Evidence map›Paper›PMID 42682292›Full record

ArticleInternational journal of surgery (London, England)2026

The value of ultrasound in the diagnosis and prediction of the severity of abdominal adhesions.

Chang Liu, Shi-Si Ding, Hui-Hui Chai, Wei-Wei Ren, Hui Zhao, Xiao-Qi Yuan, Ren-Yuan Gao, Guan-Qun Zhang, Ji-Yuan Li, Li-Ping Sun and 2 more

Abstract read
In one paragraph

Article in International journal of surgery (London, England), 2026. 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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1 · What the graph read from it

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2 · The registry

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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

12 authors.

Chang LiuDepartment of Medical Ultrasound, Center of Minimally Invasive Treatment for Tumor, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China.ORCID https://orcid.org/0000-0002-2957-0897
Shi-Si DingDepartment of Medical Ultrasound, Center of Minimally Invasive Treatment for Tumor, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China.ORCID https://orcid.org/0000-0003-2318-3367
Hui-Hui ChaiDepartment of Medical Ultrasound, Center of Minimally Invasive Treatment for Tumor, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China.
Wei-Wei RenDepartment of Medical Ultrasound, Center of Minimally Invasive Treatment for Tumor, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China.ORCID https://orcid.org/0000-0001-6028-0604
Hui ZhaoDepartment of Medical Ultrasound, Center of Minimally Invasive Treatment for Tumor, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China.ORCID https://orcid.org/0000-0001-7627-0419
Xiao-Qi YuanDepartment of Gastrointestinal Surgery, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China.
Ren-Yuan GaoDepartment of Medical Ultrasound, Center of Minimally Invasive Treatment for Tumor, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China.
Guan-Qun ZhangDepartment of Medical Ultrasound, Center of Minimally Invasive Treatment for Tumor, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China.ORCID https://orcid.org/0009-0003-7056-4223
Ji-Yuan LiDepartment of Medical Ultrasound, Center of Minimally Invasive Treatment for Tumor, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China.
Li-Ping SunDepartment of Medical Ultrasound, Center of Minimally Invasive Treatment for Tumor, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China.ORCID https://orcid.org/0000-0002-4301-4520
Chun-Qiu ChenDepartment of General Surgery, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China.
Hui-Xiong XuDepartment of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai, China.ORCID https://orcid.org/0000-0002-8699-854X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: The aim of this study was to construct a preoperative ultrasound prediction model, and compare its diagnostic performance with the existing sliding sign to predict the severity of abdominal adhesions in order to reduce the occurrence of intraoperative complications and shorten the duration of surgery. Methods: Between June 2020 and June 2022, 100 patients with a history of abdominal surgery were included in this retrospective study. All participants underwent ultrasound examination of five sites on the anterior abdominal wall before surgery. Eighteen sites were excluded because of surgical stomas, etc. Finally, 482 sites were examined by ultrasound, of which 138 (28.6%) were severe adhesions. Based on the intraoperative findings, the patients were divided into two groups: patients with severe adhesions and patients with non-severe adhesions. Finally, the data of 482 abdominal sites examined were randomly divided into a training cohort (70%) and a validation cohort (30%). The least absolute shrinkage and selection operator (LASSO) regression and multivariate binary logistic regression were used to determine the independent influencing factors, thereby constructing a clinical ultrasound prediction model for severe abdominal adhesions. The sensitivity, specificity, positive and negative predictive value (PPV and NPV) and accuracy of the model were calculated. Prediction models were established, and the area under the receiver operating curve (AUC) between models and compared with sliding sign (MA) using the DeLong test to determine the optimal model. At the same time, intraclass correlation coefficient (ICC) was used to evaluate the inter-observer agreement. Results: The LASSO regression showed that wall syndrome, traction sign, deformation of abdominal organs, two-layer peritoneal bright lines, sliding sign (head-foot direction mobility), and examination site were associated with severe adhesions. Multivariate analysis showed that traction sign, two-layer peritoneal bright lines, head-foot direction mobility and examination site (all Conclusions: In the presence of complex adhesions in the abdominal cavity, the new diagnostic model (MB) significantly improved the diagnostic performance compared with the conventional model (MA). The MB improved the AUC, specificity, PPV, and accuracy. Therefore, this model has the potential to reduce severe intraoperative complications.

Indexed as

abdominal adhesionsprediction modelsliding signtraction signultrasoundwall syndrome

Identifiers

PMID42682292
PMCPMC13249241

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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.