Analyzing Influencing Factors of Uterine Rupture in Pregnant Women with Scarred Uterus Undergoing Repeat Delivery and Evaluating the Predictive Value of Lower Uterine Anterior Wall Thickness.

OBJECTIVE

To analyze influencing factors of uterine rupture in pregnant women with a scarred uterus undergoing repeat delivery and to investigate the predictive value of transabdominal ultrasound measurement of lower uterine anterior wall thickness.

METHODS

A retrospective analysis of 159 pregnant women with scarred uterus (March 2022-May 2024) divided into rupture group (n=48) and non-rupture group (n=111). Lower uterine anterior wall thickness was measured via transabdominal ultrasound pre-delivery. Univariate/multivariate logistic regression and ROC curves were used to identify risk factors and evaluate predictive performance.

RESULTS

The rupture group had higher rates of advanced maternal age, prenatal BMI ≥30 kg/m², multiparity, single-layer cesarean suturing, and shorter inter-pregnancy intervals (all P<0.05). Lower uterine anterior wall thickness was significantly thinner in the rupture group (1.24±0.31 mm vs 2.19±0.52 mm, P<0.001). Multivariate analysis identified thinner lower uterine anterior wall thickness (OR=2.359, 95% CI:1.362-4.134) and single-layer suturing (OR=1.863, 95% CI:1.125-3.086) as independent risk factors, while longer inter-pregnancy interval was protective (OR=0.256, 95% CI:0.091-0.634; all P<0.05). ROC analysis showed AUCs of 0.821 (scar thickness), 0.783 (single-layer suturing), and 0.759 (inter-pregnancy interval); combined prediction achieved an AUC of 0.894 (95% CI:0.837-0.946), sensitivity 90.23%, specificity 84.15%. Uterine rupture was associated with worse perioperative outcomes (eg, higher transfusion rates, longer hospitalization) and adverse neonatal outcomes (lower birth weight, more preterm births; all P<0.05).

CONCLUSION

Lower uterine anterior wall thickness, single-layer suturing, and inter-pregnancy interval are key determinants of uterine rupture. Combined assessment of these factors provides high predictive accuracy (AUC=0.894) and improves risk stratification.