JMIR research protocols · 2026
Prediction of Pregnancy Complications Related to Placental Dysfunction: Protocol for a Prospective Cohort Study (Placental Health Study).
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Han Luhao, Holland Olivia, Rathnayake Hasini, de Freitas Paganoti Cristiane, Ragazini Conrado Sávio, Chan Elsa Suk Fan, Mahler Sally, Lai Carman Wing Sze, Rolnik Daniel Lorber, Perkins Anthony, Ellwood David, Kumar Sailesh, Da Silva Costa Fabricio
Аннотация
BACKGROUND: Placental dysfunction underlies major obstetric complications, including preeclampsia, fetal growth restriction, preterm birth, and stillbirth. While early identification of pregnancies at high risk of complications enables preventive interventions, current screening methods based on traditional risk factors have limited accuracy.
OBJECTIVE: This study aims to develop robust prediction models for placental dysfunction-related pregnancy disorders across 3 gestational windows and to determine the optimal delivery time by integrating maternal characteristics, biophysical measurements, and maternal circulating biomarkers.
METHODS: The Placental Health Study is a prospective cohort study conducted at the Maternal Fetal Medicine Unit at Gold Coast University Hospital, Australia. Singleton pregnancies are enrolled at 11 to 13 weeks, 26 to 28 weeks, and 35 to 36 weeks of gestation and are followed until delivery. Exclusion criteria are multiple pregnancies, major fetal anomalies, maternal age below 18 years, and inability to provide informed consent. Clinical data, standardized ultrasound and Doppler measurements, hemodynamic parameters, and biospecimens (serum, plasma, whole blood RNA, and urine) are collected at each visit. Three gestational age-specific datasets (11-13 weeks, 26-28 weeks, and 35-36 weeks) will be constructed for model development. Prediction models will be developed using multivariable logistic regression. Competing risks (Fine and Gray) models will be used as sensitivity analyses for outcomes related to gestational age at delivery. Bayesian updating will be applied to refine existing Fetal Medicine Foundation risk estimates using additional biomarkers or variables collected later in pregnancy. Developed models will undergo internal validation via 5-fold cross-validation, with performance assessed through model discrimination, calibration, and comparison against existing approaches. Collected biospecimens will also be analyzed for angiogenic markers and exploratory omics studies.
RESULTS: Recruitment occurred from June 2022 to February 2026, enrolling 1267 participants. As of submission, 1072 (84.6%) participants have completed their pregnancies, 144 (11.4%) participants remain active in follow-up, and 51 (4%) were withdrawn or had missing or duplicated data. Clinical data have undergone preliminary cleaning and descriptive analysis. Biospecimens are being processed for biomarker quantification and exploratory molecular analyses. Two to three publications are anticipated in late 2026 or early 2027.
CONCLUSIONS: This cohort establishes a comprehensive database and biospecimen repository to advance prediction of placental dysfunction-related complications. Three-stage recruitment integrated into routine clinical care enhances feasibility and translation potential, enabling dynamic risk assessment and investigation of temporal biomarker changes. Although the single-center design may limit generalizability, warranting future external validation, the interdisciplinary research team brings diverse expertise that strengthens study depth and scope.
OBJECTIVE: This study aims to develop robust prediction models for placental dysfunction-related pregnancy disorders across 3 gestational windows and to determine the optimal delivery time by integrating maternal characteristics, biophysical measurements, and maternal circulating biomarkers.
METHODS: The Placental Health Study is a prospective cohort study conducted at the Maternal Fetal Medicine Unit at Gold Coast University Hospital, Australia. Singleton pregnancies are enrolled at 11 to 13 weeks, 26 to 28 weeks, and 35 to 36 weeks of gestation and are followed until delivery. Exclusion criteria are multiple pregnancies, major fetal anomalies, maternal age below 18 years, and inability to provide informed consent. Clinical data, standardized ultrasound and Doppler measurements, hemodynamic parameters, and biospecimens (serum, plasma, whole blood RNA, and urine) are collected at each visit. Three gestational age-specific datasets (11-13 weeks, 26-28 weeks, and 35-36 weeks) will be constructed for model development. Prediction models will be developed using multivariable logistic regression. Competing risks (Fine and Gray) models will be used as sensitivity analyses for outcomes related to gestational age at delivery. Bayesian updating will be applied to refine existing Fetal Medicine Foundation risk estimates using additional biomarkers or variables collected later in pregnancy. Developed models will undergo internal validation via 5-fold cross-validation, with performance assessed through model discrimination, calibration, and comparison against existing approaches. Collected biospecimens will also be analyzed for angiogenic markers and exploratory omics studies.
RESULTS: Recruitment occurred from June 2022 to February 2026, enrolling 1267 participants. As of submission, 1072 (84.6%) participants have completed their pregnancies, 144 (11.4%) participants remain active in follow-up, and 51 (4%) were withdrawn or had missing or duplicated data. Clinical data have undergone preliminary cleaning and descriptive analysis. Biospecimens are being processed for biomarker quantification and exploratory molecular analyses. Two to three publications are anticipated in late 2026 or early 2027.
CONCLUSIONS: This cohort establishes a comprehensive database and biospecimen repository to advance prediction of placental dysfunction-related complications. Three-stage recruitment integrated into routine clinical care enhances feasibility and translation potential, enabling dynamic risk assessment and investigation of temporal biomarker changes. Although the single-center design may limit generalizability, warranting future external validation, the interdisciplinary research team brings diverse expertise that strengthens study depth and scope.