title: Comparing co-morbidities in total joint arthroplasty patients using the RxRisk-V, Elixhauser, and Charlson Measures: a cross-sectional evaluation.
abstract: BACKGROUND
  Joint arthroplasty patients have a high prevalence of co-morbidities and this impacts their surgical outcomes. There are different ways to ascertain co-morbidities and appropriate measurement is necessary. The purpose of this study was to: (1) describe the prevalence of co-morbidities in a cohort of total hip arthroplasty (THA) and knee arthroplasty (TKA) patients using two diagnoses-based measures (Charlson and Elixhauser) and one prescription-based measure (RxRisk-V); (2) compare the agreement of co-morbidities amongst the measures.
  
  
  METHODS
  A cross-sectional study of Australian veterans undergoing THAs (n = 11,848) and TKAs (n = 18,972) between 2001 and 2012 was conducted. Seventeen co-morbidities were identified using the Charlson, 30 using the Elixhauser, and 42 using the RxRisk-V measure. Agreement between co-morbidities was calculated using Kappa (κ) statistics.
  
  
  RESULTS
  Combining measures, 64 conditions were identified, of these 28 were only identified using the RxRisk-V, 11 using the Elixhauser, and 2 using the Charlson. The most prevalent conditions was pain treated with anti-inflammatories (58.7% THAs, 55.9% TKAs), pain treated with narcotics (55.0% THAs, 50.9% TKAs), hypertension (56.0% THAs and TKAs), and anticoagulation disorders (53.0% THAs, 48.6% TKAs). Diabetes was the only condition with substantial agreement (all κ > 0.6) amongst all measures. When comparing the diagnoses based algorithms, agreement was high for overlapping conditions (all κ > 0.71).
  
  
  CONCLUSIONS
  Different measures identified different co-morbidities, provided different estimates for the same co-morbidity, and had different levels of agreement for common co-morbidities. This highlights the importance of understanding co-morbidity measures and using them appropriately in studies and case-mix adjustments analyses.
authors.affiliation: Quality Use of Medicines and Pharmacy Research Centre, Medicine and Device Surveillance Centre of Research Excellence, Sansom Institute, School of Pharmacy and Medical Sciences, University of South Australia, GPO Box 2471, Adelaide, 5001, South Australia, Australia. maria.inacio@unisa.edu.au.
authors.forename: Maria C S
authors.initials: MC
authors.lastname: Inacio
authors.affiliation: Quality Use of Medicines and Pharmacy Research Centre, Medicine and Device Surveillance Centre of Research Excellence, Sansom Institute, School of Pharmacy and Medical Sciences, University of South Australia, GPO Box 2471, Adelaide, 5001, South Australia, Australia.
authors.forename: Nicole L
authors.initials: NL
authors.lastname: Pratt
authors.affiliation: Quality Use of Medicines and Pharmacy Research Centre, Medicine and Device Surveillance Centre of Research Excellence, Sansom Institute, School of Pharmacy and Medical Sciences, University of South Australia, GPO Box 2471, Adelaide, 5001, South Australia, Australia.
authors.forename: Elizabeth E
authors.initials: EE
authors.lastname: Roughead
authors.affiliation: Australian Orthopaedic Association, National Total Joint Replacement Registry, Level 6 Bice Building, Royal Adelaide Hospital, The University of Adelaide, Adelaide, 5005, SA, Australia.
authors.forename: Stephen E
authors.initials: SE
authors.lastname: Graves
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D019644:Arthroplasty, Replacement, Hip / Q000706:statistics & numerical data*; D019645:Arthroplasty, Replacement, Knee / Q000706:statistics & numerical data*; D001315:Australia / Q000453:epidemiology; D015897:Comorbidity; D003430:Cross-Sectional Studies; D016208:Databases, Factual; D005260:Female; D006801:Humans; D008297:Male; D016347:Medical Records Systems, Computerized; D012307:Risk Factors