smoking might have effects on angiogenic factors, endothelial function, and the immune system, which may contribute to the lowered risk of pre‑eclampsia.152 No significant associations have been observed between smokeless tobacco use and pregnancy‑associated hypertension in various studies.155,156 Therefore, it has been proposed that combustion products from cigarette smoke other than nicotine may be responsible for the decreased pre‑eclampsia risk seen amongst smokers.155 The combustion products of tobacco, such as carbon monoxide (CO) may be the critical vascular protective mediator against pre-eclampsia. Cudmore et al. reported that CO and CO-releasing molecules lowered sFlt-1 and sEng production in endothelial cells and placental organ cultures.157,158 It should be noted that, although smoking reduces the incidence of pre-eclampsia, it increases disease severity and worsens pregnancy outcomes in smokers who develop pre-eclampsia.159,160 The results from the subgroup analysis of a multicenter study of more than 1,000 pregnant women show that the risk of developing eclampsia is increased by five-fold in smokers compared to non-smokers. This implies that pregnant women who smoke during pregnancy are more likely to have the more severe form of the condition if they develop pre-eclampsia.161 Chappell et al. have also reported an association of smoking with increased risk of superimposed pre-eclampsia in women with CHT.162

Physical activity

Exercise and physical activity are recommended during pregnancy to improve maternal health. In their systematic review, Kasavara et al. reported that physical activity had a protective effect on the development of pre‑eclampsia (OR, 0.77; 95% CI, 0.64–0.91), while this effect was not seen in cohort studies (OR, 0.99; 95% CI, 0.93–1.05).163 However, a meta‑analysis conducted by Aune et al. reported that those women who engaged in high levels of physical activity pre‑pregnancy and continued to do so during early pregnancy, were less likely (by 35% and 21%, respectively) to develop pre‑eclampsia, compared with those who participated in low levels of physical activity.164

Micronutrient deficiencies

Vitamin D deficiency is commonly reported in women and has been investigated to assess its link with pre‑eclampsia. There have been conflicting results regarding the serum concentrations of 25‑hydroxy vitamin D and the subsequent risk of developing pre‑eclampsia,165,166 mainly owing to small sample size of these studies. A large case-control study has reported that maternal vitamin D deficiency, defined as 25‑hydroxy vitamin D <30 nmol/l, was associated with double the risk of pre‑eclampsia when compared with concentrations >50 nmol/l.167 A randomized, double-blind, placebo-controlled clinical trial concluded that vitamin D supplementation did not reduce the rate of pre-eclampsia in the intention-to-treat paradigm. However, vitamin D ≥30 ng/ml was associated with a lower risk of pre-eclampsia.168

The Vitamins in Pre-eclampsia (VIP) Trial reported that vitamin C (1,000 mg) and vitamin E (400 IU) supplements given prophylactically from the second trimester of pregnancy had no effect on reduction in the rate of pre‑eclampsia in women at risk.169 Similar findings have been reported by the WHO multicountry vitamin supplementation survey from India, South Africa, and Vietnam.170

A meta-analysis reported that there was insufficient evidence of folic acid or zinc supplementation on reducing the risk of pre-eclampsia.171

Mental health

Depression and anxiety in the first trimester of pregnancy are known to increase the risk of pre‑eclampsia by two‑ to three‑fold.172 The Finnish Genetics of Pre-eclampsia Consortium (FINNPEC) cohort revealed that mental disorders including depression were more common in women with pre-eclampsia compared to those who were unaffected (7.2% in pre-eclampsia women, 3.7% [p = 0.013] in uncomplicated pregnancies and 3.9% [p = 0.007] in pregnancy with complications other than pre‑eclampsia).173 In addition, lifetime stress and perceived stress during pregnancy may double the risk of developing pre‑eclampsia; an interaction that may be mediated by the neuropsychoimmunological pathway.174

Socioeconomic status

In LMICs, rural dwellers were twice as likely to develop pre‑eclampsia compared with those living in urban areas. Furthermore, women with concurrent anemia and poor intake of fruits and vegetables were at higher risk of pre‑eclampsia.175 Severe anemia (hemoglobin <70 g/l) was associated with a three‑fold greater risk of pre‑eclampsia in women living in LMICs.54 A lack of antenatal care and less than secondary‑level education were pertinent risk factors for risk of pre‑eclampsia in these regions.54

A large case-control study conducted in 77,406 women in San Joaquin Valley, California from 2000 to 2006 reported the association between air quality and neighborhood socioeconomic (SES) with pre-eclampsia.176 The study reported that more greenspace was inversely associated with superimposed pre-eclampsia (OR = 0.57), while high particulate matter <2.5 μm (PM 2.5) and low SES were associated with increased risks of mild and severe pre-eclampsia.176

A meta-analysis has indicated that working rotating shifts is associated with an increased odds of pre-eclampsia (OR, 1.75; 95% CI, 1.01–3.01, I2 = 75%), and gestational hypertension (OR, 1.19; 95% CI, 1.10–1.29, I2 = 0%), in addition, occupational exposures of heavy lifting (≥11 kg) increases the odds of pre-eclampsia (OR, 1.35; 95% CI, 1.07–1.71; I2 = 0%).177,178


Pre‑eclampsia research is now tailored towards development of predictive models utilizing the risk factors mentioned above along with measurable clinical and laboratory biomarkers to predict the occurrence of pre‑eclampsia. In the context of this chapter, we are talking about the prediction of a diagnosis of pre‑eclampsia (or other placental complications) occurring at some point in the future, not the prediction of complications (prognosis), or risk stratification, in either individual or populations of women whose pregnancies have been complicated by the clinical syndrome of pre‑eclampsia.

According to the WHO, a prediction test should be simple, non‑invasive, inexpensive, rapid, easy to carry out early in gestation, impose minimal discomfort or risk on the woman, be a widely available technology, and the test results must be valid, reliable, and reproducible.179,180 The performance of predictive tests is generally summarized as being poorly associated, moderately associated and strongly associated when the positive likelihood ratio (LR+) is <5, 5–9.9, and >10, respectively. Similarly, for tests that poorly, moderately, or strongly exclude risk, their performance is summarized as negative likelihood ratios (LR‑) of >0.2, 0.11–0.2, and <0.1, respectively. Tests with LRs + of 10 or greater and LRs – of 0.1 or lower are most likely to be useful in clinical practice.181 Other summary statistics used in this chapter are detection rate (DR): “true positive rate”, the proportion of positives that are correctly identified as such, and false positive rate (FPR): the proportion of negatives who are wrongly identified as positives, of the test to predict the outcome, namely pre‑eclampsia, as well as the area under the receiver‑operator characteristic curve (AUROC).182,183,184

In this section, the prediction approaches are described based on the use of a single predictor (univariable analyses) or a combination of multiple predictors (multivariable analyses). Considerable efforts have been made to identify biomarkers that can predict pre-eclampsia in the first trimester and combinations of biomarkers generally have better diagnostic performance than single biomarkers. Thus far, there are a number of first trimester combined prediction models developed from prospective cohort studies that have undergone external validation. Some of the key first trimester combined prediction models will be described. It should be remembered that nearly all the studies referred to in this section relate to women in HICs. Their relevance to women in LMICs is uncertain. It is women in LMICs who carry the greatest burden of risk for the complications of pre‑eclampsia.

Prediction Using Univariable Analyses

Maternal risk factors

At present, a checklist of maternal characteristics, which include well‑established risk factors discussed above, such as maternal age, nulliparity, pre‑existing medical conditions and history of pre‑eclampsia, is commonly used to screen for pre‑eclampsia by clinicians during antenatal visits because of its simplicity.113,,,113,185,186,187,188,189,190,191,192 Bartsch et al. published a large systematic review and meta-analysis of 92 studies, including 25,356,688 pregnancies evaluating the clinical risk factors for pre-eclampsia identified before 16 weeks’ gestation.89 The most remarkable risk factors for pre-eclampsia are history of pre-eclampsia (RR, 8.4; 95% CI, 7.1–9.9) and CHT (RR, 5.1; 95% CI, 4.0–6.5). Other clinical risk factors for pre-eclampsia include nulliparity (RR, 2.1; 95% CI, 1.9–2.4), maternal age of >35 years (RR, 1.2; 95% CI, 1.1–1.3), chronic kidney disease (RR, 1.8; 95% CI, 1.5–2.1), conception by ART (RR, 1.8; 95% CI: 1.6–2.1), pre-pregnancy BMI of >30 kg/m2 (RR, 2.8; 95% CI 2.6–3.1), and pre-gestational DM (RR, 3.7; 95% CI, 3.1–4.3).89 Several professional organizations recommend using the checklist-based approach for the identification of women at risk of pre-eclampsia using maternal factors. The approach recommended by the National Institute for Health and Care Excellence (NICE) and American College of Obstetricians and Gynecologists (ACOG) essentially treats each risk factor as a separate screening test with additive detection rate and screen-positive rate.193 Although the recognition of maternal risk factors appears clinically useful and has been widely adopted in identifying high-risk women in clinical practice, it is not sufficient for effective prediction of pre-eclampsia.194 Screening with the use of the NICE guidelines only achieves DRs of 39% (95% CI, 27–53) and 34% (95%CI, 27–41), with a 10% FPR for preterm and term pre-eclampsia, respectively.195 The respective DRs in screening with use of the US Preventive Services Task Force recommendations, which has been endorsed by ACOG, the Society for Maternal-Fetal Medicine, and the American Diabetes Association were 90% (95% CI, 79–96) and 89% (95% CI, 84–94) for preterm and term pre-eclampsia, respectively; however, the FPR has also increased to as high as 64%.195,196,197

Clinical examination

Blood pressure

Blood pressure (BP), which forms the basis of diagnosis for pre‑eclampsia in all international guidelines, is routinely measured during pregnancy. Mercury sphygmomanometers have been withdrawn owing to safety concerns and replaced with aneroid devices, but these are particularly prone to calibration errors and regular calibration is required to ensure accuracy.198 Automated oscillometric devices are convenient to use, but the physiologic changes in healthy pregnancy and pathologic changes in pre-eclampsia may affect the accuracy of these devices and they must be validated.198 As high BP is an indication of increased vascular resistance observed in pre‑eclampsia, there have been studies examining the value of BP measurements using systolic BP, diastolic BP, or MAP indices for the prediction of pre‑eclampsia.199,200,201,202 In a systematic review of 34 studies including 60,599 women, with 3,341 cases affected by pre-eclampsia, MAP predicted pre-eclampsia with a moderate AUROC of 0.76 (95% CI, 0.70–0.82), whereas systolic and diastolic BP were less effective in predicting pre‑eclampsia, with an AUROC of <0.70.202 A simplified protocol for MAP measurement could be followed with the use of automated BP monitors, which allows standardized measurements to be taken, but accurate measurements still require correct cuff size and patient positioning. Patients should be asked to rest for at least 5 min in the sitting position. During BP measurement, their back should rest against the seat, their arms supported at the level of the heart, and legs uncrossed. The BP should be measured twice from both arms simultaneously and the final MAP is calculated from the average of the four measurements.200,203,204



Proteinuria is routinely measured during pregnancy, especially in women with new‑onset hypertension occurring after 20 weeks’ gestation to establish the diagnosis of pre‑eclampsia113,185 (see Chapter 2). Underlying renal disease is a recognized clinical risk factor for pre‑eclampsia and as such, documentation of proteinuria early in pregnancy is associated with an increased risk of pre‑eclampsia (see Pre‑existing medical conditions, above). Significant attention has been devoted to the role of albuminuria, and more specifically for lower levels of albuminuria (or ‘microalbuminuria’) for the prediction of pre‑eclampsia. In a review of the published studies retrieved from a structured literature search (1980 to mid‑March 2008), a total of seven studies were performed in early pregnancy (defined as <20 weeks) and 13 studies in late pregnancy (≥20 weeks).205 Overall, the negative predictive value (NPV) of ‘microalbuminuria’ was high, but the test performance was not good enough for clinical use; this finding is consistent with most other individual prediction tests described in this section. The largest study (N = 2,486 women) performed at 11+0–13+6 weeks demonstrated an increased albumin : creatinine ratio in women who later developed pre‑eclampsia compared with those who did not; however, the combined prediction models incorporating the albumin : creatinine ratio results did not yield to significantly improved AUROCs over maternal variables alone.206 Prediction of pre‑eclampsia in early pregnancy (17–20 weeks) by estimating the albumin : creatinine ratio was also performed using high‑performance liquid chromatography (HPLC).207 In this cohort of 265 women with singleton pregnancy, six developed pre‑eclampsia; the AUROC to predict pre‑eclampsia was 0.753. Although the interpretation is of a good predictive test, this study has not been replicated and, in addition, the impact is limited by accessibility to HPLC in clinical practice, especially in LMICs. It seems that evaluation of microalbuminuria has limited value in the prediction of pre-eclampsia.208

Ultrasound markers

Transcranial Doppler velocimetry

Transcranial Doppler velocimetry is a non-invasive test that has been used to assess cerebral blood flow velocity of the maternal middle cerebral artery (MCA). The procedure is performed by placing an ultrasound transducer over the temporal region and interrogating the intracranial circulation at a depth of 35–66 mm. A higher cerebral perfusion pressure and cerebrovascular resistance have been observed in overt preeclamptic women compared with unaffected women.209 Some of these changes have been speculated to be present prior to the development of overt disease, and, therefore, could be a potential predictor. A prospective cohort study by Belfort et al. in 405 low-risk women at gestational age 12–26 weeks revealed that the predictive value of either decreased resistance index (RI) of MCA <0.54 or pulsatility index (PI) of <0.81 had a DR of 86% at a FPR of 7% for predicting pre-eclampsia (LR+ and LR- of 12.3 and 0.2, respectively).208,210

Ophthalmic artery Doppler

Ophthalmic artery (OA), the first branch of the internal carotid artery has embryological, anatomical, and functional similarities with the intracranial vasculature. Several studies have shown that low resistance and hyperperfusion in the central nervous system, demonstrated through the OA Doppler, might precede the clinical onset of pre-eclampsia. While the change in uterine artery could be the result of trophoblast invasion, the change in OA Doppler indices is more likely to be related to maternal hemodynamic changes. The waveform of ophthalmic arteries is characterized by two systolic peaks, in a meta-analysis of three studies involving 1,119 pregnancies, a first diastolic peak velocity >23.3 cm/s showed a modest DR of 61.0% (95% CI, 44.2–76.1), at a FPR of 26.8% (95% CI, 21.3–33.1) for the prediction of early-onset pre-eclampsia (AUROC 0.68; 95% CI, 0.61–0.76),211 while the ratio of the second to the first peak systolic velocity was increased in women who developed pre-eclampsia. In a study at 19 to 23 weeks’ gestation, the peak systolic velocity ratio was superior to the UTPI, MAP, PLGF, and sFlt -1 as individual biomarkers in the prediction of both preterm and term pre-eclampsia and the peak systolic velocity ratio improved the prediction of pre-eclampsia provided by all the other biomarkers.212

Uterine artery Doppler ultrasonography

In an uncomplicated pregnancy, blood flow resistance in the uterine arteries decreases with gestation owing to invasion of the spiral arteries by the trophoblasts.213,214,215 The corollary is that increased impedance to blood flow in the uterine arteries has been observed in pregnancies complicated by impaired trophoblast invasion of the spiral arteries, as occurs with placental pre‑eclampsia and FGR of placental origin.215

The change in uterine artery blood flow between the first and second trimesters has been examined by screening studies to identify pregnancies at risk of pre‑eclampsia and FGR.213 The increase in impedance in the uterine arteries is more reflective of preterm pregnancy complications than those at term, as poor placentation is more associated with early‑onset pre‑eclampsia.214,215 In the first trimester, pragmatically transabdominal ultrasound is used to obtain a sagittal section of the uterus and to locate the internal cervical os. Then, the ultrasound transducer is tilted slightly to the lateral sides of the cervix. Color Doppler flow mapping is used to identify the uterine arteries at the level of the internal cervical os. Pulsed wave Doppler is then performed to measure the left and right UTPI with the sampling gate set at 2 mm to cover the vessel. The UTPI and peak systolic velocity are measured automatically when three similar consecutive waveforms are obtained. The peak systolic velocity must be at >60 cm/s to ensure that PI measurement is of the uterine artery. The UTPI is measured with the angle of insonation <30°, and the mean UTPI of the left and right arteries is calculated.193,216 In a meta-analysis that included eight studies (n = 41,692) for the prediction of early-onset pre‑eclampsia and 11 studies (n = 39,179) for the prediction of pre‑eclampsia of any gestational age, abnormal first trimester uterine artery Doppler, defined as the RI or PI ≥90th centile, had pooled DRs of 48% (95% CI, 39–57), at FPR of 8% (95% CI, 5–11) for early-onset pre‑eclampsia, and 22% (95% CI = 18–25), at FPR of 10% (95% CI, 9–10) for late-onset pre‑eclampsia.217

Laboratory markers

High‑sensitivity C‑reactive protein

High‑sensitivity C‑reactive protein (hs‑CRP) is a systemic inflammatory marker, which is produced by the placenta and released into the maternal circulation.218 This marker can be found in fetal urine and amniotic fluid and is sensitive to inflammation and tissue damage. Studies have reported an observed increase in maternal hs‑CRP level in pre‑eclampsia and other adverse pregnancy outcomes. Kashanian et al. conducted a prospective cohort study of 394 women evaluating the predictive accuracy of serum hs‑CRP for pre‑eclampsia in the first trimester.218 The result from this study showed that using hsCRP cut-off at 4 mg/l in the first trimester was predictive of pre-eclampsia with a DR of 78.1%, at FPR of 28%. While hs‑CRP of more than 7 mg/l had a RR of 12.1 (95% CI, 6.91–21.15) for pre‑eclampsia and a RR of 9.35 (95% CI, 4.48–19.52) for severe pre-eclampsia.218

Placental growth factor

Placental growth factor is a glycosylated dimeric glycoprotein, which is a member of the vascular endothelial growth factor (VEGF) family, it is a pro‑angiogenic factor secreted by the trophoblast and binds to VEGF receptor-1.219,220 Serum PLGF during the first trimester and at the time of diagnosis in women who subsequently develop pre-eclampsia have significantly lower concentrations than those with unaffected pregnancy.219,220,221,222,223,224,225 In a case-control study of 127 pregnant women with pre-eclampsia and 609 controls, PLGF achieved DR of 55% (95% CI, 33–71) for early-onset pre-eclampsia and 33% (95% CI,24–43) for late-onset pre-eclampsia, at a 10% FPR.226 These findings were confirmed by larger subsequent studies.227,228 A systematic review and meta-analysis have demonstrated that PLGF is superior to the other biomarkers for the first-trimester prediction of pre-eclampsia. Specifically, maternal PLGF concentrations alone achieve a DR of 56% (95% CI, 52–61), at a FPR of 9% (95% CI, 8–41), with LR+ of 6.05 (95% CI, 5.55–6.55) and LR- of 0.48 (95% CI, 0.43–0.52) for the prediction of early-onset pre-eclampsia.229

Pregnancy-associated plasma protein A

Pregnancy-associated plasma protein A (PAPP-A) is an insulin-like growth factor binding protein metalloproteinase complex. It is predominantly synthesized by the placenta and has been involved in the control of trophoblast invasion of the decidua.230 Previous studies have demonstrated that PAPP-A concentrations in maternal serum during the first trimester in pregnancies complicated by pre-eclampsia are relatively low compared with normotensive pregnancies.231 A meta-analysis including 16 studies, ten for early pre-eclampsia and three for late pre-eclampsia, thresholds of PAPP-A that were most commonly used were <5th centile (five studies) and < 10th centile (four studies). The optimal cut-off level was PAPP-A <0.4 MoM. The predictive performance of PAPP-A for early pre-eclampsia was generally better than that for late pre-eclampsia.229

Placental protein-13

Placental protein-13 (PP-13) is a member of the galectin family secreted by the syncytiotrophoblast.232 It binds to proteins on the extracellular matrix between the placenta and the endometrium and is postulated to be involved in placental implantation and vascular remodeling.233 The predictive performance of PP-13 for pre‑eclampsia is controversial. One cohort study conducted in the United States included 477 singleton pregnant women at 11–14 weeks’ gestation, including 42 patients with pre-eclampsia, showed that maternal plasma PP-13 concentration was significantly lower in pre-eclamptic cases compared with controls.234 For the prediction of early-onset pre-eclampsia, PP-13 MoM alone had an AUROC of 0.85 (0.69–1.00) with a DR of 68%, at 10% FPR, and the incorporation of this biomarker with PAPP-A MoM and mean UTPI MoM did not increase the predictive performance.234

Cell-free fetal deoxyribonucleic acid

Cell-free fetal deoxyribonucleic acid (cffDNA), which comprises approximately 10–20% of the total cell-free DNA in the maternal plasma, is thought to be originated from apoptotic trophoblasts.235 Lo et al. reported a five-fold increase in circulating cffDNA plasma levels in women diagnosed with pre-eclampsia compared with normotensive pregnant women.236 Pooled data from three case-control studies, including a total of 112 cases of pre-eclampsia and 239 controls, demonstrate that the DR, FPR, LR+ and LR- are 33–67%, 5–18%, 3.7–6.6, and 0.4–0.7, respectively.208,237,238,239 Papantoniou et al. quantified the cffDNA levels in maternal plasma by determining the promoter sequence of RASSF1A gene levels at 11–13 weeks’ gestation in 24 women who subsequently developed pre-eclampsia and 48 women with uncomplicated pregnancies and reported that using a cut-off value of 512 GEq/ml, cffDNA plasma levels had a DR of 100%, at 0% FPR.240 Karapetian et al. showed that a cut-off value of cffDNA concentration 22.54 GE/ml in maternal blood at 11–14 weeks’ pregnancy had the greatest predictive value for pre-eclampsia from ROC analysis, with 85.0% DR at 18.2% FPR.241 Although cffDNA may be a promising marker for pre-eclampsia prediction in the first trimester of pregnancy, Poon et al. assessed the association between pre-eclampsia and plasma cffDNA levels measured at 11–13 weeks in 1,949 singleton gestations by using digital analysis of selected regions assays. There was no significant difference in median plasma concentration of cffDNA between women with or without pre-eclampsia after adjustment for the maternal and fetal characteristics.242

Prediction Using Multivariable Analyses

There is no single test that predicts pre‑eclampsia with sufficient accuracy to be useful clinically,179 therefore interest has grown in the development of multivariable models that include both clinical and laboratory predictors, especially during the first trimester of pregnancy.243 However, the models that have been developed by the logistic regression approach are prone to overfitting, which can overestimate screening performance; however, the models are likely to perform poorly on data that have not been used to develop the model.203,244 As a result, alternative approaches have been proposed and external validation of prediction models is considered the optimal approach, which reflects generalizability of the prediction model. The prediction models that have been developed in specific study populations should be prospectively tested in independent validation samples with patients from a different population.245,246 A recent systematic review including 68 pre-eclampsia prediction models from 70 studies with 425,125 participants has demonstrated that the most frequently used predictors are medical history, BP, and UTPI.247

Multivariate logistic regression analysis approach

A systematic review evaluated the performance of 70 models from 29 studies consisted of 22 simple risk models (maternal characteristics only) versus 48 specialized models, which include specialized tests such as the measurement of MAP, UTPI, and/or maternal biomarkers for the prediction of pre-eclampsia (17 models to predict pre-eclampsia; 31 models to predict early-onset pre-eclampsia; and 22 models to predict late-onset pre-eclampsia) and revealed that specialized models performed better than the simple model in predicting early- and late-onset pre-eclampsia.248 Therefore, a combination of various tests is recommended for the prediction of pre-eclampsia.

Poon et al. published the first version of the Fetal Medicine Foundation (FMF) prediction models derived from a prospective screening study including 7,797 singleton pregnancies with 157 cases (2%) of pre-eclampsia by using multivariate logistic regression analysis.206 The authors reported that women of Afro-Caribbean race (OR = 3.64; 95% CI, 1.84–7.21), with history of pre-eclampsia (OR, 4.02; 95% CI, 1.58–10.24), and CHT (OR, 8.70; 95% CI, 2.77–27.33), and those who conceived with ovulation induction (OR, 4.75; 95% CI, 1.55–14.53) were associated with an increased risk of early-onset pre-eclampsia. For late-onset pre-eclampsia, the risk increased with maternal age (OR, 1.04; 95% CI, 1.00–1.07), BMI (OR, 1.10; 95% CI, 1.07–1.13), family history (OR, 2.91; 95% CI, 1.63–5.21), or history of pre-eclampsia (OR, 2.18; 95% CI, 1.24–3.83). In addition, late-onset pre-eclampsia was more common in Afro-Caribbean and South Asian women (adjusted OR, 2.66–3.31).39,206 Maternal risk factors alone yield DRs of 37% and 29% for early- and late-onset pre-eclampsia, respectively, at 5% FPR.39,249 A combination of maternal factors, MAP, UTPI, PAPP-A, and PLGF at 11–13 weeks’ gestation yielded DRs of 93% and 36% for the prediction of early- and late-onset pre-eclampsia, respectively, at 5% FPR, which were superior to the DRs of the traditional approach based only on maternal risk factors.206

Bayes-theorem and competing risk algorithm approach

Bayes-theorem is a mathematical formula for determining conditional probability that provides a way to incorporate new information when it becomes available during pregnancy to revise existing prediction.203 This approach allows the “a priori” risk for developing pre-eclampsia based on maternal characteristics and medical and obstetrical history to be updated with the results of various biomarkers using competing risk (CR) analysis.39,199 The CR algorithm is based on a survival-time model, this model determines the risk for pre-eclampsia adjusted for confounding maternal and past and current obstetric history by modifying the expected mean time of delivery and adjusting this a priori risk using biomarker information using likelihoods.250 The advantage of this approach is that it allows the assessment of individual patient-specific risks of pre-eclampsia that requires delivery before a specified gestation.203,251 The CR model was first developed from a study of 58,884 singleton pregnancies at 11–13 weeks’ gestation, including 1,426 (2.4%) that subsequently developed pre-eclampsia; the estimated DRs of preterm pre-eclampsia and all cases of pre-eclampsia, at a fixed FPR of 10%, were 77% and 54%, respectively.250 In a study of 120,492 singleton pregnancies, including 2,704 pregnancies with pre-eclampsia (2.2%), using the CR model, the factors that were shown to increase the risk for pre-eclampsia were advancing maternal age, increasing weight, Afro-Caribbean and South Asian racial origin, medical history of CHT, diabetes mellitus and SLE or APS, family and history of pre-eclampsia, and conception by IVF. In contrast, the risk for pre-eclampsia decreases with increasing maternal height and in parous women with no history of pre-eclampsia.203,251 The survival-time model based on maternal factors achieved DRs of 40%, 48%, and 54%, at 10% FPR, for all, preterm, and early-onset pre-eclampsia, respectively.251 Subsequently, data from prospective screening in 35,948 singleton pregnancies, including 1058 pregnancies (2.9%) that experienced pre-eclampsia, were used to update the original algorithm; DRs of preterm and term pre-eclampsia were 75% and 47%, respectively, at an FPR of 10%.227 The incorporation of PAPP-A to the model did not improve the DR of pre-eclampsia of any gestational age at delivery. These findings are in line with previous studies.227,251,252

Multivariate Gaussian distribution model approach

Serra and Scazzocchio et al. published a study of 6,893 pregnancies in a Spanish population with an incidence of pre-eclampsia of 2.3% (n = 161), including 17 cases (0.2%) of early-onset pre-eclampsia, using a multivariate Gaussian distribution model including maternal characteristics (a priori risk), PAPP-A and PLGF assessed at 8–14 weeks and MAP and UTPI measured at 11–14 weeks. The combination of maternal characteristics, biophysical parameters, and PLGF achieved the best DR, which was 59% for a 5% FPR and 94% for a 10% FPR (AUROC, 0.96; 95% CI, 0.94–0.98). The addition of PLGF to biophysical markers significantly improved the DR from 59% to 94%, while PAPP-A did not.253 This approach might reduce the overfitting problem related to logistic regression analysis because the models were constructed using different sources such as previous knowledge, specific population marker medians, risk factor meta-analysis, and published literature.249


A systematic review evaluating the benefits and harms of pre-eclampsia screening models has indicated that among 16 models that have been validated in 4 studies (n = 7,123), only five models (four first-trimester models and one second-trimester model) are considered good or with better discrimination, with c statistics ≥ 0.80.203,254 There are four first-trimester logistic regression predictive models that had successful external validation including three models developed by the FMF: (1) maternal factors, MAP, UTPI, (2) maternal factors, MAP, UTPI, PAPP-A, and (3) maternal factors, MAP, UTPI, PAPP-A, PLGF and a model developed by Odibo et al. using a combination of maternal factors, MAP, UTPI, PAPP-A, PP-13.203,206,234,255,256 The best predictive performance was achieved by the “FMF combined test”, which is a combination of maternal factors with “triple test”: MAP, UTPI, and serum PLGF.249,257

The Fetal Medicine Foundation prediction models

The “CR model”, a novel analytical statistic approach used in the FMF prediction model is based on the concept, which hypothesizes that all women would have developed pre-eclampsia if the pregnancy were to continue indefinitely.203 Hence, there is a competition between delivery before or after the development of pre-eclampsia. The largest study to date for the development of the FMF first-trimester combined test using the CR model was reported by Tan et al.257 A total of 61,174 mixed-European pregnant women, including 1,770 (2.9%) cases of pre-eclampsia, found that the best predictive performance was achieved by a combination of maternal factors with MAP, UTPI, and serum PLGF with DRs of 90%, 75%, and 41% for very early (with delivery at <32 weeks), preterm, and term pre-eclampsia, respectively, at 10% FPR.249,257 In white women, with a risk cut-off of 1 in 100 for preterm pre-eclampsia, the screen-positive rate was 10%, and DRs for early, preterm, and term pre-eclampsia were 88%, 69%, and 40%, respectively.257 While, in Afro-Caribbean women, the same method of screening and risk cut-off yielded the screen-positive rate of 34%, and DRs for early, preterm, and term pre-eclampsia were 100%, 92%, and 75%, respectively.195,227,228 The latest version of the FMF triple test has undergone successful validation in Italian258 Australian,259 American,260 Brazilian,261,262 mixed-European,195,228,263,264,265,266,267,268 Dutch,269,270 and Asian populations.271 In the screened population in the ASPRE (Combined Multimarker Screening and Randomized Patient Treatment with Aspirin for Evidence-Based Pre-eclampsia Prevention) trial involving 26,941 singleton pregnancies from 13 maternity hospitals in 6 countries (the UK, Spain, Italy, Belgium, Greece, and Israel), the DRs of preterm and term pre-eclampsia, after adjustment for the effect of aspirin, were 77% and 43%, respectively, at an FPR of 9.2%.5

Models developed from North American populations

To date, there are several first-trimester models that have been developed from prospective cohort studies in the United States and Canada:234,272,273,274,275,276,277,278,279,280

  • Audibert et al.273 presented the first model in 2010, which was developed from 893 nulliparous women including 40 (4.5%) cases of pre-eclampsia. The combination of maternal characteristics, maternal serum PAPP-A, inhibin-A, and PLGF yielded a DR of 75% for early-onset pre-eclampsia, at 10% FPR. However, this model is limited to nulliparous women, and the measurement of PLGF was only done in half of the study population. This model could not achieve the acceptable performance in external validation in a Dutch population.270
  • Odibo et al.234 reported a prospective cohort study in 2011, which included 452 women with 42 (9.3%) cases of pre-eclampsia using UTPI and maternal serum PP-13 and PAPP-A. The model containing PAPP-A or PAPP-A alone yielded the best AUROC prediction of all types of pre-eclampsia (AUROC, 0.77), while PP-13 alone or PP-13 containing models achieved the best AUROC for prediction of early-onset pre-eclampsia (AUROC, 0.85). However, the combination of PAPP-A and PP-13 did not improve the predictive performance compared with each individual marker.234 This model passed successful external validation within the United States260 but failed external validation in a Dutch population.203,270
  • Baschat et al.274 performed a study in 2014 and included 2,441 women with 108 (4.4%) cases of pre-eclampsia. The multivariate first-trimester prediction model including maternal factors (history of previous pre-eclampsia, DM, and nulliparity), MAP, and maternal serum PAPP-A had AUROCs of 0.82 and 0.83 for all and early-onset pre-eclampsia, respectively. With this model, pre-eclampsia and early-onset pre-eclampsia were predicted with 49% and 55% DRs, respectively, at 10% FPR. It was notable that this model did not include the maternal serum PLGF concentration. These models have been externally evaluated in the UK; however, the predictive performance is lower than that of the original study.203,266
  • Sonek et al.272 published a prospective observational cohort study for first-trimester pre-eclampsia prediction in 2018. This study developed a model that combined maternal characteristics, MAP, UTPI, ultrasonographic estimated placental volume, maternal serum PAPP-A, PLGF, and alpha fetoprotein (AFP) in 1,068 pregnant women with 46 (4.31%) cases of pre-eclampsia. There were 13 (1.22%) and 33 (3.09%) cases of early-onset pre-eclampsia and late-onset pre-eclampsia, respectively. A combination of maternal characteristics, maternal serum biomarkers, and UTPI yielded the highest DR of 85% at both 5% and 10% FPR for early-onset pre-eclampsia. Using the same model, the DRs for the identification of preterm pre-eclampsia were 52% and 60%, at 5% and 10% FPR, respectively. In contrast, the identification of late-onset and term pre-eclampsia was not improved by the addition of maternal biochemical markers, UTPI, or placental volume to maternal characteristics. The DRs of late-onset pre-eclampsia and term pre-eclampsia at 10% FPR were 48% and 43%, respectively. This model has not undergone internal or external validation.203

Models developed from Spanish populations

There are three major cohort studies that have reported on the first-trimester combined prediction models developed from the Spanish population.

  • Scazzocchio et al.263 developed combined prediction models for early- and late-onset pre-eclampsia in 2013 from 5,759 singleton pregnancies, with rates of all, early-, and late-onset pre-eclampsia of 2.6%, 0.5%, and 2.1%, respectively. The prediction algorithm for early-onset pre-eclampsia was based on maternal factors, mean UTPI, and MAP, whereas the prediction algorithm for late-onset pre-eclampsia was based on maternal factors and PAPP-A. The combination of maternal factors and biomarkers achieved an AUROC of 0.960 (95% CI, 0.940–0.980) with a DR of 80.2%, at 10% FPR, for early-onset pre-eclampsia. For late-onset pre-eclampsia, the AUROC was 0.710 (95% CI, 0.658–0.763) and the DR was 39.6% at 10% FPR.263 Internal validation was conducted in 2017 by the same group of researchers, the performance of their prediction algorithms in 4,203 singleton pregnancies with rates of all, early-, and late-onset pre-eclampsia of 4.0%, 0.7%, and 3.4%, respectively, was similar to the original study. This model has undergone external validation in several prospective studies, which have reported underperformance contrary to the internal validation study performed by the original investigators.203
  • Crovetto et al.278 later developed another prediction algorithm for pre-eclampsia using maternal factors, MAP, UTPI, plasma PLGF, and sFlt-1 based on a study population of 9,462 singleton pregnancies with 57 (0.6%) and 246 (2.6%) cases with early- and late-onset pre-eclampsia, respectively. Maternal PLGF and sFlt-1 concentrations were available in 303 pre-eclamptic cases and 853 controls. The best algorithm for early- and late-onset pre-eclampsia was a combination of maternal factors, MAP, UTPI, PLGF, and sFlt-1. The model achieved DRs of 91.2% and 76.4%, at 10% FPR, for early- and late-onset pre-eclampsia, respectively.278 These models were reported to be suboptimal in external validation in a Dutch population including 3,736 women with 87 (2.3%) affected by pre-eclampsia.249,270
  • Serra and Scazzocchio et al.281 recently developed another model using a new approach “multivariate Gaussian distribution model” for early-onset pre-eclampsia screening in a cohort study of 7,908 pregnancies with 17 (0.2%) cases of early-onset pre-eclampsia. The model including maternal factors, MAP, and UTPI at 11–13 weeks’ gestation, and PLGF at 8–13+6 weeks’ gestation had been shown to achieve a DR of 94.1% at 10% FPR for the identification of early-onset pre-eclampsia. This new approach might potentially allow adaptation to a variety of populations.281 In addition, multiple approaches for UTPI measurement (transabdominal or transvaginal) and PLGF measurement from as early as 8 weeks’ gestation would allow flexibility for routine clinical practice in a setting in which Down's syndrome screening is performed using a two-step procedure.281 However, the major limitations of this model were related to the fact that the development was based on a small number of early-onset pre-eclampsia cases and the lack of internal and external validation.203


Abbreviations for Clinical Practice Guidelines are as follows ACOG (American College of Obstetricians and Gynecologists),282 NICE (National Institutes of Clinical Excellence),283 SOGC (Society of Obstetricians and Gynaecologists of Canada),113,185 AOM (Association of Ontario Midwives)284 International Society of Ultrasound in Obstetrics and Gynecology (ISUOG)216 and The International Federation of Gynecology and Obstetrics (FIGO)193

In a systematic review of international clinical practice guidelines on the hypertensive disorders of pregnancy,285 three (ACOG, AOM, SOGC) out of 13 guidelines gave recommendations for the screening or prediction of pre‑eclampsia or other hypertensive disorders of pregnancy. Well‑established clinical risk markers such as medical history were the only recommended markers for screening. None of the guidelines recommended the use of ultrasonography or biomarkers; however, two guidelines (NICE, SOGC) suggested that a combination of these tests with clinical risk markers may be useful but require further studies to make sufficient conclusions. Recently, FIGO guidelines support the use of risk-based screening using biomarkers for first-trimester prediction of pre-eclampsia over screening methods that use maternal demographic characteristics and medical history (maternal risk factors) only. The guidelines endorse the Fetal Medicine Foundation position that all pregnant women should be screened for preterm pre-eclampsia by the first-trimester combined test with maternal factors, MAP, UTPI, and PLGF as a one-step procedure.193


Evidence suggests that pre-eclampsia is predictable and preventable. Administration of low-dose aspirin initiated before 16 weeks’ gestation significantly reduces the rate of preterm pre-eclampsia. Therefore, it is necessary to identify pregnant women at risk of developing pre-eclampsia during the first trimester of pregnancy, thus allowing timely therapeutic intervention.


Key priorities when conducting research on predicting pre‑eclampsia include the following.

Standardization of definitions and analytical methods will be useful for comparison and meta‑analysis of results from prediction studies. Multivariate models need to be validated externally before they can be used in clinical practice. Assessing the validity of these models in other population needs to be carried out to assure validity and reliability of predictive performance. There is a large knowledge deficit related to risk prediction for women in LMICs. An urgent priority is to diminish this deficit.


Limited access to medical facilities and antenatal care early in pregnancy leading to delays in disease identification and treatment are major contributing factors to the increased burden of the hypertensive disorders of pregnancy in LMICs.286 Pre‑eclampsia is a heterogeneous condition with different phenotypes. Future research is required to identify the risk factors and disease presentation for pre‑eclampsia in LMICs, which may differ from that in HICs, to allow for early interventions. Also, there might be need for earlier screening and initiation of preventative treatments in LMICs owing to the severity of outcomes in these setting. Priorities for under-resourced settings may be focused around the availability of accurate, functional BP monitoring devices, facility for assessment of risk, training of staff (particularly if ultrasound is to be used for screening), ensuring reliable supply chains for the provision of aspirin, antihypertensives and magnesium sulphate and the availability of laboratory services and point-of-care tests. There should be strategies to implement prevention alongside screening.193

Screening of pre‑eclampsia using maternal factors and biomarkers is considered challenging in LMICs because nearly all risk-assessment tools have been developed exclusively in high-income countries. The risk factors of high-risk women in LMICs may be different from those in high-income countries and it is important to determine whether they have unique risk factors. There are also some other factors (e.g., diet, smoking status, and coital and partner's history), which may alter the risk, severity, and pertinent pathophysiology of pre‑eclampsia compared with that observed in high-income countries. Therefore, the prior risk models developed in HICs require validation in LMICs. FIGO has pragmatically recommended that where resources are limited, “contingent screening” for preterm pre-eclampsia can be considered. First-stage screening with a model that combines maternal factors and MAP is performed for all pregnancies and those who have positive screening result could be referred for second-stage screening with the addition of PLGF and UTPI measurements.193 In a prospective screening study including more than 120,000 singleton pregnancies, the performance of screening for preterm pre-eclampsia by this strategy was examined. At a fixed screen-positive rate of 10%, a detection rate of 71% was achieved by this two-stage screening.287 FIGO also encourages that risk assessment and resource-appropriate testing for preterm pre‑eclampsia become an integral part of routine first-trimester evaluation protocol offered at all maternal health services.193


Priorities for prediction of pre-eclampsia in under‑resourced settings. Modified from Poon LC et al. (2019).193

Initial priority

Ultimate goal

Improve access to prenatal services and encourage early booking

Risk assessment and resource-appropriate testing for preterm pre-eclampsia should become an integral part of routine first-trimester evaluation protocol offered at all maternal health services.

Given the resource constraints in LMICs, variations of the first-trimester combined test should be considered but the baseline test should be maternal risk factors combined with mean arterial pressure

All pregnant women should be screened for preterm pre-eclampsia preferably by the first-trimester combined test with maternal risk factors, mean arterial pressure, uterine artery pulsatility index, and placental growth factor as a one-step procedure.


  • Maternal characteristics, medical history and obstetric history must be recorded accurately (quality of evidence: high, strength of recommendation: strong).
  • Mean arterial pressure (MAP) should be measured as part of the risk assessment for pre-eclampsia and it should be measured by validated automated and semiautomated device (quality of evidence: high, strength of recommendation: strong).
  • In first-trimester screening, the best biochemical marker is PLGF. PAPP-A is useful if measurements of PLGF and UTPI are not available (quality of evidence: high, strength of recommendation: strong).

Modified from Poon LC et al. (2019).193


Dr LC Poon has received speaker fees and consultancy payments from Roche Diagnostics and Ferring Pharmaceuticals. Dr LC Poon have received in?kind contributions from Roche Diagnostics, PerkinElmer, Thermo Fisher Scientific, and GE Healthcare.


ACOG: American College of Obstetricians and Gynecologists

AFP: Alpha fetoprotein

ANC: Antenatal care

AOM: Association of Ontario Midwives

APS: Antiphospholipid syndrome

ART: Assisted reproductive technology

ASPRE: Aspirin for Evidence-Based Preeclampsia Prevention

AUROC: Area under the receiver operating characteristic curve

BMI: Body mass index

BP: Blood pressure

cffDNA: Cell-free fetal deoxyribonucleic acid

CHT: Chronic hypertension

CI: Confidence interval

CO: Carbon monoxide

CoNARTaS: Committee of Nordic Assisted Reproductive Technology and Safety

CR: Competing risk

DBP: Diastolic blood pressure

DC: Dichorionic

DM: Diabetes mellitus

DR: Detection rate

FGR: Fetal growth restriction

FIGO: International Federation of Gynecology and Obstetrics

FINNPEC: Finnish Genetics of Preeclampsia Consortium

FMF: Fetal Medicine Foundation

FPR: False positive rate

hCG: human chorionic gonadotropin

HDL-C: High-density lipoprotein cholesterol

HELLP: Hemolysis, elevated liver enzymes, low platelets

HICs: High-income countries

HPLC: High performance liquid chromatography

HT: Hypertension

hs-CRP: High-sensitivity C-reactive protein

ISSHP: The International Society for the Study of Hypertension in Pregnancy

IVF: In vitro fertilization

LDL-C: Low-density lipoprotein cholesterol

LMICs: Low and middle-income countries

LR: Likelihood ratio

MAP: Mean arterial pressure

MC: Monochorionic

MCA: Middle cerebral artery

MoM: Multiple of the median

MTHFR: Methylene tetrahydrofolate reductase

NICE: National Institute for Health and Care Excellence

NICU: Neonatal intensive care unit

NPV: Negative predictive value

OA: Ophthalmic artery

OR: Odds ratio

PAPP-A: Pregnancy associated plasma protein-A

PE: Preeclampsia

PI: Pulsatility index

PLGF: Placental growth factor

PM 2.5: Particulate matter diameter 2.5 µm or less

PP-13: Placental protein-13

RI: Resistance index

ROC: Receiver operating characteristic

RR: Relative risk

SBP: Systolic blood pressure

sEng: Soluble endoglin

SES: Socioeconomic status

sFlt-1: Soluble fms-like tyrosine kinase-1

SGA: Small for gestational age

SLE: Systemic lupus erythematosus

SNP: Single-nucleotide polymorphism

SOGC: Society of Obstetricians and Gynaecologists of Canada

SOMANZ: Society of Obstetric Medicine of Australia and New Zealand

TGFb: Transforming growth factor-beta

UTI: Urinary tract infection

UTPI: Uterine artery pulsatility index

VEGF: Vascular endothelial growth factor

VIP: Vitamins in Preeclampsia Trial



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