Development and Validation of Multi-Stage Prediction Models for Pre-eclampsia: A Retrospective Cohort Study on Chinese Women

Abstract

ObjectiveThe aim of this study is to develop multistage prediction models for pre-eclampsia (PE) covering almost the entire pregnancy period based on routine antenatal measurements and to propose a risk screening strategy.MethodsThis was a retrospective cohort study that included 20582 singleton pregnant women with the last menstruation between January 1, 2013 and December 31, 2019. Of the 20582 women, 717 (3.48%) developed pre-eclampsia, including 46 (0.22%) with early-onset pre-eclampsia and 119 (0.58%) preterm pre-eclampsia. We randomly divided the dataset into the training set (N = 15665), the testing set (N = 3917), and the validation set (N = 1000). Least Absolute Shrinkage And Selection Operator (LASSO) was used to do variable selection from demographic characteristics, blood pressure, blood routine examination and biochemical tests. Logistic regression was used to develop prediction models at eight periods: 5–10 weeks, 11–13 weeks, 14–18 weeks, 19–23 weeks, 24–27 weeks, 28–31 weeks, 32–35 weeks, and 36–39 weeks of gestation. We calculated the AUROC (Area Under the Receiver Operating Characteristic Curve) on the test set and validated the screening strategy on the validation set.ResultsWe found that uric acid tested from 5–10 weeks of gestation, platelets tested at 18–23 and 24–31 weeks of gestation, and alkaline phosphatase tested at 28–31, 32–35 and 36–39 weeks of gestation can further improve the prediction performance of models. The AUROC of the optimal prediction models on the test set gradually increased from 0.71 at 5–10 weeks to 0.80 at 24–27 weeks, and then gradually increased to 0.95 at 36–39 weeks of gestation. At sensitivity level of 0.98, our screening strategy can identify about 94.8% of women who will develop pre-eclampsia and reduce about 40% of the healthy women to be screened by 28–31 weeks of pregnancy.ConclusionWe developed multistage prediction models and a risk screening strategy, biomarkers of which were part of routine test items and did not need extra costs. The prediction window has been advanced to 5–10 weeks, which has allowed time for aspirin intervention and other means for PE high-risk groups.

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

“…Therefore, there is a need for a concise, evidence-based, and effective tool that clinicians can use to screen women at high risk of PE in the early second trimester. This study's Previous studies have found that maternal age ≥35 years, family history of hypertension or DM, history of PE, chronic hypertension, duration of hypertension, GDM, and pre-pregnancy BMI ≥30 kg/m 2 are all associated with an increased incidence of PE (18)(19)(20)(21)(22)(23)(24)32,33). In this study, kidney disease and a family history of PE was shown to be the most important factor in predicting PE, followed by a family history of hypertension, pBMI ≥27.7 kg/m 2 , and BP ≥130/80 mmHg in the first trimester.…”

“…The pathogenesis of PE is complex, and its etiology is somewhat unclear (1,30,31). Numerous studies have attempted to assess the predictive performance of various factors for PE, including clinical features, biomarkers, and ultrasound indicators (18)(19)(20)(21)(22)(23)(24). Identification of early risk factors is the best way to prevent PE.…”

“…An accurate diagnosis of PE using clinically accessible risk factors in the early second trimester is crucial to the prevention of PE progression. At present, many studies have assessed the predictive ability of various factors for predicting PE, including clinical features, biomarkers, and ultrasound markers (18)(19)(20)(21)(22)(23)(24). Nevertheless, no consensus has been reached on the best strategy for predicting the likelihood of PE (6,25).…”

Identification of key genes in the pathogenesis of preeclampsia via bioinformatic analysis and experimental verification

“…Preeclampsia is divided into early-onset preeclampsia and lateonset preeclampsia, based on gestational age at diagnosis or delivery. Early-onset preeclampsia, less common than late-onset preeclampsia but with more severe clinical onset features, threatens maternal and fetal health worldwide, especially in developing countries (20)(21)(22). The pathogenesis of EOPE is unclear, and an ideal early clinical biomarker for the prediction of EOPE are lacking.…”

First-trimester serum antiphosphatidylserine antibodies serve as candidate biomarkers for predicting pregnancy-induced hypertension

“…However, placental biopsy is impractical until postpartum. Even if a significant amount of research has been dedicated to separately or synergistically using maternal clinical characteristics, Doppler ultrasound indexes, blood tests, or other parameters to predict PIH, the precise early prediction of PIH remains challenging due to the lack of effective biomarkers [8][9][10][11]. Our previous study has showed that the classical early prediction biomarkers for PIH and preeclampsia, including soluble fms-like tyrosine kinase 1 (sFlt-1) and placental growth factor (PlGF), cannot be extrapolated to predict PIH occurrence in pregnancies after IVF treatment [12], suggesting the need of exploring new biomarkers for the prediction of PIH in patients who undergo IVF treatment.…”