Deconstructing the smoking-preeclampsia paradox through a counterfactual framework

Prevention of preeclampsia

Preeclampsia is a relatively common pregnancy complication and constitutes a major cause of morbidity and mortality for mothers and children worldwide. It disproportionally affects low-resource countries. Appropriate identification of individuals at increased risk and prevention of the disease and its complications remain healthcare and research priorities, and the investigation of potential interventions to prevent preeclampsia has driven much of the obstetric research in recent decades. In this article, we review the scientific literature on the topic, highlighting established benefits and remaining questions regarding different non-pharmacological and pharmacological strategies, including exercise, the timing of birth, aspirin and calcium use, among others, as well as potential novel therapies under investigation.

Ambient temperature and preeclampsia: A historical cohort study

Previous studies found inconsistent associations between ambient temperature during pregnancy and the risk of preeclampsia. If such associations are causal, they may impact the future burden of preeclampsia in the context of climate change. We used a historical cohort of 129,009 pregnancies (5074 preeclampsia cases) from southern Israel that was merged with temperature assessments from a hybrid satellite-based exposure model. Distributed-lag and cause-specific hazard models were employed to study time to all preeclampsia cases, followed by stratification according to early (≤34 weeks) and late (>34 weeks) onset disease and identify critical exposure periods. We found a positive association between temperature and preeclampsia during gestation, which was stronger in the 3rd trimester. For example, during week 33, compared to the reference temperature of 22.4 °C, the cause-specific hazard ratio (HRCS) of preeclampsia was 1.01 (95% confidence interval (CI): 1.01-1.02) when exposed to 30 °C, 1.05 (95%CI: 1.03-1.08) at 35 °C, and 1.07 (95%CI: 1.04-1.10) at 37 °C. The associations existed with both early- and late-onset preeclampsia; however, the associations with the early-onset disease were somewhat stronger, limited to the first weeks of pregnancy and the third trimester, and with larger confidence intervals. The HRCS for early preeclampsia onset, when exposed to 37 °C compared to 22.4 °C during week 33, was 1.12 (95%CI: 0.96-1.30), and for late-onset preeclampsia, the HRCS was 1.09 (95%CI: 1.05-1.13). To conclude, exposure to high temperatures at the beginning and, particularly, the end of gestation is associated with an increased risk of preeclampsia in southern Israel.

Reevaluating the protective effect of smoking on preeclampsia risk through the lens of bias

Preeclampsia is a hypertensive disorder that is usually diagnosed after 20 weeks' gestation. Despite the deleterious effect of smoking on cardiovascular disease, it has been frequently reported that smoking has a protective effect on preeclampsia risk and biological explanations have been proposed. However, in this manuscript, we present multiple sources of bias that could explain this association. First, key concepts in epidemiology are reviewed: confounder, collider, and mediator. Then, we describe how eligibility criteria, losses of women potentially at risk, misclassification, or performing incorrect adjustments can create bias. We provide examples to show that strategies to control for confounders may fail when they are applied to variables that are not confounders. Finally, we outline potential approaches to manage this controversial effect. We conclude that there is probably no single epidemiological explanation for this counterintuitive association.

Secondhand smoke exposure is associated with the risk of hypertensive disorders of pregnancy: the Japan Environment and Children's Study

Hypertensive disorders of pregnancy (HDP) are associated with poor maternal and neonatal prognoses. Although several studies have indicated an effect of secondhand smoke (SHS) exposure on HDP, such evidence is lacking in Japan. Therefore, we analyzed data from the Japan Environment and Children's Study, a large-scale epidemiological investigation, to elucidate a possible link between SHS exposure and HDP risk. Data were obtained from the all-birth fixed datasets and included information on 104,062 fetuses and their parents. SHS exposure was assessed in terms of the frequency (rarely, 1-3, or 4-7 days/week) and the daily duration of exposure (<1, 1-2, or ≥2 h(s)/day). Modified Poisson regression model analyses were performed with adjustment for known risk factors for HDP. Additionally, the population attributable fractions (PAFs) of SHS exposure and maternal smoking to HDP prevalence were estimated. The relative risks of developing HDP among individuals with SHS exposures of 4-7 days/week and ≥2 h/day were 1.18 and 1.27 (95% confidence interval: 1.02-1.36 and 0.96-1.67), respectively, compared to the reference groups (rare exposure and <1 h/day). The PAFs for the risk of HDP due to SHS exposure and perinatal smoking were 3.8% and 1.8%, respectively. Japanese women with greater exposure to SHS have a higher risk of HDP after adjustment for possible confounding factors; thus, relevant measures are required to reduce SHS exposure to alleviate HDP risk. The association between second-hand smoking exposure and hypertensive disorders of pregnancy risk was analyzed using the JECS data. The relative risks in 4-7 days/week and ≥2 h/day of SHS exposures were 1.18 and 1.27, respectively. The PAFs due to SHS exposure and maternal smoking were 3.80% and 1.81%, respectively.

Cigarette smoking during pregnancy and adverse perinatal outcomes: a cross-sectional study over 10 years

BACKGROUND

It has been shown that active exposure to tobacco is associated with adverse pregnancy outcomes including, but not limited to, intrauterine fetal death, reduced fetal weight, and higher risk of preterm birth. We want to investigate these effects in a high-income country.

METHODS

This cross-sectional study examined 20,843 pregnant women who delivered over 10 years at the Maternity Hospital of the Centre Hospitalier Universitaire Vaudois (CHUV) in Lausanne, Switzerland. The objective was to evaluate a dose-response relationship between daily cigarette use during pregnancy and possible adverse perinatal outcomes. The social and clinical characteristics as well as obstetric and neonatal outcomes were compared between the smoking and the non-smoking groups. Adjusted odds ratios (aOR) and trend analyses (p_trend) were calculated.

RESULTS

Nineteen thousand five hundred fifty-four pregnant women met the inclusion criteria and 2,714 (13.9%) of them were smokers. Even after adjusting for confounding factors, smoking during pregnancy was associated with preterm birth, birthweight < 2500 g, intrauterine growth restriction, neonatal respiratory and gastrointestinal diseases, transfer to the neonatal intensive care unit, and neonatal intensive care unit admissions > 7 days. Intrauterine death and neonatal infection were associated with heavy smoking (≥ 20 cigarettes/day). Smoking appeared to be a protective factor for pre-eclampsia and umbilical cord arterial pH below 7.1. A significant trend (p_trend < 0.05) was identified for preterm birth, intrauterine growth restriction, birthweight < 2500 g, umbilical cord arterial pH below 7.1, transfers to our neonatal intensive care unit, and neonatal intensive care unit admissions more than 7 days.

CONCLUSION

Cigarette smoking is associated with several adverse perinatal outcomes of pregnancy with a dose-dependent effect.

Causal evidence in health decision making: methodological approaches of causal inference and health decision science

Objectives

Public health decision making is a complex process based on thorough and comprehensive health technology assessments involving the comparison of different strategies, values and tradeoffs under uncertainty. This process must be based on best available evidence and plausible assumptions. Causal inference and health decision science are two methodological approaches providing information to help guide decision making in health care. Both approaches are quantitative methods that use statistical and modeling techniques and simplifying assumptions to mimic the complexity of the real world. We intend to review and lay out both disciplines with their aims, strengths and limitations based on a combination of textbook knowledge and expert experience.

Methods

To help understanding and differentiating the methodological approaches of causal inference and health decision science, we reviewed both methods with the focus on aims, research questions, methods, assumptions, limitations and challenges, and software. For each methodological approach, we established a group of four experts from our own working group to carefully review and summarize each method, followed by structured discussion rounds and written reviews, in which the experts from all disciplines including HTA and medicine were involved. The entire expert group discussed objectives, strengths and limitations of both methodological areas, and potential synergies. Finally, we derived recommendations for further research and provide a brief outlook on future trends.

Results

Causal inference methods aim for drawing causal conclusions from empirical data on the relationship of pre-specified interventions on a specific target outcome and apply a counterfactual framework and statistical techniques to derive causal effects of exposures or interventions from these data. Causal inference is based on a causal diagram, more specifically, a directed acyclic graph (DAG), which encodes the assumptions regarding the causal relations between variables. Depending on the type of confounding and selection bias, traditional statistical methods or more complex g-methods are needed to derive valid causal effects. Besides the correct specification of the DAG and the statistical model, assumptions such as consistency, positivity, and exchangeability must be checked when aiming at causal inference. Health decision science aims for guiding policy decision making regarding health interventions considering and balancing multiple competing objectives of a decision based on data from multiple sources and studies, for example prevalence studies, clinical trials and long-term observational routine effectiveness studies, and studies on preferences and costs. It involves decision analysis, a systematic, explicit and quantitative framework to guide decisions under uncertainty. Decision analyses are based on decision-analytic models to mimic the course of disease as well as aspects and consequences of the intervention in order to quantitatively optimize the decision. Depending on the type of decision problem, decision trees, state-transition models, discrete event simulation models, dynamic transmission models, or other model types are applied. Models must be validated against observed data, and comprehensive sensitivity analyses must be performed to assess uncertainty. Besides the appropriate choice of the model type and the valid specification of the model structure, it must be checked if input parameters of effects can be interpreted as causal parameters in the model. Otherwise results will be biased.

Conclusions

Both causal inference and health decision science aim for providing best causal evidence for informed health decision making. The strengths and limitations of both methods differ and a good understanding of both methods is essential for correct application but also for correct interpretation of findings from the described methods. Importantly, decision-analytic modeling should be combined with causal inference when developing guidance and recommendations regarding decisions on health care interventions.

Bias in the association between advanced maternal age and stillbirth using left truncated data

Restriction to analysis of births that survive past a specified gestational age (typically 20 weeks gestation) leads to biased exposure-outcome associations. This bias occurs when the cause of restriction (early pregnancy loss) is influenced by both the exposure and unmeasured factors that also affect the outcome. The aim of this study is to estimate the magnitude of bias resulting from left truncated data in the association between advanced maternal age and stillbirth. We simulated data for the causal pathway under a collider-stratification mechanism. Simulation parameters were based on an observed birth cohort from Western Australia and a range of plausible values for the prevalence of early pregnancy loss, unmeasured factor U and the odds ratios for the selection effects. Selection effects included the effects of maternal age on early pregnancy loss, U on early pregnancy loss, and U on stillbirth. We compared the simulation scenarios to the observed birth cohort that was truncated to pregnancies that survived beyond 20 gestational weeks. We found evidence of marginal downward bias, which was most prominent for women aged 40 + years. Overall, we conclude that the magnitude of bias due to left truncation is minimal in the association between advanced maternal age and stillbirth.

Is the lack of prior exposure to sperm antigens associated with worse neonatal and maternal outcomes? A 10 years single-center experience comparing ICSI-TESE pregnancies to ICSI pregnancies

BACKGROUND

Nowadays pathogenesis of preeclampsia is still unknown. Among the different etiological hypotheses, some authors proposed that it might be due to an abnormal immunologic response to foreign fetal antigen derived from the father's sperm. Indeed, the fetus is considered a semi allograft, being one half paternally derived in its antigenicity, and the first pathogenic insult of preeclampsia may be an abnormal maternal immune response towards this semi-allogenic implant. In the context of Artificial Reproductive Techniques, it has been shown that the use of donor and surgically retrieved spermatozoa (e.g. Testicular Sperm Extraction) increases the risk of preeclampsia, confirming the protective effect of sperm exposure on maternal complications.

OBJECTIVE

Determining whether the lack of exposure to sperm antigens is associated with worse maternal and neonatal outcomes in pregnancies obtained through intracytoplasmic sperm injection after testicular sperm extraction for obstructive azoospermia.

MATERIAL AND METHODS

This is a single-center case-control retrospective study, focusing on all first pregnancies obtained through intracytoplasmic sperm injection after testicular sperm extraction for obstructive azoospermia at Humanitas Fertility Center between January 1st, 2010 and December 31st, 2019. Controls included patients that achieved their first pregnancy with intracytoplasmic sperm injection and ejaculated sperm, for a diagnosis other than azoospermia, in the same time period. Cases were matched with controls in a 1:2 ratio, considering female age, female BMI and year of controlled ovarian stimulation. The primary outcome measure was the delivery rate, defined as the number of deliveries divided by the total number of clinical pregnancies. Secondary outcome measures focused on maternal and neonatal complications, such as miscarriage rate, rate of main obstetric complications, prematurity rate and rate of congenital malformations.

RESULTS

By analyzing overall 113 pregnancies among cases and 214 pregnancies among controls, this study showed that the delivery rate was higher in controls with respect to cases (92.06% vs 84.07%, p = 0.026); among deliveries, live births were respectively 98.95% and 100%, while only one stillbirth occurred in cases. The first trimester miscarriage rate was higher in the cases than controls (13.27% vs 6.07%, p = 0.027), while no difference was found among rate of second trimester miscarriages, therapeutic abortions and ectopic pregnancies. There was no difference regarding the rate of maternal complications, including gestational hypertension, preeclampsia, HELLP syndrome, gestational diabetes, placenta previa, placental abruption and premature rupture of the membranes. Considering neonatal complications, it was shown that twins belonging to controls had a higher prematurity rate with respect to cases (65.79% vs 50.00%) but without a statistical relevance. Lastly, the rate of congenital malformations did not differ among the two groups.

DISCUSSION

This study showed that, once couples diagnosed with obstructive azoospermia achieve a pregnancy, they have a much higher risk of miscarriage in the first trimester in respect to non-azoospermic patients. Moreover, controls had a higher delivery rate in respect to cases; however, when the fetal status at birth was compared, no difference was found between live births and stillbirths.

CONCLUSIONS

Differently from the findings in the literature, no association with preeclampsia was found. This might be related to a collider bias/left truncation bias: since azoospermic patients are at higher risk of early termination of pregnancy, it results that they do not have the possibility to develop preeclampsia and other adverse outcomes. This article is protected by copyright. All rights reserved.

Quantifying the influence of bias in reproductive and perinatal epidemiology through simulation

PURPOSE

The application of simulated data in epidemiological studies enables the illustration and quantification of the magnitude of various types of bias commonly found in observational studies. This was a review of the application of simulation methods to the quantification of bias in reproductive and perinatal epidemiology and an assessment of value gained.

METHODS

A search of published studies available in English was conducted in August 2020 using PubMed, Medline, Embase, CINAHL, and Scopus. A gray literature search of Google and Google Scholar, and a hand search using the reference lists of included studies was undertaken.

RESULTS

Thirty-nine papers were included in this study, covering information (n = 14), selection (n = 14), confounding (n = 9), protection (n = 1), and attenuation bias (n = 1). The methods of simulating data and reporting of results varied, with more recent studies including causal diagrams. Few studies included code for replication.

CONCLUSIONS

Although there has been an increasing application of simulation in reproductive and perinatal epidemiology since 2015, overall this remains an underexplored area. Further efforts are required to increase knowledge of how the application of simulation can quantify the influence of bias, including improved design, analysis and reporting. This will improve causal interpretation in reproductive and perinatal studies.

Seroprevalence of SARS-CoV-2 antibodies in Saint Petersburg, Russia: a population-based study

Properly conducted serological survey can help determine infection disease true spread. This study aims to estimate the seroprevalence of SARS-CoV-2 antibodies in Saint Petersburg, Russia accounting for non-response bias. A sample of adults was recruited with random digit dialling, interviewed and invited for anti-SARS-CoV-2 antibodies. The seroprevalence was corrected with the aid of the bivariate probit model that jointly estimated individual propensity to agree to participate in the survey and seropositivity. 66,250 individuals were contacted, 6,440 adults agreed to be interviewed and blood samples were obtained from 1,038 participants between May 27 and June 26, 2020. Naïve seroprevalence corrected for test characteristics was 9.0% (7.2-10.8) by CMIA and 10.5% (8.6-12.4) by ELISA. Correction for non-response decreased estimates to 7.4% (5.7-9.2) and 9.1% (7.2-10.9) for CMIA and ELISA, respectively. The most pronounced decrease in bias-corrected seroprevalence was attributed to the history of any illnesses in the past 3 months and COVID-19 testing. Seroconversion was negatively associated with smoking status, self-reported history of allergies and changes in hand-washing habits. These results suggest that even low estimates of seroprevalence can be an overestimation. Serosurvey design should attempt to identify characteristics that are associated both with participation and seropositivity.

A systematic review of quantitative bias analysis applied to epidemiological research

BACKGROUND

Quantitative bias analysis (QBA) measures study errors in terms of direction, magnitude and uncertainty. This systematic review aimed to describe how QBA has been applied in epidemiological research in 2006-19.

METHODS

We searched PubMed for English peer-reviewed studies applying QBA to real-data applications. We also included studies citing selected sources or which were identified in a previous QBA review in pharmacoepidemiology. For each study, we extracted the rationale, methodology, bias-adjusted results and interpretation and assessed factors associated with reproducibility.

RESULTS

Of the 238 studies, the majority were embedded within papers whose main inferences were drawn from conventional approaches as secondary (sensitivity) analyses to quantity-specific biases (52%) or to assess the extent of bias required to shift the point estimate to the null (25%); 10% were standalone papers. The most common approach was probabilistic (57%). Misclassification was modelled in 57%, uncontrolled confounder(s) in 40% and selection bias in 17%. Most did not consider multiple biases or correlations between errors. When specified, bias parameters came from the literature (48%) more often than internal validation studies (29%). The majority (60%) of analyses resulted in >10% change from the conventional point estimate; however, most investigators (63%) did not alter their original interpretation. Degree of reproducibility related to inclusion of code, formulas, sensitivity analyses and supplementary materials, as well as the QBA rationale.

CONCLUSIONS

QBA applications were rare though increased over time. Future investigators should reference good practices and include details to promote transparency and to serve as a reference for other researchers.

Original article: is the protective association between hyperemesis gravidarum and birth defects biased by pregnancy termination?

PURPOSE

We assessed whether the protective association between hyperemesis gravidarum and birth defects could be due to selection bias from exclusion of pregnancy terminations.

METHODS

We designed a cohort study of 2,115,581 live births in Canada, 1990-2016. The main exposure measure was hyperemesis gravidarum. The main outcome measure included any birth defect at delivery. We estimated risk ratios (RR) and 95% confidence intervals (CI) for the association of hyperemesis gravidarum with birth defects in log-binomial regression models, and assessed the extent of selection bias through correction factors.

RESULTS

Hyperemesis gravidarum was associated with 0.88 times the risk of birth defects in models not corrected for bias (95% CI 0.82-0.94). Correction for selection bias suggested that if screening for birth defects was associated with 1.33 times the chance of detecting birth defects and having a pregnancy termination, there would be no association with hyperemesis gravidarum. If ultrasound was associated with 2.00 times the chance of detecting birth defects and 1.50 times the risk of pregnancy termination, hyperemesis gravidarum would be associated with 1.27 times the risk of birth defects (95% CI 1.18-1.35).

CONCLUSIONS

The protective association between hyperemesis gravidarum and birth defects in previous studies may be due to selection bias.

Environmental Exposure during Pregnancy: Influence on Prenatal Development and Early Life: A Comprehensive Review

Preconception and prenatal exposure to environmental contaminants may affect future health. Pregnancy and early life are critical sensitive windows of susceptibility. The aim of this review was to summarize current evidence on the toxic effects of environment exposure during pregnancy, the neonatal period, and childhood. Alcohol use is related to foetal alcohol spectrum disorders, foetal alcohol syndrome being its most extreme form. Smoking is associated with placental abnormalities, preterm birth, stillbirth, or impaired growth and development, as well as with intellectual impairment, obesity, and cardiovascular diseases later in life. Negative birth outcomes have been linked to the use of drugs of abuse. Pregnant and lactating women are exposed to endocrine-disrupting chemicals and heavy metals present in foodstuffs, which may alter hormones in the body. Prenatal exposure to these compounds has been associated with pre-eclampsia and intrauterine growth restriction, preterm birth, and thyroid function. Metals can accumulate in the placenta, causing foetal growth restriction. Evidence on the effects of air pollutants on pregnancy is constantly growing, for example, preterm birth, foetal growth restriction, increased uterine vascular resistance, impaired placental vascularization, increased gestational diabetes, and reduced telomere length. The advantages of breastfeeding outweigh any risks from contaminants. However, it is important to assess health outcomes of toxic exposures via breastfeeding. Initial studies suggest an association between pre-eclampsia and environmental noise, particularly with early-onset pre-eclampsia. There is rising evidence of the negative effects of environmental contaminants following exposure during pregnancy and breastfeeding, which should be considered a major public health issue.

Multi-level analysis of symptoms of poison exposure reported to the Japanese Poison Information Center

Two types of analytical multi-level approach to analyse symptoms after exposure to poisons were performed using numerical data extracted from reports received by the JAPAN POISON INFORMATION CENTER on the number "Poisoning 110" during 1998-2017. In the first approach, data collected in 2017, 2012, 2007 and 2002 were used, while in the second approach, all data collected during 1999-2017 were used. In the first approach, according to the odds ratio, the order of causative agents was industrial drugs>home drugs>general drugs and in the second approach, it was industrial drugs>foods/natural poisoning>agricultural drugs. However, in the first approach, the order based on the 95% confidence interval (CI) and profile likelihood was general drugs>home drugs>industrial drugs and in the second approach, the order based on 95% CI, profile likelihood and Wald value was agricultural drugs>foods/natural poisoning>industrial drugs. These multi-prospects were developed using a multilevel approach and the most optimized model was selected from the number of numerical data and the adaptability of fit of the multi-level logistic regression models in this report.

Sources of heterogeneity when studying the cardiovascular effects of fetal growth restriction: an overview of the issues

Intrauterine growth restriction (IUGR) has been repeatedly identified as a risk factor for cardiovascular disease (CVD). A possible explanation for this association is the effect of IUGR on cardiovascular structure and function. However, the specific changes observed are not consistent among studies. In this paper, we analyze several sources of heterogeneity within and between studies related to exposure, outcome and co-variables. A broad IUGR definition might include different phenotypes, expressing heterogeneity as an outcome. Outcome heterogeneity may also be the result of the postnatal effect modification that can be explored within studies. In order to do so, it is important to move beyond mean differences between groups, for example using unsupervised, stratified or interaction analysis. Different definitions of IUGR and the inclusion of different postnatal variables as confounders are potential sources of heterogeneity between studies. Researchers should be aware that postnatal variables may play different roles throughout a person's life and are not limited to behave as confounders. Therefore, their inclusion in the statistical model needs to be carefully considered. We discuss when sources of heterogeneity need to be controlled, and when they need to be identified and shown as a result.

Educational Note: Paradoxical collider effect in the analysis of non-communicable disease epidemiological data: a reproducible illustration and web application

Classical epidemiology has focused on the control of confounding, but it is only recently that epidemiologists have started to focus on the bias produced by colliders. A collider for a certain pair of variables (e.g. an outcome Y and an exposure A) is a third variable (C) that is caused by both. In a directed acyclic graph (DAG), a collider is the variable in the middle of an inverted fork (i.e. the variable C in A → C ← Y). Controlling for, or conditioning an analysis on a collider (i.e. through stratification or regression) can introduce a spurious association between its causes. This potentially explains many paradoxical findings in the medical literature, where established risk factors for a particular outcome appear protective. We use an example from non-communicable disease epidemiology to contextualize and explain the effect of conditioning on a collider. We generate a dataset with 1000 observations, and run Monte-Carlo simulations to estimate the effect of 24-h dietary sodium intake on systolic blood pressure, controlling for age, which acts as a confounder, and 24-h urinary protein excretion, which acts as a collider. We illustrate how adding a collider to a regression model introduces bias. Thus, to prevent paradoxical associations, epidemiologists estimating causal effects should be wary of conditioning on colliders. We provide R code in easy-to-read boxes throughout the manuscript, and a GitHub repository [https://github.com/migariane/ColliderApp] for the reader to reproduce our example. We also provide an educational web application allowing real-time interaction to visualize the paradoxical effect of conditioning on a collider [http://watzilei.com/shiny/collider/].

A best practice position statement on the role of the nephrologist in the prevention and follow-up of preeclampsia: the Italian study group on kidney and pregnancy

Preeclampsia (PE) is a protean syndrome causing a transitory kidney disease, characterised by hypertension and proteinuria, ultimately reversible after delivery. Its prevalence is variously estimated, from 3 to 5% to 10% if all the related disorders, including also pregnancy-induced hypertension (PIH) and HELLP syndrome (haemolysis, increase in liver enzyme, low platelets) are included. Both nephrologists and obstetricians are involved in the management of the disease, according to different protocols, and the clinical management, as well as the role for each specialty, differs worldwide. The increased awareness of the role of chronic kidney disease in pregnancy, complicating up to 3% of pregnancies, and the knowledge that PE is associated with an increased risk for development of CKD later in life have recently increased the interest and redesigned the role of the nephrologists in this context. However, while the heterogeneous definitions of PE, its recent reclassification, an emerging role for biochemical biomarkers, the growing body of epidemiological data and the new potential therapeutic interventions lead to counsel long-term follow-up, the lack of resources for chronic patients and the increasing costs of care limit the potential for preventive actions, and suggest tailoring specific interventional strategies. The aim of the present position statement of the Kidney and Pregnancy Study Group of the Italian Society of Nephrology is to review the literature and to try to identify theoretical and pragmatic bases for an agreed management of PE in the nephrological setting, with particular attention to the prevention of the syndrome (recurrent PE, presence of baseline CKD) and to the organization of the postpartum follow-up.