Risk stratification in patients with established vascular diseaseLiterature - Dorresteijn JA, Visseren FL, Wassink AM et al; - Heart. 2013 Apr 10. [Epub ahead of print] doi:10.1136/heartjnl-2013-303640
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.
Dorresteijn JA, Visseren FL, Wassink AM et al; on behalf of the SMART Study Group.
Heart. 2013 Apr 10. [Epub ahead of print] doi:10.1136/heartjnl-2013-303640
Although guidelines suggest that all patients with symptomatic arterial disease have 20% or more absolute 10-year risk of developing recurrent vascular events [1-3], clinicians are well aware that not all patients are the same, nor their actual risk. Not all patients might need the potentially hazardous and expensive novel preventive treatments. Risk stratification could help identify which patients benefit most from aggressive therapy and expensive programmes for lifestyle improvement. Moreover, individual patients may want to know their prognosis and risk factors.
Some prediction rules for risk stratification in patients with vascular disease exist, but they are not commonly applied. Commonly used prediction models have been developed based on subjects without clinical manifestations of cardiovascular disease and have not been validated in a population with established atherosclerosis [4-7]. Consequently, these risk assessment models lack important determinants of risk that are of particular relevance in patients with established disease .
The authors therefore aimed to develop and validate practical prediction models for the long-term risk of developing recurrent vascular events in individual patients with any type of arterial disease. Model A included clinical parameters (SMART risk score) only, model B only used carotid ultrasound findings, while model C combined these data. Data was used of 5788 patients included in the SMART (Secondary Manifestations of ARTerial disease) study, a single-centre prospective cohort study . The models were developed on a random 60% of the study data (n=3489) and subsequently tested in the remaining 40%.
- The concordance statistic represents the extent to which the model can separate patients with or without a recurrent cardiovascular event.
- The concordance statistic of model A (0.675 (0.642-0.708)) was significantly better (P<0.01) as compared to model B (0.644 (0.609–0.679)). Model C (0.683 (0.650–0.717)) was significantly, but only slightly better than model A (P=0.03).
- Calibration plots and statistical tests show satisfying goodness-of-fit for 10-year predicted versus observed event-free survival of models A, B and C.
- Predicted and observed risk also match when clinically relevant risk subgroups are analysed, except that the observed risk was slightly lower (26 or 27%) in the subgroups where SMART risk score predicted high risk (30-<40%), in all models.
Discrimination of patient with different risk profiles based on clinical parameters only was only slightly smaller than the model that combined clinical with imaging data. Because a model based on clinical parameters without the need for carotid ultrasound is easy to use, the authors propose the use of the SMART risk score for prediction of 10-year risk for recurrent vascular events in patients with any type of symptomatic atherosclerotic vascular disease. Application of the model based on SMART risk score worked well in subgroups of patients with cerebrovascular disease, coronary artery disease, peripheral artery disease or abdominal aortic aneurysm. Moreover, the SMART risk score is useful for patients with established vascular disease, thus allows risk stratification of patients in the stable phase of symptomatic vascular disease.
1. Graham I, Atar D, Borch-Johnsen K, et al. European guidelines on cardiovascular disease prevention in clinical practice: executive summary. Eur Heart J 2007;28:2375–414.
2. Grundy SM, Cleeman JI, Merz CN, et al. Implications of recent clinical trials for the National Cholesterol Education Program Adult Treatment Panel III guidelines. Circulation 2004;110:227–39.
3. Smith SC Jr, Allen J, Blair SN, et al. AHA/ACC guidelines for secondary prevention for patients with coronary and other atherosclerotic vascular disease: 2006 update: endorsed by the National Heart, Lung, and Blood Institute. Circulation 2006;113:2363–72
4. Ridker PM, Buring JE, Rifai N, et al. Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: the Reynolds Risk Score. JAMA 2007;297:611–19.
5. Ridker PM, Paynter NP, Rifai N, et al. C-reactive protein and parental history improve global cardiovascular risk prediction: the Reynolds Risk Score for men. Circulation 2008;118:2243–51, 4p.
6. Wilson PW, D’Agostino RB, Levy D, et al. Prediction of coronary heart disease using risk factor categories. Circulation 1998;97:1837–47.
7. Conroy RM, Pyorala K, Fitzgerald AP, et al. Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J 2003;24:987–1003.
8.Wattanakit K, Folsom AR, Chambless LE, et al. Risk factors for cardiovascular event recurrence in the Atherosclerosis Risk in Communities (ARIC) study. Am Heart J 2005;149:606–12.
9. Simons PC, Algra A, van de Laak MF, et al. Second manifestations of ARTerial disease (SMART) study: rationale and design. Eur J Epidemiol 1999;15:773–81.
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