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Development and Underlying Structure of a Second‐Generation Appropriateness Classification System for Total Knee Arthroplasty
Author(s) -
Escobar Martinez Antonio,
Perera Robert A.,
Riddle Daniel L.
Publication year - 2021
Publication title -
arthritis care and research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.032
H-Index - 163
eISSN - 2151-4658
pISSN - 2151-464X
DOI - 10.1002/acr.24169
Subject(s) - medicine , physical therapy , osteoarthritis , arthroplasty , knee pain , multinomial logistic regression , regression analysis , surgery , machine learning , alternative medicine , pathology , computer science
Objective Evidence supports the validity of total knee arthroplasty (TKA) appropriateness classification based on the RAND Corporation and University of California Los Angeles system.The system, however, is ~2 decades old, and the science and clinical application of TKA has changed dramatically. We undertook this study to describe the methods used to develop a second‐generation system and to examine the structure of the system to determine the extent to which each of the indication criteria informed appropriateness. Methods Multivariable multinomial regression analyses determined the extent to which each of the 8 individually analyzed indication criteria informed appropriateness judgments. Classification tree analysis illustrates how the indication criteria, in combination, led to judgments of appropriate, inappropriate, or uncertain. Results An expert panel selected 8 indication criteria (i.e., age, knee pain, function, radiographic osteoarthritis severity, osteoarthritis location, psychological factors, pain catastrophizing, and comorbidities). A total of 1,008 clinical scenarios were written, based on the criteria. Regression analyses indicated that age, knee pain, function, and radiographic severity dominated prediction of appropriateness, while the other criteria played a smaller role. Classification tree analysis confirmed the regression findings. Conclusion Our second‐generation classification system, which incorporates contemporary indicators of TKA prognosis and risk, demonstrated preliminary evidence for utility in clinical practice.