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

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dc.authoridSagol, Sermet/0000-0002-4693-4090
dc.authoridMELEKOGLU, RAUF/0000-0001-7113-6691
dc.authoridSHIH, JIN-CHUNG/0000-0002-0296-4327
dc.authoridIrianti, Setyorini/0000-0003-0865-2620
dc.authoridAtes, Cagri/0000-0001-5341-4950
dc.authoridAkhmadeev, Nariman/0000-0003-0908-7256
dc.authoridSuardi, Dodi/0000-0003-3084-8101
dc.authorwosidKang, Jessica/GZA-6384-2022
dc.authorwosidMachado, Ana/HLV-9704-2023
dc.authorwosidyeniel, ahmet özgür/E-8459-2011
dc.authorwosidSagol, Sermet/GSN-9501-2022
dc.authorwosidkaraman, erbil/AFU-7129-2022
dc.authorwosidMELEKOGLU, RAUF/AAF-1614-2019
dc.authorwosidSHIH, JIN-CHUNG/AAQ-8651-2021
dc.contributor.authorShazly, Sherif A.
dc.contributor.authorHortu, Ismet
dc.contributor.authorShih, Jin-Chung
dc.contributor.authorMelekoglu, Rauf
dc.contributor.authorFan, Shangrong
dc.contributor.authorAhmed, Farhat ul Ain
dc.contributor.authorKaraman, Erbil
dc.date.accessioned2024-08-04T20:50:29Z
dc.date.available2024-08-04T20:50:29Z
dc.date.issued2022
dc.departmentİnönü Üniversitesien_US
dc.description.abstractIntroduction 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(R) 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.en_US
dc.identifier.doi10.1080/14767058.2021.1918670
dc.identifier.endpage6653en_US
dc.identifier.issn1476-7058
dc.identifier.issn1476-4954
dc.identifier.issue25en_US
dc.identifier.pmid34233555en_US
dc.identifier.scopus2-s2.0-85111741739en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage6644en_US
dc.identifier.urihttps://doi.org/10.1080/14767058.2021.1918670
dc.identifier.urihttps://hdl.handle.net/11616/100095
dc.identifier.volume35en_US
dc.identifier.wosWOS:000670531000001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherTaylor & Francis Ltden_US
dc.relation.ispartofJournal of Maternal-Fetal & Neonatal Medicineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectObstetric hemorrhageen_US
dc.subjectplacenta praeviaen_US
dc.subjectcesarean hysterectomyen_US
dc.subjectmorbidly adherent placentaen_US
dc.subjectplacenta accreta spectrumen_US
dc.subjectmachine learningen_US
dc.titlePrediction of clinical outcomes in women with placenta accreta spectrum using machine learning models: an international multicenter studyen_US
dc.typeArticleen_US

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