Prediction of clinical outcomes in women with placenta accreta spectrum using machine learning models: an international multicenter study.

Autor:	Shazly SA; Department of Obstetrics and Gynaecology, Assiut University, Assiut, Egypt., Hortu I; Department of Obstetrics and Gynaecology, Ege University School of Medicine, Izmir, Turkey., Shih JC; Department of Obstetrics and Gynaecology, National Taiwan University College of Medicine, Taipei City, Taiwan., Melekoglu R; Department of Obstetrics and Gynaecology, Inonu University, Malatya, Turkey., Fan S; Department of Obstetrics and Gynaecology, Peking University Shenzhen Hospital, Shenzhen, China., Ahmed FUA; Department of Obstetrics and Gynaecology, Fatima Memorial Hospital, Lahore, Pakistan., Karaman E; Department of Obstetrics and Gynaecology, Yuzuncu Yil University, Van, Turkey., Fatkullin I; Department of Obstetrics and Gynaecology, Kazan State Medical University, Kazan, Russia., Pinto PV; Serviço de Ginecologia e Obstetrícia, Centro Hospitalar São João, Porto, Portugal., Irianti S; Taskforce of Placenta Accreta Spectrum, Universitas Padjadjaran Bandung, Bandung, Indonesia., Tochie JN; Faculty of Medicine and Biomedical Sciences, Department of Obstetrics and Gynaecology, University of Yaoundé I, Yaoundé, Cameroon., Abdelbadie AS; Department of Obstetrics and Gynaecology, Aswan University Hospital, Aswan, Egypt., Ergenoglu AM; Department of Obstetrics and Gynaecology, Ege University School of Medicine, Izmir, Turkey., Yeniel AO; Department of Obstetrics and Gynaecology, Ege University School of Medicine, Izmir, Turkey., Sagol S; Department of Obstetrics and Gynaecology, Ege University School of Medicine, Izmir, Turkey., Itil IM; Department of Obstetrics and Gynaecology, Ege University School of Medicine, Izmir, Turkey., Kang J; Department of Obstetrics and Gynaecology, National Taiwan University College of Medicine, Taipei City, Taiwan., Huang KY; Department of Obstetrics and Gynaecology, National Taiwan University College of Medicine, Taipei City, Taiwan., Yilmaz E; Department of Obstetrics and Gynaecology, Inonu University, Malatya, Turkey., Liang Y; Department of Obstetrics and Gynaecology, Peking University Shenzhen Hospital, Shenzhen, China., Aziz H; Department of Obstetrics and Gynaecology, Fatima Memorial Hospital, Lahore, Pakistan., Akhter T; Department of Obstetrics and Gynaecology, Fatima Memorial Hospital, Lahore, Pakistan., Ambreen A; Department of Obstetrics and Gynaecology, Fatima Memorial Hospital, Lahore, Pakistan., Ateş Ç; Department of Obstetrics and Gynaecology, Yuzuncu Yil University, Van, Turkey., Karaman Y; Department of Obstetrics and Gynaecology, Lokman Hekim Hayat Hospital, Van, Turkey., Khasanov A; Department of Obstetrics and Gynaecology, Kazan State Medical University, Kazan, Russia., Larisa F; Department of Obstetrics and Gynaecology, Kazan State Medical University, Kazan, Russia., Akhmadeev N; Department of Obstetrics and Gynaecology, Kazan State Medical University, Kazan, Russia., Vatanina A; Republic Clinical Hospital, Ministry of Healthcare of Republic of Tatarstan, Kazan, Russia., Machado AP; Serviço de Ginecologia e Obstetrícia, Centro Hospitalar São João, Porto, Portugal., Montenegro N; Serviço de Ginecologia e Obstetrícia, Centro Hospitalar São João, Porto, Portugal., Effendi JS; Taskforce of Placenta Accreta Spectrum, Universitas Padjadjaran Bandung, Bandung, Indonesia., Suardi D; Taskforce of Placenta Accreta Spectrum, Universitas Padjadjaran Bandung, Bandung, Indonesia., Pramatirta AY; Taskforce of Placenta Accreta Spectrum, Universitas Padjadjaran Bandung, Bandung, Indonesia., Aziz MA; Taskforce of Placenta Accreta Spectrum, Universitas Padjadjaran Bandung, Bandung, Indonesia., Siddiq A; Taskforce of Placenta Accreta Spectrum, Universitas Padjadjaran Bandung, Bandung, Indonesia., Ofakem I; Faculty of Medicine and Biomedical Sciences, Department of Obstetrics and Gynaecology, University of Yaoundé I, Yaoundé, Cameroon., Dohbit JS; Faculty of Medicine and Biomedical Sciences, Department of Obstetrics and Gynaecology, University of Yaoundé I, Yaoundé, Cameroon., Fahmy MS; Department of Obstetrics and Gynaecology, Aswan University Hospital, Aswan, Egypt., Anan MA; Department of Obstetrics and Gynaecology, Aswan University Hospital, Aswan, Egypt.
Jazyk:	angličtina
Zdroj:	The journal of maternal-fetal & neonatal medicine : the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians [J Matern Fetal Neonatal Med] 2022 Dec; Vol. 35 (25), pp. 6644-6653. Date of Electronic Publication: 2021 Jul 07.
DOI:	10.1080/14767058.2021.1918670
Abstrakt:	Introduction: Placenta accreta spectrum is a major obstetric disorder that is associated with significant morbidity and mortality. The objective of this study is to establish a prediction model of clinical outcomes in these women. Materials and Methods: PAS-ID is an international multicenter study that comprises 11 centers from 9 countries. Women who were diagnosed with PAS and were managed in the recruiting centers between 1 January 2010 and 31 December 2019 were included. Data were reanalyzed using machine learning (ML) models, and 2 models were created to predict outcomes using antepartum and perioperative features. ML model was conducted using python ® programing language. The primary outcome was massive PAS-associated perioperative blood loss (intraoperative blood loss ≥2500 ml, triggering massive transfusion protocol, or complicated by disseminated intravascular coagulopathy). Other outcomes include prolonged hospitalization >7 days and admission to the intensive care unit (ICU). Results: 727 women with PAS were included. The area under curve (AUC) for ML antepartum prediction model was 0.84, 0.81, and 0.82 for massive blood loss, prolonged hospitalization, and admission to ICU, respectively. Significant contributors to this model were parity, placental site, method of diagnosis, and antepartum hemoglobin. Combining baseline and perioperative variables, the ML model performed at 0.86, 0.90, and 0.86 for study outcomes, respectively. Ethnicity, pelvic invasion, and uterine incision were the most predictive factors in this model. Discussion: ML models can be used to calculate the individualized risk of morbidity in women with PAS. Model-based risk assessment facilitates a priori delineation of management.
Databáze:	MEDLINE
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