A Model for Predicting Severe Intra-Abdominal Adhesions following Prior Cesarean Sections.

Abstract

Objective: The increasing rate of cesarean sections (CSs) raises concerns over severe intra-abdominal adhesions, which are associated with numerous complications. We aimed to identify risk factors and predictive tools for severe adhesions.

Design: A prospective study was conducted. Participants/Materials: Women with at least one prior CS were evaluated.

Setting: The study was conducted at a tertiary medical center from January to July 2021.

Methods: Surgeons assessed adhesions at four anatomical sites, scoring them from 0 (none) to 2 (dense), with a total possible score of 0-8. Severe adhesions were defined as a score of ≥5. Risk factors were analyzed using logistic regression to create a prediction model.

Results: Overall, 341 women were included in the study. Significant predictors included the number of previous CS, maternal body mass index, maternal morbidity at the time of the previous CS, and operation time. The model predicted severe adhesions with 79.1% accuracy, a positive predictive value of 68.4%, and a negative predictive value of 79.5%.

Limitations: Few risk factors, such as surgical history beyond cesarean sections, endometriosis, and pelvic inflammatory disease were not available. Additionally, the sample size of 341 women, while substantial, may limit the identification of further risk factors and the precision of the predictive model.

Conclusion: The severity of most cases of post-CS adhesions can be predicted by a model which considers common risk factors.