Abstract
Revisions of hip and knee arthroplasty implants and cardiac pacemakers pose a large medical and economic burden for society. Consequently, the identification of health care providers with potential for quality improvements regarding the reduction of revision rates is a central aim of quality assurance in any healthcare system. Even though the time span between initial and possible subsequent operations is a classical time-to-event endpoint, hospital-specific quality indicators are in practice often measured as revisions within a fixed follow-up period and subsequently analyzed by traditional methods like proportions or logistic regression. Methods from survival analysis, in contrast, allow the inclusion of all observations, i.e. also those with early censoring or events, and make thus more efficient and more timely use of the available data than traditional methods. This may be obvious to a statistician but in an applied context with historic traditions, the introduction of more complicated methods needs a clear presentation of their added value. We demonstrate how standard survival methods like the Kaplan–Meier estimator and a multiplicative hazards model outperform traditional methods with regard to the identification of performance outliers. Following that, we use the proposed methods to analyze 640,000 hip and knee replacement operations with about 13,000 revisions between 2015 and 2016 in more than 1200 German hospitals in the annual evaluation of quality of care. Based on the results, performance outliers are identified which are to be further investigated qualitatively with regard to their provided quality of care and possible necessary measures for improvement. Survival analysis is a sound statistical framework for analyzing data in the context of quality assurance and survival methods outperform the statistical methods that are traditionally used in this area.
Highlights
The occurrence of subsequent operations to replace or repair implanted prostheses or cardiac pacemakers and their temporal distance from the initial operation is an important indicator for the quality of both the initial implantation operation as well as the implantation product
Revision rates are an important measure of quality, both for product quality as well as for the initial implantation operation in the areas of arthroplasty and cardiac pacemakers
While registry studies usually trace implants over a long period of time and, often make use of survival analysis methods, hospital-specific and nationwide revision rates were usually analyzed by calculating simple proportions within a fixed follow-up period in the context of German quality assurance
Summary
The occurrence of subsequent operations to replace or repair implanted prostheses or cardiac pacemakers and their temporal distance from the initial operation is an important indicator for the quality of both the initial implantation operation as well as the implantation product. We show in the following how standard methods like the Kaplan–Meier estimator and a multiplicative hazard model can be utilized for analyzing an unadjusted and a risk-adjusted quality indicator, with time from initial to potential subsequent operations as outcome of interest. While these methods are standard tools in the context of registry studies (Ranstam and Robertsson 2017; Gwinnutt et al 2017), they have rarely been used in the context of sequential hospital-specific quality assurance
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