Clinical Outcomes of Placenta Previa - an Individualized Scoring System

Assiut University

Status: Not yet enrolling

Conditions: Placenta Previa

Study type: Observational

Funder types: Other

Identifiers

NCT06589180

PPISS

Details and patient eligibility

About

The objective of the study is to develop a machine learning -based prediction model for patients with placenta previa . The model will be based on patient characteristics, clinical assessment, and disease features to predict disease course, prognosis and establish an individualized plan of care. Individualized management plan comprises timing of delivery, mode of delivery, need for hospital admission, postpartum hemorrhage management plan, and perioperative care.

The primary outcome is to predict maternal complications, primarily major antepartum hemorrhage (APH), defined as blood loss of 50-1000 ml.

Full description

• Study settings: This multi-center study will invite at least 8 European-based tertiary centers. Data will be retrospectively collected from January 2018 to December 2023. with a total follow-up of at least 6 weeks postpartum. All women who will be diagnosed with PP during this period will be considered in the study.

• Inclusion Criteria:

• Pregnant women diagnosed with placenta previa at time of routine second trimester ultrasound (18 to 21 weeks of gestation).
• Aged 18 years old or above.
• Diagnosed with PP, followed-up antenatally and delivered within the same center.
• Authorization to use anonymous patient data for research purposes.

Exclusion Criteria:

• Inadequate documentation.
• Non-compliance to antenatal care.
• Previous 1 or more Cesarean deliveries.
• Previous uterine surgeries that approach the uterine cavity (myomectomy).
• Known untreated uterine septum.
• Maternal chronic medical condition that is associated with higher risk of placental insufficiency (including chronic hypertension, pregestational diabetes, chronic renal disease, autoimmune disease).
• Fetal major congenital anomalies.

Data Collection A standardized data collection spreadsheet is designed for this study and will be available for all participating centers. The sheet will comprise drop lists for all nominal, ordinal and binary variables to ensure consistency. Target data will include patient demographics (age, parity, gestational age at diagnosis, mode of conception, body mass index at booking and at delivery, pregnancy interval), hemoglobin at booking, past medical and surgical history, and the presence of any current complications in pregnancy. Diagnostic information including placental location, distance from internal os, the presence of other placental abnormalities as well as diagnostic modality will be collected. Follow-up from the 32-week scan and any subsequent scans will be explored and further follow-up information including hemoglobin levels, bleeding episodes, need for transfusion, administration of steroids, fetal growth, and maternal and neonatal outcomes, will be reviewed, and collected.

Data sheets will be sent on completion to the primary investigators to review and inquire on any unclear entry prior to data cleaning and data analysis.

Secondary outcomes include prediction of need for antenatal transfusion, emergency cesarean delivery, and massive postpartum hemorrhage (defined as blood loss > 2000 ml), and neonatal admission to neonatal intensive care unit (NICU).

Enrollment: 1,000 estimated patients

Sex: Female

Ages: 18 to 55 years old

Volunteers: No Healthy Volunteers

Inclusion criteria

Pregnant women diagnosed with placenta previa at time of routine second trimester ultrasound (18 to 21 weeks of gestation).

Aged 18 years old or above. Diagnosed with PP, followed-up antenatally and delivered within the same center.

Authorization to use anonymous patient data for research purposes

Exclusion criteria

Inadequate documentation. Non-compliance to antenatal care. Previous 1 or more Cesarean deliveries. Previous uterine surgeries that approach the uterine cavity (myomectomy). Known untreated uterine septum. Maternal chronic medical condition that is associated with higher risk of placental insufficiency (including chronic hypertension, pregestational diabetes, chronic renal disease, autoimmune disease).

Fetal major congenital anomalies