PRO-DIAG: Improved Diagnosis of Prosthetic Joint Infections
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Prosthesis-Related Infections
- Sponsor
- Aalborg University
- Enrollment
- 40
- Locations
- 1
- Primary Endpoint
- Improved diagnostic accuracy
- Last Updated
- 8 years ago
Overview
Brief Summary
Implantation of joint prostheses is currently the second largest diagnosis-related group in the Danish health service, and in view of the demographic development and spread of lifestyle diseases, this type of intervention is expected to continue to increase.
Unfortunately, 5% of patients experience significant discomforts and complications. The second most frequent and serious complication is infection. While the established laboratory analyses (culture of tissue biopsies) are good at diagnosing acute infections, they are not satisfactory to diagnose a large group of patients especially with chronic infections. This can lead to prolonged diagnosing time and even to wrong diagnosis.
Several studies have shown that analyses of prosthetic parts and the use of molecular biological methods for detecting infecting microorganisms can significantly improve diagnostics accuracy.
The purpose of this project is primarily to demonstrate that analyses of bacterial specific DNA (16S rRNA genes) can confirm or rule out infection as fast (or faster) as cultivation methods. Rapid clarification can promote targeted treatment and in order to demonstrate this, the trial is conducted as a randomized study. .
Investigators
Yijuan Xu
Post Doc
Aalborg University
Eligibility Criteria
Inclusion Criteria
- •revision of hip platelets (THA) or knee replacement (TKA) on indication of likely infection
Exclusion Criteria
- •surgery within the last 4 months. The same applies to patients with short history of illness, pronounced acute symptoms and systemic response (reducing the likelihood of biofilm infection).
Outcomes
Primary Outcomes
Improved diagnostic accuracy
Time Frame: 6 months post surgery
DNA based method can detect patients with hidden chronic infections more often than culture