Classification of Bone Loss With Failed Stemmed Components in Revision Total Knee Arthroplasty

Introduction: With the increasing number of primary total knee arthroplasties (TKA) being performed annually there is also an expected increase in the number of revision TKA, as well as failed revision TKA with stemmed components. A new classification system based upon the location and degree of bone loss in the tibial and or femoral metaphysis and diaphysis is proposed. Type 1 has no bone loss in the metaphysis and diaphysis; Type 2 has a metaphyseal bone loss. Type 3 defects are subdivided into A and B, depending on the extent of the diaphyseal bone loss. Type 4 has extensive bone loss with an expansion of the diaphyseal cortex. Method: An online survey with digital anteroposterior and lateral radiographs was sent to five arthroplasty surgeons. A total of 55 cases with stemmed femoral and tibial components, considered failures and pending revision, were reviewed. By using the proposed classification system, each femoral and tibial component was scored. Interobserver reliability was determined using the intraclass correlation coefficient for pooled data. Results: Comparisons between each individual reviewer demonstrated moderate to strong agreement overall for the reviewers using the classification, with an average correlation coefficient of 0.67 (95% CI [0.57 to 0.77]) for all cases, signifying a low variation in the scores of each case. Looking specifically at the femoral components, the ICC was 0.62 (95% CI [0.477 to 0.76]), while the ICC for the tibial components was 0.71 (95% CI [0.58 to 0.83]). Discussion: This classification, which has demonstrated moderate to strong interobserver reliability, can help surgeons determine the degree of anticipated bone loss and approach these complex cases with a preoperative plan based upon the radiographic images. This classification system will also allow standardized communication among surgeons, categorization of procedures for comparative research, and anticipated prognosis. (C) 2022 Elsevier Inc. All rights reserved.