Evidence map›Paper›PMID 39608323›Full record

ArticleGynecologic and obstetric investigation2025

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

Shai Ram, Hila Shalev-Ram, Shira Alon, Ziv Shapira, Roza Berkovitz-Shperling, Margaret Johansson-Lipinski, Yariv Yogev, Ariel Many

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Article in Gynecologic and obstetric investigation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Shai RamLis Hospital for Women, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.
Hila Shalev-RamDepartment of Obstetrics and Gynecology, Meir Medical Center, Kfar Saba, Israel.
Shira AlonDepartment of Statistics and Data Science, Hebrew University, Jerusalem, Israel.
Ziv ShapiraLis Hospital for Women, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.
Roza Berkovitz-ShperlingLis Hospital for Women, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.
Margaret Johansson-LipinskiDepartment of Obstetrics and Gynecology, Meir Medical Center, Kfar Saba, Israel.
Yariv YogevLis Hospital for Women, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.
Ariel ManyLis Hospital for Women, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThe 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.

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

settingThe study was conducted at a tertiary medical center from January to July 2021.

methodsSurgeons 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.

resultsOverall, 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.

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

Indexed as

Cesarean SectionPostoperative ComplicationsAbdomenAdultFemaleHumansLogistic ModelsPredictive Value of TestsPregnancyProspective StudiesRisk FactorsSeverity of Illness IndexTissue AdhesionsCesarean sections complicationsIntra-abdominal adhesionsPredicting modeling

Identifiers

PMID39608323
PMCPMC12129429

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.