Early prediction of preeclampsia

Preeclampsia (PE) is a major cause of perinatal and maternal morbidity and mortality. In the United Kingdom, the National Institute for Clinical Excellence (NICE) has issued guidelines on routine antenatal care recommending that at the booking visit a woman’s level of risk for PE should be determined and the subsequent intensity of antenatal care should be based on this risk assessment. This method relies on a risk scoring system derived from maternal characteristics and medical history; the performance of screening by this method is poor with detection of less than 50% of cases of preterm-PE and term-PE. The objective of this thesis is to develop a method for the estimation of the patient-specific risk for PE by combining the a priori risk based on maternal characteristics and medical history with the results of biophysical and biochemical markers obtained at 11-13 weeks’ gestation. Such early identification of high-risk pregnancies could lead to the use of pharmacological interventions, such as low-dose aspirin, which could prevent the development of the disease. The data for the thesis were derived from two types of studies: First, prospective screening in 65,771 singleton pregnancies, which provided data for maternal factors and serum pregnancy associated plasma protein-A (PAPP-A). In an unselected sequential process we also measured uterine artery pulsatility index (PI) in 45,885 of these pregnancies, mean arterial pressure (MAP) in 35,215 cases and placental growth factor (PLGF) in 14,252 cases. Second, cases-control studies for evaluating the ten most promising biochemical markers identified from search of the literature; for these studies we used stored serum or plasma samples obtained during screening and measured PLGF, Activin-A, Inhibin-A, placental protein-13 (PP13), P-selectin, Pentraxin-3 (PTX-3), soluble Endoglin (sEng), Plasminogen activator inhibitor-2 (PAI-2), Angiopoietin-2 (Ang-2) and soluble fms-like tyrosine kinase-1 (s-Flt-1). A competing risk model was developed which is based on Bayes theorem and combines the prior risk from maternal factors with the distribution of biomarkers to derive patient-specific risk for PE at different stages in pregnancy. The prior risk was derived by multiple regression analysis of maternal factors in the screening study. The distribution of biophysical and biochemical markers was derived from both the screening study and the case-control studies. The prior risk increased with advancing maternal age, increasing weight, was higher in women of Afro-Caribbean and South-Asian racial origin, those with a previous pregnancy with PE, conception by in vitro fertilization and medical history of chronic hypertension, type 1 diabetes mellitus and systemic lupus erythematosus (SLE) or antiphospholipid syndrome (APS). The estimated detection rate (DR) of PE requiring delivery at <34, <37 weeks’ gestation and all PE, at false positive rate (FPR) of 10%, in screening by maternal factors were 51, 43 and 40%, respectively. The addition of biochemical markers to maternal factors, including maternal serum PLGF and PAPPA, improved the performance of screening with respective DRs of 74, 56 and 41%. Similarly, addition of biophysical markers to maternal factors, including uterine artery PI and MAP, improved the performance of screening with respective DRs of 90, 72 and 57%. The combination of maternal factors with all the above biophysical and biochemical markers improved the respective DRs to 96, 77 and 54%. The findings of these studies demonstrate that a combination of maternal factors, biophysical and biochemical markers can effectively identify women at high-risk of developing PE

First-trimester screening for trisomies by cfDNA testing of maternal blood in singleton and twin pregnancies: factors affecting test failure.

Objectives: To examine factors affecting the failure rate to obtain a result from (cf) DNA testing of maternal blood for fetal trisomies 21, 18 and 13 in singleton and twin pregnancies in the first trimester of pregnancy. Methods: This was a prospective study in 23,495 singleton and 928 twin pregnancies undergoing screening for fetal trisomies by targeted cfDNA testing at 10+0-14+1 weeks’ gestation. Multivariate regression analysis was used to determine significant predictors of failure to obtain a result after first sampling. Results: There was no result from cfDNA testing after first sampling in 3.4% (798/23,495) of singletons, 11.3% (91/806) of DC twins and in 4.9% (6/122) of MC twins. Multivariate logistic regression analysis demonstrated that the risk of test failure first, increased with increasing maternal age (odds ratio (OR) 1.02; 95% confidence interval (CI) 1.01, 1.04) and weight (OR 1.05; 95% CI 1.04, 1.05), decreasing gestational age (OR 0.85; 95% CI 0.79, 0.91) and serum PAPP-A (OR 0.56; 95% CI 0.49, 0.64) and free ß-hCG (OR 0.67; 95% CI 0.60, 0.74), second, was higher in women of Black (OR 1.72; 95% CI 1.33, 2.20) and South Asian (OR 1.99; 95% CI 1.56, 2.52) than White racial origin, in dichorionic twin (OR 1.75; 95% CI 1.34, 2.25) than singleton pregnancy and in in vitro fertilization (OR 3.82; 95% CI 3.19, 4.55) than natural conception and third, was lower in parous (OR 0.63; 95% CI 0.55, 0.74) than nulliparous women. Conclusions: Maternal age, weight, racial origin and parity, gestational age, dichorionicity, method of conception and serum levels of free ß-hCG and PAPP-A are independent predictors of cfDNA test failure. The risk of test failure is higher in dichorionic twin than in singleton pregnancies, mainly because a higher proportion of twins are conceived by in vitro fertilization and more of the women are nulliparous.pre-print429 K

Screening for trisomies by cfDNA testing of maternal blood in twin pregnancy: update of the Fetal Medicine Foundation results and meta-analysis.

Objective: To report on the routine clinical implementation of cell-free (cf)DNA analysis of maternal blood for trisomies 21, 18 and 13 in twin pregnancies and to define the performance of the test by combining our results with those arising from systematic review of the literature. Methods: The data for the study were derived from prospective screening for trisomies 21, 18 and 13 in twin pregnancies at 10+0-14+1 weeks’ gestation. Two populations were included; first self-referred women to the Fetal Medicine Centre in London or Brugmann University Hospital in Brussels and second, women selected for the cfDNA test after routine first-trimester combined testing in one of two National Health Service hospitals in England. This dataset was used to determine the performance of screening for the three trisomies. Search of Medline, Embase, CENTRAL (The Cochrane Library), ClinicalTrials.gov and ICTRP (World Health Organization) was carried out to identify all peer-reviewed publications on clinical validation or implementation of maternal cfDNA testing for trisomies 21, 18 and 13 in twin pregnancies. Meta-analysis was then performed using our data and data from the studies identified by the literature search. Results: In our dataset of 997 twin pregnancies with a cfDNA result and known outcome, the test classified correctly 16 (94.1%) of the 17 cases of trisomy 21, 9 (90.0%) of 10 of trisomy 18, 1 (50.0%) of 2 of trisomy 13 and 963 (99.5%) of 968 cases without any of the three trisomies. The literature search identified 7 relevant studies, excluding our papers because their data are included in the current study. In the combined total of our study and the 7 studies identified by the literature search there were 56 trisomy 21 and 3,718 non-trisomy 21 twin pregnancies; the pooled weighted detection rate (DR) and false positive rate (FPR) were 98.2% (95% CI 83.2, 99.8%) and 0.05% (95% CI 0.01, 0.26%), respectively. In the combined total of 18 cases of trisomy 18 and 3,143 non-trisomy 18 pregnancies the pooled weighted DR and FPR were 88.9% (95% CI 64.8, 97.2%) and 0.03% (95% CI 0.00, 0.33%), respectively. For trisomy 13, there were only 3 affected cases and 2 (66.7%) of these were detected by the cfDNA test at FPR of 0.19% (5/2,569). Conclusions: Performance of cfDNA testing for trisomies 21 in twin pregnancies is similar to that reported for singleton pregnancies. The number of cases of trisomies 18 and 13 is too small for accurate assessment of predictive performance of the cfDNA test.pre-print445 K

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

Non-invasive prenatal assessment of trisomy 21 by multiplexed maternal plasma DNA sequencing: large scale validity study

Objectives To validate the clinical efficacy and practical feasibility of massively parallel maternal plasma DNA sequencing to screen for fetal trisomy 21 among high risk pregnancies clinically indicated for amniocentesis or chorionic villus sampling

Systematic Identification of Placental Epigenetic Signatures for the Noninvasive Prenatal Detection of Edwards Syndrome

Background: Noninvasive prenatal diagnosis of fetal aneuploidy by maternal plasma analysis is challenging owing to the low fractional and absolute concentrations of fetal DNA in maternal plasma. Previously, we demonstrated for the first time that fetal DNA in maternal plasma could be specifically targeted by epigenetic (DNA methylation) signatures in the placenta. By comparing one such methylated fetal epigenetic marker located on chromosome 21 with another fetal genetic marker located on a reference chromosome in maternal plasma, we could infer the relative dosage of fetal chromosome 21 and noninvasively detect fetal trisomy 21. Here we apply this epigenetic-genetic (EGG) chromosome dosage approach to detect Edwards syndrome (trisomy 18) in the fetus noninvasively. Principal Findings: We have systematically identified methylated fetal epigenetic markers on chromosome 18 by methylated DNA immunoprecipitation (MeDIP) and tiling array analysis with confirmation using quantitative DNA methylation assays. Methylated DNA sequences from an intergenic region between the VAPA and APCDD1 genes (the VAPAAPCDD1 DNA) were detected in pre-delivery, but not post-delivery, maternal plasma samples. The concentrations correlated positively with those of an established fetal genetic marker, ZFY, in pre-delivery maternal plasma. The ratios of methylated VAPA-APCDD1(chr18) to ZFY(chrY) were higher in maternal plasma samples of 9 male trisomy 18 fetuses than those of 27 male euploid fetuses (Mann-Whitney test, P = 0.029). We defined the cutoff value for detecting trisomy 18 fetuses as mean+1.96 SD of the EGG ratios of the euploid cases. Eight of 9 trisomy 18 and 1 of 27 euploid cases showed EGG ratios higher than the cutoff value, giving a sensitivity of 88.9% and a specificity of 96.3%. Conclusions: Our data have shown that the methylated VAPA-APCDD1 DNA in maternal plasma is redominantly derived from the fetus. We have demonstrated that this novel fetal epigenetic marker in maternal plasma is useful for the noninvasive detection of fetal trisomy 18. © Tsui et al.published_or_final_versio

Noninvasive Prenatal Diagnosis of Fetal Trisomy 18 and Trisomy 13 by Maternal Plasma DNA Sequencing

Massively parallel sequencing of DNA molecules in the plasma of pregnant women has been shown to allow accurate and noninvasive prenatal detection of fetal trisomy 21. However, whether the sequencing approach is as accurate for the noninvasive prenatal diagnosis of trisomy 13 and 18 is unclear due to the lack of data from a large sample set. We studied 392 pregnancies, among which 25 involved a trisomy 13 fetus and 37 involved a trisomy 18 fetus, by massively parallel sequencing. By using our previously reported standard z-score approach, we demonstrated that this approach could identify 36.0% and 73.0% of trisomy 13 and 18 at specificities of 92.4% and 97.2%, respectively. We aimed to improve the detection of trisomy 13 and 18 by using a non-repeat-masked reference human genome instead of a repeat-masked one to increase the number of aligned sequence reads for each sample. We then applied a bioinformatics approach to correct GC content bias in the sequencing data. With these measures, we detected all (25 out of 25) trisomy 13 fetuses at a specificity of 98.9% (261 out of 264 non-trisomy 13 cases), and 91.9% (34 out of 37) of the trisomy 18 fetuses at 98.0% specificity (247 out of 252 non-trisomy 18 cases). These data indicate that with appropriate bioinformatics analysis, noninvasive prenatal diagnosis of trisomy 13 and trisomy 18 by maternal plasma DNA sequencing is achievable