Department of Neurology, Children's Mercy Hospital, Kansas City, MO 64108, USA.
Department of Artificial Intelligence, AIbytes, LLC, Hurley, NY 12443, USA.
Brain. 2021 Dec 16;144(11):3451-3460. doi: 10.1093/brain/awab326.
Facioscapulohumeral muscular dystrophy (FSHD) is one of the most prevalent muscular dystrophies characterized by considerable variability in severity, rates of progression and functional outcomes. Few studies follow FSHD cohorts long enough to understand predictors of disease progression and functional outcomes, creating gaps in our understanding, which impacts clinical care and the design of clinical trials. Efforts to identify molecularly targeted therapies create a need to better understand disease characteristics with predictive value to help refine clinical trial strategies and understand trial outcomes. Here we analysed a prospective cohort from a large, longitudinally followed registry of patients with FSHD in the USA to determine predictors of outcomes such as need for wheelchair use. This study analysed de-identified data from 578 individuals with confirmed FSHD type 1 enrolled in the United States National Registry for FSHD Patients and Family members. Data were collected from January 2002 to September 2019 and included an average of 9 years (range 0-18) of follow-up surveys. Data were analysed using descriptive epidemiological techniques, and risk of wheelchair use was determined using Cox proportional hazards models. Supervised machine learning analysis was completed using Random Forest modelling and included all 189 unique features collected from registry questionnaires. A separate medications-only model was created that included 359 unique medications reported by participants. Here we show that smaller allele sizes were predictive of earlier age at onset, diagnosis and likelihood of wheelchair use. Additionally, we show that females were more likely overall to progress to wheelchair use and at a faster rate as compared to males, independent of genetics. Use of machine learning models that included all reported clinical features showed that the effect of allele size on progression to wheelchair use is small compared to disease duration, which may be important to consider in trial design. Medical comorbidities and medication use add to the risk for need for wheelchair dependence, raising the possibility for better medical management impacting outcomes in FSHD. The findings in this study will require further validation in additional, larger datasets but could have implications for clinical care, and inclusion criteria for future clinical trials in FSHD.
面肩肱型肌营养不良症(FSHD)是最常见的肌肉营养不良症之一,其严重程度、进展速度和功能结果存在很大差异。很少有研究能够对 FSHD 队列进行足够长时间的随访,以了解疾病进展和功能结果的预测因素,这导致我们对疾病的认识存在空白,从而影响了临床护理和临床试验的设计。为了寻找有针对性的分子治疗方法,我们需要更好地了解具有预测价值的疾病特征,以帮助完善临床试验策略并理解试验结果。在这里,我们分析了一个来自美国大型 FSHD 纵向注册研究的前瞻性队列,以确定疾病进展和功能结果(如是否需要使用轮椅)的预测因素。本研究分析了美国国家 FSHD 患者和家属注册中心登记的 578 名确诊为 FSHD 1 型患者的匿名数据。数据收集时间为 2002 年 1 月至 2019 年 9 月,平均随访时间为 9 年(0-18 年)。使用描述性流行病学技术进行数据分析,使用 Cox 比例风险模型确定使用轮椅的风险。使用随机森林模型完成了监督机器学习分析,该模型包括从登记问卷中收集的 189 个唯一特征。还创建了一个单独的仅药物模型,其中包括参与者报告的 359 种独特药物。研究结果表明,较小的等位基因大小可预测发病年龄、诊断年龄和使用轮椅的可能性更早出现。此外,与男性相比,女性总体上更容易进展到需要使用轮椅,且进展速度更快,这与遗传因素无关。使用包含所有报告的临床特征的机器学习模型表明,与疾病持续时间相比,等位基因大小对进展到需要使用轮椅的影响较小,这在临床试验设计中可能需要考虑。合并症和药物使用增加了对轮椅依赖的需求,这提高了更好的医疗管理可能会对 FSHD 结果产生影响的可能性。本研究的发现还需要在其他更大的数据集进行进一步验证,但可能会对 FSHD 的临床护理和未来临床试验的纳入标准产生影响。