Systematic review of prediction models for gestational hypertension and preeclampsia

Determinants of birth asphyxia in urban south Ethiopia

Birth asphyxia is a well-known cause of neonatal mortality, and the survivors suffer from long-lasting sequels such as seizures, intellectual disabilities, and motor disorders that are great challenges for newborns. Elucidating the determinants of birth asphyxia helps implement evidence-based practice in the local context. Thus, this study aimed at elucidating the determinants of birth asphyxia in urban south Ethiopia. A community-based unmatched nested case-control study was conducted on a cohort of 2548 pregnant women who were followed up until delivery in urban areas of Hadiya Zone, south Ethiopia. All newborns who experienced birth asphyxia (n = 118) were taken as cases. Newborns who were randomly selected from the risk-set (n = 472) were taken as controls (those without birth asphyxia). A binary logistic regression was done using R software. Induction of labor [AOR = 2.98, 95% CI: 1.20, 7.42], prolonged labor [AOR = 2.12, 95% CI: 1.02, 4.37], delivery through cesarean section [AOR = 3.81, 95% CI: 1.67, 8.72], instrumental delivery [AOR = 3.91, 95% CI: 1.72, 8.89], and low birth weight [AOR = 6.52, 95% CI: 3.40, 12.51] were determinants of birth asphyxia. Asphyxia during birth was mainly related to obstetric care and maternal nutrition, highlighting the need to pay attention during the course of labor and maternal nutrition during pregnancy. This study might have selection bias and loss of power so careful interpretation of the results is needed.

Fostering Informed Consent and Shared Decision-Making in Maternity Nursing With the Advancement of Artificial Intelligence

ABSTRACT

Artificial intelligence (AI), defined as algorithms built to reproduce human behavior, has various applications in health care such as risk prediction, medical image classification, text analysis, and complex disease diagnosis. Due to the increasing availability and volume of data, especially from electronic health records, AI technology is expanding into all fields of nursing and medicine. As the health care system moves toward automation and computationally driven clinical decision-making, nurses play a vital role in bridging the gap between the technological output, the patient, and the health care team. We explore the nurses' role in translating AI-generated output to patients and identify considerations for ensuring informed consent and shared decision-making throughout the process. A brief review of AI technology and informed consent, an identification of power dynamics that underly informed consent, and descriptions of the role of the nurse in various relationships such as nurse-AI, nurse-patient, and patient-AI are covered. Ultimately, nurses and physicians bear the responsibility of upholding and safeguarding the right to informed choice, as it is a fundamental aspect of safe and ethical patient-centered health care.

Profilin-1 levels in preeclampsia: Associations with disease and adverse neonatal outcomes

BACKGROUND

Preeclampsia is a serious pregnancy complication requiring early detection to improve outcomes. Profilin-1 (PFN1), linked to vascular dysfunction, may serve as a biomarker for diagnosing preeclampsia and predicting adverse neonatal outcomes. The aim of this study was to determine the serum Profilin-1 levels in patients diagnosed with preeclampsia and to investigate its association with disease severity and adverse neonatal outcomes.

METHODS

A prospective cross-sectional study was conducted at Etlik City Hospital involving 40 women with preeclampsia and 40 healthy controls. Serum PFN1 levels were measured by ELISA and results were compared between groups. The results were compared between the groups. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic performance of PFN1.

RESULTS

Serum PFN1 levels were significantly higher in the preeclampsia group compared to controls (46.48 [30.23-60.29] vs. 26.41 [19.65-41.76], p < 0.001). The ROC curve showed good diagnostic accuracy for PFN1 in detecting preeclampsia with an AUC of 0.741 (95 % CI: 0.631-0.832, p < 0.001), a sensitivity of 95 % and a specificity of 42.5 %. PFN1 levels were also associated with composite neonatal outcomes, with an AUC of 0.622 (95 % CI: 0.520-0.716, p = 0.042).

DISCUSSION

PFN1 is a potential biomarker for the diagnosis of preeclampsia. However, further studies are needed to validate its role in predicting adverse neonatal outcomes and to improve its specificity for clinical use.

Early Prediction of Hypertensive Disorders of Pregnancy Using Machine Learning and Medical Records from the First and Second Trimesters

Hypertensive disorders of pregnancy (HDPs) remain a major challenge in maternal health. Early prediction of HDPs is crucial for timely intervention. Most existing predictive machine learning (ML) models rely on costly methods like blood, urine, genetic tests, and ultrasound, often extracting features from data gathered throughout pregnancy, delaying intervention. This study developed an ML model to identify HDP risk before clinical onset using affordable methods. Features were extracted from blood pressure (BP) measurements, body mass index values (BMI) recorded during the first and second trimesters, and maternal demographic information. We employed a random forest classification model for its robustness and ability to handle complex datasets. Our dataset, gathered from large academic medical centers in Atlanta, Georgia, United States (2010-2022), comprised 1,190 patients with 1,216 records collected during the first and second trimesters. Despite the limited number of features, the model's performance demonstrated a strong ability to accurately predict HDPs. The model achieved an F1-score, accuracy, positive predictive value, and area under the receiver-operating characteristic curve of 0.76, 0.72, 0.75, and 0.78, respectively. In conclusion, the model was shown to be effective in capturing the relevant patterns in the feature set necessary for predicting HDPs. Moreover, it can be implemented using simple devices, such as BP monitors and weight scales, providing a practical solution for early HDPs prediction in low-resource settings with proper testing and validation. By improving the early detection of HDPs, this approach can potentially help with the management of adverse pregnancy outcomes.

First-Trimester Prediction Models Based on Maternal Characteristics for Adverse Pregnancy Outcomes: A Systematic Review and Meta-Analysis

BACKGROUND

Early risk stratification can facilitate timely interventions for adverse pregnancy outcomes, including preeclampsia (PE), small-for-gestational-age neonates (SGA), spontaneous preterm birth (sPTB) and gestational diabetes mellitus (GDM).

OBJECTIVES

To perform a systematic review and meta-analysis of first-trimester prediction models for adverse pregnancy outcomes.

SEARCH STRATEGY

The PubMed database was searched until 6 June 2024.

SELECTION CRITERIA

First-trimester prediction models based on maternal characteristics were included. Articles reporting on prediction models that comprised biochemical or ultrasound markers were excluded.

DATA COLLECTION AND ANALYSIS

Two authors identified articles, extracted data and assessed risk of bias and applicability using PROBAST.

MAIN RESULTS

A total of 77 articles were included, comprising 30 developed models for PE, 15 for SGA, 11 for sPTB and 35 for GDM. Discriminatory performance in terms of median area under the curve (AUC) of these models was 0.75 [IQR 0.69-0.78] for PE models, 0.62 [0.60-0.71] for SGA models of nulliparous women, 0.74 [0.72-0.74] for SGA models of multiparous women, 0.65 [0.61-0.67] for sPTB models of nulliparous women, 0.71 [0.68-0.74] for sPTB models of multiparous women and 0.71 [0.67-0.76] for GDM models. Internal validation was performed in 40/91 (43.9%) of the models. Model calibration was reported in 21/91 (23.1%) models. External validation was performed a total of 96 times in 45/91 (49.5%) of the models. High risk of bias was observed in 94.5% of the developed models and in 58.3% of the external validations.

CONCLUSIONS

Multiple first-trimester prediction models are available, but almost all suffer from high risk of bias, and internal and external validations were often not performed. Hence, methodological quality improvement and assessment of the clinical utility are needed.

Identifying Predictor Variables for a Composite Risk Prediction Tool for Gestational Diabetes and Hypertensive Disorders of Pregnancy: A Modified Delphi Study

A composite cardiometabolic risk prediction tool will support the systematic identification of women at increased cardiometabolic risk during pregnancy to enable early screening and intervention. This study aims to identify and select predictor variables for a composite risk prediction tool for cardiometabolic risk (gestational diabetes mellitus and/or hypertensive disorders of pregnancy) for use in the first trimester. A two-round modified online Delphi study was undertaken. A prior systematic literature review generated fifteen potential predictor variables for inclusion in the tool. Multidisciplinary experts (n = 31) rated the clinical importance of variables in an online survey and nominated additional variables for consideration (Round One). An online meeting (n = 14) was held to deliberate the importance, feasibility and acceptability of collecting variables in early pregnancy. Consensus was reached in a second online survey (Round Two). Overall, 24 variables were considered; 9 were eliminated, and 15 were selected for inclusion in the tool. The final 15 predictor variables related to maternal demographics (age, ethnicity/race), pre-pregnancy history (body mass index, height, history of chronic kidney disease/polycystic ovarian syndrome, family history of diabetes, pre-existing diabetes/hypertension), obstetric history (parity, history of macrosomia/pre-eclampsia/gestational diabetes mellitus), biochemical measures (blood glucose levels), hemodynamic measures (systolic blood pressure). Variables will inform the development of a cardiometabolic risk prediction tool in subsequent research. Evidence-based, clinically relevant and routinely collected variables were selected for a composite cardiometabolic risk prediction tool for early pregnancy.

Development of the DONOR prediction model on the risk of hypertensive complications in oocyte donation pregnancy: study protocol for a multicentre cohort study in the Netherlands

INTRODUCTION

Oocyte donation (OD) pregnancy is accompanied by a high incidence of hypertensive complications, with serious consequences for mother and child. Optimal care management, involving early recognition, optimisation of suitable treatment options and possibly eventually also prevention, is in high demand. Prediction of patient-specific risk factors for hypertensive complications in OD can provide the basis for this. The current project aims to establish the first prediction model on the risk of hypertensive complications in OD pregnancy.

METHODS AND ANALYSIS

The present study is conducted within the DONation of Oocytes in Reproduction project. For this multicentre cohort study, at least 541 OD pregnancies will be recruited. Baseline characteristics and obstetric data will be collected. Additionally, one sample of maternal peripheral blood and umbilical cord blood after delivery or a saliva sample from the child will be obtained, in order to determine the number of fetal-maternal human leucocyte antigen mismatches. Following data collection, a multivariate logistic regression model will be developed for the binary outcome hypertensive complication 'yes' and 'no'. The Prediction model Risk Of Bias ASsessment Tool will be used as guide to minimise the risk of bias. The study will be reported in line with the 'Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis' guideline. Discrimination and calibration will be determined to assess model performance. Internal validation will be performed using the bootstrapping method. External validation will be performed with the 'DONation of Oocytes in Reproduction individual participant data' dataset.

ETHICS AND DISSEMINATION

This study is approved by the Medical Ethics Committee LDD (Leiden, Den Haag, Delft), with protocol number P16.048 and general assessment registration (ABR) number NL56308.058.16. Further results will be shared through peer-reviewed journals and international conferences.

Machine Learning Algorithms Versus Classical Regression Models in Pre-Eclampsia Prediction: A Systematic Review

PURPOSE OF REVIEW

Machine learning (ML) approaches are an emerging alternative for healthcare risk prediction. We aimed to synthesise the literature on ML and classical regression studies exploring potential prognostic factors and to compare prediction performance for pre-eclampsia.

RECENT FINDINGS

From 9382 studies retrieved, 82 were included. Sixty-six publications exclusively reported eighty-four classical regression models to predict variable timing of onset of pre-eclampsia. Another six publications reported purely ML algorithms, whilst another 10 publications reported ML algorithms and classical regression models in the same sample with 8 of 10 findings that ML algorithms outperformed classical regression models. The most frequent prognostic factors were age, pre-pregnancy body mass index, chronic medical conditions, parity, prior history of pre-eclampsia, mean arterial pressure, uterine artery pulsatility index, placental growth factor, and pregnancy-associated plasma protein A. Top performing ML algorithms were random forest (area under the curve (AUC) = 0.94, 95% confidence interval (CI) 0.91-0.96) and extreme gradient boosting (AUC = 0.92, 95% CI 0.90-0.94). The competing risk model had similar performance (AUC = 0.92, 95% CI 0.91-0.92) compared with a neural network. Calibration performance was not reported in the majority of publications. ML algorithms had better performance compared to classical regression models in pre-eclampsia prediction. Random forest and boosting-type algorithms had the best prediction performance. Further research should focus on comparing ML algorithms to classical regression models using the same samples and evaluation metrics to gain insight into their performance. External validation of ML algorithms is warranted to gain insights into their generalisability.

Clinical manifestations and complications of preeclampsia and eclampsia in populations residing at high altitudes and very high altitudes: A scoping review

INTRODUCTION

Preeclampsia and eclampsia are conditions that affect gestation, characterized by high blood pressure and direct organ damage, while geographical altitude directly affects cardiovascular physiology. The aim of this review is to identify the clinical manifestations and complications of preeclampsia in pregnant women at high and very high altitudes.

METHODS

A scoping review was conducted to assess the objective. A systematic search was performed on Pubmed, Web of Science, Embase, Scopus, Scielo, and Lilacs. Studies including pregnant women with preeclampsia or eclampsia at high altitudes and very high altitudes were included, excluding non-citable documents. Results were summarized in tables based on bibliographic data, methodological aspects, and key findings.

RESULTS

Eight documents meeting the inclusion and exclusion criteria were obtained. Seven studies focused on populations in Latin America, with the highest geographical altitude being 4380 m above sea level in the city of Cerro de Pasco, Peru. One report suggests a higher admission rate to the Intensive Care Unit and a higher frequency of HELLP syndrome. Functional cardiovascular changes were also observed.

CONCLUSIONS

There are few studies directly evaluating pregnant populations at high altitudes and very high altitudes experiencing preeclampsia and eclampsia. Complications may be more frequent at high altitudes and very high altitudes with clinically unobservable cardiovascular changes.

Risk Factors for Adverse Maternal Outcomes among Patients with Severe Preeclampsia Before 34 Weeks

OBJECTIVE

This study aimed to characterize rates of maternal morbidity associated with early (<34 wk) preeclampsia with severe features and to determine factors associated with developing these morbidities.

STUDY DESIGN

Retrospective cohort study of patients with early preeclampsia with severe features at a single institution from 2013 to 2019. Inclusion criteria were admission between 23 and 34 weeks and diagnosis of preeclampsia with severe features. Maternal morbidity defined as death, sepsis, intensive care unit (ICU) admission, acute renal insufficiency (acute kidney injury [AKI]), postpartum (PP) dilation and curettage, PP hysterectomy, venous thromboembolism (VTE), PP hemorrhage (PPH), PP wound infection, PP endometritis, pelvic abscess, PP pneumonia, readmission, and/or need for blood transfusion. Death, ICU admission, VTE, AKI, PP hysterectomy, sepsis, and/or transfusion of >2 units were considered severe maternal morbidity (SMM). Simple statistics used to compare characteristics among patients experiencing any morbidity and those not. Poisson regression used to assess relative risks.

RESULTS

Of 260 patients included, 77 (29.6%) experienced maternal morbidity and 16 (6.2%) experienced severe morbidity. PPH (n = 46, 17.7%) was the most common morbidity, although 15 (5.8%) patients were readmitted, 16 (6.2%) needed a blood transfusion, and 14 (5.4%) had AKI. Patients who experienced maternal morbidity were more likely to be advanced maternal age, have preexisting diabetes, have multiples, and deliver nonvaginally (all ps < 0.05). Diagnosis of preeclampsia < 28 weeks or longer latency from diagnosis to delivery were not associated with increased maternal morbidity. In regression models, the relative risk of maternal morbidity remained significant for twins (adjusted odds ration [aOR]: 2.57; 95% confidence interval [CI]: 1.67, 3.96) and preexisting diabetes (aOR: 1.64; 95% CI: 1.04, 2.58), whereas attempted vaginal delivery was protective (aOR: 0.53; 95% CI: 0.30, 0.92).

CONCLUSION

In this cohort, more than 1 in 4 patients diagnosed with early preeclampsia with severe features experienced maternal morbidity, whereas 1 in 16 patients experienced SMM. Twins and pregestational diabetes were associated with higher risk of morbidity, whereas attempted vaginal delivery was protective. These data may be helpful in promoting risk reduction and counseling patients diagnosed with early preeclampsia with severe features.

KEY POINTS

· One in four patients diagnosed with preeclampsia w/ severe features experienced maternal morbidity.. · One in 16 patients with preeclampsia w/ severe features experienced severe maternal morbidity.. · Factors most associated with morbidity/severe morbidity were twins and pregestational diabetes.. · Patients who attempted vaginal delivery appeared to have a lower rate of morbidity..

Prepregnancy plant-based diets and risk of hypertensive disorders of pregnancy

BACKGROUND

Plant-based diets have been associated with a lower risk of cardiovascular disease in nonpregnant adults, but specific evidence for their effects on risk of hypertensive disorders of pregnancy is scarce.

OBJECTIVE

This study aimed to evaluate the prospective association between adherence to plant-based diets before pregnancy and the risk for hypertensive disorders of pregnancy. We hypothesized that women with higher adherence to plant-based diets would have a lower risk for hypertensive disorders of pregnancy.

STUDY DESIGN

We followed 11,459 parous women (16,780 singleton pregnancies) without chronic diseases, a history of preeclampsia, and cancers who participated in the Nurses' Health Study II (1991-2009), which was a prospective cohort study. Diet was assessed every 4 years using a validated food frequency questionnaire from which we calculated the plant-based diet index (higher score indicates higher adherence) to evaluate the health associations of plant-based diets among participants while accounting for the quality of plant-based foods. Participants self-reported hypertensive disorders of pregnancy, including preeclampsia and gestational hypertension. We estimated the relative risk of hypertensive disorders of pregnancy in relation to plant-based diet index adherence in quintiles using generalized estimating equations log-binomial regression while adjusting for potential confounders and accounting for repeated pregnancies for the same woman.

RESULTS

The mean (standard deviation) age at first in-study pregnancy was 35 (4) years. A total of 1033 cases of hypertensive disorders of pregnancy, including 482 cases of preeclampsia (2.9%) and 551 cases of gestational hypertension (3.3%) were reported. Women in the highest quintile of plant-based diet index were significantly associated with a lower risk for hypertensive disorders of pregnancy than women in the lowest quintile (relative risk, 0.76; 95% confidence interval, 0.62-0.93). There was an inverse dose-response relationship between plant-based diet index and risk for hypertensive disorders of pregnancy. The multivariable-adjusted relative risk (95% confidence interval) of hypertensive disorders of pregnancy for women in increasing quintiles of plant-based diet index were 1 (ref), 0.93 (0.78-1.12), 0.86 (0.72-1.03), 0.84 (0.69-1.03), and 0.76 (0.62-0.93) with a significant linear trend across quintiles (P trend=.005). This association was slightly stronger for gestational hypertension (relative risk, 0.77; 95% confidence interval, 0.60-0.99) than for preeclampsia (relative risk, 0.80; 95% confidence interval, 0.61-1.04). Mediation analysis suggested that body mass index evaluation for dietary assessment and pregnancy explained 39% (95% confidence interval, 15%-70%]) of the relation between plant-based diet index and hypertensive disorders of pregnancy and 48% (95% confidence interval, 12%-86%]) of the relation between plant-based diet index and gestational hypertension.

CONCLUSION

Higher adherence to plant-based diets was associated with a lower risk of developing hypertensive disorders of pregnancy. Much of the benefit seems to be related to improved weight control.

Preeclampsia Management and Maternal Ophthalmic Artery Doppler Measurements between 19 and 23 Weeks of Gestation

Background: The ophthalmic Doppler is a reliable and impartial way to assess the severity of preeclampsia (PE). The study aimed to assess the potential utility of Doppler measurements of the maternal ophthalmic arteries during the weeks 19-23 of gestation, both independently and in combination with established biomarkers for PE. Methods: A prospective cohort study was conducted involving women who were recruited from a variety of standard appointments, including booking, scanning, and regular prenatal visits. A total of 200 women that were divided into high-risk and low-risk groups for developing PE were involved during the period between April 2023 and November 2023. Results: The ophthalmic ratio had significantly higher values in high-risk patients than in low-risk women (p = 0.000). There was a significant relationship between PSV2/PSV1 and gestational age at birth in women with PE compared to the ones who did not develop PE. Conclusions: An ophthalmic artery Doppler can play a crucial role in the early detection of PE, allowing for timely intervention and management. Incorporating the ophthalmic artery Doppler as a screening tool for PE in Bulgaria has the potential to improve early detection, risk stratification, and overall maternal and fetal health outcomes.

Risk factors for pre-eclampsia in clinical practice guidelines: Comparison with the evidence

OBJECTIVE

To compare pre-eclampsia risk factors identified by clinical practice guidelines (CPGs) with risk factors from hierarchical evidence review, to guide pre-eclampsia prevention.

DESIGN

Our search strategy provided hierarchical evidence of relationships between risk factors and pre-eclampsia using Medline (Ovid), searched from January 2010 to January 2021.

SETTING

Published studies and CPGs.

POPULATION

Pregnant women.

METHODS

We evaluated the strength of association and quality of evidence (GRADE). CPGs (n = 15) were taken from a previous systematic review.

MAIN OUTCOME MEASURE

Pre-eclampsia.

RESULTS

Of 78 pre-eclampsia risk factors, 13 (16.5%) arise only during pregnancy. Strength of association was usually 'probable' (n = 40, 51.3%) and the quality of evidence was low (n = 35, 44.9%). The 'major' and 'moderate' risk factors proposed by 8/15 CPGs were not well aligned with the evidence; of the ten 'major' risk factors (alone warranting aspirin prophylaxis), associations with pre-eclampsia were definite (n = 4), probable (n = 5) or possible (n = 1), based on moderate (n = 4), low (n = 5) or very low (n = 1) quality evidence. Obesity ('moderate' risk factor) was definitely associated with pre-eclampsia (high-quality evidence). The other ten 'moderate' risk factors had probable (n = 8), possible (n = 1) or no (n = 1) association with pre-eclampsia, based on evidence of moderate (n = 1), low (n = 5) or very low (n = 4) quality. Three risk factors not identified by the CPGs had probable associations (high quality): being overweight; 'prehypertension' at booking; and blood pressure of 130-139/80-89 mmHg in early pregnancy.

CONCLUSIONS

Pre-eclampsia risk factors in CPGs are poorly aligned with evidence, particularly for the strongest risk factor of obesity. There is a lack of distinction between risk factors identifiable in early pregnancy and those arising later. A refresh of the strategies advocated by CPGs is needed.

WD repeat domain 5 promotes the development of late-onset preeclampsia by activating nuclear factor kappa B

PURPOSE

Over-activation of nuclear factor kappa B (NF-κB) was proven to be involved in the pathogenesis of preeclampsia. However, its regulation mechanism is not clear yet. This paper explored the role of WD repeat domain 5 (WDR5) in the development of late-onset preeclampsia and its relationship with NF-κB.

METHODS

WDR5 expression was detected in normal placentas and placentas from late-onset preeclampsia patients. CCK-8 and colony formation assays were conducted to appraise the proliferative ability of trophoblast. Migration and invasion were observed by wound healing and transwell assays. The interaction between WDR5 and NF-κB inhibitor I-kappa-B-alpha (IkBa) was verified by Co-immunoprecipitation analysis. Immunofluorescence was used to analyze the activation of NF-κB. Finally, we tested the role of WDR5 using the mice late-onset preeclampsia model.

RESULTS

WDR5 was highly expressed in the placentas of late-onset preeclampsia patients. WDR5 overexpression suppressed cell proliferation, migration, and invasion in trophoblast. WDR5 could interact with IkBa to activate NF-κB. Knockdown of NF-κB counteracted the anti-proliferative and anti-metastatic effects of WDR5 overexpression in trophoblast. In-vivo studies suggested that targeting WDR5 combated late-onset preeclampsia development.

CONCLUSIONS

Our finding provides new insights into the role of WDR5 in late-onset preeclampsia development.

Effect of Third Interstitial Fluid on Adverse Outcomes in Patients with Severe Pre-eclampsia and Twin Pregnancy: A 5-year Single-center Retrospective Study

OBJECTIVE

This study aims to identify the effect of third interstitial fluid on adverse outcomes in twin pregnancies with severe pre-eclampsia, and explore the differences in bad ending between twins and singletons.

METHODS

The present retrospective cohort study was conducted on patients with severe pre-eclampsia, who delivered in Tongji Hospital, Wuhan, China, between 2017 and 2022. The adverse outcomes in singleton and twin pregnancies with severe pre-eclampsia were initially investigated. Then, the diverse maternal and fetal consequences between singleton and twin pregnancies in patients with severe pre-eclampsia were compared after merging with the third interstitial fluid.

RESULTS

A total of 709 patients were included for the present study. Among these patients, 68 patients had twin pregnancies, and 641 patients had singleton pregnancies. The rate of postpartum hemorrhage (2.81% vs. 13.24%, P<0.001), and admission rate to the Neonatal Intensive Care Unit (NICU) after birth (30.73% vs. 63.24%, P=0.011) were significantly higher in twin pregnancies. The neonatal weight of twins was statistically lower than singletons (1964.73±510.61 g vs. 2142.92±731.25 g, P=0.008). For the groups with the third interstitial fluid, the delivery week (P=0.001) and rate of admission to the NICU after birth were significantly advanced in twin pregnancy group, when compared to singleton pregnancy group (P=0.032), and the length of hospital stay was shorter (P=0.044). Furthermore, there was no statistically significant difference between the twin pregnancy group and the singletony pregnancy group without the third interstitial fluid.

CONCLUSION

The maternal and fetal adverse outcomes of patients with severe pre-eclampsia increased in twin pregnancies, when compared to singleton pregnancies. Thus, when patients develop the third interstitial fluid, twin pregnancies would more likely lead to adverse fetal outcomes, when compared to singleton pregnancies, and there would be no significant difference in maternal adverse outcomes. More attention should be given to patients who merge with the third interstitial fluid.

The role of first trimester eosinophil count and eosinophil-based complete blood cell indices in the predictiction of preeclampsia: A case-control study

INTRODUCTION

The importance of eosinophils in the pathogenesis of preeclampsia is an question of interest and there are recent studies in the literature indicating significantly lower eosinophil count values in pregnant women with preeclampsia. The present study aims to evaluate the utility of first-trimester eosinophil count and eosinophil-based complete blood cell count indices in the prediction of preeclampsia.

METHODS

Pregnant women diagnosed with preeclampsia (n = 281) were retrospectively compared with a control group (n = 307). The utility of first trimester eosinophil count, neutrophil to eosinophil ratio (NER) (neutrophil/eosinophil), leukocyte to eosinophil ratio (LER) (leukocyte/eosinophil), eosinophil to monocyte ratio (EMR) (eosinophil/monocyte) and, eosinophil to lymphocyte ratio (ELR) (eosinophil/lymphocyte) in the prediction of preeclampsia were evaluated.

RESULTS

Optimal cut-off values for eosinophil count, NER, LER, EMR and, ELR in predicting preeclampsia were 0.07 (AUC: 0.62, 58.7% sensitivity, 56.4% specificity), 90.9 (AUC: 0.65, 61.1% sensitivity, 59.4% specificity), 125.7 (AUC: 0.64, 61.4% sensitivity, 58.4% specificity), 0.15 (AUC: 0.63, 60.1% sensitivity, 59.6% specificity) and, 0.03 (AUC: 0.62, 60.9% sensitivity, 57% specificity), respectively. Mentioned values in predicting early-onset preeclampsia were 0.07 (AUC: 0.64, 60.5% sensitivity, 50.8% specificity), 102.1 (AUC: 0.64, 62.4% sensitivity, 58.8% specificity), 140.2 (AUC: 0.65, 63.5% sensitivity, 59.1% soecificity), 0.14 (AUC: 0.66, 66.3% sensitivity, 59.2% specificity), and, 0.03 (AUC: 0.63, 60.5% sensitivity, 57.4% specificity), respectively. The optimal cut-off value for EMR in the prediction of preeclampsia with severe features was 0.16 (AUC: 0.56, 56.9% sensitivity, 53.2% specificity).

DISCUSSION

Eosinophil-based complete blood count indices may be used to predict early-onset preeclampsia with relatively low sensitivity and specificity.

Development and validation of a short tool to assess the awareness of hypertensive disorders of pregnancy: a cross-sectional study among pregnant women in Lebanon

BACKGROUND

Hypertensive disorders of pregnancy (HDPs) are responsible for most perinatal and fetal mortality. Few programs are patient-centered during pregnancy, thereby increasing the risks of misinformation and misconceptions among pregnant women and, as a result, malpractices.

OBJECTIVE

This study aims to develop and validate a form to assess the knowledge and attitudes of pregnant women about HDPs.

STUDY DESIGN

A cross-sectional pilot study was conducted over 4 months, targeting 135 pregnant women from 5 obstetrics and gynecology clinics. A self-reported survey was developed and validated, and an awareness score was generated.

RESULTS

The mean maternal age of the participants was 27.3 (5.3) years. About 80% of the participants reported that they monitored their weight during pregnancy, and 70.4% monitored their blood pressure, out of which 73.8% performed it at the doctor's clinic only. Overall, participants had a total score of 16.9 (3.1) over 25 with higher attitude scores than knowledge scores. Less than half of the patients (45.2%) knew the cut-off for hypertension. With respect to knowledge statements, higher scores were noted for statements related to the symptoms of HDPs, and lower scores were reported for statements related to some HDP complications. Older women and those who monitored their blood pressure during pregnancy had significantly higher awareness scores. Those working had higher awareness of HDPs (67.4%), whereas about half of nonworkers (53.9%) showed lower awareness scores (P=.019).

CONCLUSION

Pregnant women had moderate awareness of HDPs. The short 25-item tool developed in the present study can be used in obstetric clinics to explore the awareness of women of HDPs.

Early Pregnancy Systolic Blood Pressure Patterns Predict Early- and Later-Onset Preeclampsia and Gestational Hypertension Among Ostensibly Low-to-Moderate Risk Groups

Background Clinical risk factors, a single blood pressure (BP) measurement, current biomarkers, and biophysical parameters can effectively identify risk of early-onset preeclampsia but have limited ability to predict later-onset preeclampsia and gestational hypertension. Clinical BP patterns hold promise to improve early risk stratification for hypertensive disorders of pregnancy. Methods and Results After excluding preexisting hypertension, heart, kidney, or liver disease, or prior preeclampsia, the retrospective cohort (n=249 892) all had systolic BP <140 mm Hg and diastolic BP <90 mm Hg or a single BP elevation ≤20 weeks' gestation, prenatal care at <14 weeks' gestation, and a still or live birth delivery at Kaiser Permanente Northern California hospitals (2009-2019). The sample was randomly split into development (N=174 925; 70%) and validation (n=74 967; 30%) data sets. Predictive performance of multinomial logistic regression models for early-onset (<34 weeks) preeclampsia, later-onset (≥34 weeks) preeclampsia, and gestational hypertension was evaluated in the validation data set. There were 1008 (0.4%), 10 766 (4.3%), and 11 514 (4.6%) patients with early-onset preeclampsia, later-onset preeclampsia, and gestation hypertension, respectively. Models with 6 systolic BP trajectory groups (0-20 weeks' gestation) plus standard clinical risk factors performed substantially better than risk factors alone to predict early- and later-onset preeclampsia and gestational hypertension, with C-statistics (95% CIs) of 0.747 (0.720-0.775), 0.730 (0.722-0.739), and 0.768 (0.761-0.776) versus 0.688 (0.659-0.717), 0.695 (0.686-0.704) and 0.692 (0.683-0.701), respectively, with excellent calibration (Hosmer-Lemeshow P=0.99, 0.99, and 0.74, respectively). Conclusions Early pregnancy BP patterns up to 20 weeks' gestation plus clinical, social, and behavioral factors more accurately discriminate hypertensive disorders of pregnancy risk among low-to-moderate risk pregnancies. Early pregnancy BP trajectories improve risk stratification to reveal higher-risk individuals hidden within ostensibly low-to-moderate risk groups and lower-risk individuals considered at higher risk by US Preventive Services Task Force criteria.

Preterm Preeclampsia Risk Modelling: Examining Hemodynamic, Biochemical, and Biophysical Markers Prior to Pregnancy

Preeclampsia (PE) is a leading cause of maternal and perinatal death globally and can lead to unplanned preterm birth. Predicting risk for preterm or early-onset PE, has been investigated primarily after conception, and particularly in the early and mid-gestational periods. However, there is a distinct clinical advantage in identifying individuals at risk for PE prior to conception, when a wider array of preventive interventions are available. In this work, we leverage machine learning techniques to identify potential pre-pregnancy biomarkers of PE in a sample of 80 women, 10 of whom were diagnosed with preterm preeclampsia during their subsequent pregnancy. We explore prospective biomarkers derived from hemodynamic, biophysical, and biochemical measurements and several modeling approaches. A support vector machine (SVM) optimized with stochastic gradient descent yields the highest overall performance with ROC AUC and detection rates up to .88 and .70, respectively on subject-wise cross validation. The best performing models leverage biophysical and hemodynamic biomarkers. While preliminary, these results indicate the promise of a machine learning based approach for detecting individuals who are at risk for developing preterm PE before they become pregnant. These efforts may inform gestational planning and care, reducing risk for adverse PE-related outcomes.Clinical Relevance- This work considers the development and optimization of pre-pregnancy biomarkers for improving the identification of preterm (early-onset) preeclampsia risk prior to conception.

Systematic review highlights high risk of bias of clinical prediction models for blood transfusion in patients undergoing elective surgery

BACKGROUND

Blood transfusion can be a lifesaving intervention after perioperative blood loss. Many prediction models have been developed to identify patients most likely to require blood transfusion during elective surgery, but it is unclear whether any are suitable for clinical practice.

STUDY DESIGN AND SETTING

We conducted a systematic review, searching MEDLINE, Embase, PubMed, The Cochrane Library, Transfusion Evidence Library, Scopus, and Web of Science databases for studies reporting the development or validation of a blood transfusion prediction model in elective surgery patients between January 1, 2000 and June 30, 2021. We extracted study characteristics, discrimination performance (c-statistics) of final models, and data, which we used to perform risk of bias assessment using the Prediction model risk of bias assessment tool (PROBAST).

RESULTS

We reviewed 66 studies (72 developed and 48 externally validated models). Pooled c-statistics of externally validated models ranged from 0.67 to 0.78. Most developed and validated models were at high risk of bias due to handling of predictors, validation methods, and too small sample sizes.

CONCLUSION

Most blood transfusion prediction models are at high risk of bias and suffer from poor reporting and methodological quality, which must be addressed before they can be safely used in clinical practice.

Evaluation of reporting quality of cohort studies using real-world data based on RECORD: systematic review

OBJECTIVE

Real-world data (RWD) and real-world evidence (RWE) have been paid more and more attention in recent years. We aimed to evaluate the reporting quality of cohort studies using real-world data (RWD) published between 2013 and 2021 and analyze the possible factors.

METHODS

We conducted a comprehensive search in Medline and Embase through the OVID interface for cohort studies published from 2013 to 2021 on April 29, 2022. Studies aimed at comparing the effectiveness or safety of exposure factors in the real-world setting were included. The evaluation was based on the REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) statement. Agreement for inclusion and evaluation was calculated using Cohen's kappa. Pearson chi-square test or Fisher's exact test and Mann-Whitney U test were used to analyze the possible factors, including the release of RECORD, journal IFs, and article citations. Bonferroni's correction was conducted for multiple comparisons. Interrupted time series analysis was performed to display the changes in report quality over time.

RESULTS

187 articles were finally included. The mean ± SD of the percentage of adequately reported items in the 187 articles was 44.7 ± 14.3 with a range of 11.1-87%. Of 23 items, the adequate reporting rate of 10 items reached 50%, and the reporting rate of some vital items was inadequate. After Bonferroni's correction, the reporting of only one item significantly improved after the release of RECORD and there was no significant improvement in the overall report quality. For interrupted time series analysis, there were no significant changes in the slope (p = 0.42) and level (p = 0.12) of adequate reporting rate. The journal IFs and citations were respectively related to 2 areas and the former significantly higher in high-reporting quality articles.

CONCLUSION

The endorsement of the RECORD cheklist was generally inadequate in cohort studies using RWD and has not improved in recent years. We encourage researchers to endorse relevant guidelines when utilizing RWD for research.

Development of a Urine Metabolomics Biomarker-Based Prediction Model for Preeclampsia during Early Pregnancy

Preeclampsia (PE) is a condition that poses a significant risk of maternal mortality and multiple organ failure during pregnancy. Early prediction of PE can enable timely surveillance and interventions, such as low-dose aspirin administration. In this study, conducted at Stanford Health Care, we examined a cohort of 60 pregnant women and collected 478 urine samples between gestational weeks 8 and 20 for comprehensive metabolomic profiling. By employing liquid chromatography mass spectrometry (LCMS/MS), we identified the structures of seven out of 26 metabolomics biomarkers detected. Utilizing the XGBoost algorithm, we developed a predictive model based on these seven metabolomics biomarkers to identify individuals at risk of developing PE. The performance of the model was evaluated using 10-fold cross-validation, yielding an area under the receiver operating characteristic curve of 0.856. Our findings suggest that measuring urinary metabolomics biomarkers offers a noninvasive approach to assess the risk of PE prior to its onset.

Higher PAPP-A Values in Pregnant Women Complicated with Preeclampsia Than with Gestational Hypertension

The purpose of this study is to compare the levels of maternal serum pregnancy-associated plasma protein-A at the first trimester in pregnancies complicated by impaired placental diseases, such as preeclampsia (PE), intrauterine fetal growth restriction (IUGR), and gestational hypertension (GH), with those in pregnancies without the development of any of these outcomes to expand the knowledge of how this protein behaves in the different impaired placental diseases. This current work is an observational study based on a prospective cohort. Pregnancy-associated plasma protein-A was measured in 422 patients who had completed maternal-perinatal outcomes. Comparisons of pregnancy characteristics and the biomarker between outcome groups (PE, IUGR, gestational hypertension, and not impaired placental outcomes) were analyzed. PAPP-A MoM in the IUGR (0.8 IQR: 0.6-0.9) and GH groups (0.5 IQR: 0.3-1.4) compared to the PE group (1.06 IQR: 0.66-1.52) was significantly lower (p < 0.005). Pregnant women who developed early-onset PE (1.11 IQR 1.08-1.18) presented significant differences with the IUGR group (0.83 IQR: 0.59-0.98; p = 0.002) and those who developed preterm-PE (1.19 IQR: 0.66-1.58; p = 0.045). The results demonstrate that the levels of PAPP-A at first trimester in the sample of women who developed PE, and specially term-PE, were higher than those in women who developed GH or IUGR. The GH group had the lowest PAPP-A values in this sample of pregnant women. Research in a population with a high prevalence of preeclampsia is still lacking and deserves more extended studies to define if these patients could have different rates of PAPP-A.

Preterm Preeclampsia Risk Modelling: Examining Hemodynamic, Biochemical, and Biophysical Markers Prior to Pregnancy

Preeclampsia (PE) is a leading cause of maternal and perinatal death globally and can lead to unplanned preterm birth. Predicting risk for preterm or early-onset PE, has been investigated primarily after conception, and particularly in the early and mid-gestational periods. However, there is a distinct clinical advantage in identifying individuals at risk for PE prior to conception, when a wider array of preventive interventions are available. In this work, we leverage machine learning techniques to identify potential pre-pregnancy biomarkers of PE in a sample of 80 women, 10 of whom were diagnosed with preterm preeclampsia during their subsequent pregnancy. We explore biomarkers derived from hemodynamic, biophysical, and biochemical measurements and several modeling approaches. A support vector machine (SVM) optimized with stochastic gradient descent yields the highest overall performance with ROC AUC and detection rates up to .88 and .70, respectively on subject-wise cross validation. The best performing models leverage biophysical and hemodynamic biomarkers. While preliminary, these results indicate the promise of a machine learning based approach for detecting individuals who are at risk for developing preterm PE before they become pregnant. These efforts may inform gestational planning and care, reducing risk for adverse PE-related outcomes.

Preeclampsia: Recent Advances in Predicting, Preventing, and Managing the Maternal and Fetal Life-Threatening Condition

Preeclampsia accounts for one of the most common documented gestational complications, with a prevalence of approximately 2 to 15% of all pregnancies. Defined as gestational hypertension after 20 weeks of pregnancy and coexisting proteinuria or generalized edema, and certain forms of organ damage, it is life-threatening for both the mother and the fetus, in terms of increasing the rate of mortality and morbidity. Preeclamptic pregnancies are strongly associated with significantly higher medical costs. The maternal costs are related to the extra utility of the healthcare system, more resources used during hospitalization, and likely more surgical spending due to an elevated rate of cesarean deliveries. The infant costs also contribute to a large percentage of the expenses as the babies are prone to preterm deliveries and relevant or causative adverse events. Preeclampsia imposes a considerable financial burden on our societies. It is important for healthcare providers and policy-makers to recognize this phenomenon and allocate enough economic budgets and medical and social resources accordingly. The true cellular and molecular mechanisms underlying preeclampsia remain largely unexplained, which is assumed to be a two-stage process of impaired uteroplacental perfusion with or without prior defective trophoblast invasion (stage 1), followed by general endothelial dysfunction and vascular inflammation that lead to systemic organ damages (stage 2). Risk factors for preeclampsia including race, advanced maternal age, obesity, nulliparity, multi-fetal pregnancy, and co-existing medical disorders, can serve as warnings or markers that call for enhanced surveillance of maternal and fetal well-being. Doppler ultrasonography and biomarkers including the mean arterial pressure (MAP), uterine artery pulsatility index (UtA-PI), and serum pregnancy-associated plasma protein A (PAPP-A) can be used for the prediction of preeclampsia. For women perceived as high-risk individuals for developing preeclampsia, the administration of low-dose aspirin on a daily basis since early pregnancy has proven to be the most effective way to prevent preeclampsia. For preeclamptic females, relevant information, counseling, and suggestions should be provided to facilitate timely intervention or specialty referral. In pregnancies complicated with preeclampsia, closer monitoring and antepartum surveillance including the Doppler ultrasound blood flow study, biophysical profile, non-stress test, and oxytocin challenge test can be arranged. If the results are unfavorable, early intervention and aggressive therapy should be considered. Affected females should have access to higher levels of obstetric units and neonatal institutes. Before, during, and after delivery, monitoring and preparation should be intensified for affected gravidas to avoid serious complications of preeclampsia. In severe cases, delivery of the fetus and the placenta is the ultimate solution to treat preeclampsia. The current review is a summary of recent advances regarding the knowledge of preeclampsia. However, the detailed etiology, pathophysiology, and effect of preeclampsia seem complicated, and further research to address the primary etiology and pathophysiology underlying the clinical manifestations and outcomes is warranted.

Prediction of pre-eclampsia at St. Mary's hospital lacor, a low-resource setting in northern Uganda, a prospective cohort study

BACKGROUND

Pre-eclampsia is the second leading cause of maternal death in Uganda. However, mothers report to the hospitals late due to health care challenges. Therefore, we developed and validated the prediction models for prenatal screening for pre-eclampsia.

METHODS

This was a prospective cohort study at St. Mary's hospital lacor in Gulu city. We included 1,004 pregnant mothers screened at 16-24 weeks (using maternal history, physical examination, uterine artery Doppler indices, and blood tests), followed up, and delivered. We built models in RStudio. Because the incidence of pre-eclampsia was low (4.3%), we generated synthetic balanced data using the ROSE (Random Over and under Sampling Examples) package in RStudio by over-sampling pre-eclampsia and under-sampling non-preeclampsia. As a result, we got 383 (48.8%) and 399 (51.2%) for pre-eclampsia and non-preeclampsia, respectively. Finally, we evaluated the actual model performance against the ROSE-derived synthetic dataset using K-fold cross-validation in RStudio.

RESULTS

Maternal history of pre-eclampsia (adjusted odds ratio (aOR) = 32.75, 95% confidence intervals (CI) 6.59-182.05, p = 0.000), serum alkaline phosphatase(ALP) < 98 IU/L (aOR = 7.14, 95% CI 1.76-24.45, p = 0.003), diastolic hypertension ≥ 90 mmHg (aOR = 4.90, 95% CI 1.15-18.01, p = 0.022), bilateral end diastolic notch (aOR = 4.54, 95% CI 1.65-12.20, p = 0.003) and body mass index of ≥ 26.56 kg/m² (aOR = 3.86, 95% CI 1.25-14.15, p = 0.027) were independent risk factors for pre-eclampsia. Maternal age ≥ 35 years (aOR = 3.88, 95% CI 0.94-15.44, p = 0.056), nulliparity (aOR = 4.25, 95% CI 1.08-20.18, p = 0.051) and white blood cell count ≥ 11,000 (aOR = 8.43, 95% CI 0.92-70.62, p = 0.050) may be risk factors for pre-eclampsia, and lymphocyte count of 800 - 4000 cells/microliter (aOR = 0.29, 95% CI 0.08-1.22, p = 0.074) may be protective against pre-eclampsia. A combination of all the above variables predicted pre-eclampsia with 77.0% accuracy, 80.4% sensitivity, 73.6% specificity, and 84.9% area under the curve (AUC).

CONCLUSION

The predictors of pre-eclampsia were maternal age ≥ 35 years, nulliparity, maternal history of pre-eclampsia, body mass index, diastolic pressure, white blood cell count, lymphocyte count, serum ALP and end-diastolic notch of the uterine arteries. This prediction model can predict pre-eclampsia in prenatal clinics with 77% accuracy.

The reporting of prognostic prediction models for obstetric care was poor: a cross-sectional survey of 10-year publications

BACKGROUND

To investigate the reporting of prognostic prediction model studies in obstetric care through a cross-sectional survey design.

METHODS

PubMed was searched to identify prognostic prediction model studies in obstetric care published from January 2011 to December 2020. The quality of reporting was assessed by the TRIPOD checklist. The overall adherence by study and the adherence by item were calculated separately, and linear regression analysis was conducted to explore the association between overall adherence and prespecified study characteristics.

RESULTS

A total of 121 studies were included, while no study completely adhered to the TRIPOD. The results showed that the overall adherence was poor (median 46.4%), and no significant improvement was observed after the release of the TRIPOD (43.9 to 46.7%). Studies including both model development and external validation had higher reporting quality versus those including model development only (68.1% vs. 44.8%). Among the 37 items required by the TRIPOD, 10 items were reported adequately with an adherence rate over of 80%, and the remaining 27 items had an adherence rate ranging from 2.5 to 79.3%. In addition, 11 items had a report rate lower than 25.0% and even covered key methodological aspects, including blinding assessment of predictors (2.5%), methods for model-building procedures (4.5%) and predictor handling (13.5%), how to use the model (13.5%), and presentation of model performance (14.4%).

CONCLUSIONS

In a 10-year span, prognostic prediction studies in obstetric care continued to be poorly reported and did not improve even after the release of the TRIPOD checklist. Substantial efforts are warranted to improve the reporting of obstetric prognostic prediction models, particularly those that adhere to the TRIPOD checklist are highly desirable.

Diagnostic Accuracy of Gestosis Score in Comparison to multi-marker Screening as a Predictor of Preeclampsia at 11-14 Weeks of Pregnancy: A Cohort Study

BACKGROUND

Pre-eclampsia is a pregnancy-specific multisystemic disorder associated with adverse feto-maternal outcomes. Low-dose Aspirin therapy started in early pregnancy in high-risk women, has significantly reduced the chances of developing PE. Therefore, screening and identification of at-risk mothers are crucial. The present study was planned to study the predictive ability of gestosis score in predicting early-onset pre-eclampsia by comparing it with the multi-marker model.

MATERIAL AND METHODS

One hundred sixteen women, more than 19 years of age, with live singleton pregnancy at 11-13 weeks of gestation were recruited from the antenatal outpatient department and formed the study cohort. After a detailed history, screening for pre-eclampsia was performed both by multi-marker screening and by gestosis score. Diagnostic accuracy was compared for the two methods of screening.

RESULTS

The incidence of pre-eclampsia in the present study cohort was 26.7%. The sensitivity of gestosis score >/= 3 was 84.38% (67.21-94.72) and specificity was 93.18% (85.75-97.46 %). The positive predictive value was 81.82% (67.2%-90.81%), and the negative predictive value was 94.25 (87.98 - 97.35%). The diagnostic accuracy of the gestosis score was 90.83%.

CONCLUSION

Gestosis scoring is a potential tool that can be used as a cost-effective screening method for pre-eclampsia at 11-14 weeks of gestation in low-resource settings. The sensitivity and negative predictive value of the gestosis score is comparable to multi-marker screening using maternal factors, MAP, Uterine artery PI, PAPP-A, and PlGF.

American College of Cardiology and American Heart Association blood pressure categories-a systematic review of the relationship with adverse pregnancy outcomes in the first half of pregnancy

OBJECTIVE

A relationship between the 2017 American College of Cardiology and American Heart Association blood pressure thresholds and adverse pregnancy outcomes has been reported, but few studies have explored the diagnostic test properties of these cutoffs.

DATA SOURCES

We systematically searched electronic databases (from 2017 to 2021) for reports of blood pressure measurements in pregnancy, classified according to 2017 American College of Cardiology and American Heart Association criteria, and their relationship with pregnancy outcomes.

STUDY ELIGIBILITY CRITERIA

Studies recording blood pressure at <20 weeks gestation were included.

METHODS

Meta-analyses were used to investigate the strength of the association between blood pressure cutoffs and adverse outcomes, and the diagnostic test properties were calculated.

RESULTS

Of 23 studies included, there was a stepwise relationship between the American College of Cardiology and American Heart Association blood pressure category (when compared with normal blood pressure of <120/80 mmHg) and the strength of the association with preeclampsia. The category of elevated blood pressure had a risk ratio of 2.0 (95% prediction interval, 0.8-4.8), the stage 1 hypertension category had a risk ratio of 3.0 (95% prediction interval, 1.1-8.5), and the stage 2 hypertension category had a risk ratio of 7.9 (95% prediction interval, 1.8-35.1). Between-study variability was related to the magnitude of the association with stronger relationships in larger studies at low risk of bias and with unselected populations with multiple routine blood pressure measurements. None of the systolic blood pressure measurements of <120 mmHg, <130/80 mmHg, or <140/90 mmHg were useful to rule out the development of preeclampsia (all negative likelihood ratios >0.2). Only a blood pressure measurement of ≥140/90 mmHg was a good predictor for the development of preeclampsia (positive likelihood ratio, 5.95). The findings were similar for other outcomes.

CONCLUSION

Although a blood pressure of 120 to 140 over 80 to 90 mmHg at <20 weeks gestation is associated with a heightened risk for preeclampsia and adverse pregnancy outcomes and may assist in risk prediction in multivariable modelling, lowering the diagnostic threshold for chronic hypertension would not assist clinicians in identifying women at heightened risk.

Modeling renal autoregulation in a hemodynamic, first-trimester gestational model

The maternal cardiovascular system, led by renal volume regulatory responses, changes during pregnancy to ensure an adequate circulation for fetal development and growth. Circulatory maladjustment predisposes to hypertensive complications during pregnancy. Mathematical models can be used to gain insight in the gestational cardiovascular physiology. In this study, we developed an accurate, robust, and transparent model for renal autoregulation implemented in an existing circulatory gestational model. This renal autoregulation model aims to maintain steady glomerular pressure by the myogenic response, and glomerular filtration rate by tubuloglomerular feedback, both by inducing a change in the radius, and thus resistance, of the afferent arteriole. The modeled response of renal blood flow and the afferent arteriole following blood pressure increase were compared to published observations in rats. With solely the myogenic response, our model had a maximum deviation of 7% in change in renal blood flow and 7% in renal vascular resistance. When both the myogenic response and tubuloglomerular feedback were concurrently activated, the maximum deviation was 7% in change in renal blood flow and 5% in renal vascular resistance. These results show that our model is able to represent renal autoregulatory behavior comparable to empirical data. Further studies should focus on extending the model with other regulatory mechanisms to understand the hemodynamic changes in healthy and complicated pregnancy.

Development of a prediction model on preeclampsia using machine learning-based method: a retrospective cohort study in China

Objective: The aim of this study was to use machine learning methods to analyze all available clinical and laboratory data obtained during prenatal screening in early pregnancy to develop predictive models in preeclampsia (PE).

Material and Methods: Data were collected by retrospective medical records review. This study used 5 machine learning algorithms to predict the PE: deep neural network (DNN), logistic regression (LR), support vector machine (SVM), decision tree (DT), and random forest (RF). Our model incorporated 18 variables including maternal characteristics, medical history, prenatal laboratory results, and ultrasound results. The area under the receiver operating curve (AUROC), calibration and discrimination were evaluated by cross-validation.

Results: Compared with other prediction algorithms, the RF model showed the highest accuracy rate. The AUROC of RF model was 0.86 (95% CI 0.80–0.92), the accuracy was 0.74 (95% CI 0.74–0.75), the precision was 0.82 (95% CI 0.79–0.84), the recall rate was 0.42 (95% CI 0.41–0.44), and Brier score was 0.17 (95% CI 0.17–0.17).

Conclusion: The machine learning method in our study automatically identified a set of important predictive features, and produced high predictive performance on the risk of PE from the early pregnancy information.

The prognostic utility of soluble fms-like tyrosine kinase-1 (sFlt-1) and placental growth factor (PIGF) biomarkers for predicting preeclampsia: a secondary analysis of data from the INSPIRE trial

Objective

To compare the prognostic performance of biomarkers soluble fms-like tyrosine kinase-1 (sFlt-1), Placental Growth Factor (PIGF), and sFlt-1/PIGF ratio as continuous values or as a binary cut-off of 38 for predicting preeclampsia (PE) within 7 days.

Design

Secondary analysis of a randomised clinical trial.

Setting

Oxford University Hospitals, Oxford, United Kingdom (UK).

Population

Pregnant women between 24⁺⁰ to 37⁺⁰ weeks of gestation with a clinical suspicion of preeclampsia.

Main outcome

Onset of preeclampsia within 7 days of the initial biomarker test.

Methods

Logistic regression model for onset of preeclampsia using: (i) sFlt-1 (ii) PIGF, (iii) sFlt-1/PIGF ratio (continuous), and (iv) sFlt-1/PIGF ratio as a cut-off above or below 38.

Results

Of the total 370 women, 42 (11.3%) developed PE within 7 days of screening. Models with sFlt-1 and sFlt-1/PIGF ratio (continuous) had greater overall performance than models with PIGF or with sFlt-1/PIGF ratio as a cut-off at 38 (R²: sFlt-1 = 55%, PIGF = 38%, sFlt-1/PIGF ratio = 57%, sFlt-1/PIGF ratio as cut-off at 38 model = 46%). The discrimination performance was the highest in sFlt-1 and sFlt-1/PIGF ratio (continuous) (c-statistic, sFlt-1 = 0.94, sFlt-1/PIGF ratio (continuous) = 0.94) models compared to PIGF or sFlt-1/PIGF cut-off models (c-statistic, PIGF = 0.87, sFlt-1/PIGF cut-off = 0.89).

Conclusion

Models using continuous values of sFlt-1 only or sFlt-1/PIGF ratio had better predictive performance compared to a PIGF only or the model with sFlt-1/PIGF ratio as a cut-off at 38. Further studies based on a larger sample size are warranted to substantiate this finding.

Development and Validation of Multi-Stage Prediction Models for Pre-eclampsia: A Retrospective Cohort Study on Chinese Women

Objective

The aim of this study is to develop multistage prediction models for pre-eclampsia (PE) covering almost the entire pregnancy period based on routine antenatal measurements and to propose a risk screening strategy.

Methods

This was a retrospective cohort study that included 20582 singleton pregnant women with the last menstruation between January 1, 2013 and December 31, 2019. Of the 20582 women, 717 (3.48%) developed pre-eclampsia, including 46 (0.22%) with early-onset pre-eclampsia and 119 (0.58%) preterm pre-eclampsia. We randomly divided the dataset into the training set (N = 15665), the testing set (N = 3917), and the validation set (N = 1000). Least Absolute Shrinkage And Selection Operator (LASSO) was used to do variable selection from demographic characteristics, blood pressure, blood routine examination and biochemical tests. Logistic regression was used to develop prediction models at eight periods: 5-10 weeks, 11-13 weeks, 14-18 weeks, 19-23 weeks, 24-27 weeks, 28-31 weeks, 32-35 weeks, and 36-39 weeks of gestation. We calculated the AUROC (Area Under the Receiver Operating Characteristic Curve) on the test set and validated the screening strategy on the validation set.

Results

We found that uric acid tested from 5-10 weeks of gestation, platelets tested at 18-23 and 24-31 weeks of gestation, and alkaline phosphatase tested at 28-31, 32-35 and 36-39 weeks of gestation can further improve the prediction performance of models. The AUROC of the optimal prediction models on the test set gradually increased from 0.71 at 5-10 weeks to 0.80 at 24-27 weeks, and then gradually increased to 0.95 at 36-39 weeks of gestation. At sensitivity level of 0.98, our screening strategy can identify about 94.8% of women who will develop pre-eclampsia and reduce about 40% of the healthy women to be screened by 28-31 weeks of pregnancy.

Conclusion

We developed multistage prediction models and a risk screening strategy, biomarkers of which were part of routine test items and did not need extra costs. The prediction window has been advanced to 5-10 weeks, which has allowed time for aspirin intervention and other means for PE high-risk groups.

PREDICTION OF GESTATIONAL DIABETES AND HYPERTENSION BASED ON PREGNANCY EXAMINATION DATA

Gestational diabetes mellitus and hypertension are two common pregnancy complications, which seriously threaten the life safety of pregnant women and adversely affect the growth and development of the fetus. Therefore, it is of great significance to detect and prevent hypertension and diabetes at an early stage of pregnancy. Each pregnant woman will undergo multiple tests at different gestational weeks. This progress produces lots of pregnancy examination data. These data can reflect the dynamic changes of pregnant women’s health indicators during pregnancy. This study aims to establish gestational diabetes and hypertension prediction model with a machine learning method based on real pregnancy examination data from the hospital. We use Logistic Regression, XGBoost, LightGBM, and Neural Network Model based on LSTM to do the prediction, respectively, and compare the performance. We check the prediction accuracy at different stages of pregnancy. We found that with pregnancy examination data at all gestational weeks, the predictive AUCs for diabetes and hypertension can reach 0.92 and 0.87, respectively. At 16th gestational week, the AUCs are 0.68 for diabetes and 0.70 for hypertension. We extract the checking items which are most important and get a simplified model with a modest reduction in predictive accuracy. This study demonstrates that based on several routine pregnancy examination items we can establish a machine learning model to detect and predict gestational diabetes and hypertension. This can be used as a diagnostic aid and is conducive to early prevention and treatment.

Effects of Low-Dose Aspirin Combined with Vitamin E on the Incidence of Intrauterine Growth Restriction and Hemorheological Indexes of Pregnant Women in Patients with Gestational Hypertension

Objective

To investigate the effect of low-dose aspirin combined with vitamin E on the incidence of intrauterine growth restriction and hemorheological indexes of pregnant women in patients with gestational hypertension.

Method

134 elderly patients with chronic urticaria treated in our hospital from November 2017 to November 2020 were studied. According to the treatment methods, they were randomly divided into observation and control groups. There were 67 patients in the observation group, aged 20-37 years, with an average of (25.7 ± 2.75) years. There were 67 patients in the control group, aged 21-35 years, with an average of (26.3 ± 3.17) years. No significant difference was observed between the two groups (P > 0.05).

Results

The number of cases with postpartum hemorrhage and intrauterine growth restriction in the observation group was less than that in the control group. The total incidence rate was lower than that in the control group. There were significant differences in the above results (P < 0.05). The number of patients with preterm birth in the observation group was less than that in the control group, but there was no significant difference in the results (P > 0.05). The head circumference, abdominal circumference, biparietal diameter, and femoral length diameter in the control and observation groups increased significantly after treatment (P < 0.05). Compared with the control group, the head circumference, abdominal circumference, biparietal diameter, and femoral diameter in the observation group increased more after treatment, and the results were statistically poor (P < 0.05). The systolic blood pressure, diastolic blood pressure, and mean arterial pressure in the control and observation groups decreased significantly after treatment, and the results were statistically different (P < 0.05). Compared with the control group, the systolic blood pressure, diastolic blood pressure, and mean arterial pressure in the observation group decreased more after treatment. The results were statistically different (P < 0.05). The plasma viscosity levels, whole blood high shear viscosity, and whole blood low shear viscosity in the control and observation groups decreased significantly after treatment, and the results were statistically different (P < 0.05). Compared with the control group, plasma viscosity levels, whole blood high shear viscosity, and whole blood low shear viscosity in the observation group decreased more after treatment, and the results were statistically different (P < 0.05). The control and observation groups' fetal systolic/diastolic pressure and pulsatile index decreased significantly after treatment, and the results were statistically different (P < 0.05). Compared with the control group, the fetal systolic/diastolic blood pressure and pulsatile index in the observation group decreased more after treatment, and the results were statistically poor (P < 0.05).

Conclusion

Low-dose aspirin combined with vitamin E is effective in treating intrauterine growth restriction in patients with gestational hypertension. It can effectively control the blood pressure and blood flow of patients and newborns and improve pregnancy outcomes without increasing the incidence of adverse reactions. It is worthy of clinical promotion.

Optimising Cardiometabolic Risk Factors in Pregnancy: A Review of Risk Prediction Models Targeting Gestational Diabetes and Hypertensive Disorders

Cardiovascular disease, especially coronary heart disease and cerebrovascular disease, is a leading cause of mortality and morbidity in women globally. The development of cardiometabolic conditions in pregnancy, such as gestational diabetes mellitus and hypertensive disorders of pregnancy, portend an increased risk of future cardiovascular disease in women. Pregnancy therefore represents a unique opportunity to detect and manage risk factors, prior to the development of cardiovascular sequelae. Risk prediction models for gestational diabetes mellitus and hypertensive disorders of pregnancy can help identify at-risk women in early pregnancy, allowing timely intervention to mitigate both short- and long-term adverse outcomes. In this narrative review, we outline the shared pathophysiological pathways for gestational diabetes mellitus and hypertensive disorders of pregnancy, summarise contemporary risk prediction models and candidate predictors for these conditions, and discuss the utility of these models in clinical application.

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

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.

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

The burden of preeclampsia, a substantial contributor to perinatal morbidity and mortality, is not born equally across the population. Although the prevalence of preeclampsia has been reported to be 3% to 5%, racial and ethnic minority groups such as non-Hispanic Black women and American Indian or Alaskan Native women are widely reported to be disproportionately affected by preeclampsia. However, studies that add clarity to the causes of the racial and ethnic differences in preeclampsia are limited. Race is a social construct, is often self-assigned, is variable across settings, and fails to account for subgroups. Studies of the genetic structure of human populations continue to find more variations within racial groups than among them. Efforts to examine the role of race and ethnicity in biomedical research should consider these limitations and not use it as a biological construct. Furthermore, the use of race in decision making in clinical settings may worsen the disparity in health outcomes. Most of the existing data on disparities examine the differences between White and non-Hispanic Black women. Fewer studies have enough sample size to evaluate the outcomes in the Asian, American Indian or Alaskan Native, or mixed-race women. Racial differences are noted in the occurrence, presentation, and short-term and long-term outcomes of preeclampsia. Well-established clinical risk factors for preeclampsia such as obesity, diabetes, and chronic hypertension disproportionately affect non-Hispanic Black, American Indian or Alaskan Native, and Hispanic populations. However, with comparable clinical risk factors for preeclampsia among women of different race or ethnic groups, addressing modifiable risk factors has not been found to have the same protective effect for all women. Abnormalities of placental formation and development, immunologic factors, vascular changes, and inflammation have all been identified as contributing to the pathophysiology of preeclampsia. Few studies have examined race and the pathophysiology of preeclampsia. Despite attempts, a genetic basis for the disease has not been identified. A number of genetic variants, including apolipoprotein L1, have been identified as possible risk modifiers. Few studies have examined race and prevention of preeclampsia. Although low-dose aspirin for the prevention of preeclampsia is recommended by the US Preventive Service Task Force, a population-based study found racial and ethnic differences in preeclampsia recurrence after the implementation of low-dose aspirin supplementation. After implementation, recurrent preeclampsia reduced among Hispanic women (76.4% vs 49.6%; P<.001), but there was no difference in the recurrent preeclampsia in non-Hispanic Black women (13.7 vs 18.1; P=.252). Future research incorporating the National Institute on Minority Health and Health Disparities multilevel framework, specifically examining the role of racism on the burden of the disease, may help in the quest for effective strategies to reduce the disproportionate burden of preeclampsia on a minority population. In this model, a multilevel framework provides a more comprehensive approach and takes into account the influence of behavioral factors, environmental factors, and healthcare systems, not just on the individual.

Artificial intelligence in obstetrics

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.

Clinical Evaluation of Pinggan Yiqi Yangshen Recipe Combined with Labetalol Hydrochloride and Magnesium Sulfate in the Treatment of PIH

Background

To observe the clinical effect of Pinggan Yiqi Yangshen recipe combined with labetalol hydrochloride and magnesium sulfate in the treatment of pregnancy-induced hypertension (PIH).

Methods

A total of 126 patients with PIH diagnosed in our hospital from January 2016 to May 2018 were randomly divided into the control group and the experimental group, with 63 cases in each group. The control group was treated with labetalol combined with magnesium sulfate. On the basis of the control group, the experimental group was treated with Pinggan Yiqi Yangshen recipe. Clinical efficacy, blood pressure, renal function, and biochemical indexes were compared between the two groups. Moreover, pregnancy outcomes and adverse reactions were compared between the two groups.

Results

After treatment, the total effective rate in the experimental group was higher than in the control group. Blood pressure and mean arterial pressure in the experimental group were more significantly downregulated than the control group. Renal function indexes and biochemical indexes in the experimental group were more significant than those in the control group. The incidence of cesarean section, preterm birth, and abnormal fetal heart rate in the experimental group was significantly lower than that in the control group. There was no difference in the incidence of fetal distress, postpartum hemorrhage, neonatal asphyxia, and adverse reactions between the two groups.

Conclusion

Pinggan Yiqi Yangshen recipe combined with labetalol hydrochloride and magnesium sulfate can effectively reduce the blood pressure of patients with PIH, help patients to return to normal levels of biochemical indexes and renal function indexes, and improve pregnancy outcomes with high safety, which is worthy of further promotion and application in clinical practice.

Predictive Model for Late Stillbirth Among Antenatal Hypertensive Women

Objective

To develop a predictive model for late stillbirth among women with hypertensive disorders of pregnancy (HDP) in low- and middle-income countries.

Materials and Methods

Study was part of the WHO newborn birth defect (NBBD) project and included all stillbirths occurring in the facility from November 2015 to December 2020. The age and parity matched subjects with HDP having live birth were taken as controls. All significant predictors were analyzed and a predictive model was developed.

Results

Out of 69,007 deliveries, 1691(24.5/1000) were stillborn. HDP was seen in (390/1691, 23.0%), in 265/390 (67.4%) cases it occurred at or after 28 weeks of gestation and were included as cases. On comparing the cases with controls, the significant factors were estimated fetal weight less than 2000 gms (P < 0.001, OR 10.3), poor antenatal care (p < 0.001, OR-5.9), family history of hypertension (p < 0.018, OR-4.4) and the presence of gestational hypertension (p = 0.001, OR 2.2). The predictive model had sensitivity and specificity of 80.3% and 70.03%, respectively, the receiver operating curve showed the area under the curve(AUC) in the range of good prediction (0.846).

Conclusion

The predictive model could play a potential role in stillbirth prevention in women with HDP in low- and middle-income countries.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13224-021-01561-3.

Uterine Tissue Engineering: Where We Stand and the Challenges Ahead

Tissue engineering is an innovative approach to develop allogeneic tissues and organs. The uterus is a very sensitive and complex organ, which requires refined techniques to properly regenerate and even, to rebuild itself. Many therapies were developed in 20th century to solve reproductive issues related to uterus failure and, more recently, tissue engineering techniques provided a significant evolution in this issue. Herein we aim to provide a broad overview and highlights of the general concepts involved in bioengineering to reconstruct the uterus and its tissues, focusing on strategies for tissue repair, production of uterine scaffolds, biomaterials and reproductive animal models, highlighting the most recent and effective tissue engineering protocols in literature and their application in regenerative medicine. In addition, we provide a discussion about what was achieved in uterine tissue engineering, the main limitations, the challenges to overcome and future perspectives in this research field.

Association between the levels of CGI-58 and lipoprotein lipase in the placenta of patients with preeclampsia

Preeclampsia is an idiopathic disease of pregnancy, which seriously endangers the life of both the mother and the infant. The pathogenesis of preeclampsia has not been fully elucidated, although it is generally considered to be associated with abnormal lipid metabolism during pregnancy. Comparative gene identification-58 (CGI-58) and lipoprotein lipase (LPL) are involved in the first step of triglyceride hydrolysis and serve an important role in lipid transport in the placenta. The present study aimed therefore to investigate the association between CGI-58 and LPL in the placentas of patients with or without preeclampsia and to evaluate blood lipid levels. The patient cohort was divided into two groups, pregnant women with preeclampsia and normal pregnant women (control). According to biochemical analyses, reverse transcription-quantitative PCR, immunohistochemistry analysis and western blotting, the expression of CGI-58 and LPL in the placenta was detected, the blood lipid levels were evaluated and other clinical data were collected. Compared with the control group, triglycerides (TGs), low density lipoprotein-cholesterol (LDL-C), apolipoprotein B (ApoB) and atherosclerotic index (AI) were significantly higher in the preeclampsia group, whereas high density lipoprotein-cholesterol (HDL-C) and apolipoprotein A (ApoA) were significantly lower (P<0.05). Furthermore, the expression levels of CGI-58 and LPL in the placental tissue of the preeclampsia group was significantly lower than that of the control group (P<0.05). Linear correlation analysis demonstrated that there was a positive association between CGI-58 and LPL (r=0.602; P<0.05), that CGI-58 was positively associated with HDL-C (r=0.63; P<0.01) but negatively associated with TG and ApoB (r=0.840; P<0.01; and r=0.514; P<0.05, respectively), that LPL was positively associated with HDL-C (r=0.524; P<0.01) but negatively associated with TG and AI (r=0.659; P<0.01; and r=0.496; P<0.01, respectively). These results suggested that the expression of CGI-58 and LPL in the placenta was associated with the pathogenesis of preeclampsia and maternal lipids and the risk of preeclampsia was increased with decreasing expression levels of CGI-58 and LPL. Hence, CGI-58 and LPL may be used as important indicators for the diagnosis of preeclampsia and for the prevention of preeclampsia in pregnant women.

The role of resistin in early preeclampsia prediction

Resistin might be involved with general inflammation and endothelial dysfunction observed in preeclampsia. We aimed to investigate longitudinal changes in resistin concentrations during high-risk pregnancies and evaluate their significance in preeclampsia development. Ninety-one patients were recruited at 11-14 weeks of gestation. They were followed towards the end of each trimester and before their deliveries. Of the 91 pregnant women, 21 developed preeclampsia, while 70 women did not develop preeclampsia despite being at risk. Compared to the 1^st trimester, resistin concentration significantly increased during the 2^nd trimester (p<.001). When women were divided into groups of those who developed preeclampsia and those who did not develop preeclampsia, we noticed a significant difference only in women who did not develop preeclampsia (p<.001). Moreover, resistin concentration in the 1^st trimester was statistically higher in women who developed preeclampsia when compared to those who did not develop preeclampsia (p<.001). The analysis of the Receiver Operating Characteristics (ROC) curves indicated that inclusion of triglycerides (TG), high-sensitivity C-reactive protein (CRP), and resistin (AUC = 0.870) improved diagnostic accuracy of the basic model including demographic and clinical parameters (AUC = 0.777) for preeclampsia prediction (p<.05). If the concentration of resistin is high in the 1^st trimester, such pregnancy at risk is likely to develop preeclampsia as a complication, indicating that resistin concentration in the 1^st trimester might contribute to existing predictive and prognostic models for preeclampsia. A multi-marker model, possibly including also resistin and other clinical, metabolic, and inflammatory parameters, seems to be the best approach in late-onset preeclampsia prediction.

Assessment of plasma cell-free DNA and ST2 as parameters in gestational hypertension and preeclampsia

The objective of this study was to evaluate the differences and predictive efficacy of circulating cell-free DNA (cfDNA) and human suppression of tumorigenesis 2 (ST2) among women with uncomplicated pregnancies and patients with gestational hypertension (GH) or preeclampsia (PE). This study included patients with GH (n = 41), patients with PE (n = 62), and women with uncomplicated pregnancies (n = 148). The cfDNA concentration was determined by qPCR, and the ST2 levels were measured by ELISA. A receiver operating characteristic curve was employed to measure the diagnostic performance of cfDNA and ST2. Our results showed that ST2 but not cfDNA was increased in the middle and third trimesters of normal pregnancy; ST2 and cfDNA were increased in GH and PE patients compared to women with uncomplicated pregnancies. More importantly, plasma cfDNA and ST2 served as diagnostic biomarkers for GH and PE, and the AUCs were 0.883 and 0.734 for GH and 0.838 and 0.816 for PE, respectively. Moreover, their combination significantly elevated the diagnostic efficiency for GH and PE, with AUCs of 0.906 and 0.916, respectively. Plasma cfDNA and ST2 could be used as parameters for GH and PE.

Current Resources for Evidence-Based Practice, November 2020

An extensive review of new resources to support the provision of evidence-based care for women and infants. The current column includes a discussion of diversity in the maternity care workforce and commentaries on reviews focused on burnout in midwifery and a cross-national comparison of guidelines for uncomplicated childbirth.

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

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 singleton 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.