Model For Prediction Of Preeclampsia Research Articles

Prediction Model for Pre-Eclampsia in a Low-Resource Setting: A Systematic Literature Review

Prediction Model for Pre-Eclampsia in a Low-Resource Setting: A Systematic Literature Review

Real-time pre-eclampsia prediction model based on IoT and machine learning

Pre-eclampsia (PET) is a hypertensive disease that occurs during pregnancy or in the postpartum period. It complicates 2% to 8% of all pregnancies and is one of the causes of more than 50,000 maternal deaths and over 500,000 fetal deaths worldwide annually. Adverse birth outcomes due to pregnancy complications have been associated with three delays: delay in recognizing the complication, delay in reaching an appropriate facility, and delay in receiving adequate care when the facility is reached. Thus prevention, timely detection, and care of pregnancy complications can prevent maternal deaths and morbidity. The Internet of Things (IoT) and machine learning (ML) technologies have become the new revolution of research in the field of healthcare. These technologies can be utilized to interconnect various sensors, monitor the health status of a patient, and predict the occurrence of an ailment. This study has designed and prototyped a pre-eclampsia monitoring model based on IoT and machine learning for remotely monitoring the health status of an expectant woman and her unborn child, to enhance early diagnosis of pre-eclampsia and improve birth outcomes. The study involved researching the on most appropriate biosensors and then designing and prototyping the pre-eclampsia watch. To build the pre-eclampsia prediction model the best ML algorithm was empirically analysed. A Naïve Bayes pre-eclampsia prediction model was found to perform better in identifying pregnant women who are at risk of pre-eclampsia after evaluation of various pre-eclampsia models built using decision trees, Naïve Bayes, K Nearest Neighbor (KNN), logistic regression, support vector machines (SVM) and Artificial neural networks (ANN). Lastly, the predictive model was integrated with the pre-eclampsia model to assist in early diagnosis of pre-eclampsia. The prototype generates alerts when the expectant woman is at risk of Pre-eclampsia. The pre-eclampsia watch model can securely capture and transmit expectant women's vital to the cloud for processing and provide timely alerts when the woman is at risk. Further research on the performance and efficacy of the model in a real environment will be done by experimenting with it in a purposively selected sample.

Exploration of the molecular characteristics and potential clinical significance of shared immune-related genes between preterm preeclampsia and term preeclampsia

BackgroundPreeclampsia is a severe obstetric disorder that significantly affects the maternal and neonatal peri-partum safety and long-term quality of life. However, there is limited research exploring the common mechanisms and potential clinical significance between early-onset preeclampsia and full-term preeclampsia from an immunological perspective.MethodsIn this study, data analysis was conducted. Initially, immune-related co-expressed genes involving both subtypes of preeclampsia were identified through Weighted Gene Co-expression Network Analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were further employed to investigate the shared pathways regulated by immune-related genes. Binary logistic regression identified co-expressed genes with diagnostic value for preeclampsia, and a diagnostic model was constructed. Gene Set Enrichment Analysis (GSEA) predicted the potential biological functions of the selected genes. Lasso and Cox regression analyses identified genes closely associated with gestational duration, and a risk score model was established. A 4-gene feature, immune-related gene model for predicting the risk of preterm birth in preeclamptic pregnant women, was developed and validated through qPCR experiments. Immune cell infiltration analysis determined differences in immune cell infiltration between the two subtypes of preeclampsia.ResultsThis study identified 4 immune-related co-expressed genes (CXCR6, PIK3CB, IL1RAP, and OSMR). Additionally, diagnostic and preterm birth risk prediction models for preeclampsia were constructed based on these genes. GSEA analysis suggested the involvement of these genes in the regulation of galactose metabolism, notch signaling pathway, and RIG-I like receptor signaling pathway. Immune pathway analysis indicated that the activation of T cell co-inhibition could be a potential intervention target for immunotherapy in early-onset preeclampsia.ConclusionOur study provides promising insights into immunotherapy and mechanistic research for preeclampsia, discovering novel diagnostic and intervention biomarkers, and offering personalized diagnostic tools for preeclampsia.

Risk prediction for preeclampsia in CKD patients: development of a model in a retrospective cohort.

Chronic kidney disease (CKD) mayaffect women of childbearing age and maylead to substantial maternal and foetal morbidity and mortalityin pregnancy. There is a lack of prediction models for preeclampsia and adverse pregnancy outcomes in pregnant women with CKD. This study aimed to create a prediction nomogram for these issues. This retrospective cohort study included clinical data from 627 women with CKD and their 627 pregnancies at Peking University First Hospital between January 1, 2009, and December 31, 2022. Multivariate logistic regression analysis was conducted to identify independent prognostic factors and develop a nomogram for predicting the occurrence of preeclampsia. The identified risk factors were utilised to construct the nomogram, which was subsequentlyinternally validated using receiver operating characteristic (ROC) analysis and calibration curve assessment. According to our multivariate analysis, age, blood urea nitrogen (BUN), serum creatinine (Scr), mean arterial pressure (MAP), 24-h proteinuria, and CKD stage were identified as predictors of preeclampsia. Additionally, Scr, MAP, BUN, and 24-h proteinuria were found to be predictors of adverse pregnancy outcomes. The nomogram for predicting preeclampsia had an area under the ROC curve of 0.910, while the nomogram for predicting adverse pregnancy outcomes had an area under the ROC curve of 0.906. Both models demonstrated excellent discriminatory ability. A nomogram based on 24-h proteinuria, serum creatinine, serum urea and age, and MAP allows predicting the occurrence of preeclampsia and other adversepregnancy-related outcomes in CKD patients.

A dynamic prediction model for preeclampsia using the sFlt-1/PLGF ratio combined with multiple factors

ObjectivePreeclampsia (PE) is a pregnancy-related multi-organ disease and a significant cause of incidence rate and mortality of pregnant women and newborns worldwide. Delivery remains the only available treatment for PE. This study aims to establish a dynamic prediction model for PE.MethodsA total of 737 patients who visited our hospital from January 2021 to June 2022 were identified according to the inclusion and exclusion criteria, forming the primary dataset. Additionally, 176 singleton pregnant women who visited our hospital from July 2022 to November 2022 comprised the verification set. We investigated different gestational weeks of sFlt-1/PLGF (soluble FMS-like tyrosine kinase-1, placental growth factor) ratio combined with maternal characteristics and routine prenatal laboratory results in order to predict PE in each trimester. Multivariate logistic regression was used to establish the prediction model for PE at different gestational weeks. The discrimination, calibration, and clinical validity were utilized to evaluate predictive models as well as models in external validation queues.ResultsAt 20–24 weeks, the obtained prediction model for PE yielded an area under the curve of 0.568 (95% confidence interval, 0.479–0.657). At 25–29 weeks, the obtained prediction model for PE yielded an area under the curve of 0.773 (95% confidence interval, 0.703–0.842)and 0.731 (95% confidence interval, 0.653–0.809) at 30–34 weeks. After adding maternal factors, uterine artery pulsation index(Ut-IP), and other laboratory indicators to the sFlt-1/PLGF ratio, the predicted performance of PE improved. It found that the AUC improved to 0.826(95% confidence interval, 0.748 ∼ 0.904) at 20–24 weeks, 0.879 (95% confidence interval, 0.823 ∼ 0.935) at 25–29 weeks, and 0.862(95% confidence interval, 0.799 ∼ 0.925) at 30–34 weeks.The calibration plot of the prediction model indicates good predictive accuracy between the predicted probability of PE and the observed probability. Furthermore, decision-curve analysis showed an excellent clinical application value of the models.ConclusionUsing the sFlt-1/PLGF ratio combined with multiple factors at 25–29 weeks can effectively predict PE, but the significance of re-examination in late pregnancy is not significant.

Prediction model of preeclampsia using machine learning based methods: a population based cohort study in China.

Preeclampsia is a disease with an unknown pathogenesis and is one of the leading causes of maternal and perinatal morbidity. At present, early identification of high-risk groups for preeclampsia and timely intervention with aspirin is an effective preventive method against preeclampsia. This study aims to develop a robust and effective preeclampsia prediction model with good performance by machine learning algorithms based on maternal characteristics, biophysical and biochemical markers at 11-13+6 weeks' gestation, providing an effective tool for early screening and prediction of preeclampsia. This study included 5116 singleton pregnant women who underwent PE screening and fetal aneuploidy from a prospective cohort longitudinal study in China. Maternal characteristics (such as maternal age, height, pre-pregnancy weight), past medical history, mean arterial pressure, uterine artery pulsatility index, pregnancy-associated plasma protein A, and placental growth factor were collected as the covariates for the preeclampsia prediction model. Five classification algorithms including Logistic Regression, Extra Trees Classifier, Voting Classifier, Gaussian Process Classifier and Stacking Classifier were applied for the prediction model development. Five-fold cross-validation with an 8:2 train-test split was applied for model validation. We ultimately included 49 cases of preterm preeclampsia and 161 cases of term preeclampsia from the 4644 pregnant women data in the final analysis. Compared with other prediction algorithms, the AUC and detection rate at 10% FPR of the Voting Classifier algorithm showed better performance in the prediction of preterm preeclampsia (AUC=0.884, DR at 10%FPR=0.625) under all covariates included. However, its performance was similar to that of other model algorithms in all PE and term PE prediction. In the prediction of all preeclampsia, the contribution of PLGF was higher than PAPP-A (11.9% VS 8.7%), while the situation was opposite in the prediction of preterm preeclampsia (7.2% VS 16.5%). The performance for preeclampsia or preterm preeclampsia using machine learning algorithms was similar to that achieved by the fetal medicine foundation competing risk model under the same predictive factors (AUCs of 0.797 and 0.856 for PE and preterm PE, respectively). Our models provide an accessible tool for large-scale population screening and prediction of preeclampsia, which helps reduce the disease burden and improve maternal and fetal outcomes.

Externally validated prediction models for pre-eclampsia: systematic review and meta-analysis.

This systematic review and meta-analysis aimed to evaluate the performance of existing externally validated prediction models for pre-eclampsia (PE) (specifically, any-onset, early-onset, late-onset and pretermPE). A systematic search was conducted in five databases (MEDLINE, EMBASE, Emcare, CINAHL and Maternity & Infant Care Database) and using Google Scholar/reference search to identify studies based on the Population, Index prediction model, Comparator, Outcome, Timing and Setting (PICOTS) approach until 20 May 2023. We extracted data using the CHARMS checklist and appraised the risk of bias using the PROBAST tool. A meta-analysis of discrimination and calibration performance was conducted when appropriate. Twenty-three studies reported 52 externally validated prediction models for PE (one preterm, 20 any-onset, 17 early-onset and 14 late-onset PE models). No model had the same set of predictors. Fifteen any-onset PE models were validated externally once, two were validated twice and three were validated three times, while the Fetal Medicine Foundation (FMF) competing-risks model for preterm PE prediction was validated widely in 16 different settings. The most common predictors were maternal characteristics (prepregnancy body mass index, prior PE, family history of PE, chronic medical conditions and ethnicity) and biomarkers (uterine artery pulsatility index and pregnancy-associated plasma protein-A). The FMF model for preterm PE (triple test plus maternal factors) had the best performance, with a pooled area under the receiver-operating-characteristics curve (AUC) of 0.90 (95% prediction interval (PI), 0.76-0.96), and was well calibrated. The other models generally had poor-to-good discrimination performance (median AUC, 0.66 (range, 0.53-0.77)) and were overfitted on external validation. Apart from the FMF model, only two models that were validated multiple times for any-onset PE prediction, which were based on maternal characteristics only, produced reasonable pooled AUCs of 0.71 (95% PI, 0.66-0.76) and 0.73 (95% PI, 0.55-0.86). Existing externally validated prediction models for any-, early- and late-onset PE have limited discrimination and calibration performance, and include inconsistent input variables. The triple-test FMF model had outstanding discrimination performance in predicting preterm PE in numerous settings, but the inclusion of specialized biomarkers may limit feasibility and implementation outside of high-resource settings. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Validation of the first-trimester machine learning model for predicting pre-eclampsia in an Asian population.

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. 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. 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). 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.

First-trimester preterm preeclampsia prediction model for prevention with low-dose aspirin.

Preeclampsia (PE) is a major maternal and fetal threat. Previous risk-scoring methods in guidelines lacked precision. The Fetal Medicine Foundation (FMF) proposed a first-trimester PE screening model using Bayes' theorem. FMF prediction model combines maternal characteristics and medical/obstetrical history to determine prior risk and further incorporate maternal blood pressure, maternal serum biomarkers, and uterine Doppler pulsatility index expressed as multiples of the median (MoM) to estimate posterior risk. Low-dose aspirin is one ofthe potential PE prevention strategies. Initiating it before 16 weeks is crucial. Aspirin's antiplatelet and anti-inflammatory properties align with PE's pathophysiology. Dosing and resistance warrant further study, but a standard regimen of 150 mg nightly, starting before 16 weeks, is widely supported. Clinical trials, including ASPRE, affirm aspirin's role in PE prevention. Starting aspirin based on FMF screening significantly reduces preterm PE and associated complications. Emerging research explores predictors like maternal ophthalmic arterial waveform. Regional variations, especially in Asian populations, are considered. Machine learning and AI show promise, but examiner expertise remains essential for accurate prediction. In conclusion, integrating FMF's first-trimester PE screening with low-dose aspirin offers a promising strategy. Further advancements may enhance precision and broaden prevention efforts.

Pre-pregnancy body mass index combined with peripheral blood PLGF, DCN, LDH, and UA in a risk prediction model for pre-eclampsia.

This study analyzes the levels of peripheral blood placental growth factor (PLGF), body mass index (BMI), decorin (DCN), lactate dehydrogenase (LDH), uric acid (UA), and clinical indicators of patients with preeclampsia (PE), and establishes a predictive risk model of PE, which can provide a reference for early and effective prediction of PE. 81 cases of pregnant women with PE who had regular prenatal checkups and delivered in Jinshan Branch of Shanghai Sixth People's Hospital from June 2020 to December 2022 were analyzed, and 92 pregnant women with normal pregnancies who had their antenatal checkups and delivered at the hospital during the same period were selected as the control group. Clinical data and peripheral blood levels of PLGF, DCN, LDH, and UA were recorded, and the two groups were subjected to univariate screening and multifactorial logistic regression analysis. Based on the screening results, the diagnostic efficacy of PE was evaluated using the receiver operating characteristic (ROC) curve. Risk prediction nomogram model was constructed using R language. The Bootstrap method (self-sampling method) was used to validate and produce calibration plots; the decision curve analysis (DCA) was used to assess the clinical benefit rate of the model. There were statistically significant differences in age, pre-pregnancy BMI, gestational weight gain, history of PE or family history, family history of hypertension, gestational diabetes mellitus, and history of renal disease between the two groups (P < 0.05). The results of multifactorial binary logistic stepwise regression revealed that peripheral blood levels of PLGF, DCN, LDH, UA, and pre-pregnancy BMI were independent influences on the occurrence of PE (P < 0.05). The area under the curve of PLGF, DCN, LDH, UA levels and pre-pregnancy BMI in the detection of PE was 0.952, with a sensitivity of 0.901 and a specificity of 0.913, which is better than a single clinical diagnostic indicator. The results of multifactor analysis were constructed as a nomogram model, and the mean absolute error of the calibration curve of the modeling set was 0.023, suggesting that the predictive probability of the model was generally compatible with the actual value. DCA showed the predictive model had a high net benefit in the range of 5% to 85%, suggesting that the model has clinical utility value. The occurrence of PE is related to the peripheral blood levels of PLGF, DCN, LDH, UA and pre-pregnancy BMI, and the combination of these indexes has a better clinical diagnostic value than a single index. The nomogram model constructed by using the above indicators can be used for the prediction of PE and has high predictive efficacy.

Validation of machine-learning model for first-trimester prediction of pre-eclampsia using cohort from PREVAL study.

Effective first-trimester screening for pre-eclampsia (PE) can be achieved using a competing-risks model that combines risk factors from the maternal history with multiples of the median (MoM) values of biomarkers. A new model using artificial intelligence through machine-learning methods has been shown to achieve similar screening performance without the need for conversion of raw data of biomarkers into MoM. This study aimed to investigate whether this model can be used across populations without specific adaptations. Previously, a machine-learning model derived with the use of a fully connected neural network for first-trimester prediction of early (< 34 weeks), preterm (< 37 weeks) and all PE was developed and tested in a cohort of pregnant women in the UK. The model was based on maternal risk factors and mean arterial blood pressure (MAP), uterine artery pulsatility index (UtA-PI), placental growth factor (PlGF) and pregnancy-associated plasma protein-A (PAPP-A). In this study, the model was applied to a dataset of 10 110 singleton pregnancies examined in Spain who participated in the first-trimester PE validation (PREVAL) study, in which first-trimester screening for PE was carried out using the Fetal Medicine Foundation (FMF) competing-risks model. The performance of screening was assessed by examining the area under the receiver-operating-characteristics curve (AUC) and detection rate (DR) at a 10% screen-positive rate (SPR). These indices were compared with those derived from the application of the FMF competing-risks model. The performance of screening was poor if no adjustment was made for the analyzer used to measure PlGF, which was different in the UK and Spain. Therefore, adjustment for the analyzer used was performed using simple linear regression. The DRs at 10% SPR for early, preterm and all PE with the machine-learning model were 84.4% (95% CI, 67.2-94.7%), 77.8% (95% CI, 66.4-86.7%) and 55.7% (95% CI, 49.0-62.2%), respectively, with the corresponding AUCs of 0.920 (95% CI, 0.864-0.975), 0.913 (95% CI, 0.882-0.944) and 0.846 (95% CI, 0.820-0.872). This performance was achieved with the use of three of the biomarkers (MAP, UtA-PI and PlGF); inclusion of PAPP-A did not provide significant improvement in DR. The machine-learning model had similar performance to that achieved by the FMF competing-risks model (DR at 10% SPR, 82.7% (95% CI, 69.6-95.8%) for early PE, 72.7% (95% CI, 62.9-82.6%) for preterm PE and 55.1% (95% CI, 48.8-61.4%) for all PE) without requiring specific adaptations to the population. A machine-learning model for first-trimester prediction of PE based on a neural network provides effective screening for PE that can be applied in different populations. However, before doing so, it is essential to make adjustments for the analyzer used for biochemical testing. © 2023 International Society of Ultrasound in Obstetrics and Gynecology.

Different risk-assessment models for prediction of preeclampsia and fetal growth restriction in the first trimester in a high-risk pregnancy – which models are better?

Different risk-assessment models for prediction of preeclampsia and fetal growth restriction in the first trimester in a high-risk pregnancy – which models are better?

ЦИФРОВЫЕ РЕСУРСЫ В ПРЕДИКЦИИ ПРЕЭКЛАМПСИИ И ЗАДЕРЖКИ РОСТА ПЛОДА

Abstact. Introduction. Preeclampsia and fetal growth restriction are pregnancy complications related to Great Obstetrical Syndromes and having a similar pathogenesis. No antipathogenetic therapy methods have been found for both pathologies, but there is a means of prevention: Acetylsalicylic acid. The drug is effective only in high-risk pregnant women; therefore, various models are being developed to predict preeclampsia and fetal growth restriction, including digital models. They aim to help select patients who need that prophylaxis. Aim. This study is aiming to create a digital model for the early prediction of preeclampsia, based on the patient’s electronic medical card. Materials and Methods. The investigation involved 231 pregnant women. An algorithm was developed for the early prediction of preeclampsia and fetal growth restriction, based on detecting a combination of systemic and local hemodynamic markers: High variability of blood pressure between visits (long-term variability) and high resistance of blood flow in uterine arteries. Long-term (intervisit) variability of blood pressure is calculated for each trimester: First, the arithmetic mean is calculated among the values of systolic blood pressure at successive visits; second, the standard deviation is computed based on the result. Uterine arteries are checked using Doppler ultrasound technology at 11-13 weeks of gestation: Resistivity of blood flow (peripheral resistivity) in the right and left uterine arteries is assessed. Results and Discussion. For this new prediction algorithm, a digital model was developed, the ECAPP program (registration certificate No. 2018660666 – Electronic Prenatal Record with assessing the risk of developing preeclampsia based on blood pressure variability and uterine blood flow resistance). Conclusions. The IT resource presented has a potential for the effective early prediction of preeclampsia, as well as for predicting fetal growth restriction. Keywords: preeclampsia, fetal growth restriction, prognosis, digital model.

Risk Prediction Model of Early-Onset Preeclampsia Based on Risk Factors and Routine Laboratory Indicators.

Background: Globally, 10-15% of maternal deaths are statistically attributable to preeclampsia. Compared with late-onset PE, the severity of early-onset PE remains more harmful with higher morbidity and mortality. Objective: To establish an early-onset preeclampsia prediction model by clinical characteristics, risk factors and routine laboratory indicators were investigated from pregnant women at 6 to 10 gestational weeks. Methods: The clinical characteristics, risk factors, and 38 routine laboratory indicators (6-10 weeks of gestation) including blood lipids, liver and kidney function, coagulation, blood count, and other indicators of 91 early-onset preeclampsia patients and 709 normal controls without early-onset preeclampsia from January 2010 to May 2021 in Peking University Third Hospital (PUTH) were retrospectively analyzed. A logistic regression, decision tree model, and support vector machine (SVM) model were applied for establishing prediction models, respectively. ROC curves were drawn; area under curve (AUCROC), sensitivity, and specificity were calculated and compared. Results: There were statistically significant differences in the rates of diabetes, antiphospholipid syndrome (APS), kidney disease, obstructive sleep apnea (OSAHS), primipara, history of preeclampsia, and assisted reproductive technology (ART) (p < 0.05). Among the 38 routine laboratory indicators, there were no significant differences in the levels of PLT/LYM, NEU/LYM, TT, D-Dimer, FDP, TBA, ALP, TP, ALB, GLB, UREA, Cr, P, Cystatin C, HDL-C, Apo-A1, and Lp(a) between the two groups (p > 0.05). The levels of the rest indicators were all statistically different between the two groups (p < 0.05). If only 12 risk factors of PE were analyzed with the logistic regression, decision tree model, and support vector machine (SVM), and the AUCROC were 0.78, 0.74, and 0.66, respectively, while 12 risk factors of PE and 38 routine laboratory indicators were analyzed with the logistic regression, decision tree model, and support vector machine (SVM), and the AUCROC were 0.86, 0.77, and 0.93, respectively. Conclusions: The efficacy of clinical risk factors alone in predicting early-onset preeclampsia is not high while the efficacy increased significantly when PE risk factors combined with routine laboratory indicators. The SVM model was better than logistic regression model and decision tree model in early prediction of early-onset preeclampsia incidence.

Development of a Urine Metabolomics Biomarker-Based Prediction Model for Preeclampsia during Early Pregnancy

Preeclampsia (PE) is a condition that poses a significant risk of maternal mortality and multiple organ failure during pregnancy. Early prediction of PE can enable timely surveillance and interventions, such as low-dose aspirin administration. In this study, conducted at Stanford Health Care, we examined a cohort of 60 pregnant women and collected 478 urine samples between gestational weeks 8 and 20 for comprehensive metabolomic profiling. By employing liquid chromatography mass spectrometry (LCMS/MS), we identified the structures of seven out of 26 metabolomics biomarkers detected. Utilizing the XGBoost algorithm, we developed a predictive model based on these seven metabolomics biomarkers to identify individuals at risk of developing PE. The performance of the model was evaluated using 10-fold cross-validation, yielding an area under the receiver operating characteristic curve of 0.856. Our findings suggest that measuring urinary metabolomics biomarkers offers a noninvasive approach to assess the risk of PE prior to its onset.

Noninvasive preeclampsia prediction using plasma cell–free RNA signatures

Preeclampsia, especially preterm preeclampsia and early-onset preeclampsia, is a life-threating pregnancy disorder, and the heterogeneity and complexity of preeclampsia make it difficult to predict risk and to develop treatments. Plasma cell-free RNA carries unique information from human tissue and may be useful for noninvasive monitoring of maternal, placental, and fetal dynamics during pregnancy. This study aimed to investigate various RNA biotypes associated with preeclampsia in plasma and to develop classifiers to predict preterm preeclampsia and early-onset preeclampsia before diagnosis. We performed a novel, cell-free RNA sequencing method termed polyadenylation ligation-mediated sequencing to investigate the cell-free RNA characteristics of 715 healthy pregnancies and 202 pregnancies affected by preeclampsia before symptom onset. We explored differences in the abundance of different RNA biotypes in plasma between healthy and preeclampsia samples and built preterm preeclampsia and early-onset preeclampsia prediction classifiers using machine learning methods. Furthermore, we validated the performance of the classifiers using the external and internal validation cohorts and assessed the area under the curve and positive predictive value. We detected 77 genes, including messenger RNA (44%) and microRNA (26%), that were differentially expressed in healthy mothers and mothers with preterm preeclampsia before symptom onset, which could separate participants with preterm preeclampsia from healthy samples and that played critical functional roles in preeclampsia physiology. We developed 2 classifiers for predicting preterm preeclampsia and early-onset preeclampsia before diagnosis based on 13 cell-free RNA signatures and 2 clinical features (invitro fertilization and mean arterial pressure), respectively. Notably, both classifiers showed enhanced performance when compared with the existing methods. The preterm preeclampsia prediction model achieved 81% area under the curve and 68% positive predictive value in an independent validation cohort (preterm, n=46; control, n=151); the early-onset preeclampsia prediction model had an area under the curve of 88% and a positive predictive value of 73% in an external validation cohort (early-onset preeclampsia, n=28; control, n=234). Furthermore, we demonstrated that downregulation of microRNAs may play vital roles in preeclampsia through the upregulation of preeclampsia-relevant target genes. In this cohort study, a comprehensive transcriptomic landscape of different RNA biotypes in preeclampsia was presented and 2 advanced classifiers with substantial clinical importance for preterm preeclampsia and early-onset preeclampsia prediction before symptom onset were developed. We demonstrated that messenger RNA, microRNA, and long noncoding RNA can simultaneously serve as potential biomarkers of preeclampsia, holding the promise of prevention of preeclampsia in the future. Abnormal cell-free messenger RNA, microRNA, and long noncoding RNA molecular changes may help to elucidate the pathogenic determinants of preeclampsia and open new therapeutic windows to effectively reduce pregnancy complications and fetal morbidity.

Risk prediction model of early-onset preeclampsia based on risk factors and routine laboratory indicators

Background: 10% - 15% of maternal deaths are statistically attributable to preeclampsia. Compared with late-onset PE, the severity of early-onset PE remains greater harm, with higher morbidity and mortality. Objective: To establish an early-onset preeclampsia prediction model by clinical characteristics, risk factors and routine laboratory indicators from 6 to 10 gestational weeks of pregnant women. Methods: The clinical characteristics, risk factors and 38 routine laboratory indicators (6 - 10 weeks of gestation) including blood lipids, liver and kidney function, coagulation, blood count and other indicators of 91 early-onset preeclampsia patients and 709 normal controls without early-onset preeclampsia from January 2010 to May 2021 in Peking University Third Hospital (PUTH) were retrospectively analyzed. Logistic regression, Decision tree model and Support vector machine (SVM) model were applied for establishing prediction models, respectively. ROC curves were drawn, and the area under the curve (AUCROC), sensitivity and specificity was calculated and compared. Results: There were statistically significant differences in the rates of diabetes, Antiphospholipid Syndrome (APS), kidney disease, Obstructive Sleep Apnea (OSAHS), primipara, history of preeclampsia and Assisted Reproductive Technology (ART) (p &lt; 0.05). Among the 38 routine laboratory indicators, there were no significant differences in the levels of PLT/LYM, NEU/LYM, TT, D-Dimer, FDP, TBA, ALP, TP, ALB, GLB, UREA, Cr, P, Cystatin C, HDL- C, Apo-A1, and Lp(a) between the two groups (p &gt; 0.05). The levels of the rest indicators were all statistically different between the two groups (p &lt; 0.05). If only 12 risk factors of PE were analyzed by logistic regression, decision tree model, and the Support Vector Machine (SVM), the AUCROC were 0.78, 0.74 and 0.66 respectively, while 12 risk factors of PE and 38 routine laboratory indicators were analyzed by logistic regression, decision tree model and the support vector machine(SVM), the AUCROC were 0.86, 0.77 and 0.93 respectively. Conclusion: The efficacy of clinical risk factors alone in predicting early-onset preeclampsia is not high, while the efficacy increased significantly when PE risk factors were combined with routine laboratory indicators. The SVM model was better than the logistic regression model and decision tree model in the early prediction of early-onset preeclampsia incidence.

Fibrinogen beta chain may be a potential predict biomarker for pre-eclampsia: A preliminary study

ObjectiveThere are no approaches for the early detection of pre-eclampsia (PE). Using parallel reaction monitoring proteomics, we investigated 79 maternal serum protein changes before PE onset and its predictive capability. MethodsWe conducted a nested case-control study with 60 PE patients and 60 normotensive pregnant women matched for age and gestational week. These differentially expressed proteins were quantified using the data-dependent acquisition (DDA) combined parallel response monitoring (PRM) approach, and a PE prediction model was developed using the least absolute shrinkage and selection operator (LASSO) regression. We further examined the link between these biomarkers and PE using bioinformatics. ELISA assay was used to investigate the expression and clinical significance of the critical variables. ResultsAmong the 79 analyzed proteins, we identified 11 serum proteins with significantly abnormal expression. Fibrinogen beta chain (FGB) was likely connected with the progression of PE due to the positive correlation between their levels and those of hypertension and proteinuria. In addition, an early prediction model for PE with an AUC of 92% was developed using LASSO regression. ConclusionOur research employs predictive algorithms and screens for relevant variables, which could result in a potential approach to enhancing PE prediction.

Early prediction and longitudinal modeling of preeclampsia from multiomics

Preeclampsia is a complex disease of pregnancy whose physiopathology remains unclear. We developed machine-learning models for early prediction of preeclampsia (first 16weeks of pregnancy) and over gestation by analyzing six omics datasets from a longitudinal cohort of pregnant women. For early pregnancy, a prediction model using nine urine metabolites had the highest accuracy and was validated on an independent cohort (area under the receiver-operating characteristic curve [AUC]= 0.88, 95% confidence interval [CI] [0.76, 0.99] cross-validated; AUC= 0.83, 95% CI [0.62,1] validated). Univariate analysis demonstrated statistical significance of identified metabolites. An integrated multiomics model further improved accuracy (AUC= 0.94). Several biological pathways were identified including tryptophan, caffeine, and arachidonic acid metabolisms. Integration with immune cytometry data suggested novel associations between immune and proteomic dynamics. While further validation in a larger population is necessary, these encouraging results can serve as a basis for a simple, early diagnostic test for preeclampsia.

Development and validation of the prediction models for preeclampsia: a retrospective, single-center, case-control study

Preeclampsia (PE) is a major cause of adverse maternal and infant outcomes. Accurate screening of PE is currently the focus of clinical attention. This study aimed to develop a model for predicting PE. A retrospective case-control study was conducted with 916 pregnant women who received care at the Second Hospital of Tianjin Medical University (October 2018 to July 2020). Women were randomly divided into the training (n=680) and testing (n=236) sets based on a ratio of 3:1. Demographic and clinical data of women were collected. In training set, logistic regression (LR), classification tree (CT) model, and random forest (RF) algorithm were used to develop prediction models for PE. Using the testing set was to validate these prediction models. The predictive performance of three models were assessed by the area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Of the total 916 women, 237 had PE. The family history of hypertension, pre-pregnancy body mass index (pBMI), blood pressure (BP) ≥130/80 mmHg in early pregnancy, age, chronic hypertension, and duration of hypertension were the predictors of PE. The AUCs for the LR, CT, and RF models were 0.778, 0.850, and 0.871, respectively (all P<0.05 for all pair-wise comparisons). The RF had the best predictive efficiency with sensitivity, specificity, PPV, and NPV of 79.6%, 94.7%, 79.6%, and 94.7%, respectively. The RF model could be a practical screening approach for predicting PE, which is helpful for clinicians to identify high-risk individuals and prevent the occurrence of adverse pregnancy outcomes.