Methods
Patient Cohort and Data Collection
A total of 1015 patients were initially enrolled, and 940 patients were
involved in the final analysis. Patients enrolled between June 2014 and
May 2017 underwent scar defect repair in Shanghai First Maternity &
Infant Hospital, and those enrolled between May 2017 and May 2021
underwent repair in Xinhua Hospital (The enrolled subjects were not
replicated with the previous articles published by our team). All VR
surgeries were performed by a single doctor. Seventy-five patients were
lost to follow-up at 3 or 6 months after CSD repair. According to the
results of our previous studies, the scar stabilizes 3 months after CSD
repair surgery, and there is no change after 6
months16. Therefore, we used MRI measurements of CSD
at 6 months postoperative, in combination with a statistical analysis of
age, uterine position, number of C-sections, the timing of cesarean
section(selective or emergency operation), menstrual cycle, menstrual
period and CSD size before and after repair surgery. According to the
analysis results, the key factors determining the prognosis of
diverticulum repair were identified, and the scoring model was
established (eFigure 1 in the Supplement ).
This study was approved by the Ethics Committee of Xinhua Hospital
Affiliated with Shanghai Jiao Tong University School (XHEC-H-2018-002)
of Medicine and Shanghai First Maternity & Infant Hospital affiliated
with Tongji University (KS1512). All patients signed written informed
consent to participate in this study.
Surgical Procedures
The surgical techniques are described in detail in our previous
publication15, and the most important steps of the
procedure are summarized below.
At a distance of 0.5 cm below the site of the reflexed vesica-cervical
area, an anterior incision was made from the 3 o’clock to the 9 o’clock
position using an electric knife. After entry into the abdominal cavity
and complete exposure of the cervix, the location of the uterine defect
was determined, and the thickness of the lower uterine segment of the
CSD could be gauged through contact with the surgeon’s index finger and
sounding. The CSD tissue was resected, the incision tissues were trimmed
to healthy myometrium with dissecting scissors, and the incision was
closed with a double layer of 1–0 absorbable interrupted sutures. All
operations were performed by the same surgeon with extensive vaginal
surgical experience.
Statistical Analysis
In the analyses, β coefficients and ORs are presented as estimates of
effect, and the reported statistical significance levels were all
two-sided, with statistical significance set at 0.05. Statistical
analysis was conducted with R software (3.5.1; R Foundation for
Statistical Computing) and SAS version 9.4 (SAS Institute Inc., Cary,
NC).
A descriptive analysis of patients and a demographic comparison between
the suboptimal healing group and the optimal healing group were
performed. The distributions of the baseline characteristics of the
patients are represented by the mean ± SD or as a number (n) with a
percentage (%). Differences between groups were assessed as follows:
t-test and ANOVA were used for continuous variables and multiple
comparisons, and the χ2 test was used for categorical variables.
Multivariate linear regression was used to assess the association
between CSD parameters and optimal healing, CSD parameters and menstrual
improvement, and CSD parameters and TRM after adjustment for potential
confounding variables.
These patients were randomly divided into two cohorts: a training cohort
(634, 70%) and an internal validation cohort (306, 30%). After
multivariable logistic regression analysis, clinical candidate
predictors were applied to develop an individualized prediction model by
using the training cohort. Forward and backward stepwise regression was
applied by using the likelihood ratio test with Akaike’s information
criterion as the stopping rule. To provide a quantitative tool for
prediction, we built nomograms based on multivariate logistic analysis
of the training cohort. We selected the point that yielded the highest
Youden’s index (i.e., specificity + sensitivity - 1) on the receiver
operating characteristic (ROC) curve of the training set as the optimal
cutoff value and used it to calculate which score combination for every
patient represented the sum of scores for each risk factor.
Calibration curves were plotted to assess the calibration of the
nomogram using the Hosmer‒Lemeshow test. To quantify the discrimination
performance of the nomogram, Harrell’s C-index was measured. The
nomogram was subjected to bootstrapping validation (1,000 bootstrap
resamples) to calculate a relatively corrected C-index. We also
evaluated the nomogram using the area under the curve (AUC) of the ROC
with a 95% confidence interval (95% CI). Internal validation was
carried out using data from 306 patients. Decision curve analysis was
performed to determine the clinical usefulness of the nomogram by
quantifying the net benefits at different threshold probabilities.