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2020 Frank Stinchfield Award: Identifying who will fail following irrigation and debridement for prosthetic joint infection

2020Journal article Open access
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dc.contributor.authorShohat, Noam
dc.contributor.authorGoswami, Karan
dc.contributor.authorTan, Timothy L.
dc.contributor.authorYayac, Michael
dc.contributor.authorSoriano, Alex
dc.contributor.authorSousa, Ricardo
dc.contributor.authorWouthuyzen-Bakker, Marjan
dc.contributor.authorParvizi, Javad
dc.date.accessioned2021-11-09T15:34:54Z
dc.date.available2021-11-09T15:34:54Z
dc.date.issued2020
dc.description.abstractAims: Failure of irrigation and debridement (I&D) for prosthetic joint infection (PJI) is influenced by numerous host, surgical, and pathogen-related factors. We aimed to develop and validate a practical, easy-to-use tool based on machine learning that may accurately predict outcome following I&D surgery taking into account the influence of numerous factors. Methods: This was an international, multicentre retrospective study of 1,174 revision total hip (THA) and knee arthroplasties (TKA) undergoing I&D for PJI between January 2005 and December 2017. PJI was defined using the Musculoskeletal Infection Society (MSIS) criteria. A total of 52 variables including demographics, comorbidities, and clinical and laboratory findings were evaluated using random forest machine learning analysis. The algorithm was then verified through cross-validation. Results: Of the 1,174 patients that were included in the study, 405 patients (34.5%) failed treatment. Using random forest analysis, an algorithm that provides the probability for failure for each specific patient was created. By order of importance, the ten most important variables associated with failure of I&D were serum CRP levels, positive blood cultures, indication for index arthroplasty other than osteoarthritis, not exchanging the modular components, use of immunosuppressive medication, late acute (haematogenous) infections, methicillin-resistant Staphylococcus aureus infection, overlying skin infection, polymicrobial infection, and older age. The algorithm had good discriminatory capability (area under the curve = 0.74). Cross-validation showed similar probabilities comparing predicted and observed failures indicating high accuracy of the model. Conclusion: This is the first study in the orthopaedic literature to use machine learning as a tool for predicting outcomes following I&D surgery. The developed algorithm provides the medical profession with a tool that can be employed in clinical decision-making and improve patient care. Future studies should aid in further validating this tool on additional cohorts. Cite this article: Bone Joint J 2020;102-B(7 Supple B):11-19.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationShohat N, Goswami K, Tan TL, et al. 2020 Frank Stinchfield Award: Identifying who will fail following irrigation and debridement for prosthetic joint infection. Bone Joint J. 2020;102-B(7_Supple_B):11-19. doi:10.1302/0301-620X.102B7.BJJ-2019-1628.R1pt_PT
dc.identifier.doi10.1302/0301-620X.102B7.BJJ-2019-1628.R1pt_PT
dc.identifier.issn2049-4394
dc.identifier.urihttp://hdl.handle.net/10400.16/2533
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherBritish Editorial Society of Bone & Joint Surgerypt_PT
dc.relation.publisherversionhttps://online.boneandjoint.org.uk/doi/full/10.1302/0301-620X.102B7.BJJ-2019-1628.R1?rfr_dat=cr_pub++0pubmed&url_ver=Z39.88-2003&rfr_id=ori%3Arid%3Acrossref.org#pt_PT
dc.subjectFailurept_PT
dc.subjectIrrigation and debridementpt_PT
dc.subjectProsthetic joint infectionpt_PT
dc.subjectTotal hip arthroplastypt_PT
dc.title2020 Frank Stinchfield Award: Identifying who will fail following irrigation and debridement for prosthetic joint infectionpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.conferencePlaceEnglandpt_PT
oaire.citation.endPage19pt_PT
oaire.citation.issue7_Supple_Bpt_PT
oaire.citation.startPage11pt_PT
oaire.citation.titleThe Bone & Joint Journalpt_PT
oaire.citation.volume102-Bpt_PT
person.familyNameSousa
person.givenNameRicardo
person.identifier.ciencia-id8C18-816F-6E4A
person.identifier.orcid0000-0003-4293-7347
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication2f21d3c0-c752-499f-9bcc-d87d9fd31d5c
relation.isAuthorOfPublication.latestForDiscovery2f21d3c0-c752-499f-9bcc-d87d9fd31d5c

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