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.

中文翻译：

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面肩肱型肌营养不良症患者功能结果的预测因素


面肩肱型肌营养不良症 (FSHD) 是最常见的肌营养不良症之一，其严重程度、进展速度和功能结果的差异很大。很少有研究对 FSHD 队列进行足够长时间的跟踪来了解疾病进展和功能结果的预测因素，这在我们的理解中造成了差距，这影响了临床护理和临床试验的设计。确定分子靶向疗法的努力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了解试验结果。在这里，我们分析了来自美国 FSHD 患者的大型纵向跟踪登记的前瞻性队列，以确定结果的预测因素，例如需要使用轮椅。这项研究分析了美国国家 FSHD 患者和家庭成员登记处登记的 578 名确诊为 1 型 FSHD 的个体的去识别化数据。数据收集时间为 2002 年 1 月至 2019 年 9 月，包括平均 9 年（范围 0-18）的跟踪调查。使用描述性流行病学技术分析数据，并使用 Cox 比例风险模型确定使用轮椅的风险。监督机器学习分析是使用随机森林模型完成的，包括从注册表调查问卷中收集的所有 189 个独特特征。创建了一个单独的纯药物模型，其中包括参与者报告的 359 种独特药物。在这里，我们发现较小的等位基因大小可以预测较早的发病年龄、诊断和使用轮椅的可能性。此外，我们发现，与男性相比，女性总体上更有可能使用轮椅，而且速度更快，与遗传无关。 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，等位基因大小对轮椅使用进展的影响很小，这在试验设计中可能很重要。医疗合并症和药物使用增加了需要轮椅依赖的风险，从而提高了更好的医疗管理影响 FSHD 结局的可能性。这项研究的结果需要在更多、更大的数据集中进行进一步验证，但可能对临床护理以及未来 FSHD 临床试验的纳入标准产生影响。