Regular, evidence-based assignment of patients to etiologic stroke categories is essential to enable valid comparison among studies. We designed an algorithm (SSS-TOAST) that incorporated recent advances in stroke imaging and epidemiology to identify the most probable TOAST category in the presence of evidence for multiple mechanisms. Based on the weight of evidence, each TOAST subtype was subdivided into 3 subcategories as "evident", "probable", or "possible". Classification into the subcategories was determined via predefined specific clinical and imaging criteria. These criteria included published risks of ischemic stroke from various mechanisms and published reports of the strength of associations between clinical and imaging features and particular stroke mechanisms. Two neurologists independently assessed 50 consecutively admitted patients with acute ischemic stroke through reviews of abstracted data from medical records. The number of patients classified as "undetermined-unclassified" per the original TOAST system decreased from 38-40% to 4% using the SSS-TOAST system. The kappa value for inter-examiner reliability was 0.78 and 0.90 for the original TOAST and SSS-TOAST respectively. The SSS-TOAST system successfully classifies patients with acute ischemic stroke into determined etiologic categories without sacrificing reliability. The SSS-TOAST is a dynamic algorithm that can accommodate modifications as new epidemiological data accumulate and diagnostic techniques advance.

ADC maps and DWI can successfully differentiate PLES from early cerebral ischemia, thus playing a pivotal role in treatment decisions. PLES is associated with a wider variety of conditions than has been previously reported and is not always reversible. Hyperintense DWI signal in patients with the syndrome likely marks a tissue stage of permanent brain injury.

Background and Purpose-The SSS-TOAST is an evidence-based classification algorithm for acute ischemic stroke designed to determine the most likely etiology in the presence of multiple competing mechanisms. In this article, we present an automated version of the SSS-TOAST, the Causative Classification System (CCS), to facilitate its utility in multicenter settings. Methods-The CCS is a web-based system that consists of questionnaire-style classification scheme for ischemic stroke (http://ccs.martinos.org). Data entry is provided via checkboxes indicating results of clinical and diagnostic evaluations. The automated algorithm reports the stroke subtype and a description of the classification rationale. We evaluated the reliability of the system via assessment of 50 consecutive patients with ischemic stroke by 5 neurologists from 4 academic stroke centers. Results-The kappa value for inter-examiner agreement was 0.86 (95% CI, 0.81 to 0.91) for the 5-item CCS (large artery atherosclerosis, cardio-aortic embolism, small artery occlusion, other causes, and undetermined causes), 0.85 (95% CI, 0.80 to 0.89) with the undetermined group broken into cryptogenic embolism, other cryptogenic, incomplete evaluation, and unclassified groups (8-item CCS), and 0.80 (95% CI, 0.76 to 0.83) for a 16-item breakdown in which diagnoses were stratified by the level of confidence. The intra-examiner reliability was 0.90 (0.75-1.00) for 5-item, 0.87 (0.73-1.00) for 8-item, and 0.86 (0.75-0.97) for 16-item CCS subtypes. Conclusions-The web-based CCS allows rapid analysis of patient data with excellent intra-and inter-examiner reliability, suggesting a potential utility in improving the fidelity of stroke classification in multicenter trials or research databases in which accurate subtyping is critical.