TY - JOUR
T1 - Risk Scores and Machine Learning to Identify Patients With Acute Periprosthetic Joints Infections That Will Likely Fail Classical Irrigation and Debridement
AU - Wouthuyzen-Bakker, Marjan
AU - Shohat, Noam
AU - Parvizi, Javad
AU - Soriano, Alex
N1 - Publisher Copyright:
© Copyright © 2021 Wouthuyzen-Bakker, Shohat, Parvizi and Soriano.
PY - 2021/5/3
Y1 - 2021/5/3
N2 - 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.
AB - 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.
KW - debridement
KW - failure
KW - implant retention
KW - machine learning
KW - periprosthetic joint infection
KW - risk score
UR - http://www.scopus.com/inward/record.url?scp=85106024586&partnerID=8YFLogxK
U2 - 10.3389/fmed.2021.550095
DO - 10.3389/fmed.2021.550095
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C2 - 34012968
AN - SCOPUS:85106024586
SN - 2296-858X
VL - 8
JO - Frontiers in Medicine
JF - Frontiers in Medicine
M1 - 550095
ER -