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First-trimester risk stratification algorithm for placenta accreta spectrum.

Alesha White1, Jessica E Pruszynski2, Quyen N Do3

  • 1Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX (White, Pruszynski, Twickler, Spong, and Herrera); Parkland Health, Dallas, TX (White, Twickler, Spong, and Herrera).

American Journal of Obstetrics & Gynecology MFM
|January 21, 2026
PubMed
Summary
This summary is machine-generated.

A new first-trimester ultrasound algorithm improves the detection of placenta accreta spectrum (PAS) in high-risk pregnancies. This tool offers higher sensitivity and specificity than previous methods for early PAS diagnosis.

Keywords:
first trimestermaternal morbidityplacenta accreta spectrumplacenta accreta spectrum riskplacenta accreta spectrum risk stratification

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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.