Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK.
Unit of Medical Genetics and Neurogenetics, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, 20133, Italy.
Ann Clin Transl Neurol. 2023 Nov;10(11):2000-2012. doi: 10.1002/acn3.51886. Epub 2023 Aug 28.
The Scale for Assessment and Rating of Ataxia (SARA) is widely used in different types of ataxias and has been chosen as the primary outcome measure in the European natural history study for Friedreich ataxia (FA).
To assess distribution and longitudinal changes of SARA scores and its single items, we analyzed SARA scores of 502 patients with typical-onset FA (<25 years) participating in the 4-year prospective European FA Consortium for Translational Studies (EFACTS). Pattern of disease progression was determined using linear mixed-effects regression models. The chosen statistical model was re-fitted in order to estimate parameters and predict disease progression. Median time-to-change and rate of score progression were estimated using the Kaplan-Meier method and weighted linear regression models, respectively.
SARA score at study enrollment and age at onset were the major predictive factors of total score progression during the 4-year follow-up. To a less extent, age at evaluation also influenced the speed of SARA progression, while disease duration did not improve the prediction of the statistical model. Temporal dynamics of total SARA and items showed a great variability in the speed of score increase during disease progression. Gait item had the highest annual progression rate, with median time for one-point score increase of 1 to 2 years.
Analyses of statistical properties of SARA suggest a variable sensitivity of the scale at different disease stages, and provide important information for population selection and result interpretation in future clinical trials.
共济失调评定量表(SARA)广泛应用于不同类型的共济失调中,并且已被选为弗里德里希共济失调(FA)欧洲自然史研究的主要结局指标。
为了评估 SARA 评分及其单项的分布和纵向变化,我们分析了 502 例典型起病 FA(<25 岁)患者的 SARA 评分,这些患者参与了为期 4 年的前瞻性欧洲 FA 转化研究联合会(EFACTS)。使用线性混合效应回归模型确定疾病进展模式。为了估计参数和预测疾病进展,重新拟合了所选的统计模型。使用 Kaplan-Meier 方法和加权线性回归模型分别估计中位数时间变化和评分进展率。
研究入组时的 SARA 评分和发病年龄是 4 年随访期间总分进展的主要预测因素。在较小程度上,评估时的年龄也影响 SARA 进展的速度,而疾病持续时间并不能改善统计模型的预测。总 SARA 和项目的时间动态显示,在疾病进展过程中,评分增加的速度存在很大的变异性。步态项目的年进展率最高,中位数为每增加 1 分至 2 分的时间。
对 SARA 统计特性的分析表明,该量表在不同疾病阶段的敏感性不同,为未来临床试验中的人群选择和结果解释提供了重要信息。