Prediction rule for cardiovascular events and mortality in peripheral arterial disease patients: data from the prospective Second Manifestations of ARTerial disease (SMART) cohort study

Abstract

Background: Patients with peripheral arterial disease (PAD) are at high risk of secondary cardiovascular death and events such as myocardial infarction or stroke. To minimize this elevated risk, cardiovascular risk factors should be treated in all PAD patients. Secondary risk management may benefit from a prediction tool to identify PAD patients at the highest risk who could be referred for an additional extensive workup. Stratifying PAD patients according to their risk of secondary events could aid in achieving optimal therapy compliance. To this end we developed a prediction model for secondary cardiovascular events in PAD patients.

Methods: The model was developed using data from 800 PAD patients who participated in the Second Manifestations of ARTerial disease (SMART) cohort study. From the baseline characteristics, 13 candidate predictors were selected for the model development. Missing values were imputed by means of single regression imputation. Continuous predictors were truncated and transformed where necessary, followed by model reduction by means of backward stepwise selection. To correct for over-fitting, a bootstrapping technique was applied. Finally, a score chart was created that divides patients in four risk categories that have been linked to the risk of a cardiovascular event during 1- and 5-year follow-up.

Results: During a mean follow-up of 4.7 years, 120 events occurred (27% nonfatal myocardial infarction, 21% nonfatal stroke, and 52% mortality from vascular causes), corresponding to a 1- and 5-year cumulative incidence of 3.1% and 13.2%, respectively. Important predictors for the secondary risk of a cardiovascular event are age, history of symptomatic cardiovascular disease, systolic blood pressure, high-density lipoprotein cholesterol, smoking behavior, ankle-brachial pressure index, and creatinine level. The risk of a cardiovascular event in a patient as predicted by the model was 0% to 10% and 1% to 28% for the four risk categories at 1- and 5-year follow-up, respectively. The discriminating capacity of the prediction model, indicated by the c statistic, was 0.76 (95% confidence interval, 0.71-0.80).

Conclusion: A prediction model can be used to predict secondary cardiovascular risk in PAD patients. We propose such a prediction model to allow for the identification of PAD patients at the highest risk of a cardiovascular event or cardiovascular death, which may be a viable tool in vascular secondary health care practice.