Prediction of vaginal birth after cesarean deliveries using machine learning.

Journal: American journal of obstetrics and gynecology
PMID: 32007491

Abstract

BACKGROUND: Efforts to reduce cesarean delivery rates to 12-15% have been undertaken worldwide. Special focus has been directed towards parturients who undergo a trial of labor after cesarean delivery to reduce the burden of repeated cesarean deliveries. Complication rates are lowest when a vaginal birth is achieved and highest when an unplanned cesarean delivery is performed, which emphasizes the need to assess, in advance, the likelihood of a successful vaginal birth after cesarean delivery. Vaginal birth after cesarean delivery calculators have been developed in different populations; however, some limitations to their implementation into clinical practice have been described. Machine-learning methods enable investigation of large-scale datasets with input combinations that traditional statistical analysis tools have difficulty processing.

Authors

  • Michal Lipschuetz
    The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel; Obstetrics & Gynecology Division, Hadassah-Hebrew University Medical Center, Jerusalem, Israel.
  • Joshua Guedalia
    The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel.
  • Amihai Rottenstreich
    Obstetrics & Gynecology Division, Hadassah-Hebrew University Medical Center, Jerusalem, Israel.
  • Michal Novoselsky Persky
    Obstetrics & Gynecology Division, Hadassah-Hebrew University Medical Center, Jerusalem, Israel.
  • Sarah M Cohen
    Obstetrics & Gynecology Division, Hadassah-Hebrew University Medical Center, Jerusalem, Israel.
  • Doron Kabiri
    Obstetrics & Gynecology Division, Hadassah-Hebrew University Medical Center, Jerusalem, Israel.
  • Gabriel Levin
    Obstetrics & Gynecology Division, Hadassah-Hebrew University Medical Center, Jerusalem, Israel.
  • Simcha Yagel
    Obstetrics & Gynecology Division, Hadassah-Hebrew University Medical Center, Jerusalem, Israel. Electronic address: simcha.yagel@gmail.com.
  • Ron Unger
    Roni Shouval, Hila Mishan-Shamay, Avichai Shimoni, and Arnon Nagler, The Chaim Sheba Medical Center, Tel-Hashomer; Roni Shouval, Ori Bondi, and Ron Unger, Bar-Ilan University, Ramat-Gan, Israel; Myriam Labopin, Norbert C. Gorin, Emmanuelle Polge, Arnon Nagler, and Mohamad Mohty, European Group for Blood and Marrow Transplantation; Myriam Labopin and Mohamad Mohty, Sorbonne Universités, Centre de Recherche (CDR) Saint-Antoine; Myriam Labopin and Mohamad Mohty, Institut National de la Santé et de la Recherche Médicale, CDR Saint-Antoine; Myriam Labopin and Mohamad Mohty, Assistance Publique-Hôpitaux de Paris, Hôpital Saint-Antoine, Paris, France; Fabio Ciceri, San Raffaele Scientific Institute, Milan; Andrea Bacigalupo, Ospedale San Martino, Genoa, Italy; Jordi Esteve, Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain; Sebastian Giebel, Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Gliwice, Poland; Christoph Schmid, Ludwig-Maximilians-University, Munich; Nicolaus Kroger, University Medical Center Hamburg Eppendorf, Hamburg, Germany; Mahmoud Aljurf, King Faisal Specialist Hospital & Research Centre, Riyadh, Saudi Arabia; Charles Craddock, Queen Elizabeth Hospital, Birmingham, United Kingdom; Jan J. Cornelissen, Erasmus University Medical Center, Rotterdam, the Netherlands; and Frederic Baron, University of Liège, Liège, Belgium.
  • Yishai Sompolinsky
    Obstetrics & Gynecology Division, Hadassah-Hebrew University Medical Center, Jerusalem, Israel.

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