Hypertensive disorders of pregnancy (HDP) are one of the most commonly occurring complications of pregnancy and include chronic hypertension, gestational hypertension, and pre-eclampsia. New developments in early pregnancy screening to identify women at high risk for pre-eclampsia combined with targeted aspirin prophylaxis could greatly reduce the number of affected pregnancies. Furthermore, recent advances in the diagnosis of pre-eclampsia, such as placental growth factor based testing, have been shown to improve the identification of those pregnancies at highest risk of severe complications. Evidence from trials has refined the target blood pressure and timing of delivery to manage chronic hypertension and pre-eclampsia with non-severe features, respectively. Importantly, a wealth of epidemiological data now links HDP to future cardiovascular disease and diabetes decades after an affected pregnancy. This review discusses the current guidelines and research data on the prevention, diagnosis, management, and postnatal follow-up of HDP. It also discusses the gap in knowledge regarding the long term risks for cardiovascular disease following HDP and illustrates the importance of improving adherence to postnatal guidelines to monitor hypertension and the need for more research focused on primary prevention of future cardiovascular disease in women identified as being at high risk because of HDP.

Background: Preterm delivery (<37 weeks gestational age) affects 11% of all pregnancies, but data are conflicting whether preterm birth is associated with long‐term adverse maternal cardiovascular outcomes. We aimed to systematically evaluate and summarize the evidence on the relationship between preterm birth and future maternal risk of cardiovascular diseases. Methods and Results: A systematic search of MEDLINE and EMBASE was performed to identify relevant studies that evaluated the association between preterm birth and future maternal risk of composite cardiovascular disease, coronary heart disease, stroke, and death caused by cardiovascular or coronary heart disease and stroke. We quantified the associations using random effects meta‐analysis. Twenty‐one studies with over 5.8 million women, including over 338 000 women with previous preterm deliveries, were identified. Meta‐analysis of studies that adjusted for potential confounders showed that preterm birth was associated with an increased risk of maternal future cardiovascular disease (risk ratio [ RR ] 1.43, 95% confidence interval [CI], 1.18, 1.72), cardiovascular disease death ( RR 1.78, 95% CI , 1.42, 2.21), coronary heart disease ( RR 1.49, 95% CI , 1.38, 1.60), coronary heart disease death ( RR 2.10, 95% CI , 1.87, 2.36), and stroke ( RR 1.65, 95% CI , 1.51, 1.79). Sensitivity analysis showed that the highest risks occurred when the preterm deliveries occurred before 32 weeks gestation or were medically indicated. Conclusions: Preterm delivery is associated with an increase in future maternal adverse cardiovascular outcomes, including a 2‐fold increase in deaths caused by coronary heart disease. These findings support the assessment of preterm delivery in cardiovascular risk assessment in women.

Women with pregnancy-induced hypertension, defined as gestational hypertension and preeclampsia/eclampsia, are at increased risk of long-term cardiovascular disease, but less is known about the spectrum of acute cardiovascular outcomes, especially across racial/ethnic groups. We evaluated the risk of cardiovascular events at delivery associated with gestational hypertension and preeclampsia/eclampsia, compared with no pregnancy-induced hypertension, overall and by race/ethnicity. We used the 2016 to 2018 National Inpatient Sample data. International Classification of Diseases , Tenth Revision , Clinical Modification codes identified delivery hospitalizations and clinical diagnoses. Using survey weights, cardiovascular events were examined using logistic regression by pregnancy-induced hypertension status, with subsequent stratification by race/ethnicity. Among 11 304 996 deliveries in 2016 to 2018, gestational hypertension occurred in 614 995 (5.4%) and preeclampsia in 593 516 (5.2%). Black women had higher odds for preeclampsia independent of underlying comorbidities (adjusted odds ratio, 1.45 [95% CI, 1.42–1.49]) and had the highest rates for several complications (peripartum cardiomyopathy, 506; heart failure, 660; acute renal failure, 953; and arrhythmias, 418 per 100 000 deliveries). After adjustment for socioeconomic factors and comorbidities, preeclampsia/eclampsia was associated with increased risk of cardiovascular events in women of all races/ethnicities. However, risk was highest among Asian/Pacific Islander women and lowest among Black women. In sum, while Black women were the most likely to experience preeclampsia, Asian/Pacific women were the most at risk for acute cardiovascular complications during delivery hospitalization.

Pre-eclampsia (PE) complicates 2%–8% of all pregnancies and is an important cause of perinatal morbidity and mortality worldwide. In order to reduce these complications and to develop possible treatment modalities, it is important to identify women at risk of developing PE. The use of biomarkers in early pregnancy would allow appropriate stratification into high and low risk pregnancies for the purpose of defining surveillance in pregnancy and to administer interventions. We used formal methods for a systematic review and meta-analyses to assess the accuracy of all biomarkers that have been evaluated so far during the first and early second trimester of pregnancy to predict PE. We found low predictive values using individual biomarkers which included a disintegrin and metalloprotease 12 (ADAM-12), inhibin-A, pregnancy associated plasma protein A (PAPP-A), placental growth factor (PlGF) and placental protein 13 (PP-13). The pooled sensitivity of all single biomarkers was 0.40 (95% CI 0.39–0.41) at a false positive rate of 10%. The area under the Summary of Receiver Operating Characteristics Curve (SROC) was 0.786 (SE 0.02). When a combination model was used, the predictive value improved to an area under the SROC of 0.893 (SE 0.03). In conclusion, although there are multiple potential biomarkers for PE their efficacy has been inconsistent and comparisons are difficult because of heterogeneity between different studies. Therefore, there is an urgent need for high quality, large-scale multicentre research in biomarkers for PE so that the best predictive marker(s) can be identified in order to improve the management of women destined to develop PE.

Several recent reports have described associations between gestational diabetes (GDM) and changes to the epigenomic landscape where the DNA samples were derived from either cord or placental sources. We employed genome-wide 450K array analysis to determine changes to the epigenome in a unique cohort of maternal blood DNA from 11 pregnant women prior to GDM development relative to matched controls. Hierarchical clustering segregated the samples into 2 distinct clusters comprising GDM and healthy pregnancies. Screening identified 100 CpGs with a mean β-value difference of ≥0.2 between cases and controls. Using stringent criteria, 5 CpGs (within COPS8, PIK3R5, HAAO, CCDC124, and C5orf34 genes) demonstrated potentials to be clinical biomarkers as revealed by differential methylation in 8 of 11 women who developed GDM relative to matched controls. We identified, for the first time, maternal methylation changes prior to the onset of GDM that may prove useful as biomarkers for early therapeutic intervention.

One of the leading forms of cancer is colorectal cancer (CRC), which is responsible for increasing mortality in young people. The aim of this paper is to provide an experimental modification of deep learning of Xception with Swish and assess the possibility of developing a preliminary colorectal polyp screening system by training the proposed model with a colorectal topogram dataset in two and three classes. The results indicate that the proposed model can enhance the original convolutional neural network model with evaluation classification performance by achieving accuracy of up to 98.99% for classifying into two classes and 91.48% for three classes. For testing of the model with another external image, the proposed method can also improve the prediction compared to the traditional method, with 99.63% accuracy for true prediction of two classes and 80.95% accuracy for true prediction of three classes.