Reverse Dorr Classification of Lateral Knee Radiographs: A Qualitative Assessment of Bone Quality in Total Knee Arthroplasty
Year: 2026
Type: article
Study design: Observational Study
Abstract: Background The Dorr classification is the standard radiographic method for assessing proximal femur bone quality and guiding implant selection in total hip arthroplasty (THA); however, a similar method has not been established for total knee arthroplasty (TKA). Previous studies have demonstrated that radiographic parameters of the distal femur, specifically the ratio of the inner vs outer cortical diameter on lateral knee radiographs, are associated with bone mineral density. The aim of this study was to develop an efficient, reproducible, and qualitative classification system of the distal femur that correlates with bone mineral density to guide implant selection in TKA. Methods A retrospective review of 200 TKA patients with preoperative dual-energy radiograph absorptiometry (DXA) within 1 year of TKA at a single institution was performed. Preoperative lateral knee radiographs were independently reviewed and the degree of anterior cortical taper of the distal femur between the trochlea and a point one patella height (Reverse Dorr 1 [RD1]) or two patella heights (RD2) proximal to the trochlea was graded as Reverse Dorr A, B, or C. Results The Reverse Dorr classification system was associated with DXA T-score, intraoperative physician assessment, and demonstrated moderate to strong interobserver agreement. Reverse Dorr A femurs had a 59-70% predicted probability of normal bone, whereas Reverse Dorr C femurs had a 66-76% predicted probability of osteoporosis. Conclusions The Reverse Dorr classification system is an efficient and reproducible assessment of preoperative bone quality and may serve to inform implant selection in TKA.
Source: Arthroplasty Today
Institution: University of Wisconsin–Madison
Language: English
Subfield: Surgery
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,500
OpenAlex ID: W7221562234