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Prevention of Prosthetic Joint Infection: From Traditional Approaches towards Quality Improvement and Data Mining
J. Gallo, E. Nieslanikova,
Language English Country Switzerland
Document type Journal Article, Review
Grant support
VES 17-29680A
Ministerstvo Zdravotnictví Ceské Republiky
University Hospital Olomouc, 00098892
Ministerstvo zdravotnictví České Republiky
NV17-29680A
MZ0
CEP Register
Digital library NLK
Full text - Article
NLK
Free Medical Journals
from 2012
PubMed Central
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Europe PubMed Central
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ProQuest Central
from 2019-01-01
Open Access Digital Library
from 2012-01-01
Open Access Digital Library
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Health & Medicine (ProQuest)
from 2019-01-01
ROAD: Directory of Open Access Scholarly Resources
from 2012
PubMed
32664491
DOI
10.3390/jcm9072190
Knihovny.cz E-resources
- Publication type
- Journal Article MeSH
- Review MeSH
A projected increased use of total joint arthroplasties will naturally result in a related increase in the number of prosthetic joint infections (PJIs). Suppression of the local peri-implant immune response counters efforts to eradicate bacteria, allowing the formation of biofilms and compromising preventive measures taken in the operating room. For these reasons, the prevention of PJI should focus concurrently on the following targets: (i) identifying at-risk patients; (ii) reducing "bacterial load" perioperatively; (iii) creating an antibacterial/antibiofilm environment at the site of surgery; and (iv) stimulating the local immune response. Despite considerable recent progress made in experimental and clinical research, a large discrepancy persists between proposed and clinically implemented preventative strategies. The ultimate anti-infective strategy lies in an optimal combination of all preventative approaches into a single "clinical pack", applied rigorously in all settings involving prosthetic joint implantation. In addition, "anti-infective" implants might be a choice in patients who have an increased risk for PJI. However, further progress in the prevention of PJI is not imaginable without a close commitment to using quality improvement tools in combination with continual data mining, reflecting the efficacy of the preventative strategy in a particular clinical setting.
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