Screening for pregnancy complications at 11-13 weeks’ gestation

Background: The current approach to prenatal care, which was established more than 80 years ago, is characterised by a high concentration of visits in the third-trimester of pregnancy which implies that firstly, most complications occur at this late stage of pregnancy and secondly, most adverse outcomes are unpredictable during the first or even the second trimester. Objectives: The objective of this thesis is to provide evidence that most pregnancy complications are predictable as early as 12 weeks’ gestation. The pregnancy complications examined include fetal aneuploidies, fetal structural defects, preeclampsia, preterm birth, gestational diabetes mellitus and fetal macrosomia. Methods: I have critically examined fourteen articles reporting on screening for pregnancy complications at 11-13 weeks’ gestation, where more than 90,000 singleton pregnancies were prospectively assessed at 11-13 weeks’ gestation as part of a routine prenatal visit for screening for trisomy 21. We recorded a series of maternal characteristics and history, measured maternal weight and height, performed a detailed ultrasound examination of the fetus, measured maternal uterine artery Doppler pulsatility index and maternal mean arterial pressure and collected blood for analysis of biomarkers for prospective or retrospective analysis. All data were prospectively entered into our data base as well as the pregnancy outcomes as soon as they became available. Ethical approval was obtained for these studies. Multivariate regression analysis was used to define the contribution of each maternal characteristic and history in predicting each adverse outcome and those with a significant contribution formed an algorithm to estimate the background risk (a priori risk) for each one of these complications. The potential value of biophysical and biochemical markers in improving the performance of the a priori risk in predicting adverse pregnancy outcomes, was evaluated. Results: First trimester effective screening for adverse pregnancy outcomes was provided by a combination of maternal factors and biophysical or biochemical markers. The developed predictive models could correctly identify the vast majority of aneuploidies, early preeclampsia and more than half of the cases of spontaneous preterm birth and gestational diabetes. First trimester prediction of fetal macrosomia was less effective compared with other complications. First trimester examination of fetal anatomy was feasible resulting in a high detection of fetal non-chromosomal defects, including more than half of fetal cardiac defects. Conclusions: Assessment of the mother and fetus at 11-13 weeks’ gestation can provide effective early identification of the high risk group of pregnancies with fetal and maternal adverse outcomes

Can quantification of serum glycans predict pre-eclampsia?

Objectives: To determine if concentrations of placental glycans and glycan components are altered in pre-eclamspia and to determine if serum levels can predict pre-eclampsia. Methods: Serum samples were collected from women in the third trimester of singleton pregnancy but before the onset of pre-eclampsia (n=10) and also from women during unaffected pregnancies at the same gestational age. Tissues were collected from the basal plate of placentas collected at delivery following uncomplicated singleton pregnancy (term and preterm) and from pregnancies complicated by preeclampsia (n=8). Pre-eclampsia was diagnosed according to International Society for the Study of Hypertension in Pregnancy criteria. Glycan components were isolated using a combination of enzyme digestion, molecular weight filtration and ion exchange chromatography, and then derivatised prior to separation using hydrophilic interaction liquid chromatography. Components were detected using electrospray ionisation operated in positive ion mode with single ion monitoring. Results: Specific glycan components (designated glycan 1, 2 and 3) were significantly altered in the serum from women who went on to have pre-eclampsia compared to those who had an unaffected pregnancy. Interestingly levels of the same biomarkers were also elevated in nulliparous versus multiparous pregnancy. Biomarkers were also significantly altered in placental tissues from pregnancies complicated by preeclampsia. Conclusion: This study suggests that altered glycan levels may contribute to impaired placental development and that the glycome is a potential diagnostic target for pre-eclampsia, and possibly other disorders of pregnancy

Machine learning-enabled maternal risk assessment for women with pre-eclampsia (the PIERS-ML model): a modelling study.

Affecting 2-4% of pregnancies, pre-eclampsia is a leading cause of maternal death and morbidity worldwide. Using routinely available data, we aimed to develop and validate a novel machine learning-based and clinical setting-responsive time-of-disease model to rule out and rule in adverse maternal outcomes in women presenting with pre-eclampsia. We used health system, demographic, and clinical data from the day of first assessment with pre-eclampsia to predict a Delphi-derived composite outcome of maternal mortality or severe morbidity within 2 days. Machine learning methods, multiple imputation, and ten-fold cross-validation were used to fit models on a development dataset (75% of combined published data of 8843 patients from 11 low-income, middle-income, and high-income countries). Validation was undertaken on the unseen 25%, and an additional external validation was performed in 2901 inpatient women admitted with pre-eclampsia to two hospitals in south-east England. Predictive risk accuracy was determined by area-under-the-receiver-operator characteristic (AUROC), and risk categories were data-driven and defined by negative (-LR) and positive (+LR) likelihood ratios. Of 8843 participants, 590 (6·7%) developed the composite adverse maternal outcome within 2 days, 813 (9·2%) within 7 days, and 1083 (12·2%) at any time. An 18-variable random forest-based prediction model, PIERS-ML, was accurate (AUROC 0·80 [95% CI 0·76-0·84] vs the currently used logistic regression model, fullPIERS: AUROC 0·68 [0·63-0·74]) and categorised women into very low risk (-LR 0·2 and +LR 10·0; 11 [1·0%] women). Adverse maternal event rates were 0% for very low risk, 2% for low risk, 5% for moderate risk, 26% for high risk, and 91% for very high risk within 48 h. The 2901 women in the external validation dataset were accurately classified as being at very low risk (0% with outcomes), low risk (1%), moderate risk (4%), high risk (33%), or very high risk (67%). The PIERS-ML model improves identification of women with pre-eclampsia who are at lowest and greatest risk of severe adverse maternal outcomes within 2 days of assessment, and can support provision of accurate guidance to women, their families, and their maternity care providers. University of Strathclyde Diversity in Data Linkage Centre for Doctoral Training, the Fetal Medicine Foundation, The Canadian Institutes of Health Research, and the Bill & Melinda Gates Foundation. [Abstract copyright: Copyright © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license

Machine learning-enabled maternal risk assessment for women with pre-eclampsia (the PIERS-ML model) : a modelling study

Background Affecting 2–4% of pregnancies, pre-eclampsia is a leading cause of maternal death and morbidity worldwide. Using routinely available data, we aimed to develop and validate a novel machine learning-based and clinical setting-responsive time-of-disease model to rule out and rule in adverse maternal outcomes in women presenting with pre-eclampsia. Methods We used health system, demographic, and clinical data from the day of first assessment with pre-eclampsia to predict a Delphi-derived composite outcome of maternal mortality or severe morbidity within 2 days. Machine learning methods, multiple imputation, and ten-fold cross-validation were used to fit models on a development dataset (75% of combined published data of 8843 patients from 11 low-income, middle-income, and high-income countries). Validation was undertaken on the unseen 25%, and an additional external validation was performed in 2901 inpatient women admitted with pre-eclampsia to two hospitals in south-east England. Predictive risk accuracy was determined by area-under-the-receiver-operator characteristic (AUROC), and risk categories were data-driven and defined by negative (–LR) and positive (+LR) likelihood ratios. Findings Of 8843 participants, 590 (6·7%) developed the composite adverse maternal outcome within 2 days, 813 (9·2%) within 7 days, and 1083 (12·2%) at any time. An 18-variable random forest-based prediction model, PIERS-ML, was accurate (AUROC 0·80 [95% CI 0·76–0·84] vs the currently used logistic regression model, fullPIERS: AUROC 0·68 [0·63–0·74]) and categorised women into very low risk (–LR 0·2 and +LR 10·0; 11 [1·0%] women). Adverse maternal event rates were 0% for very low risk, 2% for low risk, 5% for moderate risk, 26% for high risk, and 91% for very high risk within 48 h. The 2901 women in the external validation dataset were accurately classified as being at very low risk (0% with outcomes), low risk (1%), moderate risk (4%), high risk (33%), or very high risk (67%). Interpretation The PIERS-ML model improves identification of women with pre-eclampsia who are at lowest and greatest risk of severe adverse maternal outcomes within 2 days of assessment, and can support provision of accurate guidance to women, their families, and their maternity care providers

Metformin in Pregnancy Study (MiPS): Protocol for a systematic review with individual patient data meta-analysis

AbstractIntroduction Gestational diabetes mellitus (GDM) is a common disorder of pregnancy and contributes to adverse pregnancy outcomes. Metformin is often used for the prevention and management of GDM; however, its use in pregnancy continues to be debated. The Metformin in Pregnancy Study aims to use individual patient data (IPD) meta-analysis to clarify the efficacy and safety of metformin use in pregnancy and to identify relevant knowledge gaps.Methods and analysis MEDLINE, EMBASE and all Evidence-Based Medicine will be systematically searched for randomised controlled trials (RCT) testing the efficacy of metformin compared with placebo, usual care or other interventions in pregnant women. Two independent reviewers will assess eligibility using prespecified criteria and will conduct data extraction and quality appraisal of eligible studies. Authors of included trials will be contacted and asked to contribute IPD. Primary outcomes include maternal glycaemic parameters and GDM, as well as neonatal hypoglycaemia, anthropometry and gestational age at delivery. Other adverse maternal, birth and neonatal outcomes will be assessed as secondary outcomes. IPD from these RCTs will be harmonised and a two-step meta-analytic approach will be used to determine the efficacy and safety of metformin in pregnancy, with a priori adjustment for covariates and subgroups to examine effect moderators of treatment outcomes. Sensitivity analyses will assess heterogeneity, risk of bias and the impact of trials which have not provided IPD.Ethics and disseminationAll IPD will be deidentified and studies contributing IPD will have ethical approval from their respective local ethics committees. This study will provide robust evidence regarding the efficacy and safety of metformin use in pregnancy, and may identify subgroups of patients who may benefit most from this treatment modality. Findings will be published in peer-reviewed journals and disseminated at scientific meetings, providing much needed evidence to inform clinical and public health actions in this area.</p

GWAS meta-analysis of intrahepatic cholestasis of pregnancy implicates multiple hepatic genes and regulatory elements.

Intrahepatic cholestasis of pregnancy (ICP) is a pregnancy-specific liver disorder affecting 0.5-2% of pregnancies. The majority of cases present in the third trimester with pruritus, elevated serum bile acids and abnormal serum liver tests. ICP is associated with an increased risk of adverse outcomes, including spontaneous preterm birth and stillbirth. Whilst rare mutations affecting hepatobiliary transporters contribute to the aetiology of ICP, the role of common genetic variation in ICP has not been systematically characterised to date. Here, we perform genome-wide association studies (GWAS) and meta-analyses for ICP across three studies including 1138 cases and 153,642 controls. Eleven loci achieve genome-wide significance and have been further investigated and fine-mapped using functional genomics approaches. Our results pinpoint common sequence variation in liver-enriched genes and liver-specific cis-regulatory elements as contributing mechanisms to ICP susceptibility

A comprehensive analysis of common genetic variation around six candidate loci for intrahepatic cholestasis of pregnancy.

OBJECTIVES: Intrahepatic cholestasis of pregnancy (ICP) has a complex etiology with a significant genetic component. Heterozygous mutations of canalicular transporters occur in a subset of ICP cases and a population susceptibility allele (p.444A) has been identified in ABCB11. We sought to expand our knowledge of the detailed genetic contribution to ICP by investigation of common variation around candidate loci with biological plausibility for a role in ICP (ABCB4, ABCB11, ABCC2, ATP8B1, NR1H4, and FGF19). METHODS: ICP patients (n=563) of white western European origin and controls (n=642) were analyzed in a case-control design. Single-nucleotide polymorphism (SNP) markers (n=83) were selected from the HapMap data set (Tagger, Haploview 4.1 (build 22)). Genotyping was performed by allelic discrimination assay on a robotic platform. Following quality control, SNP data were analyzed by Armitage's trend test. RESULTS: Cochran-Armitage trend testing identified six SNPs in ABCB11 together with six SNPs in ABCB4 that showed significant evidence of association. The minimum Bonferroni corrected P value for trend testing ABCB11 was 5.81×10(-4) (rs3815676) and for ABCB4 it was 4.6×10(-7)(rs2109505). Conditional analysis of the two clusters of association signals suggested a single signal in ABCB4 but evidence for two independent signals in ABCB11. To confirm these findings, a second study was performed in a further 227 cases, which confirmed and strengthened the original findings. CONCLUSIONS: Our analysis of a large cohort of ICP cases has identified a key role for common variation around the ABCB4 and ABCB11 loci, identified the core associations, and expanded our knowledge of ICP susceptibility

GWAS meta-analysis of intrahepatic cholestasis of pregnancy implicates multiple hepatic genes and regulatory elements

Intrahepatic cholestasis of pregnancy (ICP) is a pregnancy-specific liver disorder affecting 0.5–2% of pregnancies. The majority of cases present in the third trimester with pruritus, elevated serum bile acids and abnormal serum liver tests. ICP is associated with an increased risk of adverse outcomes, including spontaneous preterm birth and stillbirth. Whilst rare mutations affecting hepatobiliary transporters contribute to the aetiology of ICP, the role of common genetic variation in ICP has not been systematically characterised to date. Here, we perform genome-wide association studies (GWAS) and meta-analyses for ICP across three studies including 1138 cases and 153,642 controls. Eleven loci achieve genome-wide significance and have been further investigated and fine-mapped using functional genomics approaches. Our results pinpoint common sequence variation in liver-enriched genes and liver-specific cis-regulatory elements as contributing mechanisms to ICP susceptibility

Personalized stratification of pregnancy care for small for gestational age neonates from biophysical markers at mid-gestation

Antenatal identification of pregnancies at high-risk to deliver small for gestational age (SGA) neonates, may improve the management of the condition and reduce the associated adverse perinatal outcome. In a series of publications we have developed a new competing risks model for SGA prediction and we demonstrated that the new approach has a superior performance to that of the traditional methods. The next step in shaping the appropriate management of SGA is the timely assessment of these high-risk pregnancies according to an antenatal stratification plan. To demonstrate the stratification of pregnancy care based on individual patient risk derived from the application of the competing risks model for SGA that combines maternal factors with sonographic estimated fetal weight (EFW) and uterine artery pulsatility index (UtA-PI) at mid-gestation. This was a prospective observational non-intervention study in 96,678 women with singleton pregnancies undergoing routine ultrasound examination at 19-24 weeks of gestation, which included recording of EFW and measurement of UtA-PI. The competing risk model for SGA was used to create a patient specific stratification curve capable to define a specific timing for a repeat ultrasound examination after 24 weeks. We examined different stratification plans with the intention of detecting about 80%, 85%, 90% and 95% of SGA neonates with birth weight <3 and <10 percentiles at any gestational age at delivery until 36 weeks; all pregnancies would be offered a routine ultrasound examination at 36 weeks. The stratification of pregnancy care for SGA can be based on a patient specific stratification curve. Factors from maternal history, low EFW and increased UtA-PI shift the personalized risk curve towards higher risks. The degree of shifting defines the timing for assessment for each pregnancy. If the objective of our antenatal plan was to detect 80%, 85%, 90% and 95% of SGA neonates at any gestational age at delivery until 36 weeks, the median (range) proportions (%) of population examined per week would be 3.15 (1.9, 3.7), 3.85 (2.7, 4.5), 4.75 (4.0, 5.4) and 6.45 (3.7, 8.0) for SGA < 3 percentile and 3.8 (2.5, 4.6) ,4.6 (3.6, 5.4), 5.7 (3.8, 6.4) and 7.35 (3.3, 9.8) for SGA < 10 percentile, respectively. The competing risks model provides an effective personalized continuous stratification of pregnancy care for SGA which is based on the individual characteristics and the biophysical marker levels recorded at the mid-gestation scan. [Abstract copyright: Copyright © 2022. Published by Elsevier Inc.