Format

Send to

Choose Destination
See comment in PubMed Commons below
Ann Surg. 2013 Jun;257(6):1168-73. doi: 10.1097/SLA.0b013e31827b9761.

A model for predicting the risk of carotid artery disease.

Author information

1
Department of Health Evidence and Policy, Mount Sinai School of Medicine, New York, NY 10029, USA. Giampaolo.greco@mountsinai.org

Abstract

OBJECTIVE:

To develop a model for the identification of individuals at risk for carotid stenosis (CS) that could be useful in a clinical setting when trying to decide whether screening is worthwhile.

BACKGROUND:

Evidence that aggressive medical therapy and life style changes reduce the risk of stroke in individuals with CS is increasing and has led to a renewed interest in screening for CS.

METHODS:

Data on demographics and risk factors were obtained from 2,885,257 individuals who had carotid Duplex scans by Life Line Screening between 2003 and 2008. Multivariable logistic regression analysis was used to identify independent risk factors for CS (>50% stenosis). A scoring system was developed where risk factors were assigned a weighted score. Predictive ability was assessed by calculating C statistics and r2.

RESULTS:

In the screened cohort, 71,004 patients (2.4%) had CS. Independent risk factors include advanced age, smoking, peripheral arterial disease, high blood pressure, coronary artery disease, diabetes, cholesterol, and abdominal aortic aneurysm. African Americans, Asians, and Hispanics had reduced risk than whites. Exercise and consumption of fruit, vegetables, and nuts had a modest protective effect. A predictive scoring system was created that identifies individuals with CS more efficiently (C statistic = 0.753) than previously published models.

CONCLUSIONS:

We provide a model that enables identification of individuals who have a high probability of having CS. This model can be helpful in designing targeted screening programs that are cost-effective.

PMID:
23333880
DOI:
10.1097/SLA.0b013e31827b9761
[Indexed for MEDLINE]
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for Lippincott Williams & Wilkins
    Loading ...
    Support Center