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Original article
Development and validation of a prediction rule for recurrent vascular events based on a cohort study of patients with arterial disease: the SMART risk score
  1. Johannes A N Dorresteijn1,
  2. Frank L J Visseren1,
  3. Annemarie M J Wassink1,
  4. Martijn J A Gondrie2,
  5. Ewout W Steyerberg3,
  6. Paul M Ridker4,
  7. Nancy R Cook4,
  8. Yolanda van der Graaf2,
  9. on behalf of the SMART Study Group
  1. 1Department of Vascular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
  2. 2Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
  3. 3Department of Public Health, Center for Medical Decision Making, Erasmus Medical Center, Rotterdam, The Netherlands
  4. 4Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
  1. Correspondence to Dr Frank L J Visseren, Department of Vascular Medicine, University Medical Center Utrecht, PO Box 85500, Utrecht 3508 GA, The Netherlands; F.L.J.Visseren{at}


Objectives To enable risk stratification of patients with various types of arterial disease by the development and validation of models for prediction of recurrent vascular event risk based on vascular risk factors, imaging or both.

Design Prospective cohort study.

Setting University Medical Centre.

Patients 5788 patients referred with various clinical manifestations of arterial disease between January 1996 and February 2010.

Main outcome measures 788 recurrent vascular events (ie, myocardial infarction, stroke or vascular death) that were observed during 4.7 (IQR 2.3 to 7.7) years’ follow-up.

Results Three Cox proportional hazards models for prediction of 10-year recurrent vascular event risk were developed based on age and sex in addition to clinical parameters (model A), carotid ultrasound findings (model B) or both (model C). Clinical parameters were medical history, current smoking, systolic blood pressure and laboratory biomarkers. In a separate part of the dataset, the concordance statistic of model A was 0.68 (95% CI 0.64 to 0.71), compared to 0.64 (0.61 to 0.68) for model B and 0.68 (0.65 to 0.72) for model C. Goodness-of-fit and calibration of model A were adequate, also in separate subgroups of patients having coronary, cerebrovascular, peripheral artery or aneurysmal disease. Model A predicted <20% risk in 59% of patients, 20–30% risk in 19% and >30% risk in 23%.

Conclusions Patients at high risk for recurrent vascular events can be identified based on readily available clinical characteristics.

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