Intelligent Noninvasive Diagnosis of Aneuploidy:Raw Values and Highly Imbalanced Dataset

The objective of this paper is to introduce a noninvasive diagnosis procedure for aneuploidy and to minimize the social and financial cost of prenatal diagnosis tests that are performed for fetal aneuploidies in an early stage of pregnancy. We propose a method by using artificial neural networks trained with data from singleton pregnancy cases, while undergoing first trimester screening. Three different datasets' with a total of 122 362 euploid and 967 aneuploid cases were used in this study. The data for each case contained markers collected from the mother and the fetus. This study, unlike previous studies published by the authors for a similar problem differs in three basic principles: 1) the training of the artificial neural networks is done by using the markers' values in their raw form (unprocessed), 2) a balanced training dataset is created and used by selecting only a representative number of euploids for the training phase, and 3) emphasis is given to the financials and suggest hierarchy and necessity of the available tests. The proposed artificial neural networks models were optimized in the sense of reaching a minimum false positive rate and at the same time securing a 100% detection rate for Trisomy 21. These systems correctly identify other aneuploidies (Trisomies 13&18, Turner, and Triploid syndromes) at a detection rate greater than 80%. In conclusion, we demonstrate that artificial neural network systems can contribute in providing noninvasive, effective early screening for fetal aneuploidies with results that compare favorably to other existing methods

Maternal serum concentrations of pregnancy associated placental protein A and pregnancy specific β-1-glycoprotein in multifetal pregnancies before and after fetal reduction

Placental function in multifetal pregnancies before and after embryo reduction was investigated by measuring maternal serum concentrations of pregnancy associated placental protein-A (PAPP-A) and pregnancy specific β-1-glycoprotein (SP-1). Three groups of pregnant women were studied following assisted reproduction; groups 1 and 2, were 12 singleton and 12 twin pregnancies respectively, and group 3 comprised 12 women with multifetal pregnancies undergoing embryo reduction. PAPP-A and SP-1 were measured serially at 8-21 weeks gestation. In all pregnancies, maternal serum PAPP-A and SP-1 increased with gestation. In twin pregnancies the mean concentrations of SP-1 were significantly higher than in singletons at all gestations, whereas for PAPP-A, concentrations were similar between these groups. In multifetal pregnancies before embryo reduction, the serum concentrations of both proteins were significantly higher than in twin pregnancies. Following reduction, the concentrations of PAPP-A remained significantly higher than for twins throughout, whereas the concentrations of SP-1 gradually converged towards those of twins; by 19 weeks there was no difference between the means of the two groups. These findings suggest that circulating concentrations of SP-1 reflect total placenta mass, which is proportional to the number of live fetuses, whereas the pattern of PAPP-A changes suggests that this protein is produced by the placenta, decidua and other tissue

Regulatory T cells in the peripheral blood of women with gestational diabetes: a systematic review and meta-analysis

BackgroundGestational diabetes (GDM) affects approximately 14% of pregnancies globally and is associated with short- and long-term complications for both the mother and child. In addition, GDM has been linked to chronic low-grade inflammation with recent research indicating a potential immune dysregulation in pathophysiology and a disparity in regulatory T cells.ObjectiveThis systematic review and meta-analysis aimed to determine whether there is an association between GDM and the level of Tregs in the peripheral blood.MethodsLiterature searches were conducted in PubMed, Embase, and Ovid between the 7th and 14th of February 2022. The inclusion criteria were any original studies published in the English language, measuring differentiated Tregs in women with GDM compared with glucose-tolerant pregnant women. Meta-analysis was performed between comparable Treg markers. Statistical tests were used to quantify heterogeneity: τ2, χ2, and I2. Study quality was assessed using a modified version of the Newcastle-Ottawa scale.ResultsThe search yielded 223 results: eight studies were included in the review and seven in the meta-analysis (GDM = 228, control = 286). Analysis of Tregs across all trimesters showed significantly lower Treg numbers in women with GDM (SMD, −0.76; 95% CI, −1.37, −0.15; I2 = 90%). This was reflected in the analysis by specific Treg markers (SMD −0.55; 95% CI, −1.04, −0.07; I2 = 83%; third trimester, five studies). Non-significant differences were found within subgroups (differentiated by CD4+FoxP3+, CD4+CD127−, and CD4+CD127−FoxP3) of both analyses.ConclusionGDM is associated with lower Treg numbers in the peripheral maternal blood. In early pregnancy, there is clinical potential to use Treg levels as a predictive tool for the subsequent development of GDM. There is also a potential therapeutic intervention to prevent the development of GDM by increasing Treg populations. However, the precise mechanism by which Tregs mediate GDM remains unclear.Systematic review registrationhttps://www.crd.york.ac.uk/prospero, identifier CRD42022309796

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

Maternal Arterial Stiffness in Women Who Subsequently Develop Pre-Eclampsia

BACKGROUND/OBJECTIVES: Pre-eclampsia (PE) is associated with profound changes in the maternal cardiovascular system. The aim of the present study was to assess whether alterations in the maternal arterial stiffness precede the onset of PE in at risk women. METHODOLOGY/PRINCIPAL FINDINGS: This was a cross sectional study involving 70 pregnant women with normal and 70 women with abnormal uterine artery Doppler examination at 22-24 weeks of gestation. All women had their arterial stiffness (augmentation index and pulse wave velocity of the carotid-femoral and carotid-radial parts of the arterial tree) assessed by applanation tonometry in the second trimester of pregnancy, at the time of the uterine artery Doppler imaging. Among the 140 women participating in the study 29 developed PE (PE group) and 111 did not (non-PE group). Compared to the non-PE group, women that developed PE had higher central systolic (94.9 ± 8.6 mmHg vs 104.3 ± 11.1 mmHg; p  =  < 0.01) and diastolic (64.0 ± 6.0 vs 72.4 ± 9.1; p < 0.01) blood pressures. All the arterial stiffness indices were adjusted for possible confounders and expressed as multiples of the median (MoM) of the non-PE group. The adjusted median augmentation index was similar between the two groups (p  =  0.84). The adjusted median pulse wave velocities were higher in the PE group compared to the non-PE group (carotid-femoral: 1.10 ± 0.14 MoMs vs 0.99 ± 0.11 MoMs; p < 0.01 and carotid-radial: 1.08 ± 0.12 MoMs vs 1.0 ± 0.11 MoMs; p < 0.01). CONCLUSIONS/SIGNIFICANCE: Increased maternal arterial stiffness, as assessed by pulse wave velocity, predates the development of PE in at risk women