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Abstract Number: 190

Hospital Volume Predicts Outcomes and Complications after Total Shoulder Arthroplasty

Jasvinder A. Singh1 and Rekha Ramachandaran2, 1University of Alabama and VA Medical Center, Birmingham, AL, 2University of Alabama at Birmingham, Birmingham, AL

Meeting: 2014 ACR/ARHP Annual Meeting

Keywords: Arthroplasty, outcomes and shoulder disorders

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Session Information

Session Title: Orthopedics, Low Back Pain and Rehabilitation

Session Type: Abstract Submissions (ACR)

Background/Purpose: To assess the association of hospital procedure volume for total shoulder arthroplasty (TSA) with patient outcomes and complications.

Methods: We used the Nationwide Inpatient Sample (NIS) from 1998-2011 to study the association of hospital annual TSA procedure volume with patient characteristics and TSA outcomes, including discharge disposition (home vs. inpatient facility), length of index hospitalization, post-arthroplasty periprosthetic fracture and revision.  Annual hospital TSA volume was categorized as <5, 5-9, 10-14, 15-24 and ³25 TSA procedures annually. 

Results:   Patients receiving TSA at higher volume hospitals were more likely to be female (p<0.0001), of White race (p<0.0001). Compared to low volume hospitals (<5, 5-9, 10-14 procedures annually), patients receiving TSA at higher volume hospitals (15-24, ³25) had significantly lower likelihood of: (1) being discharged to an inpatient medical facility, 16.5%, 13.4%, 13.0%, 12.7% and 11.5% (p<0.0001); (2) hospital stay >median, 46.6%, 40.4%, 36.6%, 34.4% and 29.2% (p<0.0001); (3) post-arthroplasty fracture, 1.2%, 0.8%, 0.9%, 0.6% and 0.8% (p=0.0004); (4) transfusion, 8%, 7.1%, 6.7%, 7.1% and 5.5% (p=0.006); and (5) TSA revision, 0.5%, 0.3%, 0.2%, 0.3%, 0.3% (p=0.045), respectively.

Conclusion: In this study, we found that higher annual hospital TSA volume was associated with better TSA outcomes.  These findings document the impact of annual hospital TSA volume on TSA outcomes.  Patients, surgeons and policy-makes should be aware of these findings and take them into account in decision-making, policy decisions and resource allocation.  


Disclosure:

J. A. Singh,

takeda, savient,

2,

takeda, savient, regeneron, allergan,

5;

R. Ramachandaran,
None.

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