Comparison of placental growth factor and fetal flow Doppler ultrasonography to identify fetal adverse outcomes in women with hypertensive disorders of pregnancy: an observational study.

BACKGROUND: Hypertensive disorders of pregnancy and intrauterine growth restriction (IUGR) are leading causes of maternal and perinatal morbidity and mortality. Failure to detect intrauterine growth restriction in women at high risk has been highlighted as a significant avoidable cause of serious fetal outcome. In this observational study we compare fetal flow using Doppler ultrasonography with a new test for placental growth factor (PlGF) to predict fetal adverse events. METHODS: Eighty-nine women with hypertensive disorders of pregnancy (24 with chronic hypertension, 17 with gestational hypertension, 12 with HELLP syndrome, 19 with preeclampsia and 17 with superimposed preeclampsia) were enrolled. A single maternal blood sample to measure free PlGF (Alere Triage) taken before 35 weeks of pregnancy was compared to the last Doppler ultrasound measurement of fetal flow before delivery. PlGF was classified as normal (PlGF>/=100 pg/ml), low (12<PlGF<100) or very low (PlGF</=12 pg/ml). A positive test for abnormal fetal flow was defined as either signs of centralisation of the fetal circulation or diastolic block or reverse flow in the umbilical artery or descending aorta; this was a criterion for delivery. Fetal outcomes were intrauterine growth restriction and birth before 37 weeks of pregnancy. RESULTS: In total 61/89 women had a preterm birth and 22 infants had IUGR. Of those who delivered preterm, 20/20 women with abnormal fetal flow and 36/41 (87.8%) women with normal fetal flow had low or very low PlGF. Of those infants with IUGR, 22/22 had low or very low maternal PlGF and 10/22 had abnormal fetal flow. CONCLUSIONS: PlGF may provide useful information before 35th gestational week to identify fetuses requiring urgent delivery, and those at risk of later adverse outcomes not identified by fetal flow Doppler ultrasonography

Maternal educational level and risk of gestational hypertension: the Generation R Study.

We examined whether maternal educational level as an indicator of socioeconomic status is associated with gestational hypertension. We also examined the extent to which the effect of education is mediated by maternal substance use (that is smoking, alcohol consumption and illegal drug use), pre-existing diabetes, anthropometrics (that is height and body mass index (BMI)) and blood pressure at enrolment. This was studied in 3262 Dutch pregnant women participating in the Generation R Study, a population-based cohort study. Level of maternal education was established by questionnaire at enrolment, and categorized into high, mid-high, mid-low and low. Diagnosis of gestational hypertension was retrieved from medical records using standard criteria. Odds ratios (OR) of gestational hypertension for educational levels were calculated, adjusted for potential confounders and additionally adjusted for potential mediators. Adjusted for age and gravidity, women with mid-low (OR: 1.52; 95% CI: 1.02, 2.27) and low education (OR: 1.30; 95% CI: 0.80, 2.12) had a higher risk of gestational hypertension than women with high education. Additional adjustment for substance use, pre-existing diabetes, anthropometrics and blood pressure at enrolment attenuated these ORs to 1.09 (95% CI: 0.70, 1.69) and 0.89 (95% CI: 0.50, 1.58), respectively. These attenuations were largely due to the effects of BMI and blood pressure at enrolment. Women with relatively low educational levels have a higher risk of gestational hypertension, which is largely due to higher BMI and blood pressure levels from early pregnancy. The higher risk of gestational hypertension in these women is probably caused by pre-existing hypertensive tendencies that manifested themselves during pregnancy

Immunoregulatory gene polymorphisms in women with preeclampsia

The costimulatory molecules CD28, cytotoxic T-lymphocyte antigen-4 (CTLA-4) (cytotoxic T-lymphocyte-associated antigen-4) and inducible costimulator (ICOS) are believed to have a critical modulatory role in the immune response. However, few studies have been performed on the role of these immune regulatory molecules and their polymorphisms in women with preeclampsia (PE). the aim of our study was to evaluate the CTLA4 (+49 A/G) (rs 231775), CD28 (+17 T/C) (rs 3116496) and ICOS (-1564 T/C) (rs 4675378) gene polymorphisms in Brazilian women with PE. This case-control study included 130 patients with PE and 261 control women without any obstetric or systemic disorders. Genomic DNA was extracted from peripheral blood, and the polymorphism genotyping was performed by digesting the PCR products with the restriction endonucleases BbvI (CTLA-4), Afel (CD28) and AluI (ICOS). Data were analyzed by X(2) or Fisher's exact test; a P-value of < 0.05 was considered as significant. There were significant differences in the ICOS genotype and allelic frequencies between the PE and control groups (P=0.01 and P=0.01, respectively). We found a significantly lower frequency of the ICOS (-1564) T allele in women with mild PE compared with the controls. There were no differences in the CTLA-4 (+49 A/G) and CD28 (+17 T/C) genotypes and allelic frequencies between the PE patients and controls. Our data suggest that PE is associated with ICOS, but is not associated with the CTLA-4 or CD28 gene polymorphisms. Hypertension Research (2011) 34, 384-388; doi:10.1038/hr.2010.247; published online 16 December 2010Fundacao de Amparo a PesquisaCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Universidade Federal de São Paulo, Dept Obstet, BR-01415002 São Paulo, BrazilUniversidade Federal de São Paulo, Dept Obstet, BR-01415002 São Paulo, BrazilFundacao de Amparo a Pesquisa: 07/57446-0Web of Scienc

External validation of prognostic models to predict stillbirth using the International Prediction of Pregnancy Complications (IPPIC) Network database: an individual participant data meta-analysis

Objective Stillbirth is a potentially preventable complication of pregnancy. Identifying women at high risk of stillbirth can guide decisions on the need for closer surveillance and timing of delivery in order to prevent fetal death. Prognostic models have been developed to predict the risk of stillbirth, but none has yet been validated externally. In this study, we externally validated published prediction models for stillbirth using individual participant data (IPD) meta-analysis to assess their predictive performance. Methods MEDLINE, EMBASE, DH-DATA and AMED databases were searched from inception to December 2020 to identify studies reporting stillbirth prediction models. Studies that developed or updated prediction models for stillbirth for use at any time during pregnancy were included. IPD from cohorts within the International Prediction of Pregnancy Complications (IPPIC) Network were used to validate externally the identified prediction models whose individual variables were available in the IPD. The risk of bias of the models and cohorts was assessed using the Prediction study Risk Of Bias ASsessment Tool (PROBAST). The discriminative performance of the models was evaluated using the C-statistic, and calibration was assessed using calibration plots, calibration slope and calibration-in-the-large. Performance measures were estimated separately in each cohort, as well as summarized across cohorts using random-effects meta-analysis. Clinical utility was assessed using net benefit. Results Seventeen studies reporting the development of 40 prognostic models for stillbirth were identified. None of the models had been previously validated externally, and the full model equation was reported for only one-fifth (20%, 8/40) of the models. External validation was possible for three of these models, using IPD from 19 cohorts (491 201 pregnant women) within the IPPIC Network database. Based on evaluation of the model development studies, all three models had an overall high risk of bias, according to PROBAST. In the IPD meta-analysis, the models had summary C-statistics ranging from 0.53 to 0.65 and summary calibration slopes ranging from 0.40 to 0.88, with risk predictions that were generally too extreme compared with the observed risks. The models had little to no clinical utility, as assessed by net benefit. However, there remained uncertainty in the performance of some models due to small available sample sizes. Conclusions The three validated stillbirth prediction models showed generally poor and uncertain predictive performance in new data, with limited evidence to support their clinical application. The findings suggest methodological shortcomings in their development, including overfitting. Further research is needed to further validate these and other models, identify stronger prognostic factors and develop more robust prediction models. (c) 2021 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.Peer reviewe

External validation of prognostic models predicting pre-eclampsia : individual participant data meta-analysis

Abstract Background Pre-eclampsia is a leading cause of maternal and perinatal mortality and morbidity. Early identification of women at risk during pregnancy is required to plan management. Although there are many published prediction models for pre-eclampsia, few have been validated in external data. Our objective was to externally validate published prediction models for pre-eclampsia using individual participant data (IPD) from UK studies, to evaluate whether any of the models can accurately predict the condition when used within the UK healthcare setting. Methods IPD from 11 UK cohort studies (217,415 pregnant women) within the International Prediction of Pregnancy Complications (IPPIC) pre-eclampsia network contributed to external validation of published prediction models, identified by systematic review. Cohorts that measured all predictor variables in at least one of the identified models and reported pre-eclampsia as an outcome were included for validation. We reported the model predictive performance as discrimination (C-statistic), calibration (calibration plots, calibration slope, calibration-in-the-large), and net benefit. Performance measures were estimated separately in each available study and then, where possible, combined across studies in a random-effects meta-analysis. Results Of 131 published models, 67 provided the full model equation and 24 could be validated in 11 UK cohorts. Most of the models showed modest discrimination with summary C-statistics between 0.6 and 0.7. The calibration of the predicted compared to observed risk was generally poor for most models with observed calibration slopes less than 1, indicating that predictions were generally too extreme, although confidence intervals were wide. There was large between-study heterogeneity in each model’s calibration-in-the-large, suggesting poor calibration of the predicted overall risk across populations. In a subset of models, the net benefit of using the models to inform clinical decisions appeared small and limited to probability thresholds between 5 and 7%. Conclusions The evaluated models had modest predictive performance, with key limitations such as poor calibration (likely due to overfitting in the original development datasets), substantial heterogeneity, and small net benefit across settings. The evidence to support the use of these prediction models for pre-eclampsia in clinical decision-making is limited. Any models that we could not validate should be examined in terms of their predictive performance, net benefit, and heterogeneity across multiple UK settings before consideration for use in practice. Trial registration PROSPERO ID: CRD42015029349