Susanne Baertl, Markus Rupp, Maximilian Kerschbaum, Mario Morgenstern, Florian Baumann, Christian Pfeifer, Michael Worlicek, Daniel Popp, Derek F Amanatullah, Volker Alt
{"title":"The PJI-TNM classification for periprosthetic joint infections.","authors":"Susanne Baertl, Markus Rupp, Maximilian Kerschbaum, Mario Morgenstern, Florian Baumann, Christian Pfeifer, Michael Worlicek, Daniel Popp, Derek F Amanatullah, Volker Alt","doi":"10.1302/2046-3758.131.BJR-2023-0012.R2","DOIUrl":null,"url":null,"abstract":"<p><strong>Aims: </strong>This study aimed to evaluate the clinical application of the PJI-TNM classification for periprosthetic joint infection (PJI) by determining intraobserver and interobserver reliability. To facilitate its use in clinical practice, an educational app was subsequently developed and evaluated.</p><p><strong>Methods: </strong>A total of ten orthopaedic surgeons classified 20 cases of PJI based on the PJI-TNM classification. Subsequently, the classification was re-evaluated using the PJI-TNM app. Classification accuracy was calculated separately for each subcategory (reinfection, tissue and implant condition, non-human cells, and morbidity of the patient). Fleiss' kappa and Cohen's kappa were calculated for interobserver and intraobserver reliability, respectively.</p><p><strong>Results: </strong>Overall, interobserver and intraobserver agreements were substantial across the 20 classified cases. Analyses for the variable 'reinfection' revealed an almost perfect interobserver and intraobserver agreement with a classification accuracy of 94.8%. The category 'tissue and implant conditions' showed moderate interobserver and substantial intraobserver reliability, while the classification accuracy was 70.8%. For 'non-human cells,' accuracy was 81.0% and interobserver agreement was moderate with an almost perfect intraobserver reliability. The classification accuracy of the variable 'morbidity of the patient' reached 73.5% with a moderate interobserver agreement, whereas the intraobserver agreement was substantial. The application of the app yielded comparable results across all subgroups.</p><p><strong>Conclusion: </strong>The PJI-TNM classification system captures the heterogeneity of PJI and can be applied with substantial inter- and intraobserver reliability. The PJI-TNM educational app aims to facilitate application in clinical practice. A major limitation was the correct assessment of the implant situation. To eliminate this, a re-evaluation according to intraoperative findings is strongly recommended.</p>","PeriodicalId":9074,"journal":{"name":"Bone & Joint Research","volume":"13 1","pages":"19-27"},"PeriodicalIF":4.7000,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10766470/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bone & Joint Research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1302/2046-3758.131.BJR-2023-0012.R2","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CELL & TISSUE ENGINEERING","Score":null,"Total":0}
引用次数: 0
Abstract
Aims: This study aimed to evaluate the clinical application of the PJI-TNM classification for periprosthetic joint infection (PJI) by determining intraobserver and interobserver reliability. To facilitate its use in clinical practice, an educational app was subsequently developed and evaluated.
Methods: A total of ten orthopaedic surgeons classified 20 cases of PJI based on the PJI-TNM classification. Subsequently, the classification was re-evaluated using the PJI-TNM app. Classification accuracy was calculated separately for each subcategory (reinfection, tissue and implant condition, non-human cells, and morbidity of the patient). Fleiss' kappa and Cohen's kappa were calculated for interobserver and intraobserver reliability, respectively.
Results: Overall, interobserver and intraobserver agreements were substantial across the 20 classified cases. Analyses for the variable 'reinfection' revealed an almost perfect interobserver and intraobserver agreement with a classification accuracy of 94.8%. The category 'tissue and implant conditions' showed moderate interobserver and substantial intraobserver reliability, while the classification accuracy was 70.8%. For 'non-human cells,' accuracy was 81.0% and interobserver agreement was moderate with an almost perfect intraobserver reliability. The classification accuracy of the variable 'morbidity of the patient' reached 73.5% with a moderate interobserver agreement, whereas the intraobserver agreement was substantial. The application of the app yielded comparable results across all subgroups.
Conclusion: The PJI-TNM classification system captures the heterogeneity of PJI and can be applied with substantial inter- and intraobserver reliability. The PJI-TNM educational app aims to facilitate application in clinical practice. A major limitation was the correct assessment of the implant situation. To eliminate this, a re-evaluation according to intraoperative findings is strongly recommended.