Machine learning models accurately predict recurrent infection following revision total knee arthroplasty for periprosthetic joint infection.
Artificial intelligence
Machine learning
Periprosthetic joint infection
Revision total knee arthroplasty
Risk factors
Journal
Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA
ISSN: 1433-7347
Titre abrégé: Knee Surg Sports Traumatol Arthrosc
Pays: Germany
ID NLM: 9314730
Informations de publication
Date de publication:
Aug 2022
Aug 2022
Historique:
received:
19
08
2021
accepted:
28
10
2021
pubmed:
12
11
2021
medline:
27
7
2022
entrez:
11
11
2021
Statut:
ppublish
Résumé
This study aimed to develop and validate machine-learning models for the prediction of recurrent infection in patients following revision total knee arthroplasty for periprosthetic joint infection. A total of 618 consecutive patients underwent revision total knee arthroplasty for periprosthetic joint infection. The patient cohort included 165 patients with confirmed recurrent periprosthetic joint infection (PJI). Potential risk factors including patient demographics and surgical characteristics served as input to three machine-learning models which were developed to predict recurrent periprosthetic joint. The machine-learning models were assessed by discrimination, calibration and decision curve analysis. The factors most significantly associated with recurrent PJI in patients following revision total knee arthroplasty for PJI included irrigation and debridement with/without modular component exchange (p < 0.001), > 4 prior open surgeries (p < 0.001), metastatic disease (p < 0.001), drug abuse (p < 0.001), HIV/AIDS (p < 0.01), presence of Enterococcus species (p < 0.01) and obesity (p < 0.01). The machine-learning models all achieved excellent performance across discrimination (AUC range 0.81-0.84). This study developed three machine-learning models for the prediction of recurrent infections in patients following revision total knee arthroplasty for periprosthetic joint infection. The strongest predictors were previous irrigation and debridement with or without modular component exchange and prior open surgeries. The study findings show excellent model performance, highlighting the potential of these computational tools in quantifying increased risks of recurrent PJI to optimize patient outcomes. IV.
Identifiants
pubmed: 34761306
doi: 10.1007/s00167-021-06794-3
pii: 10.1007/s00167-021-06794-3
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2582-2590Informations de copyright
© 2021. The Author(s) under exclusive licence to European Society of Sports Traumatology, Knee Surgery, Arthroscopy (ESSKA).
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