*Data available from clinical trials or registries about the usefulness/efficacy in different subpopulations, such as sex, age, history of diabetes, history of prior myocardial infarction, history of heart failure, and prior aspirin use. For comparative effectiveness recommendations (Class I and IIa; Level of Evidence A and B only), studies that support the use of comparator verbs should involve direct comparisons of the treatments or strategies being evaluated. Meschia et al Guidelines for the Primary Prevention of Stroke 3757 Kingdom and found to vary across groups, but the suitability of the scale to predict outcomes has not been fully established.20 Alternative prediction models have been developed using other cohorts and different sets of stroke risk factors. Retaining most of the Framingham covariates, one alternative stroke risk scoring system omits cigarette smoking and antihypertensive medication and adds time to walk 15 feet and serum creati- Another score is derived from a mixed cohort of stroke and stroke-free patients and includes history of stroke, marital status, blood pressure (BP) as a categorical variable, high-den- sity lipoprotein (HDL) cholesterol, impaired expiratory flow, physical disability, and a depression score.22 Several studies have generated risk assessment tools for use in patients with AF (see Atrial Fibrillation). Risk models have also been developed for other populations. For example, a stroke prediction model derived for use in Chinese adults in Taiwan included age, sex, SBP, diastolic BP (DBP), family history of stroke, AF, and dia- betes mellitus and was found to have a discriminative capacity similar to or better than those of other available stroke models.23 The model, however, has not been independently validated. 