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

• Type: Article in International Scientific Journal
• Date: 2021

Authors

Shazly, SA (Author) Other; Hortu, I (Author) Other; Shih, JC (Author) Other; Melekoglu, R (Author) Other; Fan, SR (Author) Other; Ahmed, FU (Author) Other; Karaman, E (Author) Other; Fatkullin, I (Author) Other; Pinto, PV (Author) FMUP; Irianti, S (Author) Other; Tochie, JN (Author) Other; Abdelbadie, AS (Author) Other; Ergenoglu, AM (Author) Other; Yeniel, AO (Author) Other; Sagol, S (Author) Other; Itil, IM (Author) Other; Kang, J (Author) Other; Huang, KY (Author) Other; Yilmaz, E (Author) Other; Liang, YH (Author) Other; Aziz, H (Author) Other; Akhter, T (Author) Other; Ambreen, A (Author) Other; Ates, C (Author) Other; Karaman, Y (Author) Other; Khasanov, A (Author) Other; Larisa, F (Author) Other; Akhmadeev, N (Author) Other; Vatanina, A (Author) Other; Machado, AP (Author) Other; Montenegro, N (Author) Other; Effendi, JS (Author) Other; Suardi, D (Author) Other; Pramatirta, AY (Author) Other; Aziz, MA (Author) Other; Siddiq, A (Author) Other; Ofakem, I (Author) Other; Dohbit, JS (Author) Other; Fahmy, MS (Author) Other; Anan, MA (Author) Other

Journal

Title: Journal of Maternal-Fetal and Neonatal Medicine

ISSN: 1476-7058

Publisher: Taylor & Francis

Indexing

ISI Web of Knowledge - 4 Citations

Scopus - 3 Citations

Other information

Authenticus ID: P-00V-604

DOI: 10.1080/14767058.2021.1918670

Abstract (EN): 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(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.

Language: English

Type (Professor's evaluation): Dissemination

No. of pages: 10