Actively Recruiting
Combining Image-clinical Model Based on Deep Learning and Radiomics to Predict Multidrug-resistant Klebsiella Pneumoniae Liver Abscess
Led by Shengjing Hospital · Updated on 2024-07-17
550
Participants Needed
1
Research Sites
8 weeks
Total Duration
On this page
Sponsors
S
Shengjing Hospital
Lead Sponsor
T
The First Affiliated Hospital of China University of Science and Technology (Anhui Provincial)
Collaborating Sponsor
AI-Summary
What this Trial Is About
Researchers are evaluating a deep learning-based model to predict infections caused by multidrug-resistant Klebsiella pneumoniae in patients with liver abscess, an infection of the abdomen with significant mortality risk. The study aims to improve early detection of resistant bacteria using clinical data and imaging to guide better treatment decisions, addressing challenges due to resistance and prior antibiotic use. Liver abscesses caused by Klebsiella pneumoniae have become more common, especially in China, making this research important for managing infections effectively. This observational study collects clinical features, laboratory results, and CT images from patients with pyogenic liver abscesses confirmed by surgery or interventional procedures. Patients undergo abdominal enhanced CT scans before their treatment. The study compares cases with resistant and non-resistant bacterial infections to develop and evaluate a predictive model for multidrug-resistant infections based on this combined data. During the study, researchers obtain bacterial cultures and drug resistance test results within the first week before any surgical or interventional treatment, up to 4 weeks. They use this information to validate the model's accuracy in identifying resistant bacteria early. Participants contribute their clinical data and imaging to this research without receiving experimental treatment. The study continues until March 2025, aiming to improve infection management through advanced prediction methods.
CONDITIONS
Brief Title
A Joint Model Based on Deep Learning to Predict Multidrug-resistant Klebsiella Pneumoniae Liver Abscess
Who Can Participate
Eligibility Criteria
You may qualify if you...
- Patients diagnosed with pyogenic liver abscess confirmed by surgery or interventional procedure
- Patients who have had abdominal enhanced CT scans before surgery or interventional procedure
- Adults aged 18 years or older
You will not qualify if you...
- Patients diagnosed with other types of liver abscess such as amoebic liver abscess
AI-Screening
AI-Powered Screening
Complete this quick 3-step screening to check your eligibility
Your Study Journey
Duration - 2 to 4 weeks
Participants are screened for eligibility to participate in the trial.
1 to 2 visits depending on timing of diagnosis and imaging
Duration - Up to 4 weeks
Participants undergo abdominal enhanced CT scans and bacterial culture with drug resistance testing before surgery or interventional procedure to identify the type of liver abscess and presence of multidrug-resistant organisms.
1 visit for CT scan and sample collection
Duration - Up to study completion in March 2025
Participants are observed through clinical data collection and monitoring to understand disease progression and treatment response.
Visits as needed for clinical assessments
Trial Site Locations
Total: 1 location
1
Shengjing hospital of China medical university
Shenyang, Liaoning, China, 110004
Actively Recruiting
Research Team
Z
Zhihui Chang
How is the study designed?
Study Type
OBSERVATIONAL
Masking
N/A
Allocation
N/A
Model
N/A
Primary Purpose
N/A
Number of Arms
2
Similar Trials
Frequently Asked Questions
Have more questions? Get in touch with our team for quick support
Not the Right Trial for You?
Explore thousands of other clinical trials that might be a better match.
Sign up to get personalized trial recommendations delivered to your inbox.
Already have an account? Log in here