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To determine whether a history of previous Cesarean delivery (CD) impacts uterine artery (UtA) Doppler indices throughout pregnancy. Women with and without CD (NCD) were prospectively enrolled for sequential assessments of the UtA mean/median pulsatility index (UtA-PI), resistance index (UtA-RI), and systolic/diastolic ratio (UtA-S/D) at 11–13 + 6, 14–19 + 6, 30–34 + 6, and 35–37 + 6 weeks' gestation. Data from 269/269, 246/257, 237/254, and 219/242 CD/NCD participants from each gestational period were available for analysis. Multiples of the median (MoMs) of UtA Doppler indices showed biphasic temporal (Δ) pattern; with an initial dropping until the second trimester, then a subsequent elevation until late in pregnancy (p < 0.05). The measurements and Δs of the UtA indices between CD and NCD were not different (p > 0.05). Mixed-effects modelling ruled out effects from nulliparity (n = 0 and 167 for CD and NCD, respectively) (p > 0.05). History of CD neither influenced the measurements nor the temporal changes of the UtA Doppler indices throughout pregnancy. The biphasic Δs of UtA Doppler indices added to the longitudinal data pool, and may aid in future development of a more personalized prediction using sequential/contingent methodologies, which may reduce the false results from the current cross-sectional screening. [ABSTRACT FROM AUTHOR]

Background: Preterm labour prediction has been relied on history of previous preterm birth and cervical length of current pregnancy. However, universal cervical length measurement has some limitation. We aim to find a surrogate marker of cervical length to close the gap in preterm prevention program and lower uterine wall thickness seems promising. We generate the nomogram of lower uterine wall thickness during 18-22 weeks of gestation and evaluate the accuracy of LUW thickness as a predictor of preterm delivery before 37 weeks.Methods: This prospective cohort study included 524 Thai singleton pregnant women at 18-22 weeks of gestation between November 2016 and October 2017. After signing informed consent, transabdominal ultrasonography was performed to examine fetal anatomical structures and to measure LUW thickness. The results were blinded to the caregivers. The outcomes of all pregnancies were followed. The LUW thickness at 10th percentiles was established and was correlated with the outcomes of pregnancy. The performance of LUW thickness at 10th percentile as a predictor of preterm delivery was calculated. The intra-observer and inter-observer reliability of measurement were assessed by intraclass correlation coefficient and Bland-Altman plot.Results: Of the 524 pregnant women, 64 (12.2%) delivered before 37 weeks of gestation. The reference value of lower uterine wall thickness at 18-22 weeks was established. Mean and 10th percentile of LUW thickness were 6.2 and 4.5 mm respectively. The inter-observer and intra-observer variation of measurement were small (intraclass correlation coefficient = 0.926 and 0.989 respectively). Using LUW thickness at less than 4.5 mm as a predictor of preterm delivery, we found a 2.37 folds increased risk of preterm delivery after adjustment of other factors (p = 0.037). Sensitivity, specificity, positive predictive value and negative predictive value were 14% (95% CI: 6.64-25.02), 92.8% (95% CI: 90.06-95.12), 22.5% (95% CI: 12.66-36.76) and 88% (95% CI: 86.92-89.08) respectively.Conclusions: The measurement of LUW thickness by transabdominal ultrasonography is feasible and reproducible. The risk of delivery before 37 weeks of gestation is increased significantly if the LUW thickness at 18-22 weeks is less than 4.5 mm.Trial Registration: The study protocol was approved by institutional ethical committee (COA No. Si 657/2016). [ABSTRACT FROM AUTHOR]

Three-dimensional ultrasound (3DUS) may provide additional information for prenatal assessment of twins. It may improve the diagnostic confidence of dating, nuchal translucency (NT) and chorionicity assignment in twin pregnancies. The "virtual 3DUS placentoscopy" can guide selective fetoscopic laser photocoagulation (SFLP) to treat twin-twin transfusion syndrome (TTTS). Volumetric assessment of the dysmorphic acardiac twin with the Virtual Organ Computer-aided Analysis (VOCAL) software is more accurate than the conventional ultrasound measurement. Twin anemia polycythemia (TAP) sequence and selective intrauterine growth restriction (sIUGR) may be clinically monitored with 3DUS placental volume (PV) and power Doppler vascular indices. Congenital anomalies are more common in twins. Evaluation of fetal anomalies with 3DUS could assist perinatal management. The 3DUS power Doppler can provide a better understanding of true and false umbilical cord knots, which are commonly found in monoamniotic (MA) twins. Single demise in monochorionic (MC) twin pregnancies can cause severe neurologic morbidity in the surviving co-twin. Prenatal prediction of brain injury in the surviving co-twin with unremarkable neurosonographic examination is difficult. The 3DUS power Doppler may aid in prenatal detection of subtle abnormal cerebral perfusion. Prenatal assessment of conjoined twins with 3DUS is important if emergency postnatal surgical separation is anticipated. There is no significant additional advantage in using real-time 3DUS to guide prenatal interventions. Assessment of the cervix and pelvic floor during twin pregnancies is enhanced with 3DUS. Due to lack of high-quality studies, routine prenatal 3DUS in twin pregnancies needs to be balanced with risks of excessive ultrasound exposure. [ABSTRACT FROM AUTHOR]

Objective: To derive and validate a population-specific multivariate approach for birth weight (BW) prediction based on quantitative intrapartum assessment of maternal characteristics by means of an algorithmic method in low-risk women. Methods: The derivation part (n = 200) prospectively explored 10 variables to create the best-fit algorithms (70% correct estimates within ±10% of actual BW) for prediction of BW at term; vertex presentation with engagement. The algorithm was then cross validated with samples of unrelated cases (n = 280) to compare the accuracy with the routine abdominal palpation method. Results: The best-fit algorithms were parity-specific. The derived simplified algorithms were (1) BW (g) = 100 [(0.42 × symphysis-fundal height (SFH; cm)) + gestational age at delivery (GA; weeks) − 25] in nulliparous, and (2) BW (g) = 100 [(0.42 × SFH (cm)) + GA − 23] in multiparous. Cross validation showed an overall 69.3% accuracy within ±10% of actual BW, which exceeded routine abdominal palpation (60.4%) (P = 0.019). The algorithmic BW prediction was significantly more accurate than routine abdominal palpation in women with the following characteristics: BW 2500–4000 g, multiparous, pre-pregnancy weight <50 kg, current weight <60 kg, height <155 cm, body mass index (BMI) <18.5 kg/m2, cervical dilatation 3–5 cm, station <0, intact membranes, SFH 30–39 cm, maternal abdominal circumference (mAC) <90 cm, mid-upper arm circumference (MUAC) <25 cm and female gender of the neonates (P < 0.05). Conclusion: An overall accuracy of term BW prediction by our simplified algorithms exceeded that of routine abdominal palpation. [ABSTRACT FROM AUTHOR]

BACKGROUND: This trial aimed to assess the efficacy, acceptability, and safety of a first-trimester screen-and-prevent strategy for preterm preeclampsia in Asia. METHODS: Between August 1, 2019, and February 28, 2022, this multicenter stepped wedge cluster randomized trial included maternity/diagnostic units from 10 regions in Asia. The trial started with a period where all recruiting centers provided routine antenatal care without study-related intervention. At regular 6-week intervals, one cluster was randomized to transit from nonintervention phase to intervention phase. In the intervention phase, women underwent first-trimester screening for preterm preeclampsia using a Bayes theorem-based triple-test. High-risk women, with adjusted risk for preterm preeclampsia =1 in 100, received low-dose aspirin from <16 weeks until 36 weeks. RESULTS: Overall, 88.04% (42 897 of 48 725) of women agreed to undergo first-trimester screening for preterm preeclampsia. Among those identified as high-risk in the intervention phase, 82.39% (2919 of 3543) received aspirin prophylaxis. There was no significant difference in the incidence of preterm preeclampsia between the intervention and non-intervention phases (adjusted odds ratio [aOR], 1.59 [95% CI, 0.91-2.77]). However, among high-risk women in the intervention phase, aspirin prophylaxis was significantly associated with a 41% reduction in the incidence of preterm preeclampsia (aOR, 0.59 [95% CI, 0.37-0.92]). In addition, it correlated with 54%, 55%, and 64% reduction in the incidence of preeclampsia with delivery at <34 weeks (aOR, 0.46 [95% CI, 0.23-0.93]), spontaneous preterm birth <34 weeks (aOR, 0.45 [95% CI, 0.22-0.92]), and perinatal death (aOR, 0.34 [95% CI, 0.12-0.91]), respectively. There was no significant between-group difference in the incidence of aspirin-related severe adverse events. CONCLUSIONS: The implementation of the screen-and-prevent strategy for preterm preeclampsia is not associated with a significant reduction in the incidence of preterm preeclampsia. However, low-dose aspirin effectively reduces the incidence of preterm preeclampsia by 41% among high-risk women. The screen-and-prevent strategy for preterm preeclampsia is highly accepted by a diverse group of women from various ethnic backgrounds beyond the original population where the strategy was developed. These findings underpin the importance of the widespread implementation of the screen-andprevent strategy for preterm preeclampsia on a global scale. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the performance of an artificial intelligence (AI) and machine learning (ML) model for first‐trimester screening for pre‐eclampsia in a large Asian population. Methods: This was a secondary analysis of a multicenter prospective cohort study in 10 935 participants with singleton pregnancies attending for routine pregnancy care at 11–13+6 weeks of gestation in seven regions in Asia between December 2016 and June 2018. We applied the AI+ML model for the first‐trimester prediction of preterm pre‐eclampsia (<37 weeks), term pre‐eclampsia (≥37 weeks), and any pre‐eclampsia, which was derived and tested in a cohort of pregnant participants in the UK (Model 1). This model comprises maternal factors with measurements of mean arterial pressure, uterine artery pulsatility index, and serum placental growth factor (PlGF). The model was further retrained with adjustments for analyzers used for biochemical testing (Model 2). Discrimination was assessed by area under the receiver operating characteristic curve (AUC). The Delong test was used to compare the AUC of Model 1, Model 2, and the Fetal Medicine Foundation (FMF) competing risk model. Results: The predictive performance of Model 1 was significantly lower than that of the FMF competing risk model in the prediction of preterm pre‐eclampsia (0.82, 95% confidence interval [CI] 0.77–0.87 vs. 0.86, 95% CI 0.811–0.91, P = 0.019), term pre‐eclampsia (0.75, 95% CI 0.71–0.80 vs. 0.79, 95% CI 0.75–0.83, P = 0.006), and any pre‐eclampsia (0.78, 95% CI 0.74–0.81 vs. 0.82, 95% CI 0.79–0.84, P < 0.001). Following the retraining of the data with adjustments for the PlGF analyzers, the performance of Model 2 for predicting preterm pre‐eclampsia, term pre‐eclampsia, and any pre‐eclampsia was improved with the AUC values increased to 0.84 (95% CI 0.80–0.89), 0.77 (95% CI 0.73–0.81), and 0.80 (95% CI 0.76–0.83), respectively. There were no differences in AUCs between Model 2 and the FMF competing risk model in the prediction of preterm pre‐eclampsia (P = 0.135) and term pre‐eclampsia (P = 0.084). However, Model 2 was inferior to the FMF competing risk model in predicting any pre‐eclampsia (P = 0.024). Conclusion: This study has demonstrated that following adjustment for the biochemical marker analyzers, the predictive performance of the AI+ML prediction model for pre‐eclampsia in the first trimester was comparable to that of the FMF competing risk model in an Asian population. Synopsis: Machine learning model achieved comparable predictive performance to that of the FMF competing risk model in predicting preterm and term pre‐eclampsia in an Asian population. [ABSTRACT FROM AUTHOR]