Does race or ethnicity play a role in the origin, pathophysiology, and outcomes of preeclampsia? An expert review of the literature

In the face of the obesity epidemic around the world, attention should be focused on the role of maternal obesity in the development of pregnancy. The purpose of this analysis was to evaluate the prediction of preeclampsia (PE) and isolated gestational hypertension (GH) for a number of maternal factors, in order to investigate the importance of pre-pregnancy obesity (body mass index, BMI ≥ 30 kg/m2), compared to other risk factors (e.g., prior PE, pregnancy weight gain (GWG), infertility treatment, interpregnancy interval, family history, the lack of vitamin supplementation, urogenital infection, and socioeconomic factors). In total, 912 women without chronic diseases were examined in a Polish prospective cohort of women with a single pregnancy (recruited in 2015-2016). Separate analyses were performed for the women who developed GH (n = 113) vs. 775 women who remained normotensive, as well as for those who developed PE (n = 24) vs. 775 controls. The probability of each disease was assessed for the base prediction model (age + primiparity) and for the model extended by one (test) variable, using logistic regression. Three measures were used to assess the prediction: area under curve (AUC) of the base and extended model, integrated discrimination improvement (IDI) (the index shows the difference between the value of the mean change in the predicted probability between the group of sick and healthy women when a new factor is added to the model), and net reclassification improvement (NRI) (the index focuses on the reclassification table describing the number of women in whom an upward or downward shift in the disease probability value occurred after a new factor had been added, including results for healthy and sick women). In the GH prediction, AUC increased most strongly when we added BMI (kg/m2) as a continuous variable (AUC = 0.716, p < 0.001) to the base model. The highest IDI index was obtained for prior GH/PE (IDI = 0.068, p < 0.001). The addition of BMI as a continuous variable or BMI ≥ 25 kg/m2 improved the classification for healthy and sick women the most (NRI = 0.571, p < 0.001). In the PE prediction, AUC increased most strongly when we added BMI categories (AUC = 0.726, p < 0.001) to the base model. The highest IDI index was obtained for prior GH/PE (IDI = 0.050, p = 0.080). The addition of BMI categories improved the classification for healthy and sick women the most (NRI = 0.688; p = 0.001). After summing up the results of three indexes, the probability of hypertension in pregnancy was most strongly improved by BMI, including BMI ≥ 25 kg/m2 for the GH prediction, and BMI ≥ 30 kg/m2 for the PE prediction. Main conclusions: Pre-pregnancy BMI was the most likely factor to increase the probability of developing hypertension in pregnancy, compared to other risk factors. Hierarchies of PE and GH risk factors may suggest different (or common) mechanisms of their development.

Pre-Pregnancy Obesity vs. Other Risk Factors in Probability Models of Preeclampsia and Gestational Hypertension.

The placenta is a central structure in pregnancy and has pleiotropic functions. This organ grows incredibly rapidly during this period, acting as a mastermind behind different fetal and maternal processes. The relevance of the placenta extends far beyond the pregnancy, being crucial for fetal programming before birth. Having integrative knowledge of this maternofetal structure helps significantly in understanding the development of pregnancy either in a proper or pathophysiological context. Thus, the aim of this review is to summarize the main features of the placenta, with a special focus on its early development, cytoarchitecture, immunology, and functions in non-pathological conditions. In contraposition, the role of the placenta is examined in preeclampsia, a worrisome hypertensive disorder of pregnancy, in order to describe the pathophysiological implications of the placenta in this disease. Likewise, dysfunction of the placenta in fetal growth restriction, a major consequence of preeclampsia, is also discussed, emphasizing the potential clinical strategies derived. Finally, the emerging role of the placenta in maternal chronic venous disease either as a causative agent or as a consequence of the disease is equally treated.

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The Pivotal Role of the Placenta in Normal and Pathological Pregnancies: A Focus on Preeclampsia, Fetal Growth Restriction, and Maternal Chronic Venous Disease

This study reviews recent advances on the application of artificial intelligence for the early diagnosis of various maternal-fetal conditions such as preterm birth and abnormal fetal growth. It is found in this study that various machine learning methods have been successfully employed for different kinds of data capture with regard to early diagnosis of maternal-fetal conditions. With the more popular use of artificial intelligence, ethical issues should also be considered accordingly. 

/pdf/artificial-intelligence-in-obstetrics-2cnkssyr.pdf

Artificial intelligence in obstetrics

Introduction Prediction models for gestational hypertension and preeclampsia have been developed with data and assumptions from developed countries. Their suitability and application for low resource settings have not been tested. This review aimed to identify and assess the methodological quality of prediction models for gestational hypertension and pre-eclampsia with reference to their application in low resource settings. Methods Using combinations of keywords for gestational hypertension, preeclampsia and prediction models seven databases were searched to identify prediction models developed with maternal data obtained before 20 weeks of pregnancy and including at least three predictors (Prospero registration CRD 42017078786). Prediction model characteristics and performance measures were extracted using the CHARMS, STROBE and TRIPOD checklists. The National Institute of Health quality assessment tools for observational cohort and cross-sectional studies were used for study quality appraisal. Results We retrieved 8,309 articles out of which 40 articles were eligible for review. Seventy-seven percent of all the prediction models combined biomarkers with maternal clinical characteristics. Biomarkers used as predictors in most models were pregnancy associated plasma protein-A (PAPP-A) and placental growth factor (PlGF). Only five studies were conducted in a low-and middle income country. Conclusions Most of the studies evaluated did not completely follow the CHARMS, TRIPOD and STROBE guidelines in prediction model development and reporting. Adherence to these guidelines will improve prediction modelling studies and subsequent application of prediction models in clinical practice. Prediction models using maternal characteristics, with good discrimination and calibration, should be externally validated for use in low and middle income countries where biomarker assays are not routinely available.

/pdf/systematic-review-of-prediction-models-for-gestational-29b57h11sv.pdf

Systematic review of prediction models for gestational hypertension and preeclampsia.

We aim to assess residents' perspectives and clinical utility of obtaining family history (FH) as well as to improve the rate of FH documentation in electronic medical record (EMR) at an internal medicine resident continuity clinic at a community hospital. The residents' perspectives were assessed with questionnaires. The study period was divided into the first 10-week Phase 1 in which genetic education interventions were delivered by residents, and the second 10-week Phase 2 with minimal intervention. FH documentation in EMR was reviewed and compared to a 4-week baseline (Phase 0). We found that time constraint was the most reported barrier. We reviewed 1197 patient visits; FH was recorded in 34% (67/200), 52% (272/522), and 50% (239/475) during Phase 0, Phase1, and Phase 2, respectively. Genetic education significantly increased the rate of FH documentation in Phase 1 from baseline, which was maintained in Phase 2 despite removal of interventions. The mean age of patients with documented FH was younger than those without documentation (48 years vs 51 years; p < 0.001). Documented FH of cancers and coronary artery disease lacked important details, such as age at diagnosis, in 62% (86/138) and 51% (41/81) of them, respectively. Out of 511 patients that had documented FH, we identified 66 patients (13%) where positive family history could alter medical management. In conclusion, resident-led structured genetic education effectively increased family history documentation in EMR in internal medicine resident continuity clinic and showed clinical utility. 

Increased family history documentation in internal medicine resident continuity clinic at a community hospital through resident-led structured genetic education program

Shreya Madhavaram

Papers

Systematic review of prediction models for gestational hypertension and preeclampsia.

Increased family history documentation in internal medicine resident continuity clinic at a community hospital through resident-led structured genetic education program