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Original Investigation
Association of VA Surgeons
January 9, 2019

Risk Prediction Tools to Improve Patient Selection for Carotid Endarterectomy Among Patients With Asymptomatic Carotid Stenosis

Author Affiliations
  • 1Division of General Internal Medicine, Department of Medicine, University of California, San Francisco
  • 2Medical Service, San Francisco Veterans Affairs Medical Center, San Francisco, California
  • 3Northern California Institute of Research and Education, San Francisco
  • 4Department of Neurology, University of California, Los Angeles
  • 5VA Greater Los Angeles Healthcare System, Los Angeles, California
  • 6Department of Internal Medicine, Indiana University School of Medicine, Indianapolis
  • 7Department of Neurology, Indiana University School of Medicine, Indianapolis
  • 8Health Services Research and Development, Center for Health Information and Communication, Richard L. Roudebush VA Medical Center, Department of Veterans Affairs, Indianapolis, Indiana
  • 9Division of General Internal Medicine, UT Southwestern Medical Center, Dallas, Texas
  • 10Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
  • 11Visiting Scholar, Department of Medicine, University of California, San Francisco
  • 12Department of Surgery, University of Nebraska, Omaha
  • 13Omaha VA Medical Center, Omaha, Nebraska
JAMA Surg. Published online January 9, 2019. doi:10.1001/jamasurg.2018.5119
Key Points

Question  Which patients with asymptomatic carotid stenosis are unlikely to benefit from carotid endarterectomy?

Findings  Among 2325 veterans in this cohort study that used Veterans Administration and Medicare data, a risk prediction tool (Carotid Mortality Index) based on 23 candidate variables and a simpler model based on the number of 4 key comorbidities (any cancer, chronic obstructive pulmonary disease, congestive heart failure, and chronic kidney disease [the 4C model]) were developed to identify patients at higher risk of 5-year mortality. The models were internally validated.

Meaning  Study results suggest that both the Carotid Mortality Index and the 4C model may be used to inform clinicians whether a patient will live long enough to benefit from carotid endarterectomy.

Abstract

Importance  Randomized clinical trials have demonstrated that patients with asymptomatic carotid stenosis are eligible for carotid endarterectomy (CEA) if the 30-day surgical complication rate is less than 3% and the patient’s life expectancy is at least 5 years.

Objective  To develop a risk prediction tool to improve patient selection for CEA among patients with asymptomatic carotid stenosis.

Design, Setting, and Participants  In this cohort study, veterans 65 years and older who received both carotid imaging and CEA in the Veterans Administration between January 1, 2005, and December 31, 2009 (n = 2325) were followed up for 5 years. Data were analyzed from January 2005 to December 2015. A risk prediction tool (the Carotid Mortality Index [CMI]) based on 23 candidate variables identified in the literature was developed using Veterans Administration and Medicare data. A simpler model based on the number of 4 key comorbidities that were prevalent and strongly associated with 5-year mortality was also developed (any cancer in the past 5 years, chronic obstructive pulmonary disease, congestive heart failure, and chronic kidney disease [the 4C model]). Model performance was assessed using measures of discrimination (eg, area under the curve [AUC]) and calibration. Internal validation was performed by correcting for optimism using 500 bootstrapped samples.

Main Outcome and Measure  Five-year mortality.

Results  Among 2325 veterans, the mean (SD) age was 73.74 (5.92) years. The cohort was predominantly male (98.8%) and of white race/ethnicity (94.4%). Overall, 29.5% (n = 687) of patients died within 5 years of CEA. On the basis of a backward selection algorithm, 9 patient characteristics were selected (age, chronic kidney disease, diabetes, chronic obstructive pulmonary disease, any cancer diagnosis in the past 5 years, congestive heart failure, atrial fibrillation, remote stroke or transient ischemic attack, and body mass index) for the final logistic model, which yielded an optimism-corrected AUC of 0.687 for the CMI. The 4C model had slightly worse discrimination (AUC, 0.657) compared with the CMI model; however, the calibration curve was similar to the full model in most of the range of predicted probabilities.

Conclusions and Relevance  According to results of this study, use of the CMI or the simpler 4C model may improve patient selection for CEA among patients with asymptomatic carotid stenosis.

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