RESUMEN
Introduction: 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. Methods: 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. Results: 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%). Discussion: 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.
