The traditional Tönnis Classification System has inherent drawbacks as it is vulnerable to the subjectivity of a four-grade system. A two-grade classification could potentially be more reliable. The purpose of this study is to (1) compare the inter-observer and intra-observer reliability of the traditional Tönnis Classification System and a simplified Binary Tönnis Classification System for hip osteoarthritis and to (2) evaluate the clinical applicability of both systems. Our hypothesis is that the proposed Binary Tönnis Classification System will have better reliability and agreement for surgical decision-making.
Forty consecutive patients were selected to participate in this study. Patients were included in this study if they were between 35 and 60 years old. Patients were excluded if they had prior hip surgeries or conditions. All radiographs were randomized and blinded by a non-observer. Five fellowship-trained hip surgeons from a single center, in a fully crossed design, analyzed and graded all the radiographs utilizing the traditional Tönnis Classification System and the proposed Binary Tönnis Classification System. Intra- and inter-observer reliability values for both the systems were calculated using the Cohen's κ coefficient. A multi-rater κ was calculated using the weighted Fleiss method.
The study sample contained 40 anterosuperior hip radiographs. For the traditional Tönnis Classification System, the weighted κ showed a fair inter-observer reliability (κ = 0.474) and excellent intra-observer reliability (κ mean = 0.866). For the proposed Binary Tönnis Classification System, both inter-observer and intra-observer reliability demonstrated excellent values, (κ = 0.858 and 0.928, respectively). On average, the Binary Tönnis Classification System correctly captured 87% of cases. When the traditional Tönnis Classification System was dichotomized, the capture rate was 84%.
A simplified binary Tönnis Classification System demonstrates better reliability and clinical implementation than the traditional Tönnis Classification System.