Multiplexed serum biomarkers to discriminate nonviable and ectopic pregnancy.

Journal: Fertility and sterility
Published Date: Apr 26, 2024

Abstract

OBJECTIVE: To evaluate combinations of candidate biomarkers to develop a multiplexed prediction model for identifying the viability and location of an early pregnancy. In this study, we assessed 24 biomarkers with multiple machine learning-based methodologies to assess if multiplexed biomarkers may improve the diagnosis of normal and abnormal early pregnancies.

Authors

  • Kurt T Barnhart
    Department of Obstetrics and Gynecology, University of Pennsylvania, Philadelphia, Pennsylvania. Electronic address: kbarnhart@pennmedicine.upenn.edu.
  • Kassie J Bollig
    Department of Obstetrics and Gynecology, University of Pennsylvania, Philadelphia, Pennsylvania.
  • Suneeta Senapati
    Department of Obstetrics and Gynecology, University of Pennsylvania, Philadelphia, Pennsylvania.
  • Peter Takacs
    Department of Obstetrics and Gynecology, Eastern Virginia Medical School, Norfolk, Virginia.
  • Jared C Robins
    Northwestern University, Chicago, Illinois.
  • Daniel J Haisenleder
    Department of Internal Medicine and the Center for Research in Reproduction, University of Virginia, Charlottesville, Virginia.
  • Lynn A Beer
    Center for Systems & Computational Biology, The Wistar Institute, Philadelphia, Pennsylvania.
  • Ricardo F Savaris
    Department of Gynecology and Obstetrics, School of Medicine, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.
  • Nathanael C Koelper
    Department of Obstetrics and Gynecology, University of Pennsylvania, Philadelphia, Pennsylvania.
  • David W Speicher
    Center for Systems and Computational Biology, The Wistar Institute, Philadelphia, PA 19104.
  • Jesse Chittams
  • Jingxuan Bao
    Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania.
  • Zixuan Wen
    Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania.
  • Yanbo Feng
    INSA CVL, University of Orléans, PRISME, EA 4229, 18022 Bourges, France. Electronic address: yanbo.feng@insa-cvl.fr.
  • Mansu Kim
    Department of Artificial Intelligence, Catholic University of Korea, Bucheon, South Korea.
  • Sunni Mumford
    Department of Obstetrics and Gynecology, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania.
  • Li Shen
    Department of Clinical Pharmacy, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.
  • Phyllis Gimotty
    Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania.

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