ReviewFrontiers in medicine2021
Risk Scores and Machine Learning to Identify Patients With Acute Periprosthetic Joints Infections That Will Likely Fail Classical Irrigation and Debridement.
Review in Frontiers in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- The Application of artificial intelligence in periprosthetic joint infection.Journal of advanced research · 2026Review
- Beyond Cultures: The Evolving Role of Molecular Diagnostics, Synovial Biomarkers and Artificial Intelligence in the Diagnosis of Prosthetic Joint Infections.Journal of clinical medicine · 2025Review
- Predicting periprosthetic joint Infection: Evaluating supervised machine learning models for clinical application.Journal of orthopaedic translation · 2025Article
- Debridement, antimicrobial therapy, and implant retention (DAIR) as curative strategy for acute periprosthetic hip and knee infections: a position paper of the European Bone & Joint Infection Society (EBJIS).Journal of bone and joint infection · 2025Article
- Imaging in Periprosthetic Joint Infection Diagnosis: A Comprehensive Review.Microorganisms · 2024Review
- Validation of a Classification System for Optimal Application of Debridement, Antibiotics, and Implant Retention in Prosthetic Joint Infections following Total Knee Arthroplasty: A Retrospective Review.Antibiotics (Basel, Switzerland) · 2024Article
- Investigation of the effect of rifampicin resistance and risk factors on recovery rates after DAIR procedure in patients with prosthetic joint infection.Journal of orthopaedic surgery and research · 2023Article
- Application of machine learning in the prevention of periprosthetic joint infection following total knee arthroplasty: a systematic review.Arthroplasty (London, England) · 2023Review
- Methodological Challenges in Predicting Periprosthetic Joint Infection Treatment Outcomes: A Narrative Review.Frontiers in rehabilitation sciences · 2022Review
- [Septic revision arthroplasty: how to confirm diagnosis, plan surgery and manage follow-up treatment].Der Orthopade · 2021Review
- A machine learning-based model for "In-time" prediction of periprosthetic joint infection.Digital healthArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The most preferred treatment for acute periprosthetic joint infection (PJI) is surgical debridement, antibiotics and retention of the implant (DAIR). The reported success of DAIR varies greatly and depends on a complex interplay of several host-related factors, duration of symptoms, the microorganism(s) causing the infection, its susceptibility to antibiotics and many others. Thus, there is a great clinical need to predict failure of the "classical" DAIR procedure so that this surgical option is offered to those most likely to succeed, but also to identify those patients who may benefit from more intensified antibiotic treatment regimens or new and innovative treatment strategies. In this review article, the current recommendations for DAIR will be discussed, a summary of independent risk factors for DAIR failure will be provided and the advantages and limitations of the clinical use of preoperative risk scores in early acute (post-surgical) and late acute (hematogenous) PJIs will be presented. In addition, the potential of implementing machine learning (artificial intelligence) in identifying patients who are at highest risk for failure of DAIR will be addressed. The ultimate goal is to maximally tailor and individualize treatment strategies and to avoid treatment generalization.
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Registered trials
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