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Predictive Model of Surgical Time for Revision Total Hip Arthroplasty

Abstract Background Maximizing operating room utilization relies upon accurate estimates of surgical control time (SCT). Here, we demonstrate using a variety of case and patient-specific factors in a multivariate regression model of surgical time for revision total hip arthroplasty (THA). We hypothe...

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Bibliographic Details
Published in:The Journal of arthroplasty 2017-07, Vol.32 (7), p.2214-2218
Main Authors: Wu, Albert, MD, Weaver, Michael J., MD, FACS, Heng, Marilyn, MD MPH, FRCSC, Urman, Richard D., MD, MBA
Format: Article
Language:English
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Summary:Abstract Background Maximizing operating room utilization relies upon accurate estimates of surgical control time (SCT). Here, we demonstrate using a variety of case and patient-specific factors in a multivariate regression model of surgical time for revision total hip arthroplasty (THA). We hypothesize that these variables are better predictors of actual SCT (aSCT) than a surgeon’s own prediction (pSCT). Methods All revision THAs from October 2008 to September 2014 from one institution were accessed. Variables for each case included aSCT, pSCT, patient age, gender, BMI, ASA-PS class, active infection, periprosthetic fracture, bone loss, heterotopic ossification, and implantation / explantation of a well-fixed acetabular / femoral component. These were incorporated in a stepwise fashion into a multivariate regression model for aSCT with a significant cutoff of 0.15. This was compared to a univariate regression model of aSCT that only used pSCT. Results 516 revision THAs were analyzed. After stepwise selection, patient age and ASA-PS were excluded from the model. The most significant increase in aSCT was seen with implantation of a new femoral component (24.0 min), following by explantation of a well-fixed femoral component (18.7 min), and significant bone loss (15.0 min). Overall, the multivariate model had an improved r2 of 0.49, compared to 0.16 from only using pSCT. Conclusion A multivariate regression model can assist surgeons in more accurately predicting the duration of revision THAs. The strongest predictors of increased aSCT are explantation of a well-fixed femoral component, placement of an entirely new femoral component, and presence of significant bone loss.
ISSN:0883-5403
1532-8406
DOI:10.1016/j.arth.2017.01.056