Small Bowel ObstructionNCT06711107
Predicting NOM Failure in Bowel Obstruction
An observational study of Small Bowel Obstruction and Intestinal Pseudo-Obstruction, run by Centre Hospitalier Universitaire de Nice at 1 site in France.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Small Bowel Obstruction, Intestinal Pseudo-Obstruction
- How many people
- 370 participants
- Where
- 1 siteFrance
- Study dates
- 1 Jan 2025to 1 Apr 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: 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
Who may be able to join
- 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."
Who cannot join
- 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."
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Predicting Failure of Non-operative Management of Adhesive Small Bowel Obstruction Through Deep Learning.
"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.
Read more
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."
- Medical subject headings
- Intestinal Pseudo-Obstruction
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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 |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 90 days from patients hospital discharge.
Secondary outcomes (8)
Performance evaluation: accuracy
The number of true positives and true negatives among all predictions. It varies between 0 (no correct prediction) to 1 (full correct predictions).
Measured: 90 days from patients hospital discharge.
Performance evaluation: precision
The number of true positives divided by all the positive predictions (true positives and false positives). It varies between 0 (no correct prediction) to 1 (full correct predictions)
Measured: 90 days from patients hospital discharge.
Performance evaluation: recall
The number of true positives divided by the actual positive instances in the dataset (true positives and false negatives). It varies between 0 (no correct prediction) to 1 (full correct predictions).
Measured: 90 days from patients hospital discharge.
Performance evaluation : balanced accuracy
the aritmethic mean of sensitivity and specificity. It varies between 0 (no correct prediction) to 1 (full correct predictions).
Measured: 90 days from patients hospital discharge.
Performance evaluation:F1-score
It combines precision and recall. It ranges from 0-100%, and a higher F1 score denotes a better quality classifier.
Measured: 90 days from patients hospital discharge.
Performance evaluation: Confusion matrix
A visual representation of true positives, false positives, true negatives, and false negatives. It is depicted through a table.
Measured: 90 days from patients hospital discharge.
Performance evaluation :Log-loss
It indicates how close the prediction probability is to the corresponding actual/true value (0 or 1 in case of binary classification). The more the predicted probability diverges from the actual value, the higher is the log-loss value.
Measured: 90 days from patients hospital discharge.
Performance evaluation: Cohen's Kappa
A metric used to measure the level of agreement between two raters which can be a useful tool to gauge the performance of a classification model. It accounts for the fact that the raters may happen to agree on some items purely by chance. It varies between 0 (no correct prediction) to 1 (full correct predictions).
Measured: 90 days from patients hospital discharge.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted2 Dec 2024
- Study start1 Jan 2025
- Primary completion1 Feb 2025
- Study completion1 Apr 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Small Bowel ObstructionStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 370 participants (actual)
Sponsor and investigators
- Lead sponsor
- Centre Hospitalier Universitaire de Nice
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06711107
- Sponsor's protocol ID
- 24Chirdig01
- Status last verified
- Apr 2025
- First submitted
- 18 Nov 2024
- Last updated
- 10 Apr 2025
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 10 Apr 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Predicting Failure of Non-operative Management of Adhesive Small Bowel Obstruction Through Deep Learning.
- ClinicalTrials.gov ID
- NCT06711107
- Acronym / sponsor ID
- 24Chirdig01
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 370 participants (actual)
- Lead sponsor
- Centre Hospitalier Universitaire de NiceUniversity, hospital or other
- Study start
- 1 Jan 2025 (actual)
- Primary completion
- 1 Feb 2025 (actual)
- Study completion
- 1 Apr 2026 (estimated)
- First posted
- 2 Dec 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
"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."
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| 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 |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- 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
Key inclusion criteria (4 listed)
- 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."
Key exclusion criteria (3 listed)
- 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."
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Performance of Deep Learning model in predicting NOM failure
Time frame: 90 days from patients hospital discharge.
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.
8 secondary outcomes
Performance evaluation: accuracy
Time frame: 90 days from patients hospital discharge.
Performance evaluation: precision
Time frame: 90 days from patients hospital discharge.
Performance evaluation: recall
Time frame: 90 days from patients hospital discharge.
Performance evaluation : balanced accuracy
Time frame: 90 days from patients hospital discharge.
Performance evaluation:F1-score
Time frame: 90 days from patients hospital discharge.
Performance evaluation: Confusion matrix
Time frame: 90 days from patients hospital discharge.
Performance evaluation :Log-loss
Time frame: 90 days from patients hospital discharge.
Performance evaluation: Cohen's Kappa
Time frame: 90 days from patients hospital discharge.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 10 Apr 2025.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Small Bowel Obstruction and Intestinal Pseudo-Obstruction, run by Centre Hospitalier Universitaire de Nice at 1 site in France.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: under way, not looking for participants, as of the sponsor's last update on 10 Apr 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in France. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Performance of Deep Learning model in predicting NOM failure (time frame: 90 days from patients hospital discharge.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 10 Apr 2025.