Mikko Peltola1, Antti Malmivaara, Mika Paavola. 1. National Institute for Health and Welfare, Centre for Health and Social Economics (CHESS), Mannerheimintie 166, 00270 Helsinki, Finland. E-mail address for M. Peltola: mikko.peltola@thl.fi.
Abstract
BACKGROUND: The risk of early revision is increased for the first patients operatively treated with a newly introduced knee prosthesis. In this study, we explored the learning curves associated with ten knee implant models to determine their effect on early revision risk. METHODS: We studied register data from all seventy-five surgical units that performed knee arthroplasty in Finland from 1998 to 2007. Of 54,925 patients (66,098 knees), 39,528 patients (46,363 knees) underwent arthroplasty for osteoarthritis of the knee with the ten most common total knee implants and were followed with complete data until December 31, 2010, or the time of death. We used a Cox proportional-hazards regression model for calculating the hazard ratios for early revision for the first fifteen arthroplasties and subsequent increments of numbers of arthroplasties. RESULTS: We found large differences among knee implants at the introduction with regard to the risk of early revision, as well as for the overall risk of early revision. A learning curve was found for four implant models, while six models did not show a learning effect on the risk of early revision. The survivorship of the studied prostheses showed substantial differences. CONCLUSIONS: Knee implants have model-specific learning curves and early revision risks. Some models are more difficult to implement than others. The manufacturers should consider the learning effect when designing implants and instrumentation. The surgeons should thoroughly familiarize themselves with the new knee implants before use.
BACKGROUND: The risk of early revision is increased for the first patients operatively treated with a newly introduced knee prosthesis. In this study, we explored the learning curves associated with ten knee implant models to determine their effect on early revision risk. METHODS: We studied register data from all seventy-five surgical units that performed knee arthroplasty in Finland from 1998 to 2007. Of 54,925 patients (66,098 knees), 39,528 patients (46,363 knees) underwent arthroplasty for osteoarthritis of the knee with the ten most common total knee implants and were followed with complete data until December 31, 2010, or the time of death. We used a Cox proportional-hazards regression model for calculating the hazard ratios for early revision for the first fifteen arthroplasties and subsequent increments of numbers of arthroplasties. RESULTS: We found large differences among knee implants at the introduction with regard to the risk of early revision, as well as for the overall risk of early revision. A learning curve was found for four implant models, while six models did not show a learning effect on the risk of early revision. The survivorship of the studied prostheses showed substantial differences. CONCLUSIONS: Knee implants have model-specific learning curves and early revision risks. Some models are more difficult to implement than others. The manufacturers should consider the learning effect when designing implants and instrumentation. The surgeons should thoroughly familiarize themselves with the new knee implants before use.
Authors: Kevin J Bozic; Atul F Kamath; Kevin Ong; Edmund Lau; Steve Kurtz; Vanessa Chan; Thomas P Vail; Harry Rubash; Daniel J Berry Journal: Clin Orthop Relat Res Date: 2014-12-03 Impact factor: 4.176