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

推出亚当氏后循环卒中量表(ASPOS):一种用于评估和预测后循环卒中的新型验证工具

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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.

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