TOAST stroke subtype classification in clinical practice: implications for the Get With The Guidelines-Stroke nationwide registry.

Abstract

The TOAST (Trial of ORG 10172 in Acute Stroke Treatment) is the most commonly used ischemic stroke subtype classification system worldwide and a required field in the US National Get With The Guidelines-Stroke (GWTG-Stroke) registry. However, stroke diagnostics have advanced substantially since the TOAST classification was designed 30 years ago, potentially making it difficult to apply reliably. In this prospective diagnostic accuracy study, we analyzed consecutive ischemic stroke patients admitted to a Comprehensive Stroke Center between July-October 2021. Clinical practice TOAST classification diagnoses rendered by the stroke team in the electronic medical record (EMR) at discharge were retrieved from GWTG-Stroke registry and compared to a reference ("gold") standard diagnosis derived from agreement between two expert raters after review of the EMR and patient imaging. Among 49 patients; age was 72.3 years (±12.1), 53% female, and presenting NIHSS median 3 (IQR 1-11). Work-up included: brain imaging in 100%; cardiac rhythm assessment in 100%; cervical/cerebral vessel imaging in 98%; TTE ± TEE in 92%; and TCD emboli evaluation in 51%. Reference standard diagnoses were: LAA-6%, SVD-14%, CE-39%, OTH-10%, UND-M (more than one cause)-20%, and UND-C (cryptogenic)-10%. GWTG-Stroke TOAST diagnoses agreed with reference standard diagnoses in 30/49 (61%). Among the 6 subtype diagnoses, specificity was generally high (84.8%-97.7%), but sensitivity suboptimal for LAA (33%), OTH (60%), UND-M (10%), and UND-C (20%). Positive predictive value was suboptimal for 5 of the 6 subtypes: LAA (13%), SVD (58%), OTH (75%), UND-M (50%), and UND-C (50%). Clinical practice TOAST classification subtype diagnoses entered into the GWTG-Stroke registry were accurate in only 61% of patients, a performance rate that, if similarly present at other centers, would hamper the ability of the national registry to provide dependable insights into subtype-related care. Development of an updated ischemic stroke subtype classification system, with algorithmic logic embedded in electronic medical records, is desirable.