Predicting Placenta Accreta Spectrum Disorder Through Machine Learning Using Metabolomic and Lipidomic Profiling and Clinical Characteristics
Metabolomic and lipidomic profiling performed similarly to, and not better than, clinical risk factors using machine learning to predict PAS among patients with PAS with previa and previa alone.
Authors
- Jordan Burgess (ORCID: https://orcid.org/0000-0002-6195-9202)
- Michael Snyder (ORCID: https://orcid.org/0000-0003-0784-7987)
- Samuel M. Lancaster
- Nima Aghaeepour (ORCID: https://orcid.org/0000-0002-6117-8764)
- Sarah E. Miller (ORCID: https://orcid.org/0000-0002-5558-1813)
- Elisa Zhang (ORCID: https://orcid.org/0000-0003-1294-2372)
- Katherine Bianco (ORCID: https://orcid.org/0000-0001-5842-6723)
- Samantha L. Kruger
- Deirdre J. Lyell (ORCID: https://orcid.org/0000-0002-6853-9956)
- Robert M. Silver (ORCID: https://orcid.org/0000-0002-5794-3152)
- Kévin Contrepois (ORCID: https://orcid.org/0000-0001-9678-5161)
- Karl G. Sylvester (ORCID: https://orcid.org/0000-0002-8559-0155)
- Ivana Marić (ORCID: https://orcid.org/0000-0002-9441-521X)
- Brett D. Einerson (ORCID: https://orcid.org/0000-0003-1928-4333)
- David K. Stevenson (ORCID: https://orcid.org/0000-0002-7127-0281)
- Keyla Badillo
Institutions
- Pediatrics and Genetics (US)
Publication Details
- Journal
- Obstetrics and Gynecology
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1097/aog.0000000000005922
- Citations
- 1
- Primary Topic
- Maternal and fetal healthcare
- Type
- article
- Field-Weighted Citation Impact
- 3.49
Funders
- Stanford Maternal and Child Health Research Institute