拉什模型
肌萎缩侧索硬化
比例(比率)
物理医学与康复
物理疗法
心理学
医学
地理
发展心理学
地图学
疾病
病理
作者
Umberto Manera,Luca Solero,Christina Fournier,Antonio Canosa,Rosario Vasta,Alessandro Bombaci,Maurizio Grassano,Francesca Palumbo,Maria Claudia Torrieri,Paolina Salamone,Federico Casale,Giuseppe Fuda,Cristina Moglia,Andrea Calvo,Adriano Chiò
标识
DOI:10.1080/21678421.2021.2013892
摘要
Introduction: The Amyotrophic Lateral Sclerosis (ALS) functional rating scale – revised (ALSFRS-R) is the most widely used tool for the clinical monitoring in ALS patients. Despite his usefulness as a multidimensional scale, the combined score derived from different domains is not linearly related to symptoms severity. The Rasch-Built Overall ALS Disability Scale (ROADS) has recently been developed to overcome some of these limitations. Objectives: To validate the Italian version of the ROADS scale and assess the reliability of its administration to patients versus their respective caregivers and the correlation to the corresponding ALSFRS-R. Methods: In the Turin ALS Center, the ROADS Scale questionnaire was administered together with ALSFRS-R to 55 ALS patients and their caregivers during regular follow-up assessments. Correlation analysis was performed using Spearman’s rho, Bland-Altman difference plots, Cronbach’s alpha coefficient and Intraclass correlation coefficient (ICC), one-way random effects were used for proper comparison. Results: Their correlation coefficient between patients and caregivers ROADS was found to be very high (ICC 0.95, p < 0.001). Stratifying for age, sex, site of onset, type of caregiver, disease duration, and progression rate, ICC values that did not change significantly among the considered categories. We also found a high correlation between ROADS and ALSFRS-R total score (patients’ correlation coefficient: 0.88). Conclusions: The Italian version of the ROADS scale is a valid and reliable tool to monitor disease burden, showing a high level of agreement between the responses given by patients and caregivers.
科研通智能强力驱动
Strongly Powered by AbleSci AI