title: Which Clinical and Patient Factors Influence the National Economic Burden of Hospital Readmissions After Total Joint Arthroplasty?
abstract: BACKGROUND
  The Affordable Care Act of 2010 advanced the economic model of bundled payments for total joint arthroplasty (TJA), in which hospitals will be financially responsible for readmissions, typically at 90 days after surgery. However, little is known about the financial burden of readmissions and what patient, clinical, and hospital factors drive readmission costs.
  
  
  OBJECTIVE
  (1) What is the incidence, payer mix, and demographics of THA and TKA readmissions in the United States? (2) What patient, clinical, and hospital factors are associated with the cost of 30- and 90-day readmissions after primary THA and TKA? (3) Are there any differences in the economic burden of THA and TKA readmissions between payers? (4) What types of THA and TKA readmissions are most costly to the US hospital system?
  
  
  METHODS
  The recently developed Nationwide Readmissions Database from the Healthcare Cost and Utilization Project (2006 hospitals from 21 states) was used to identify 719,394 primary TJAs and 62,493 90-day readmissions in the first 9 months of 2013 based on International Classification of Diseases, 9th Revision, Clinical Modification codes. We classified the reasons for readmissions as either procedure- or medical-related. Cost-to-charge ratios supplied with the Nationwide Readmissions Database were used to compute the individual per-patient cost of 90-day readmissions as a continuous variable in separate general linear models for THA and TKA. Payer, patient, clinical, and hospital factors were treated as covariates. We estimated the national burden of readmissions by payer and by the reason for readmission.
  
  
  RESULTS
  The national rates of 30- and 90-day readmissions after THA were 4% (95% confidence interval [CI], 4.2%-4.5%) and 8% (95% CI, 7.5%-8.1%), respectively. The national rates of 30- and 90-day readmissions after primary TKA were 4% (95% CI, 3.8%-4.0%) and 7% (95% CI, 6.8%-7.2%), respectively. The five most important variables responsible for the cost of 90-day THA readmissions (in rank order, based on the Type III F-statistic, p < 0.001) were length of stay (LOS), all patient-refined diagnosis-related group (APR DRG) severity, type of readmission (that is, medical- versus procedure-related), hospital ownership, and age. Likewise, the five most important variables responsible for the cost of 90-day TKA readmissions were LOS, APR DRG severity, gender, hospital procedure volume, and hospital ownership. After adjusting for covariates, mean 90-day readmission costs reimbursed by private insurance were, on average, USD 1324 and USD 1372 greater than Medicare (p < 0.001) for THA and TKA, respectively. In the 90 days after TJA, two-thirds of the total annual readmission costs were covered by Medicare. In 90 days after THA, more readmissions were still associated with procedure-related complications, including infections, dislocations, and periprosthetic fractures, which in aggregate account for 59% (95% CI, 59.1%-59.6%) of the total readmission costs to the US healthcare system. For TKA, 49% of the total readmission cost (95% CI, 48.8%-49.6%) in 90 days for the United States was associated with procedure issues, most notably including infections.
  
  
  CONCLUSIONS
  Hospital readmissions up to 90 days after TJA represent a massive economic burden on the US healthcare system. Approximately half of the total annual economic burden for readmissions in the United States is medical and unrelated to the joint replacement procedure and half is related to procedural complications.
  
  
  CONCLUSIONS
  This national study underscores LOS during readmission as a primary cost driver, suggesting that hospitals and doctors further optimize, to the extent possible, the clinical pathways for the hospitalization of readmitted patients. Because patients readmitted as a result of infection, dislocation, and periprosthetic fractures are the most costly types of readmissions, efforts to reduce the LOS for these types of readmissions will have the greatest impact on their economic burden. Additional clinical research is needed to determine the extent to which, if any, the LOS during readmissions can be reduced without sacrificing quality or access of care.
authors.affiliation: Exponent Inc, 3440 Market Street, Suite 600, Philadelphia, PA, 19104, USA. skurtz@exponent.com.|School of Biomedical Engineering, Science, and Health Systems, Drexel University, Philadelphia, PA, USA. skurtz@exponent.com.
authors.forename: Steven M
authors.initials: SM
authors.lastname: Kurtz
authors.affiliation: Exponent Inc, Menlo Park, CA, USA.
authors.forename: Edmund C
authors.initials: EC
authors.lastname: Lau
authors.affiliation: Exponent Inc, 3440 Market Street, Suite 600, Philadelphia, PA, 19104, USA.
authors.forename: Kevin L
authors.initials: KL
authors.lastname: Ong
authors.affiliation: Hospital for Joint Diseases, New York, NY, USA.
authors.forename: Edward M
authors.initials: EM
authors.lastname: Adler
authors.affiliation: OrthoIndy, Indianapolis, IN, USA.
authors.forename: Frank R
authors.initials: FR
authors.lastname: Kolisek
authors.affiliation: Stryker Orthopaedics, Mahwah, NJ, USA.
authors.forename: Michael T
authors.initials: MT
authors.lastname: Manley
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D019644:Arthroplasty, Replacement, Hip / Q000009:adverse effects / Q000191:economics*; D019645:Arthroplasty, Replacement, Knee / Q000009:adverse effects / Q000191:economics*; D057225:Data Mining; D016208:Databases, Factual; D003953:Diagnosis-Related Groups / Q000191:economics; D005260:Female; D017721:Hospital Costs; D061847:Hospitals, High-Volume; D061846:Hospitals, Low-Volume / Q000191:economics; D006801:Humans; D007902:Length of Stay / Q000191:economics; D008297:Male; D006278:Medicare / Q000191:economics; D008875:Middle Aged; D010359:Patient Readmission / Q000191:economics*; D011183:Postoperative Complications / Q000191:economics / Q000628:therapy; D017064:Process Assessment, Health Care / Q000191:economics*; D018570:Risk Assessment; D012307:Risk Factors; D012737:Sex Factors; D013997:Time Factors; D016896:Treatment Outcome; D014481:United States