Use of first-trimester placenta growth factor concentration to predict hypertensive disorders of pregnancy in a low-risk Asian population

Efficient isolation of encoded microparticles in a degassed micromold for highly sensitive and multiplex immunoassay with signal amplification

Multiplex detection of low-abundance protein biomarkers in biofluids can contribute to diverse biomedical fields such as early diagnosis and precision medicine. However, conventional techniques such as digital ELISA, microarray, and hydrogel-based assay still face limitations in terms of efficient protein detection due to issues with multiplexing capability, sensitivity, or complicated assay procedures. In this study, we present the degassed micromold-based particle isolation technique for highly sensitive and multiplex immunoassay with enzymatic signal amplification. Using degassing treatment of nanoporous polydimethylsiloxane (PDMS) micromold, the encoded particles are isolated in the mold within 5 min absorbing trapped air bubbles into the mold by air suction capability. Through 10 min of signal amplification in the isolated spaces by fluorogenic substrate and horseradish peroxidase labeled in the particle, the assay signal is amplified with one order of magnitude compared to that of the standard hydrogel-based assay. Using the signal amplification assay, vascular endothelial growth factor (VEGF) and chorionic gonadotropin beta (CG beta), the preeclampsia-related protein biomarkers, are quantitatively detected with a limit of detection (LoD) of 249 fg/mL and 476 fg/mL in phosphate buffer saline. The multiplex immunoassay is conducted to validate negligible non-specific detection signals and robust recovery rates in the multiplex assay. Finally, the VEGF and CG beta in real urine samples are simultaneously and quantitatively detected by the developed assay. Given the high sensitivity, multiplexing capability, and process simplicity, the presented particle isolation-based signal amplification assay holds significant potential in biomedical and proteomic fields.

Ultrasound placental image texture analysis using artificial intelligence to predict hypertension in pregnancy

BACKGROUND

The placental pathological changes in hypertensive disorders of pregnancy (HDP) starts early in pregnancy, the deep convolutional neural networks (CNN) can identify these changes before its clinical manifestation.

OBJECTIVE

To compare the placental quantitative ultrasound image texture of women with HDP to those with the normal outcome.

METHODS

The cases were enrolled in the first trimester of pregnancy, good quality images of the placenta were taken serially in the first, second, and third trimester of pregnancy. The women were followed till delivery, those with normal outcomes were controls, and those with HDP were cases. The images were processed and classified using validated deep learning tools.

RESULTS

Total of 429 cases were fully followed till delivery, 58 of them had HDP (13.5%). In the first trimester, there was a significant difference in the placental length (p = .033), uterine artery PI (p = .019), biomarkers PAPP-A (p = .001) PlGF (p = .013) and placental image texture (p = .001) between the cases and controls. In the second trimester the uterine artery PI, serum PAPP-A (p = .010) and PlGF (p = .005) levels were significantly low among women who developed hypertension later on pregnancy. The image texture disparity between the two groups was highly significant (p < .001). The model "resnext 101_32x8d" had Cohen kappa score of 0.413 (moderate) and the accuracy score of 0.710 (good). In the first trimester the best sensitivity and specificity was observed for abnormal placental image texture (70.6% and 76.6%, respectively) followed by PlGF (64% and 50%, respectively), in the second trimester the abnormal image texture had the highest sensitivity and specificity (60.4% and 73.3%, respectively) followed by uterine artery PI (58.6% and 54.7%, respectively). Similarly in the third trimester, uterine artery PI had sensitivity and specificity of 60.3% and specificity of 50.7%, whereas the abnormal image texture had sensitivity and specificity of 83.5%.

CONCLUSION

Ultrasound placental analysis using artificial intelligence (UPAAI) is a promising technique, would open avenues for more research in this field.

Prediction of hypertension in pregnancy in high risk women using maternal factors and serial placental profile in second and third trimester

INTRODUCTION

To evaluate the role of placental profile markers in second and third trimester of pregnancy in predicting hypertensive disorders of pregnancy (HDP) in women at high risk of preeclampsia.

METHOD

Women who were at high risk of preeclampsia underwent βhCG, ultrasound assessment of placental length, thickness and its ratio, uterine artery Doppler at 20-24 weeks and 28-32 weeks of gestation, the outcome at delivery was noted. Those who developed HDP were cases and those with normal outcome were controls. The placental profile markers among cases and controls were compared.

RESULTS

Hypertensive disorders of pregnancy was seen in 72/160 (45%) high risk women The serum β hCG levels at 20-24 weeks (p = 0.001) and 28-32 weeks (p = 0.018) was significantly high in women who had preeclampsia. Placental thickness was found to be less in among all subgroups of HDP, for preeclampsia, it was significantly low at 20-24 weeks (AUC- 0.743; sensitivity- 75%, specificity- 66.3%) and 28 weeks (AUC -0.764, sensitivity - 75.0% specificity - 78.7%). Uterine artery S/D ratio was considerable high in women with chronic hypertension (AUC -0.765), gestational hypertension (AUC -0.771) and preeclampsia (AUC -0.726) at 20-24 weeks. In preeclampsia group, uterine artery PI was highest and the best marker at 20-24 weeks (AUC -0.935, sensitivity - 100.0%, specificity - 87.6%).

DISCUSSION

The placental profile markers may be used to provide closer follow up in high risk pregnancies with abnormal placental profile levels, while less intense follow up in those with normal levels, thus channelizing the resources.

Current update of first trimester preeclampsia screening in Asia

In consideration of high prevalence of preeclampsia, enormous studies attempted to look for strategies in early gestation. Hence, a powerful screening should be built up in first trimester. Then, Aspirin could be administrated for proper prevention. The objective of this article is reviewing the screening for preeclampsia in first trimester recently. To identify the high-risk group precisely, an effective model should be recommended to Asian population. Articles related to first trimester screening of PE in Asia from databases of PubMed, ScienceDirect and Scopus were searched for this narrative review. The criteria included randomized clinical trials, observational prospective and retrospective cohort studies, case-control studies, systematic review and meta-analysis and professional review articles. Screening models combining maternal factors, biophysical factors, ultrasound studies and biochemical factors achieved high predictive performance of preeclampsia. In Asia, the detection rate of the Fetal Medicine Foundation is superior to those of the American College of Obstetricians and Gynecologists and the National Institute for Health and Care Excellence. Consequently, this effective model from the Fetal Medicine Foundation should be continuously used for screening in first trimester for the Asian.

Prospective evaluation of screening performance of first-trimester prediction models for preterm preeclampsia in an Asian population

BACKGROUND

The administration of aspirin <16 weeks gestation to women who are at high risk for preeclampsia has been shown to reduce the rate of preterm preeclampsia by 65%. The traditional approach to identify such women who are at risk is based on risk factors from maternal characteristics, obstetrics, and medical history as recommended by the American College of Obstetricians and Gynecologists and the National Institute for Health and Care Excellence. An alternative approach to screening for preeclampsia has been developed by the Fetal Medicine Foundation. This approach allows the estimation of patient-specific risks of preeclampsia that requires delivery before a specified gestational age with the use of Bayes theorem-based model.

OBJECTIVE

The purpose of this study was to examine the diagnostic accuracy of the Fetal Medicine Foundation Bayes theorem-based model, the American College of Obstetricians and Gynecologists, and the National Institute for Health and Care Excellence recommendations for the prediction of preterm preeclampsia at 11-13⁺⁶ weeks gestation in a large Asian population STUDY DESIGN: This was a prospective, nonintervention, multicenter study in 10,935 singleton pregnancies at 11-13⁺⁶ weeks gestation in 11 recruiting centers across 7 regions in Asia between December 2016 and June 2018. Maternal characteristics and medical, obstetric, and drug history were recorded. Mean arterial pressure and uterine artery pulsatility indices were measured according to standardized protocols. Maternal serum placental growth factor concentrations were measured by automated analyzers. The measured values of mean arterial pressure, uterine artery pulsatility index, and placental growth factor were converted into multiples of the median. The Fetal Medicine Foundation Bayes theorem-based model was used for the calculation of patient-specific risk of preeclampsia at <37 weeks gestation (preterm preeclampsia) and at any gestation (all preeclampsia) in each participant. The performance of screening for preterm preeclampsia and all preeclampsia by a combination of maternal factors, mean arterial pressure, uterine artery pulsatility index, and placental growth factor (triple test) was evaluated with the adjustment of aspirin use. We examined the predictive performance of the model by the use of receiver operating characteristic curve and calibration by measurements of calibration slope and calibration in the large. The detection rate of screening by the Fetal Medicine Foundation Bayes theorem-based model was compared with the model that was derived from the application of American College of Obstetricians and Gynecologists and National Institute for Health and Care Excellence recommendations.

RESULTS

There were 224 women (2.05%) who experienced preeclampsia, which included 73 cases (0.67%) of preterm preeclampsia. In pregnancies with preterm preeclampsia, the mean multiples of the median values of mean arterial pressure and uterine artery pulsatility index were significantly higher (mean arterial pressure, 1.099 vs 1.008 [P<.001]; uterine artery pulsatility index, 1.188 vs 1.063[P=.006]), and the mean placental growth factor multiples of the median was significantly lower (0.760 vs 1.100 [P<.001]) than in women without preeclampsia. The Fetal Medicine Foundation triple test achieved detection rates of 48.2%, 64.0%, 71.8%, and 75.8% at 5%, 10%, 15%, and 20% fixed false-positive rates, respectively, for the prediction of preterm preeclampsia. These were comparable with those of previously published data from the Fetal Medicine Foundation study. Screening that used the American College of Obstetricians and Gynecologists recommendations achieved detection rate of 54.6% at 20.4% false-positive rate. The detection rate with the use of National Institute for Health and Care Excellence guideline was 26.3% at 5.5% false-positive rate.

CONCLUSION

Based on a large number of women, this study has demonstrated that the Fetal Medicine Foundation Bayes theorem-based model is effective in the prediction of preterm preeclampsia in an Asian population and that this method of screening is superior to the approach recommended by American College of Obstetricians and Gynecologists and the National Institute for Health and Care Excellence. We have also shown that the Fetal Medicine Foundation prediction model can be implemented as part of routine prenatal care through the use of the existing infrastructure of routine prenatal care.