Patient and Perioperative Variables Affecting 30-Day Readmission for Surgical Complications After Hip and Knee Arthroplasties: A Matched Cohort Study.

BACKGROUND

Changes in reimbursement for total hip and knee arthroplasties (THA and TKA) have placed increased financial burden of early readmission on hospitals and surgeons. Our purpose was to characterize factors of 30-day readmission for surgical complications after THA and TKA at a single, high-volume orthopedic specialty hospital.

METHODS

Patients with a diagnosis of osteoarthritis and who were readmitted within 30 days of their unilateral primary THA or TKA procedure between 2010 and 2014. Readmitted patients were matched to nonreadmitted patients 1:2. Patient and perioperative variables were collected for both cohorts. A conditional logistic regression was performed to assess both the patient and perioperative factors and their predictive value toward 30-day readmission.

RESULTS

Twenty-one thousand eight hundred sixty-four arthroplasties (THA = 11,105; TKA = 10,759) were performed between 2010 and 2014 at our institution, in which 60 patients (THA = 37, TKA = 23) were readmitted during this 5-year period. The most common reasons for readmission were fracture (N = 14), infection (N = 14), and dislocation (N = 9). Thirty-day readmission for THA was associated with increased procedure time (P = .05), length of stay (LOS) shorter than 2 days (P = .04), discharge to a skilled nursing facility (P = .05), and anticoagulation use other than aspirin (P = .02). Thirty-day readmission for TKA was associated with increased tourniquet time (P = .02), LOS <3 days (P < .01), and preoperative depression (P = .02). In the combined THA/TKA model, a diagnosis of depression increased 30-day readmission (odds ratio 3.5 [1.4-8.5]; P < .01).

CONCLUSION

Risk factors for 30-day readmission for surgical complications included short LOS, discharge destination, increased procedure/tourniquet time, potent anticoagulation use, and preoperative diagnosis of depression. A focus on risk factor modification and improved risk stratification models are necessary to optimize patient care using readmission rates as a quality benchmark.