No Difference in Short-Term Revision Rate and Early Mortality in Uncemented Versus Cemented Total Hip Arthroplasty: A Regression Discontinuity Analysis in the Dutch Arthroplasty Register Data

Controversy persists over whether cemented or uncemented fixation is more effective in reducing revision and mortality risks following primary total hip arthroplasty (THA). Despite a shift towards uncemented THA in Europe, Australia, and the US, no consensus exists on superior outcomes. This ambiguity in evidence from randomized controlled trials (RCTs) and observational studies necessitates advanced research methodologies to derive more definitive conclusions. This study investigates the causal impact of THA fixation type on 2-year and 5-year revision rates, along with 90-day mortality, utilizing a regression discontinuity (RD) design in scenarios where fixation choice is guided by patient age. Employing data from the Dutch Arthroplasty Register, we conducted a cohort study on primary THAs for osteoarthritis from 2007 to 2019. A “fuzzy” RD design was executed to compute the Local Average Treatment Effect for subjects around the age-based selection threshold for fixation type. The main outcome of interest was the revision rate at 2 years post-operation. Analysis for the 2-year revision endpoint, covering any cause, included 2,344 females and 1,671 males across 5 hospitals each, with no significant variation in revision rates observed. For the 5-year mark, 1,058 females in 3 hospitals and 214 males in 1 hospital were examined, similarly showing no significant differences. Mortality within 90 days post-operation was also investigated in 5 female and 7 male cohorts, with 2,180 and 2,145 surgeries respectively, yielding no substantial disparities. In conclusion, the RD analysis revealed no notable differences in revision rates at 2 and 5 years or in early mortality based on the fixation method used in THA. These outcomes suggest that the age-based preference for THA fixation may not influence the revision or mortality risk, underscoring the value of RD design in deriving causal insights from observational data.