Preeclampsia Incidence and Its Maternal and Neonatal Outcomes With Associated Risk Factors

Khan, Bisma; Yar, Razia Allah; Khakwani, Ayesha khan; Karim, Sajilah; Ali, Hafiz Arslan

doi:10.7759/cureus.31143

Cited by 27 publications

(21 citation statements)

References 18 publications

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“…Although we recognize that PE incidence in our cohort (pPE, 2.7%; all-PE, 4.1%) remains similar to that reported in other populations 33 , even after eliminating from the study patients at high risk of PE who took and adhered to aspirin treatment, this may be explained by the high prevalence of pre-existing diabetes, chronic hypertension, antiphospholipid syndrome and PE in previous pregnancy, which oscillated between 8.4% and 29.8%.…”

Section: Comparison With Existing Literaturesupporting

confidence: 89%

Performance of machine‐learning approach for prediction of pre‐eclampsia in a middle‐income country

Torres‐Torres,

Villafan‐Bernal,

Martinez‐Portilla

et al. 2024

Ultrasound in Obstet & Gyne

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ObjectivePreeclampsia (PE) is a serious complication of pregnancy associated with maternal and fetal morbidity and mortality. Early‐onset and preterm preeclampsia have long‐term health implications for both mothers and infants. Current prediction models have limitations and may not be applicable in resource‐limited settings. Machine learning (ML) algorithms offer a potential solution for developing accurate and efficient prediction models.MethodsWe conducted a prospective cohort study in Mexico City to develop a first‐trimester prediction model for preterm preeclampsia (pPE) using ML. Maternal characteristics and locally developed multiples of the median (MoMs) for mean arterial pressure (MAP), uterine artery pulsatility index (UtA‐PI), and serum placental growth factor (PlGF) were used for variable selection. The dataset was split into training, validation, and test sets. An elastic net method was employed for predictor selection, and model performance was evaluated using area under the curve (AUC) and detection rates (DR) at 10% false positive rates (FPR).ResultsThe final analysis included 3,050 pregnant women, of whom 124 (4.07%) developed PE. The ML model showed good performance, with AUCs of 0.897, 0.963, and 0.778 for pPE, early‐onset preeclampsia (ePE), and any type of preeclampsia (all‐PE), respectively. The DRs at 10% FPR were 76.5%, 88.2%, and 50.1% for pPE, ePE, and all‐PE, respectively. The ML model outperformed previous prediction models and showed better performance than external validations of existing algorithms.ConclusionsOur ML model demonstrated high accuracy in predicting pPE and ePE using first‐trimester maternal characteristics and locally developed MoMs. The model could provide an efficient and accessible tool for early prediction of preeclampsia, facilitating timely interventions and improved maternal and fetal outcomes.This article is protected by copyright. All rights reserved.

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Section: Comparison With Existing Literaturesupporting

confidence: 89%

Performance of machine‐learning approach for prediction of pre‐eclampsia in a middle‐income country

Torres‐Torres,

Villafan‐Bernal,

Martinez‐Portilla

et al. 2024

Ultrasound in Obstet & Gyne

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“…PE patients experienced greater rates of maternal death, ICU admission, postpartum hemorrhage, antepartum hemorrhage, and blood transfusion than normotensive patients, also an Indian study reported placental abruption and coagulopathy were the most frequent consequences [25]. The commonest perinatal outcome is preterm birth followed by IUGR, stillbirth, and newborn ended up with early neonatal death [24][25][26]30].…”

Section: Complications Of Pementioning

confidence: 99%

Perspectives on the Pathogenesis and Complications of PE

Subbaiya¹,

Saravanan²,

Rajendran³

2023

Women's Health Problems - A Global Perspective [Working Title]

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Preeclampsia is a multisystem disorder characterized by hypertension and proteinuria after 20 weeks of gestation. Globally, it is the leading cause of fetal and maternal morbidity and mortality. Nearly 8–10% of women develop hypertension during pregnancy worldwide. Although the actual pathogenesis of PE has not been fully understood, the only cure for the disease is delivery. So, the growing evidence suggests that improper spiral artery remodeling creates placental hypoxia and leads to altered immune response followed by endothelial dysfunction, the release of angiogenic and antiangiogenic factors, and various other vasoactive factors into the maternal circulation. Reliable biochemical markers are needed for the diagnosis of PE at the earliest. MMPs are differentially expressed as a result of the trophoblast invasion’s distinct temporal features. Early in the gestational period, MMPs create the conditions for the ensuing incursion to the placental bed. Endothelial dysfunction is the cause of the clinical sign of the mother such as impairment of the hepatic endothelium causing the HELLP syndrome to develop, impairment of the cerebral endothelium causing refractory neurological problems, or even eclampsia. Also, this chapter reveals the various maternal consequences like HELLP syndrome, Seizure, future cardiovascular events, and end-organ dysfunction; fetal complications include premature delivery, respiratory distress, IUGR, etc.

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“…Pre-eclampsia has a worldwide incidence of 2%-8% of pregnancies and is the top-tier red flag for risk of maternal and fetal mortality. 11 Immune tolerance failure is an important cause of pre-eclampsia. 12 Deficient states of HLA-G have been implicated in the progression of pre-eclampsia via poor spiral artery remodeling, and failure to prevent maternal immune activation against fetal alloantigens.…”

Section: Introductionmentioning

confidence: 99%

“…Pre‐eclampsia has a worldwide incidence of 2%–8% of pregnancies and is the top‐tier red flag for risk of maternal and fetal mortality 11 . Immune tolerance failure is an important cause of pre‐eclampsia 12 .…”

Section: Introductionmentioning

confidence: 99%

Biomarker role of maternal soluble human leukocyte antigen G in pre‐eclampsia: A meta‐analysis

Bhattarai,

Shah,

Dahal

et al. 2024

Immunity Inflam & Disease

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IntroductionHuman leukocyte antigen‐G (HLA‐G) is a non‐classical class I HLA molecule shown to regulate the immunomodulation of maternal immune cells to prevent fetal tissue destruction. Low levels of freely circulating maternal soluble HLA‐G (sHLA‐G) have been observed in pre‐eclampsia, however, no pooled evidence exists. This meta‐analysis aimed to generate pooled findings on the association of sHLA‐G levels with pre‐eclampsia and is the first study to perform a trimester‐wise comparison of the levels of sHLA‐G in preeclamptic cases and normal pregnant controls.MethodsThe databases PubMed, Emba, Web of Science, and Google Scholar through May 31, 2023. Preeclamptic women were defined as cases and normal pregnancies as controls. Data on the level of sHLA‐G in cases and controls was extracted and subjected to a meta‐analysis using a random‐effects model. The pooled effect was expressed in terms of standardized mean difference (SMD). Sensitivity analysis was performed to investigate the effect of the exclusion of each study on the pooled results. Publication bias was assessed statistically.ResultsNine studies with altogether 567 PE cases and 1132 normal pregnancy controls were included in the meta‐analysis. The first and third trimester levels of sHLA‐G in PE cases were significantly lower than that of normal pregnant controls: (SMD: −0.84 [−1.29; −0.38]; p = .003; I2 = 54%) and (SMD: −0.39 [−0.71; −0.06]; p = .02; I2 = 79%) respectively. Sensitivity analysis revealed significant fluctuations in the pooled findings when few studies were excluded, raising questions on the consistency of results among studies.ConclusionAlthough we found that first and third‐trimester sHLA‐G levels in pre‐eclampsia are significantly lower, taking into consideration the inconsistent results from the sensitivity analysis, our findings advocate the demand for more studies with larger sample sizes to generate solid ground pooled evidence on the predictive role of sHLA‐G in pre‐eclampsia.

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Preeclampsia Incidence and Its Maternal and Neonatal Outcomes With Associated Risk Factors

Cited by 27 publications

References 18 publications

Performance of machine‐learning approach for prediction of pre‐eclampsia in a middle‐income country

Performance of machine‐learning approach for prediction of pre‐eclampsia in a middle‐income country

Perspectives on the Pathogenesis and Complications of PE

Biomarker role of maternal soluble human leukocyte antigen G in pre‐eclampsia: A meta‐analysis

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