Study Population
The “Placenta Accreta Spectrum International Database (PAS-ID)” is an
international database that was launched by Middle-East Obstetrics and
Gynaecology Graduate Education (MOGGE) Foundation to conduct the current
study (ClinicalTrials.gov identifier: NCT04384510). The database was
created on January 21st, 2020 and received
contribution from a consortium of 11 tertiary centers located in 9
countries that represent 3 continents. These centers are referral
centers for complex PAS cases and they all offer both cesarean
hysterectomy and uterine preservation procedures.
Data of all patients with PAS who were managed in these centers between
January 1st, 2010 and December 31st,
2019 were retrospectively collected. Patients were considered eligible
if they received clinical and histopathological diagnosis of PAS and
were managed, delivered, and followed-up for 6 weeks postpartum by their
respective study site. Exclusion of candidates was made if relevant
documented information and follow-up was deficient (e.g. single
antenatal visit) or if no authorization to use anonymous patient data
was provided for research purposes. Data were collected using a
standardized spreadsheet, which included 57 variables that comprise
patient baseline information (e.g. age, parity, body mass index
“BMI”, ethnicity, smoking status), obstetric and gynaecologic data(e.g. obstetric complications, previous CS, prior gynaecologic
surgeries), medical history, antepartum and intrapartum disease
characteristics (e.g. PAS type, complete versus focal uterine wall
invasion, bladder invasion, parametrial invasion, placental location),
diagnosis (antepartum versus intrapartum diagnosis, imaging
modality, and gestational age at diagnosis), antepartum hemoglobin
level, intraoperative details (e.g. hysterectomy versus uterine
preservation, uterus preserving procedures used either surgical or
IR-related, success of uterine preservation, use of preoperative or
intraoperative sonographic assessment, type of uterine incision and its
relation to the placenta, intraoperative blood loss, transfused blood
products, surgical complications), maternal outcomes (success of
uterine preservation, length of hospital stay, admission to intensive
care unit [ICU], postoperative complications), and neonatal outcome(APGAR score at 1 and 5 minutes, admission to NICU, need for
respiratory support, neonatal morbidity and mortality). Data collection
was completed on June 15th, 2020. Institutional review
board (IRB) approval was obtained from all participating centers.
Study Outcomes
Primary outcome of this study was massive PAS-associated blood loss,
which we defined as intraoperative blood loss ≥ 2500 ml, blood loss that
required massive blood transfusion (transfusion of ≥ 10 units of packed
red blood cells [RBCs] within 24 hours), or blood loss that was
complicated by intraoperative disseminated intravascular coagulopathy
(DIC). Secondary outcomes included maternal admission to ICU and
prolonged hospital stay (postpartum hospital stay for more than 7 days).
Prediction models
PAS-ID was used to establish an antepartum prediction model to calculate
a score that presents probability of peripartum massive PAS-associated
blood loss, admission to ICU and prolonged hospital stay. “MOGGE
placenta accreta risk-antepartum score” or “MOGGE PAR-A score” aims
at predicting these outcomes once PAS diagnosis is made antenatally.
“MOGGE placenta accreta risk-peripartum score” or “MOGGE PAR-P
score” is a second scoring system that was created to predict the same
outcomes using baseline features in conjugation with disease- and
surgery-related peripartum variables. This score is designed to
calculate probability of unfavorable outcomes of a management strategy
and clinical scenario(s) in priori, and would, thereby, assist
designation of management.
Statistical analysis Conventional statistics Variables were described as means and standard deviations for continuous
variables, and numbers (percentages) for categorical variables. Missing
data were generally less than 5% in all variables. For reason of
comparison, a prediction model of the primary outcome was created using
conventional statistics. Data were randomly split into a model
development group and model validation group in a 4:1 ratio. Within
model development group, each independent variable was tested using
univariable logistic regression. Results were expressed in unadjusted
odds ratio (OR) and 95% confidence interval (CI). Variables that
exhibited a p-value of less than 0.2 on univariable logistic regression
were included in a multivariable logistic regression model and adjusted
ORs (aORs) were calculated. The diagnostic performance of prediction
model was evaluated using receiver operating characteristic (ROC) curve,
which was applied to both model development and validation groups.
Statistical analysis for this part was performed using STATA 16 software
(StataCorp, College Station, TX).ML prediction model ML model was applied using python® programing language (Spyder 3.3.6)
with Scikit‐learn (ML library package) through Anaconda 3.0 platform.
For purpose of training and validation, data were randomly assigned to a
train set (0.8) and test set (0.2). The model was developed using the
train set and was applied to the test set to assess internal validation.
A ‘train/test split’ technique was considered over k-fold cross-validity
because it is associated with unbiased performance regardless of sample
size (8). A logistic regression algorithm with gradient descent was
performed on a train set using L-BFGS solver with a maximum iteration
set to 1000. Algorithms were all successfully converged at less than 10
iterations in all models. Each model was evaluated using Jaccard index,
confusion matrix, weighted precision, recall, F1 score, and log loss
were calculated. A ROC curve was used to assess diagnostic performance
of each model through the test set to assess model validity. Intercept
value and coefficients of each model were used to calculate probability
of the specific outcome. Range of calculated probability of each outcome
among women who did and did not develop this outcome was graphed using a
“box and whisker” plot. The graph was created to provide a reference
to facilitate interpretation of calculated probabilities in clinical
setting.