Discussion
PAS-ID is a multicenter international database that includes data of 797
patients from 11 centers that present 9 countries from Europe, Asia and
Africa. The current study was conducted to establish prediction models
of different critical outcomes of PAS using emerging ML models. Two
prediction models were created to determine risk of adverse maternal
outcomes, namely massive PAS-associated peripartum blood loss, admission
to ICU and prolonged hospitalization, using antepartum and peripartum
inputs. Diagnostic performance of all models ranged between 0.80 and
0.90, which is defined as ‘excellent’ for a diagnostic test (9).
Internal validity was demonstrated by consistency of diagnostic
performance between train and test sets in all models.
Although PAS has been one of the most concerningly mounting obstetric
complications in contemporary practice, evidence-based recommendations
on management of PAS are limited and mostly represent level 3, 4 or good
practice point recommendations (10). Cesarean hysterectomy seems to be
the most acceptable approach whenever possible (3, 10). However, if
cesarean hysterectomy is rejected by the patient, practice
recommendations are generally less determinate. Of conservative options,
leaving the placenta in situ was considered by the Royal College of
Obstetricians and Gynaecologists (RCOG) in women who are highly
motivated to preserve the uteri (10). This approach was considered
investigational by the American College of Obstetricians and
Gynaecologists (ACOG) and no recommendations were made in its regard
(3). This practice does not seem to be prevalent though (4). Other
uterine preserving techniques were less endorsed.
Nevertheless, management of PAS is globally diverse and is not enclosed
by these recommendations (4). This may be attributed to paucity of data.
Also, most data come from case reports and case studies that lack a
study design and selection criteria, and likely reflect a single surgeon
or team experience. Conduction of prospective studies, pilot studies,
and clinical trials on management of PAS may be restricted by ethical
considerations and recruitment difficulties and therefore, conclusions
from large retrospective studies may present the first step to support
future prospective studies and enhance evidence on current widely
adopted management strategies.
To our knowledge, PAS-ID may present the first international multicenter
database that investigates clinical outcomes of PAS in centers that
offer both cesarean hysterectomy and conservative management. The
database is one of the largest databases available on PAS in the
literature and it conveys a wide range of practice. The current study
applied ML algorithms, which tends to provide accurate prediction and
enclose complex and hidden relations between studied variables and
outcomes. Although ML is generally used with large databases to permit
model learning, current prediction models seem to perform better that
traditional statistics and to yield consistent performance on untested
data and excellent internal validity. Clinically, MOGGE PAR-A score can
be used to determine high risk group, who may benefit from additional
interventions (e.g. prophylactic IR procedures). Similarly, MOGGE PAR-P
score may determine women in whom significant bleeding is anticipated.
The score may be used to outline intraoperative management in priori, by
calculating risk using different scenarios. Specifically, it may help to
avoid unnecessary steps, which may not seem to lower risk of these
outcomes (e.g. internal iliac artery ligation), to determine whether
certain measurements may be helpful (e.g. preoperative or intraoperative
ultrasound), and whether some intraoperative steps would be safe to do
(e.g. delayed cord clamping).
The current study is limited by the retrospective nature of the study.
Although the study was based on an international database,
generalizability to some regions of the world, that were not represented
in this data, cannot be determined. Although PAS-related research is
associated with inherited risk of reflecting a specific team practice,
our data were representative of management approaches that are widely
recognizable in the literature. Complexity of ML models may present a
barrier to their applicability. However, these calculations can be
programmed into an application to facilitate their use in clinical
practice. For this purpose, a simple tool (MOGGE PAR score, version 1.0)
was created to enable use of these models and is available at
(https://www.mogge-obgyn.com/clinical-studies) for research purposes
(Figure S1).
In conclusion, utilization of ML
algorithms may provide an individualized tool to determine women with
PAS who are at high risk of significant morbidity and to optimize
management plan in priori based on available information. Prospective
validation of these scores may permit robust evidence-based
recommendations on management of PAS, converge current treatment
options, and determine required training and skills that would be deemed
satisfactory to manage PAS if uterine preservation is considered.
Acknowledgements: none
Disclosure of Interests: The authors have no conflicts of
interest. No financial disclosure to declare