First-trimester risk stratification algorithm for placenta accreta spectrum

Area of Science:

• Obstetrics and Gynecology
• Maternal-Fetal Medicine
• Diagnostic Imaging

Background:

• Antenatal diagnosis of placenta accreta spectrum (PAS) is challenging, often leading to suboptimal maternal outcomes due to late or missed diagnoses.
• Current risk-stratification protocols for PAS lack standardization, particularly concerning first-trimester ultrasound findings.
• Early identification of PAS is crucial for improving patient outcomes and planning delivery management.

Purpose of the Study:

• To develop and evaluate a novel risk stratification algorithm for predicting PAS in the first trimester of pregnancy among symptomatic patients.
• To compare the diagnostic performance of this new algorithm against the presence of low implantation with a history of cesarean delivery (CD).

Main Methods:

• A retrospective observational study analyzed first-trimester sonograms (January 2021-February 2024) from patients with known delivery outcomes.
• Low implantation was defined as gestational sac or trophoblastic change within 5 cm of the external os.
• The new algorithm incorporated gestational sac location, decidual basalis assessment, and smallest myometrial thickness (SMT); its performance was compared to low implantation with prior CD.

Main Results:

• The new algorithm demonstrated superior performance for PAS detection compared to low implantation with prior CD.
• Sensitivity, specificity, and positive predictive value (PPV) for the new algorithm were 90%, 99.6%, and 90%, respectively.
• Inter-reader agreement for SMT measurement was substantial (kappa=0.65), indicating reliable application of the algorithm.

Conclusions:

• The developed first-trimester risk stratification algorithm shows significantly improved sensitivity, specificity, and PPV for PAS compared to low implantation with prior CD.
• The algorithm exhibits high inter-reader agreement, suggesting its potential for reliable clinical use.
• Prospective studies are warranted to validate this promising risk stratification tool for early PAS detection.