Clinical Features vs CT Findings to Estimate Need for Surgery in Small Bowel Obstruction

Schulwolf, Sara; Brower, Charles; Karam, Alessandra; Havens, Joaquim; Shokoohi, Hamid; Duggan, Nicole M.; Goldsmith, Andrew J.

doi:10.1001/jamanetworkopen.2023.41376

JAMA Netw Open

2023

DOI: 10.1001/jamanetworkopen.2023.41376

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Clinical Features vs CT Findings to Estimate Need for Surgery in Small Bowel Obstruction

Sara Schulwolf,

Charles Brower,

Alessandra Karam

et al.

Abstract: This comparative effectiveness research compares the ability of clinical features vs clinical features with computed tomography (CT) to estimate the need for small bowel obstruction surgery.

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2024

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Cited by 2 publications

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Development and validation of deep learning models for bowel obstruction on plain abdominal radiograph

Li,

Zhu,

Wang

et al. 2024

J Int Med Res

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Objective Artificial intelligence (AI) could help medical practitioners in analyzing radiological images to determine the presence and site of bowel obstruction. This retrospective diagnostic study proposed a series of deep learning (DL) models for diagnosing bowel obstruction on abdominal radiograph. Methods A total of 2082 upright plain abdominal radiographs were retrospectively collected from four hospitals. The images were labeled as normal, small bowel obstruction and large bowel obstruction by three senior radiologists based on comprehensive examinations and interventions within 48 hours after admission. Gradient-weighted class activation mapping was used to visualize the inferential explanation. Results In the validation set, the Xception-backboned model achieved the highest accuracy (0.863), surpassing the VGG16 (0.847) and ResNet models (0.836). In the test set, the Xception model (accuracy: 0.807) outperformed other models and a junior radiologist (0.780) but not a senior radiologist (0.840). In the AI-aided diagnostic framework, the junior and senior radiologists made their judgements while aware of the Xception model predictions. Their accuracy significantly improved to 0.887 and 0.913, respectively. Conclusions We developed and validated DL-based computer vision models for diagnosing bowel obstruction on plain abdominal radiograph. DL-based computer-aided diagnostic systems could reduce medical practitioners’ workloads and improve diagnostic accuracy.

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Development and validation of deep learning models for bowel obstruction on plain abdominal radiograph

Li,

Zhu,

Wang

et al. 2024

J Int Med Res

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Clinical and Radiological Factors Associated with Nonoperative Management Failure for Small Bowel Obstruction: A Retrospective Study from a Resource-Limited Setting

Al-wageeh,

Alyhari,

Ahmed

et al. 2024

TCRM

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Background Recognizing factors that predict non-operative management (NOM) failure for patients with small bowel obstruction (SBO) aids in limiting surgical intervention when needed. This study investigated the predictive factors for NOM failure in SBO patients in a resource-limited setting. Material and Method A retrospective study included 165 patients who were diagnosed with SBO and were admitted and managed at Althora General Hospital, IBB, Yemen, from April 2022 to March 2024. Patients’ baseline characteristics and profiles along with factors associated with failure of NOM were investigated and analyzed with univariate and multivariate analysis. Results 51 (30.4%) of included cohorts were managed non-operatively. The mean age was 47.7±16.9 years, and males were disproportionally presented (62.7%). The majority of patients presented with abdominal pain (96.1%). Failure of NOM was seen in 18 (35.3%) patients and intra-operative findings were adhesive bands, volvulus, intussusception, and mesenteric ischemia in 7 (38.9%), 5 (27.8%), 4 (22.2%), and 2 (11.1%) patients, respectively. Bowel resection was performed in 11 (61.1%), and 4 of them needed a colostomy diversion. Postoperative complications occurred in 13 (25.5%) patients, including fever, paralytic ileus, surgical site infection, and reoperation in 13 (25.5%), 5 (9.8%), 4 (7.8%), and 2 (3.9%) patients, respectively. Sixteen patients were discharged, and two patients died due to mesenteric ischemia. Among NOM successful patients, recurrence has occurred in 8 patients. In multivariate analysis, poor bowel wall enhancement (OR: 8.59; 95% CI: 1.14–64.59, p=0.037) and high level of obstruction (OR: 11.64; 95% CI: 1.34–100.85, p=0.026) in computed tomography (CT) scan were independently associated with NOM failure. Conclusion Poor bowel wall enhancement and significant obstruction on CT images are critical indicators for selecting SBO patients requiring urgent surgery. However, evaluating the advantages of surgical intervention versus NOM demands a comprehensive analysis of surgical risks, comorbidities, and the presence of bowel strangulation or ischemia.

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Clinical Features vs CT Findings to Estimate Need for Surgery in Small Bowel Obstruction

Abstract: This comparative effectiveness research compares the ability of clinical features vs clinical features with computed tomography (CT) to estimate the need for small bowel obstruction surgery.

Cited by 2 publications

References 7 publications

Development and validation of deep learning models for bowel obstruction on plain abdominal radiograph

Development and validation of deep learning models for bowel obstruction on plain abdominal radiograph

Clinical and Radiological Factors Associated with Nonoperative Management Failure for Small Bowel Obstruction: A Retrospective Study from a Resource-Limited Setting

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