Literature DB >> 32004175

Veterans Undergoing Total Hip and Knee Arthroplasty: 30-day Outcomes as Compared to the General Population.

Nicholas B Frisch1, P Maxwell Courtney, Brian Darrith, Laurel A Copeland, Tad L Gerlinger.   

Abstract

INTRODUCTION: The Veterans Affairs (VA) health system is vital to providing joint replacement care to our retired service members but has come under recent scrutiny. The purpose of this study was to compare the short-term outcomes after total hip arthroplasty (THA) and total knee arthroplasty (TKA) between the VA cohort and the general cohort.
METHODS: We retrospectively reviewed 10.460 patients with primary THA and TKA from the Veterans Affairs Corporate Data Warehouse. As a control group, we queried the American College of Surgeons-National Surgical Quality Improvement Program database and identified 58,820 patients with primary THA and TKA over the same time period. We compared length of stay, mortality rates, 30-day complication rates, and 30-day readmissions. We performed a multivariate logistic regression analysis to identify the independent effect of the VA system on adverse outcomes.
RESULTS: Veterans are more likely to be men (93% versus 41%, P < 0.001) and have increased rates of medical comorbidities (all P < 0.001). The rate of short-term complications (all P < 0.001) were all higher in the VA cohort. When controlling for demographics and medical comorbidities, VA patients were more likely to have a readmission (P < 0.001), prolonged length of stay > 4 days (P < 0.001), and experience a complication within 30 days (P < 0.001). DISCUSSION: Despite controlling for higher rates of medical comorbidities, VA patients undergoing primary THA and TKA had poorer short-term outcomes than the civilian cohort. Additional research is needed to ensure our veteran cohort is appropriately optimized and address the discrepancy with the outcomes of the civilian.

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Year:  2020        PMID: 32004175     DOI: 10.5435/JAAOS-D-19-00775

Source DB:  PubMed          Journal:  J Am Acad Orthop Surg        ISSN: 1067-151X            Impact factor:   3.020


  2 in total

1.  A machine learning approach to identify distinct subgroups of veterans at risk for hospitalization or death using administrative and electronic health record data.

Authors:  Ravi B Parikh; Kristin A Linn; Jiali Yan; Matthew L Maciejewski; Ann-Marie Rosland; Kevin G Volpp; Peter W Groeneveld; Amol S Navathe
Journal:  PLoS One       Date:  2021-02-19       Impact factor: 3.240

2.  Estimating the Cost of Surgical Care Purchased in the Community by the Veterans Health Administration.

Authors:  Todd H Wagner; Jeanie Lo; Erin Beilstein-Wedel; Megan E Vanneman; Michael Shwartz; Amy K Rosen
Journal:  MDM Policy Pract       Date:  2021-11-16
  2 in total

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