An evidence-based causative classification system for acute ischemic stroke

Abstract

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.