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Mortality following hip arthroplasty--inappropriate use of National Joint Registry (NJR) data
Sarah L Whitehouse1, Benjamin J R F Bolland2, Jonathan R Howell2
1Orthopaedic Research Unit, Institute of Health and Biomedical Innovation, Queensland University of Technology, The Prince Charles Hospital, Chermside, Queensland, Australia.
Insights
Hip replacement surgery shows lower mortality rates than the general population. However, many factors influence outcomes, and current data sets may not fully capture all variables for accurate analysis.
Area of Science:
- Orthopedic surgery
- Public health research
- Biostatistics
Background:
- Hip arthroplasty is a common procedure with significant implications for patient outcomes.
- Interpreting mortality rates requires careful consideration of numerous confounding variables.
- Existing data sets may have limitations in capturing all relevant factors.
Purpose of the Study:
- To analyze mortality rates following hip arthroplasty using a large dataset.
- To identify significant demographic and clinical factors influencing mortality.
- To assess the adequacy of available data for comprehensive outcome analysis.
Main Methods:
- Utilized the 2011 National Joint Registry (NJR) data set.
- Employed Cox proportional hazards models to analyze mortality.
- Included relevant variables such as age, ASA grade, diagnosis, gender, provider type, hip type, and surgeon grade.
Main Results:
- Mortality rates in hip arthroplasty patients were lower compared to age-matched controls across all hip types.
- Age, ASA grade, diagnosis, gender, provider type, hip type, and surgeon grade significantly impacted mortality.
- Schemper's statistic indicated that only 18.98% of mortality variation was explained by the NJR data variables.
Conclusions:
- Hip arthroplasty is associated with favorable mortality outcomes relative to the general population.
- While several factors significantly affect mortality, the NJR data set has limitations in accounting for all confounders.
- Using NJR data alone to study outcomes influenced by numerous unmeasured variables is inappropriate.
Abstract:
Mortality following hip arthroplasty is affected by a large number of confounding variables each of which must be considered to enable valid interpretation. Relevant variables available from the 2011 NJR data set were included in the Cox model. Mortality rates in hip arthroplasty patients were lower than in the age-matched population across all hip types. Age at surgery, ASA grade, diagnosis, gender, provider type, hip type and lead surgeon grade all had a significant effect on mortality. Schemper's statistic showed that only 18.98% of the variation in mortality was explained by the variables available in the NJR data set. It is inappropriate to use NJR data to study an outcome affected by a multitude of confounding variables when these cannot be adequately accounted for in the available data set.
