Placental dysfunction represents a critical pathogenic mechanism underlying pregnancy complications, including preeclampsia, fetal growth restriction (FGR), preterm birth, and stillbirth [1,2]. These disorders collectively affect around 10% of pregnancies worldwide, significantly contributing to maternal and perinatal mortality and morbidity [3-6]. They impose a substantial health care burden through increased surveillance requirements, emergency cesarean deliveries, and neonatal intensive care [7-9], and also increase long-term cardiovascular and metabolic disease risks for both mothers and offspring [10,11].

Preeclampsia and FGR frequently coexist with evidence of impaired placentation and placental function, increasing the risks of preterm birth and stillbirth [12,13]. Research has shown that these complications share common pathogenic pathways characterized by complex maternal-fetal-placental interactions [2,3,14]. Placental dysfunction acts as the primary pathophysiological driver, originating from impaired trophoblast invasion and uteroplacental spiral artery remodeling during early pregnancy, resulting in reduced placental perfusion and insufficiency [1,15,16].

Early risk identification of high-risk pregnancies enables targeted preventive interventions and reduces unnecessary intensive care for low-risk cases. Traditional screening methods based on maternal risk factors lack precision, but recent advances in biophysical and biochemical assessments have improved early risk assessment accuracy [17]. Doppler ultrasound measurements from maternal uterine and ophthalmic arteries can provide valuable insights into endothelial health and placental insufficiency [13,18,19]. Additionally, maternal serum angiogenic factors, specifically soluble fms-like tyrosine kinase-1 (sFlt-1) and placental growth factor (PlGF) have emerged as robust biomarkers for screening and diagnosing preeclampsia and other placenta-related complications [20,21].

The first-trimester prediction model developed by the Fetal Medicine Foundation (FMF), integrating maternal factors, mean arterial pressure (MAP), uterine artery pulsatility index (UtAPI), and PlGF, identifies approximately 90% of early preeclampsia (requiring delivery <32 weeks), 75% of preterm preeclampsia (delivery <37 weeks), and 41% of term preeclampsia cases at a 10% screen-positive rate [22]. This approach also detects 56% of early small for gestational age (SGA; delivery <32 weeks) and 46% of preterm SGA (delivery <37 weeks) neonates [23]. Clinical trials have demonstrated that prophylactic aspirin (>100 mg) combined with the first-trimester prediction model effectively prevents preterm preeclampsia and its associated adverse outcomes [24].

Despite these advances, predicting and preventing late-onset or term preeclampsia and FGR remains challenging due to their heterogeneous pathophysiology. These conditions exhibit less pronounced angiogenic biomarker alterations and weaker associations with placental dysfunction than early-onset cases [25]. Although causing fewer adverse outcomes, term complications have a higher prevalence, thus constituting a substantial health care burden [26]. Furthermore, low-dose aspirin appears to be less effective in preventing term preeclampsia [24]. Recent research proposes that risk stratification at 35 to 36 weeks of gestation with planned birth may offer a potential preventive strategy for managing term preeclampsia [27]. However, determining optimal delivery timing based on individualized risk stratification requires more investigation. Emerging evidence links maternal cardiovascular maladaptation and altered hemodynamic profiles with preeclampsia and FGR development, providing new directions for improving prediction models [28,29]. Current research remains limited on combining hemodynamic measurements with placental dysfunction markers to enhance prediction accuracy.

This prospective cohort study aims to develop enhanced prediction models for placental dysfunction–related complications and define optimal delivery timing. Candidate predictors will be selected a priori based on four guiding principles: (1) established inclusion in validated prediction models and current screening approaches (maternal characteristics, MAP, uterine artery Doppler, and angiogenic biomarkers used in the FMF prediction models), (2) biological plausibility and prior evidence linking placental dysfunction and maternal cardiovascular maladaptation with preeclampsia and FGR (eg, ophthalmic artery Doppler and arterial stiffness measures), (3) feasibility of implementation within routine maternity care pathways, and (4) the potential incremental predictive value of novel biomarkers derived from stored biospecimens and exploratory omics approaches. By integrating clinical factors, biophysical parameters, and maternal circulating biomarkers across 3 critical gestational windows, this study will improve risk stratification and guide clinical decision-making in perinatal care. Furthermore, the comprehensive dataset and biobank will facilitate investigation of novel biomarkers and pathophysiological mechanisms underlying placental dysfunction.

Recruitment for the Placental Health Study commenced in June 2022. By the close of recruitment in February 2026, a total of 1267 participants were enrolled. Of these, 1072 (84.6%) participants had a completed pregnancy, 144 (11.4%) participants are still to complete their pregnancy, and 51 (4%) had withdrawn or with missing or duplicated data. Comprehensive clinical data, including maternal demographics, medical and obstetric history, ultrasound measurements, Doppler indices, hemodynamic parameters, pathology laboratory results, and pregnancy outcomes, were collected through integration with electronic medical records (Table 1). Data were recorded in REDCap. Maternal serum, plasma, whole blood for RNA extraction, and urine samples have been collected and processed according to standardized operating procedures. Biospecimens are stored at −80 °C.

A subset of samples has undergone preliminary biomarker quantification, including measurement of angiogenic markers (sFlt-1 and PlGF), and additional omics-based analyses are underway. Interim analyses have been conducted comparing clinical variables and biomarkers between participants with and without complications. It is anticipated that 2 to 3 primary publications will be ready in late 2026 or early 2027, including evaluation of maternal circulating biomarkers and exploratory analyses of novel biomarker candidates identified through omics techniques.

This protocol describes a prospective cohort study aimed at developing advanced prediction models for placental dysfunction–related pregnancy complications, allowing for dynamic and personalized accurate risk assessment throughout the pregnancy. First- and second-trimester prediction models aim to enhance early risk assessment for placental dysfunction–related complications (preeclampsia, FGR, preterm birth, and stillbirth), and the third-trimester model focuses on predicting the onset of term complications (preeclampsia and FGR) and determining optimal delivery timing. This comprehensive strategy advances personalized obstetric care through evidence-based risk stratification.

The FMF first-trimester prediction model shows superior performance compared with traditional risk-factor–based checklists [17]. By combining maternal factors with measurements of MAP, UtAPI, and serum PlGF, this model is the most effective method for predicting early-onset and preterm preeclampsia, achieving detection rates of 90% and 75%, respectively [22,43]. This model has been validated across diverse populations, consistently demonstrating predictive performance comparable to the original studies and has been tested in randomized clinical trials [24,44-48]. Importantly, when coupled with prophylactic low-dose aspirin administration starting before 16 weeks’ gestation in high-risk pregnancies, this screening strategy reduces preterm preeclampsia by more than 60% [24,49]. In addition, the model achieves detection rates of 56% for early SGA fetuses (<32 weeks) and 46% for preterm SGA neonates (<10th percentile), with aspirin use potentially reducing the incidence of preterm SGA by 20% and early SGA by 40% [23].

These advancements in preterm preeclampsia screening have prompted updates to national [50,51] and international [52] guidelines, recommending implementation of the prediction model with prophylactic low-dose aspirin to reduce preterm preeclampsia incidence and associated adverse outcomes. A cost-effectiveness analysis in Australia demonstrated significant benefits of this screening strategy, including prevention of 31 preterm preeclampsia cases and cost savings of approximately Aus $1.43 million (Aus $1=US $0.72 as of August 27, 2020) over a 2-year period [53]. Despite these clinical benefits, the FMF first-trimester prediction model has not yet become a standard component of routine maternity care for all health care providers in Australia. Gold Coast University Hospital has implemented FMF-based first-trimester preeclampsia screening in routine clinical care, providing a valuable setting to generate evidence supporting broader application in clinical practice.

Current prediction models still have limited accuracy for predicting term preeclampsia and SGA due to their complex etiology. Although considered less severe, term complications constitute most cases, occurring at least twice as frequently as preterm preeclampsia, and impose a significant health care burden [26]. While the first-trimester screening combined with low-dose aspirin effectively prevents preterm preeclampsia, it showed limited efficacy for term preeclampsia [24]. A prediction model at 35 to 36  weeks’ gestation, using maternal factors and biomarkers, can identify approximately 70% of pregnancies at risk of developing term preeclampsia [54]. In addition, planned birth at term is a potential prevention strategy for term preeclampsia [55,56]. A recent secondary analysis from a prospective nonintervention cohort study has demonstrated that a risk-stratified approach based on a combined prediction model at 35 to 36  weeks’ gestation with timed birth is likely able to reduce the risk of term preeclampsia [27]. However, research on individualized risk assessment models at late gestation to determine optimal delivery timing remains limited. This cohort study will facilitate the development of refined prediction models and optimal delivery timing strategies to reduce term preeclampsia incidence and improve pregnancy outcomes.

This project aims to enhance existing prediction models for term preeclampsia and SGA through the evaluation of additional biomarkers across 3 critical gestational stages, investigating both routine antenatal measurements and specialized markers of placental and cardiovascular dysfunction, including Doppler ultrasound parameters, maternal hemodynamic indices, and angiogenic markers. Doppler ultrasound assessment of uterine arteries can reflect placental perfusion status, with elevated values indicating increased resistance and impaired placental blood flow [57,58]. Maternal hemodynamic parameters, including ophthalmic artery Doppler indices, MAP, and augmentation index, provide insights into cardiovascular adaptation and peripheral vascular function [59-61]. Angiogenic markers (sFlt-1, PlGF, and the sFlt-1:PlGF ratio) indicating angiogenic imbalances serve as biochemical predictors of placental dysfunction [20,62,63] and spontaneous preterm labor [64]. Furthermore, our study establishes a valuable biobank for identifying novel molecular biomarkers and investigating underlying mechanisms of placental dysfunction through omics techniques. Incorporating these diverse markers and novel biomarker candidates into existing prediction models has the potential to enhance accuracy for late-onset pregnancy complications.

The key strength of this prospective cohort study lies in its comprehensive approach to implementing and improving the prediction of placental dysfunction–related complications throughout pregnancy, from first-trimester screening to late-gestation risk stratification in a general-risk population. The proposed dynamic prediction framework, if validated, could benefit implementation of targeted surveillance protocols and timely interventions, addressing current gaps in evidence-based obstetric care. Furthermore, the study establishes a valuable database and perinatal biorepository, providing valuable resources for novel biomarker discovery and investigation of associations between clinical phenotypes and biological mechanisms underlying maternal systemic changes and placental dysfunction. Some limitations should be considered. First, this study is limited to singleton pregnancies, which may restrict its applicability to multiple pregnancies. Second, the single-center design potentially constrains the generalizability of the findings to other populations and clinical settings. Third, despite incorporating diverse clinical, biochemical, and biophysical markers, some important predictors may remain unmeasured. In addition, some biomarkers in the final prediction models may be expensive or technically challenging to measure in routine clinical practice, potentially limiting widespread adoption. External validation in independent populations is planned as a future phase of research and will evaluate model transportability and generalizability across different clinical settings and populations. Despite the limitations, this research holds significant potential to advance our understanding of placental dysfunction–related pregnancy complications and enhance obstetric care through improved risk prediction and personalized intervention strategies.