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Objective: To examine the external validity of the Fetal Medicine Foundation (FMF) competing‐risks model for the prediction of small‐for‐gestational age (SGA) at 11–14 weeks' gestation in an Asian population. Methods: This was a secondary analysis of a multicenter prospective cohort study in 10 120 women with a singleton pregnancy undergoing routine assessment at 11–14 weeks' gestation. We applied the FMF competing‐risks model for the first‐trimester prediction of SGA, combining maternal characteristics and medical history with measurements of mean arterial pressure (MAP), uterine artery pulsatility index (UtA‐PI) and serum placental growth factor (PlGF) concentration. We calculated risks for different cut‐offs of birth‐weight percentile (< 10th, < 5th or < 3rd percentile) and gestational age at delivery (< 37 weeks (preterm SGA) or SGA at any gestational age). Predictive performance was examined in terms of discrimination and calibration. Results: The predictive performance of the competing‐risks model for SGA was similar to that reported in the original FMF study. Specifically, the combination of maternal factors with MAP, UtA‐PI and PlGF yielded the best performance for the prediction of preterm SGA with birth weight < 10th percentile (SGA < 10th) and preterm SGA with birth weight < 5th percentile (SGA < 5th), with areas under the receiver‐operating‐characteristics curve (AUCs) of 0.765 (95% CI, 0.720–0.809) and 0.789 (95% CI, 0.736–0.841), respectively. Combining maternal factors with MAP and PlGF yielded the best model for predicting preterm SGA with birth weight < 3rd percentile (SGA < 3rd) (AUC, 0.797 (95% CI, 0.744–0.850)). After excluding cases with pre‐eclampsia, the combination of maternal factors with MAP, UtA‐PI and PlGF yielded the best performance for the prediction of preterm SGA < 10th and preterm SGA < 5th, with AUCs of 0.743 (95% CI, 0.691–0.795) and 0.762 (95% CI, 0.700–0.824), respectively. However, the best model for predicting preterm SGA < 3rd without pre‐eclampsia was the combination of maternal factors and PlGF (AUC, 0.786 (95% CI, 0.723–0.849)). The FMF competing‐risks model including maternal factors, MAP, UtA‐PI and PlGF achieved detection rates of 42.2%, 47.3% and 48.1%, at a fixed false‐positive rate of 10%, for the prediction of preterm SGA < 10th, preterm SGA < 5th and preterm SGA < 3rd, respectively. The calibration of the model was satisfactory. Conclusion: The screening performance of the FMF first‐trimester competing‐risks model for SGA in a large, independent cohort of Asian women is comparable with that reported in the original FMF study in a mixed European population. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology. [ABSTRACT FROM AUTHOR]

A trial conducted in Asia aimed to assess the effectiveness, acceptability, and safety of a first trimester screen-and-prevent strategy for preterm pre-eclampsia (PE). The trial involved 48,725 pregnant women in ten regions across Asia from 2019 to 2022. The results showed that 88.04% of women agreed to undergo first trimester screening for preterm PE, and among those identified as high-risk, 82.39% received low-dose aspirin (LDA). The use of LDA was associated with a significant reduction in the incidence of preterm PE, as well as other adverse outcomes. The study concluded that the screen-and-prevent strategy is highly accepted among women from diverse ethnic backgrounds, and LDA is considered a safe intervention during pregnancy. [Extracted from the article]

This article discusses a study that evaluated the performance of a machine learning model for predicting pre-eclampsia (PE) in a large Asian population. The study used data from 10,935 women with singleton pregnancies and applied the machine learning model for first trimester screening. The results showed that after adjusting for biochemical marker analyzers, the predictive performance of the model was comparable to that of the Fetal Medicine Foundation competing risk model in Asian populations. This study suggests that the machine learning model could be a useful tool for predicting PE in Asian populations. [Extracted from the article]

This article discusses a study that examined the external validity of a new prediction model for small for gestational age (SGA) in an Asian population. The study used the Fetal Medicine Foundation (FMF) competing risk model, which combines maternal characteristics and medical history with measurements of mean arterial pressure (MAP), uterine artery pulsatility index (UtA-PI), and serum placental growth factor (PlGF) to predict SGA. The study found that the predictive performance of the model for SGA in the Asian population was similar to that reported in the original FMF study on a mixed European population. The article also mentions that the prophylactic use of aspirin did not have a significant effect on the incidence of SGA. Overall, the study suggests that the FMF SGA prediction model can be implemented as part of routine prenatal care for Asian women. [Extracted from the article]

Cohort or cross-sectional test accuracy studies reporting on PE outcome, allowing to tabulate 2x2 tables, with follow-up available for > 85%, evaluating performance of sFlt-1/ PlGF ratio. To estimate predictive performance of the sFlt-1/PlGF ratio at risk cut-offs 38 and 85 for diagnosis of pre-eclampsia (PE) within the following 1 or 4 weeks. EP01.06: Accuracy sFlt-1/PlGF ratio in detecting pre-eclampsia in symptomatic women: systematic review and meta-analysis. [Extracted from the article]

Objectives: To determine the predictive performance of the urine Congo red point-of-care test for the identification of preeclampsia in women presenting with suspected preeclampsia., Methods: A prospective multi-center cohort study was conducted to include women with suspected preeclampsia ( n  = 244). The urine Congo red test was determined (score range 1-8). The diagnosis of preeclampsia was based on criteria proposed by The American College of Obstetricians and Gynecologists. The primary outcome was the predictive performance (sensitivity, specificity, negative and positive predictive values, as well as likelihood ratios) of the Congo red kit test for the diagnosis of preeclampsia., Results: Fifty-four percent (131/244) of women with suspected preeclampsia subsequently developed preeclampsia. The sensitivity and specificity of the urine Congo red test were 49.6% and 94.7%, respectively, when using a cutoff for Congo red ≥4. The test had a significant positive correlation with the level of urine protein (Pearson correlation 0.61, p -value <.01). Intra- and inter-observer reliabilities were good (intra-class correlation coefficient and Cohen's kappa coefficient of 0.88 and 0.75, respectively; p  < .01)., Conclusion: The urine Congo red kit test has a high positive predictive performance for the identification of preeclampsia with high reproducibility. This test may be used as a bed side test to rule-in the diagnosis of preeclampsia in women presenting with suspected preeclampsia.

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-and-prevent strategy for preterm preeclampsia on a global scale., Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT03941886., Competing Interests: L.C.P. has received speaker fees and consultancy payments from Roche Diagnostics and Ferring Pharmaceuticals. In addition, she has received in-kind contributions from Roche Diagnostics, Revvity Inc (formerly PerkinElmer Life and Analytical Sciences), Thermo Fisher Scientific, Ningbo Aucheer Biological Technology Co, Ltd, and GE HealthCare. D.S.S. has received in-kind contributions from Revvity Inc, Thermo Fisher Scientific, Roche Diagnostics, Diabetomics, and Ningbo Aucheer Biological Technology Co, Ltd. R.K.P. is chief executive officer of Ritz Medical Co Ltd, a genetic testing company, and holds shares in and receives executive compensation from it. The other authors report no conflicts.

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., (© 2024 The Authors. International Journal of Gynecology & Obstetrics published by John Wiley & Sons Ltd on behalf of International Federation of Gynecology and Obstetrics.)

Objective: This study aimed to: (1) identify all relevant studies reporting on the diagnostic accuracy of maternal circulating placental growth factor) alone or as a ratio with soluble fms-like tyrosine kinase-1), and of placental growth factor-based models (placental growth factor combined with maternal factors±other biomarkers) in the second or third trimester to predict subsequent development of preeclampsia in asymptomatic women; (2) estimate a hierarchical summary receiver-operating characteristic curve for studies reporting on the same test but different thresholds, gestational ages, and populations; and (3) select the best method to screen for preeclampsia in asymptomatic women during the second and third trimester of pregnancy by comparing the diagnostic accuracy of each method., Data Sources: A systematic search was performed through MEDLINE, Embase, CENTRAL, ClinicalTrials.gov, and the World Health Organization International Clinical Trials Registry Platform databases from January 1, 1985 to April 15, 2021., Study Eligibility Criteria: Studies including asymptomatic singleton pregnant women at >18 weeks' gestation with risk of developing preeclampsia were evaluated. We included only cohort or cross-sectional test accuracy studies reporting on preeclampsia outcome, allowing tabulation of 2×2 tables, with follow-up available for >85%, and evaluating performance of placental growth factor alone, soluble fms-like tyrosine kinase-1- placental growth factor ratio, or placental growth factor-based models. The study protocol was registered on the International Prospective Register Of Systematic Reviews (CRD 42020162460)., Methods: Because of considerable intra- and interstudy heterogeneity, we computed the hierarchical summary receiver-operating characteristic plots and derived diagnostic odds ratios, β, θ i , and Λ for each method to compare performances. The quality of the included studies was evaluated by the QUADAS-2 tool., Results: The search identified 2028 citations, from which we selected 474 studies for detailed assessment of the full texts. Finally, 100 published studies met the eligibility criteria for qualitative and 32 for quantitative syntheses. Twenty-three studies reported on performance of placental growth factor testing for the prediction of preeclampsia in the second trimester, including 16 (with 27 entries) that reported on placental growth factor test alone, 9 (with 19 entries) that reported on the soluble fms-like tyrosine kinase-1-placental growth factor ratio, and 6 (16 entries) that reported on placental growth factor-based models. Fourteen studies reported on performance of placental growth factor testing for the prediction of preeclampsia in the third trimester, including 10 (with 18 entries) that reported on placental growth factor test alone, 8 (with 12 entries) that reported on soluble fms-like tyrosine kinase-1-placental growth factor ratio, and 7 (with 12 entries) that reported on placental growth factor-based models. For the second trimester, Placental growth factor-based models achieved the highest diagnostic odds ratio for the prediction of early preeclampsia in the total population compared with placental growth factor alone and soluble fms-like tyrosine kinase-1-placental growth factor ratio (placental growth factor-based models, 63.20; 95% confidence interval, 37.62-106.16 vs soluble fms-like tyrosine kinase-1-placental growth factor ratio, 6.96; 95% confidence interval, 1.76-27.61 vs placental growth factor alone, 5.62; 95% confidence interval, 3.04-10.38); placental growth factor-based models had higher diagnostic odds ratio than placental growth factor alone for the identification of any-onset preeclampsia in the unselected population (28.45; 95% confidence interval, 13.52-59.85 vs 7.09; 95% confidence interval, 3.74-13.41). For the third trimester, Placental growth factor-based models achieved prediction for any-onset preeclampsia that was significantly better than that of placental growth factor alone but similar to that of soluble fms-like tyrosine kinase-1-placental growth factor ratio (placental growth factor-based models, 27.12; 95% confidence interval, 21.67-33.94 vs placental growth factor alone, 10.31; 95% confidence interval, 7.41-14.35 vs soluble fms-like tyrosine kinase-1-placental growth factor ratio, 14.94; 95% confidence interval, 9.42-23.70)., Conclusion: Placental growth factor with maternal factors ± other biomarkers determined in the second trimester achieved the best predictive performance for early preeclampsia in the total population. However, in the third trimester, placental growth factor-based models had predictive performance for any-onset preeclampsia that was better than that of placental growth factor alone but similar to that of soluble fms-like tyrosine kinase-1-placental growth factor ratio. Through this meta-analysis, we have identified a large number of very heterogeneous studies. Therefore, there is an urgent need to develop standardized research using the same models that combine serum placental growth factor with maternal factors ± other biomarkers to accurately predict preeclampsia. Identification of patients at risk might be beneficial for intensive monitoring and timing delivery., (Copyright © 2023 Elsevier Inc. All rights reserved.)

The study aimed to investigate whether serum sFlt-1 (soluble fms-like tyrosine kinase-1) at 11-13 weeks' gestation in pregnancies that subsequently developed preeclampsia was different from those without preeclampsia and compare screening performance of the International Prediction of Pregnancy Complications (IPPIC) reported models, which include various combinations of maternal factors, systolic blood pressure, diastolic blood pressure, PlGF (placental growth factor) and sFlt-1 and the competing risk (CR) models, which include various combinations of maternal factors, mean arterial pressure (MAP) and PlGF for predicting any-onset, early-onset, and late-onset preeclampsia. This was a prospective multicenter study in 7877 singleton pregnancies. The differences of the predictive performance between the IPPIC and CR models were compared. There were 141 women (1.79%) who developed preeclampsia, including 13 cases (0.17%) of early-onset preeclampsia and 128 cases (1.62%) of late-onset preeclampsia. In pregnancies that developed preeclampsia compared to unaffected pregnancies, median serum sFlt-1 levels and its MoMs were not significantly different ( p >0.05). There was no significant association between gestational age at delivery and log 10 sFlt-1 and log 10 sFlt-1 MoM ( p >0.05). The areas under the curve of CR models were significantly higher than the IPPIC models for the prediction of any-onset and late-onset preeclampsia but not for early-onset preeclampsia. In conclusion, there are no significant differences in the maternal serum sFlt-1 levels at 11-13 weeks' gestation between women who subsequently develop preeclampsia and those who do not. Moreover, the CR models for the prediction of any-onset and late-onset preeclampsia perform better than the IPPIC reported model.