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Insurance status predicts acuity of thoracic aortic operations.

Publication ,  Journal Article
Andersen, ND; Hanna, JM; Ganapathi, AM; Bhattacharya, SD; Williams, JB; Gaca, JG; McCann, RL; Hughes, GC
Published in: J Thorac Cardiovasc Surg
November 2014

OBJECTIVE: Nonelective case status is the strongest predictor of mortality for thoracic aortic operations. We hypothesized that underinsured patients were more likely to require nonelective thoracic aortic surgery because of reduced access to preventative cardiovascular care and elective surgical services. METHODS: Between June 2005 and August 2011, 826 patients were admitted to a single aortic referral center and underwent 1 or more thoracic aortic operations. Patients with private insurance or Medicare (insured group, n=736; 89%) were compared with those with Medicaid or no insurance (underinsured group, n=90; 11%). RESULTS: The proportion of patients requiring nonelective surgery was higher for underinsured than insured patients (56% vs 26%, P<.0001). Multivariable analysis revealed underinsurance to be the strongest independent predictor of nonelective case status (odds ratio [OR], 2.67; P<.0001). Preoperative use of lipid-lowering medications (OR, 0.63; P<.009) or a history of aortic surgery (OR, 0.48; P<.001) was associated with a decreased risk of nonelective operation. However, after adjustment for differences in preoperative characteristics and case status, underinsurance did not confer an increased risk of procedural morbidity or mortality (adjusted OR, 0.94; P=.83) or late death (adjusted hazard ratio, 0.83, P=.58) when compared with insured patients. CONCLUSIONS: Underinsured patients were at the greatest risk of requiring nonelective thoracic aortic operation, possibly because of decreased use of lipid-lowering therapies and aortic surveillance. These data imply that greater access to preventative cardiovascular care may reduce the need for nonelective thoracic aortic surgery and lead to improved survival from thoracic aortic disease.

Duke Scholars

Published In

J Thorac Cardiovasc Surg

DOI

EISSN

1097-685X

Publication Date

November 2014

Volume

148

Issue

5

Start / End Page

2082 / 2086

Location

United States

Related Subject Headings

  • Vascular Surgical Procedures
  • United States
  • Treatment Outcome
  • Risk Factors
  • Retrospective Studies
  • Respiratory System
  • Proportional Hazards Models
  • Private Sector
  • Odds Ratio
  • Multivariate Analysis
 

Citation

APA
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ICMJE
MLA
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Andersen, N. D., Hanna, J. M., Ganapathi, A. M., Bhattacharya, S. D., Williams, J. B., Gaca, J. G., … Hughes, G. C. (2014). Insurance status predicts acuity of thoracic aortic operations. J Thorac Cardiovasc Surg, 148(5), 2082–2086. https://doi.org/10.1016/j.jtcvs.2014.03.013
Andersen, Nicholas D., Jennifer M. Hanna, Asvin M. Ganapathi, Syamal D. Bhattacharya, Judson B. Williams, Jeffrey G. Gaca, Richard L. McCann, and G Chad Hughes. “Insurance status predicts acuity of thoracic aortic operations.J Thorac Cardiovasc Surg 148, no. 5 (November 2014): 2082–86. https://doi.org/10.1016/j.jtcvs.2014.03.013.
Andersen ND, Hanna JM, Ganapathi AM, Bhattacharya SD, Williams JB, Gaca JG, et al. Insurance status predicts acuity of thoracic aortic operations. J Thorac Cardiovasc Surg. 2014 Nov;148(5):2082–6.
Andersen, Nicholas D., et al. “Insurance status predicts acuity of thoracic aortic operations.J Thorac Cardiovasc Surg, vol. 148, no. 5, Nov. 2014, pp. 2082–86. Pubmed, doi:10.1016/j.jtcvs.2014.03.013.
Andersen ND, Hanna JM, Ganapathi AM, Bhattacharya SD, Williams JB, Gaca JG, McCann RL, Hughes GC. Insurance status predicts acuity of thoracic aortic operations. J Thorac Cardiovasc Surg. 2014 Nov;148(5):2082–2086.
Journal cover image

Published In

J Thorac Cardiovasc Surg

DOI

EISSN

1097-685X

Publication Date

November 2014

Volume

148

Issue

5

Start / End Page

2082 / 2086

Location

United States

Related Subject Headings

  • Vascular Surgical Procedures
  • United States
  • Treatment Outcome
  • Risk Factors
  • Retrospective Studies
  • Respiratory System
  • Proportional Hazards Models
  • Private Sector
  • Odds Ratio
  • Multivariate Analysis