A Joint Model Based on Deep Learning to Predict Multidrug-resistant Klebsiella Pneumoniae Liver Abscess (CLASS2401)

Combining Image-clinical Model Based on Deep Learning and Radiomics to Predict Multidrug-resistant Klebsiella Pneumoniae Liver Abscess

The goal of this observational study is to train a deep learning-based model to predict multidrug-resistant Klebsiella pneumoniae liver abscess and evaluate it on a multi-center database.

Study Overview

• Status: Recruiting
• Conditions: Liver Abscess

Detailed Description

Liver abscess is one of the most common abdominal organ infections worldwide, with a mortality rate that once reached as high as 70%. Before the widespread use of antibiotics, suppurative appendicitis was the main causative factor for liver abscesses. In recent years, with the widespread application of antibiotics and the promotion and popularization of interventional therapy, the main causative factor for liver abscesses has gradually shifted from suppurative appendicitis to biliary tract diseases, and the mortality rate of liver abscesses has also been gradually declining. However, due to the increasing number of infections caused by various multidrug-resistant organisms (MDROs), the reported mortality rate currently remains between 2% and 18%.

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.

• Study Type: Observational
• Enrollment (Estimated): 550

Contacts and Locations

• Study Contact: Name: Zhihui Chang; Phone Number: +86 18940258437; Email: Changzh@sj-hospital.org

Study Locations

• China
  • Liaoning
    • Shenyang, Liaoning, China, 110004
      • Recruiting
      • Shengjing Hospital Of China Medical University
      • Contact: Zhihui Chang; Email: Changzh@sj-hospital.org

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

Pyogenic liver abscess patients in 14years of a multicenter project.

Description

Inclusion Criteria:

• Patients diagnosed as pyogenic liver abscess and was proved by surgery or interventional process.
• Patients had accepted abdominal enhance CT scans before surgery or interventional process.

Exclusion Criteria:

• Patients diagnosed with other types of liver abscess such as amoeba.

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort
Patients with ESBL(-) liver abscess
Patients with ESBL(+) liver abscess

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Result of bacteria culture	The result of bacteria culture of abscess decides which group the patients belong. This result was obtained from bacteria culture tests which were done by department of laboratory medicine.	The first time before surgery or interventional process, usually within 1 week, up to 4 weeks.
Result of drug resistant test	Our destination is to find out the potential differences between Esbl(+) & Esbl(-) liver abscess.	The first time before surgery or interventional process, usually within 1 week, up to 4 weeks.

Collaborators and Investigators

• Sponsor: Shengjing Hospital
• Collaborators: Guilin Medical University, China; Xijing Hospital; Hebei General Hospital; The People's Hospital of Liaoning Province; Sir Run Run Shaw Hospital; Hebei Medical University Third Hospital; The First Affiliated Hospital of Dalian Medical University; Fourth People's Hospital of Shenyang; The First Affiliated Hospital of China University of Science and Technology (Anhui Provincial)

Study record dates

Study Major Dates

• Study Start (Actual): January 1, 2024
• Primary Completion (Estimated): December 30, 2024
• Study Completion (Estimated): March 1, 2025

Study Registration Dates

• First Submitted: July 3, 2024
• First Submitted That Met QC Criteria: July 11, 2024
• First Posted (Actual): July 17, 2024

Study Record Updates

• Last Update Posted (Actual): July 17, 2024
• Last Update Submitted That Met QC Criteria: July 11, 2024
• Last Verified: July 1, 2024

More Information

Terms related to this study

• Keywords: Deep-learning; Liver Abscess
• Additional Relevant MeSH Terms: Digestive System Diseases; Pathologic Processes; Infections; Respiratory Tract Infections; Respiratory Tract Diseases; Lung Diseases; Inflammation; Liver Diseases; Suppuration; Abdominal Abscess; Pneumonia; Abscess; Liver Abscess
• Other Study ID Numbers: 2024PS679K

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No