Introducing Adam’s Scale of Posterior Stroke (ASPOS): A Novel Validated Tool to Assess and Predict Posterior Circulation Strokes

Author:

Wiśniewski AdamORCID,Filipska Karolina,Piec Katarzyna,Jaskólski Filip,Ślusarz Robert

Abstract

Background: Assessing the severity of posterior circulation strokes, due to the variety of symptoms, is a significant clinical problem. Current clinimetric scales show lower accuracy in the measurement of posterior stroke severity, compared with that of anterior strokes. The aim of the study was to design a validated tool, termed Adam’s Scale of Posterior Stroke (ASPOS), for better assessment and prediction of posterior stroke. Methods: This prospective, observational study involved 126 posterior circulation ischemic stroke subjects. Four researchers, previously trained in ASPOS, randomized the stroke severity using a novel tool and other appropriate stroke scales (The National Institute of Health Stroke Scale—NIHSS, modified Rankin Scale—mRS, Glasgow Coma Scale, Barthel Index, or Israeli Vertebrobasilar Stroke Scale—IVBSS) to assess the psychometric properties, reliability, and validity of ASPOS and investigate its predictive value. Results: ASPOS reached a Cronbach’s alpha coefficient of 0.7449, indicating good internal consistency. The Bland–Altman analysis showed a good coefficient of repeatability (CR) of 0.46, a 95% confidence interval (CI) of 0.41–0.53, and excellent intraclass correlation coefficients or weighted kappa values (>0.90), reflecting high reliability and reproducibility. Highly significant correlations with other scales confirmed the construct and predictive validity of ASPOS. A total ASPOS score of three points indicated a significantly increased probability of severe stroke based on the NIHSS, compared to a total ASPOS of 1–2 points (odds ratio (OR) 141; 95% CI: 6.72–2977.66; p = 0.0014). Conclusions: We developed a novel, valid, and reliable tool to assess posterior circulation strokes. This can contribute to a more comprehensive estimation of posterior stroke and, additionally, due to its predictive properties, it can be used to more accurately select candidates for specific treatments.

Publisher

MDPI AG

Subject

General Neuroscience

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