Introduction
Placenta accreta spectrum (PAS) refers to a group of placentation
disorders that are characterized by trophoblastic invasion beyond the
physiologic decidual–myometrial junction zone (1). PAS is identified as
one of the most serious pregnancy-related disorders because it is
associated with substantial risk of massive obstetric hemorrhage, blood
transfusion, surgical injuries, and thereby high risk of maternal
intensive care unit (ICU) admission, reoperation, and prolonged
hospitalization (2). Unfortunately, burden of PAS morbidity has been
significantly aggravated as a result of the rising trend of cesarean
section delivery (CS) among contemporary population (2).
To date, the most widely supported approach in management is PAS is
cesarean hysterectomy without trying to separate placenta (placenta
in-situ) (3). Although this approach may be associated with improved
maternal outcomes, uterine preservation is routinely offered as an
alternative or even considered as the primary approach in several
regions of the world (4). Interventional radiology (IR) is another
option that may reduce peripartum bleeding regardless of management
approach (5). Despite being widely adopted, uterine preserving
procedures are generally not robustly supported by evidence and data on
clinical outcomes of these procedures are limited (6). Given the
seriousness of PAS and presence of several proposed interventions,
calculation of individualized probability of intrapartum and postpartum
serious morbidity based on patient demographics, disease
characteristics, and different treatment options may facilitate
treatment decision and proper use of resources.
Machine learning (ML) is a subset of artificial intelligence, where a
computer gains cumulative experience from an existing database, to be
capable of making accurate predictions of studied outcomes (7).
Generally, ML may provide more accurate prediction, reveal more complex
relations between features and outcomes, and provide a scalable and
readily applicable clinical tool compared to traditional statistics (7).
The current study presents an international multicenter center of women
with PAS who were managed conservatively or by cesarean hysterectomy.
The study aimed at creating antepartum and peripartum prediction models
of peripartum clinical outcomes, using ML technology, to enhance
decision making with regard to PAS.