All you stats geeks out there will love the January 6, 2016 study in The Journal of Bone & Joint Surgery by Schilling and Bozic. We at OrthoBuzz are going to skip the gory statistical details for the most part and focus on the essential findings.
First the premise and purpose of the study: Because measuring and improving health care outcomes are nowadays top priorities, risk adjustment—methods to account for differences in patient characteristics across providers—has become a contentious issue. General risk-assessment models tend not to be well-tailored to orthopaedic procedures. So Schilling and Bozic developed a series of risk-adjustment models specific to 30-day morbidity and mortality following hip fracture repair (HFR), total hip arthroplasty (THA), and total knee arthroplasty (TKA). To develop their models, they used prospectively collected clinical data from the National Surgical Quality Improvement Program.
Here are the major findings: For THA and TKA, risk-adjustment models using age, sex, and American Society of Anesthesiologists (ASA) physical status classification were nearly as predictive as models using many additional covariates. HFR model discrimination improved with the addition of comorbidities and laboratory values. Vital signs did not improve model discrimination for any of the procedures.
The study confirms that it is possible to provide adequate risk adjustment for analyzing outcomes of these procedures using only a handful of the most predictive variables commonly available within the operative record. “More parsimonious models are a viable alternative when the adequacy of risk adjustment must be weighed against the cost and burden of large-scale data extraction from the clinical record,” the authors conclude.