A Joint Model Based on Deep Learning to Predict Multidrug-resistant Klebsiella Pneumoniae Liver Abscess
NCT ID: NCT06506318
Last Updated: 2024-07-17
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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RECRUITING
550 participants
OBSERVATIONAL
2024-01-01
2025-03-01
Brief Summary
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Detailed Description
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In the treatment of liver abscesses, it is very important to apply antibiotic therapy as early and as quickly as possible after diagnosis. Particularly in China, with its large population and varying medical conditions across different regions, for many primary medical institutions that lack the ability to carry out interventional treatments, antibiotic therapy is the only means of treating such infectious diseases as liver abscesses. However, precise antibiotic therapy relies on the results of bacterial cultures and drug susceptibility tests. Because many patients have received treatment at other medical institutions or have self-administered antibiotics before coming to the hospital, the results of blood cultures are somewhat affected. Currently, the common pathogens of community-acquired liver abscesses are mainly Gram-negative enterobacteria, among which Klebsiella pneumoniae (KP) and Escherichia coli are the majority, with a major shift from Escherichia coli to Klebsiella pneumoniae. In China, Klebsiella pneumoniae has become the primary pathogen of community-acquired liver abscesses. Therefore, empirical treatment regimens typically prioritize the treatment of infections caused by Gram-negative enterobacteria, especially KP. However, infections caused by MDROs often result in poor or directly ineffective treatment due to resistance to empirically chosen antibiotics, thereby delaying treatment, causing greater economic burdens on patients, and consuming more medical resources. Infections caused by MDROs have become a global public health issue of great concern. The increasing occurrence of MDRO infections and the emergence of new types of MDROs pose higher demands on clinical physicians. Under this new trend of pathogenic bacteria, it is unacceptable to rely solely on empirical diagnostic and treatment methods but need efficient and convenient new ways to guide the selection of clinical treatment regimens.
Among MDRO infections in liver abscess patients, the largest proportion is caused by extended-spectrum beta-lactamase (ESBL)-producing enterobacteria, mainly ESBL-producing Klebsiella pneumoniae and Escherichia coli. Therefore, if it could be based on general understanding on the most predominant Gram-negative bacteria and explore the clinical manifestations, biochemical indicators, CT images, and other characteristics and differences between liver abscesses caused by Gram-negative enterobacteria and other types of pathogens, the differences between ESBL bacteria and other non-resistant bacteria as well as other MDROs within Gram-negative bacteria would be explored. Ultimately, constructing a predictive model that can identify the types of pathogenic bacteria and the presence of MDROs in the early stages of the disease through basic clinical characteristics, laboratory indicators, and CT images would have significant theoretical and practical value.
Conditions
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Study Design
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CASE_CONTROL
RETROSPECTIVE
Study Groups
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Patients with ESBL(-) liver abscess
No interventions assigned to this group
Patients with ESBL(+) liver abscess
No interventions assigned to this group
Eligibility Criteria
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Inclusion Criteria
* Patients had accepted abdominal enhance CT scans before surgery or interventional process.
Exclusion Criteria
18 Years
ALL
No
Sponsors
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The First Affiliated Hospital of China University of Science and Technology (Anhui Provincial)
OTHER
Guilin Medical University, China
OTHER
Hebei General Hospital
OTHER
Xijing Hospital
OTHER
Hebei Medical University Third Hospital
OTHER
The First Affiliated Hospital of Dalian Medical University
OTHER
The People's Hospital of Liaoning Province
OTHER
Fourth People's Hospital of Shenyang
OTHER
Sir Run Run Shaw Hospital
OTHER
Shengjing Hospital
OTHER
Responsible Party
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Zhihui Chang
Clinical Professor
Locations
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Shengjing hospital of China medical university
Shenyang, Liaoning, China
Countries
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Central Contacts
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Facility Contacts
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Other Identifiers
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2024PS679K
Identifier Type: -
Identifier Source: org_study_id
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