Department of Community Health Sciences, Department of Clinical Neurosciences and Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada.
Neurosciences Research Centre, Molecular and Clinical Sciences Research Institute, St. George's, University of London, London, UK.
Eur J Neurol. 2021 Dec;28(12):3999-4009. doi: 10.1111/ene.15031. Epub 2021 Aug 4.
Several clinical and demographic factors relate to anatomic spread of adult-onset isolated dystonia, but a predictive model is still lacking. The aims of this study were: (i) to develop and validate a predictive model of anatomic spread of adult-onset isolated dystonia; and (ii) to evaluate whether presence of tremor associated with dystonia influences model predictions of spread.
Adult-onset isolated dystonia participants with focal onset from the Dystonia Coalition Natural History Project database were included. We developed two prediction models, one with dystonia as sole disease manifestation ("dystonia-only") and one accepting dystonia OR tremor in any body part as disease manifestations ("dystonia OR tremor"). Demographic and clinical predictors were selected based on previous evidence, clinical plausibility of association with spread, or both. We used logistic regressions and evaluated model discrimination and calibration. Internal validation was carried out based on bootstrapping.
Both predictive models showed an area under the curve of 0.65 (95% confidence intervals 0.62-0.70 and 0.62-0.69, respectively) and good calibration after internal validation. In both models, onset of dystonia in body regions other than the neck, older age, depression and history of neck trauma were predictors of spread.
This predictive modeling of spread in adult-onset isolated dystonia based on accessible predictors (demographic and clinical) can be easily implemented to inform individuals' risk of spread. Because tremor did not influence prediction of spread, our results support the argument that tremor is a part of the dystonia syndrome, and not an independent or coincidental disorder.
一些临床和人口统计学因素与成人起病的特发性局灶性肌张力障碍的解剖扩散有关,但仍缺乏预测模型。本研究的目的是:(i)开发和验证成人起病的特发性局灶性肌张力障碍解剖扩散的预测模型;(ii)评估与肌张力障碍相关的震颤是否影响扩散模型的预测。
纳入来自肌张力障碍联盟自然病史研究数据库的局灶起病的成年特发性肌张力障碍患者。我们开发了两种预测模型,一种仅接受以肌张力障碍为唯一疾病表现的病例(“仅肌张力障碍”),另一种接受任何部位的肌张力障碍或震颤为疾病表现的病例(“肌张力障碍或震颤”)。根据先前的证据、与扩散相关的临床可能性或两者兼而选取出人口统计学和临床预测因素。我们使用逻辑回归并评估了模型的区分度和校准度。内部验证基于 bootstrap 方法进行。
两种预测模型的曲线下面积分别为 0.65(95%置信区间为 0.62-0.70 和 0.62-0.69),内部验证后校准度良好。在这两种模型中,除颈部以外的身体部位起病的肌张力障碍、年龄较大、抑郁和颈部外伤史都是扩散的预测因素。
基于可获得的预测因素(人口统计学和临床)对成人起病的特发性局灶性肌张力障碍的扩散进行预测建模,可以方便地告知个体的扩散风险。因为震颤并没有影响对扩散的预测,所以我们的结果支持这样的观点,即震颤是肌张力障碍综合征的一部分,而不是独立的或偶发的疾病。