Predicting NOM Failure in Bowel Obstruction
Predicting Failure of Non-operative Management of Adhesive Small Bowel Obstruction Through Deep Learning.
1 other identifier
observational
370
1 country
1
Brief Summary
"This study aims to collect data on patients with small bowel obstruction (SBO) admitted to hospitals in France and Italy from May 2022 to October 2024 to develop a deep convolutional neural network (DCNN) model. This model will analyze anonymized CT scans to assess the effectiveness of non-operative management (NOM) for SBO, supporting decisions on surgical intervention. Eligible patients are those diagnosed with SBO due to abdominal adhesions who initially received NOM for at least 24 hours. Patients with other SBO causes, early surgery within 24 hours, or those without a CT scan diagnosis are excluded. Data collection spans hospitals in Antibes, Nice, Milan, and Vimercate, targeting consecutive SBO cases with adhesive etiology. To perform an external validation of the DCNN, data will also be retrospectively collected from patients admitted to the Antibes hospital between May 2021 and April 2022 with adhesive SBO. This validation set includes patients who underwent NOM successfully and those who needed surgery after NOM failure. The DCNN model will be applied to anonymized, non-contrast and contrast-enhanced portal-phase CT scans of these patients, with researchers blinded to each patient's NOM outcome to prevent bias. The model's performance will then be evaluated using accuracy metrics consistent with those used in initial model testing, ensuring the reliability of results when applied to external cases. NOM, after adhesive SBO diagnosis via clinical exams, blood tests, and CT scans, includes fasting, analgesics, antiemetics, and fluids as per current guidelines, without necessarily using nasogastric tubes or contrast agents. Patients are re-evaluated after 24 hours to determine whether NOM should continue or if surgery is necessary. NOM is deemed effective if patients experience symptom resolution, stool passage, and no recurrence within 90 days. NOM failure is defined by the need for laparoscopic or laparotomic surgery, based on symptoms' persistence, worsening, or radiological indicators of blockage despite adequate NOM. Data collection, registered with the French National Committee for Data Protection, includes variables like age, sex, medical history, symptoms, blood tests, CT-scan findings, NOM details, and surgical information. Radiological data, including Digital Imaging and Communication in Medicine (DICOM) files of CT scans, will be anonymized and converted to the Neuroimaging Informatics Technology Initiative (NIfTI) format for secure storage and analysis. The NIfTI data files will be randomly split into training and test datasets in an 80%-20% ratio, processed separately for non-contrast and contrast-enhanced CT scans. Data augmentation, including random rotation, flipping, zooming, translation, and noise addition, will be applied to improve model accuracy and reduce overfitting. Different DCNN models will be trained and tested and furtherly undergo external validation to produce a tool capable of predicting NOM failure and need for surgery in patients with adhesive SBO."
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Jan 2025
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
First Submitted
Initial submission to the registry
November 18, 2024
CompletedFirst Posted
Study publicly available on registry
December 2, 2024
CompletedStudy Start
First participant enrolled
January 1, 2025
CompletedPrimary Completion
Last participant's last visit for primary outcome
February 1, 2025
CompletedStudy Completion
Last participant's last visit for all outcomes
April 1, 2026
CompletedApril 10, 2025
April 1, 2025
1 month
November 18, 2024
April 9, 2025
Conditions
Outcome Measures
Primary Outcomes (1)
Performance of Deep Learning model in predicting NOM failure
To measure the degree of agreement between the prediction made by the Deep Learning model and the actual success or failure of non-operative management for small bowel obstruction in the real situation. This will be measured through Area Under the Receiver Operator Characteristic curve (AUROC) assesment. AUROC varies between 0.5 and 1, corresponding to no class separation capacity and full class separation capacity, respectively.
90 days from patients hospital discharge.
Secondary Outcomes (8)
Performance evaluation: accuracy
90 days from patients hospital discharge.
Performance evaluation: precision
90 days from patients hospital discharge.
Performance evaluation: recall
90 days from patients hospital discharge.
Performance evaluation : balanced accuracy
90 days from patients hospital discharge.
Performance evaluation:F1-score
90 days from patients hospital discharge.
- +3 more secondary outcomes
Study Arms (1)
SBO patients
"After adhesive SBO diagnosis is performed through clinical examination, blood tests, and abdominal CT-scan, and need for urgent surgical exploration excluded, patients are admitted to the General/Digestive Surgery Department for NOM. NOM consists in fasting, analgesics, antiemetics, and fluids administration, with or without electrolytes correction. Nasogastric tube positioning and/or hyperosmolar water-soluble contrast administration is not deemed necessary for the patient to be included in this study, thus allowing us to evaluate different management strategies for adhesive SBO. After at least 24 hours of NOM, patients are re-evaluated for NOM prosecution or surgical treatment. NOM is considered effective when patients achieved symptoms remission, flatus and stools evacuation, and realimentation without symptoms recurrence for at least 90 days. NOM failure is defined as need for surgery, both laparoscopic or laparotomic, to treat adhesive SBO. Surgical management depends on single
Eligibility Criteria
The team will collect data from consecutive patients admitted to the Digestive Surgery Department of the Hospital of Antibes (Centre Hospitalier d'Antibes Juan-les-Pins, France) from May the 1st 2022 to October the 31st 2024, to the Digestive and Emergency Surgery Department (Nice University Hospital , France) from the 1st of May 2022 to the 31st of October 2024 , to the General Surgery and Trauma Center of the University Hospital Niguarda in Milan (Grande Ospedale Metropolitano Niguarda, Italy) from the 1st of May 2023 and the 31st of October 2024, and to the General Surgery Department of the Vimercate Hospital (Ospedale di Vimercate, Italy) from the 1st of May 2023 and the 31st of October 2024 with the diagnosis of SBO. Moreover, to perform external validation of the DCNN model, the team will collect data of patients admitted to the Digestive Surgery Department of the Antibes Juan-les-Pins hospital from the 1st of May 2021 to the 30th of April 20
You may qualify if:
- SBO diagnosed through clinical evaluation, blood tests, and an abdominal CT-scan performed at the admission in the Emergency Department.
- SBO secondary to single or multiple abdominal adhesions.
- Initial treatment of SBO by NOM for at least 24 hours.
- Non-opposition to the anonymous data processing by the included patients."
You may not qualify if:
- admission for SBO other than of adhesive etiology (hernias, bowel or other abdominal neoplasms, foreign bodies, functional obstruction, etc.).
- surgical treatment of SBO within 24 hours from admission or rather NOM duration \< 24 hours.
- SBO diagnosis performed without CT-scan."
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (1)
Andrea CHIERICI
Nice, 06000, France
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Design
- Study Type
- observational
- Observational Model
- COHORT
- Time Perspective
- RETROSPECTIVE
- Sponsor Type
- OTHER
- Responsible Party
- SPONSOR
Study Record Dates
First Submitted
November 18, 2024
First Posted
December 2, 2024
Study Start
January 1, 2025
Primary Completion
February 1, 2025
Study Completion
April 1, 2026
Last Updated
April 10, 2025
Record last verified: 2025-04