Transition from a relapsing-remitting to the secondary progressive phenotype is an important milestone in the clinical evolution of multiple sclerosis. In the absence of reliable imaging or biological markers of phenotype transition, assignment of current phenotype status relies on retrospective evaluation of the medical history of an individual. Here, we sought to determine if demographic and clinical information from multiple sclerosis patients can be used to accurately assign current disease phenotypes: either relapsing-remitting or secondary progressive status. Data from the most recent clinical visit of 14,387 multiple sclerosis patients were extracted from the Swedish Multiple Sclerosis Registry. Decision trees based on sex, symptom onset age, Expanded Disability Scale Status score, and age & disease duration at the most recent clinic visit, were examined to build a classifier to determine disease phenotype. Validation was conducted using an independent cohort of multiple sclerosis patients from British Columbia, Canada, and a previously published classifier to assign phenotype was also tested. Clinical records of 100 randomly selected patients were used to manually categorize phenotype by three independent neurologists. A decision tree (the classifier) containing only most recently available disability score and age obtained 89.3% (95% confidence intervals (CI): 88.8% to 89.8%) classification accuracy, defined as concordance with the latest reported status in the registry. Replication in an independent cohort from British Columbia resulted in 82.0% (95%CI: 81.0% to 83.1%) accuracy. A previously published classification algorithm with slight modifications achieved 77.8% (95%CI: 77.1% to 78.4%) accuracy when assigning disease phenotype. With complete patient history data, three neurologists obtained 84.7% accuracy on average compared with 85 for the classifier using the same data. The model is easily interpretable and could allow research studies and randomized clinical trials to estimate the probability of patients having already reached the secondary progressive stage when they have not yet been retrospectively assigned this status, and to standardize definitions of disease phenotype across different cohorts. Clinically, this model could assist neurologists by providing additional information about the probability of having secondary progressive disease. This could also benefit patients who may be introduced to new therapies targeting progressive multiple sclerosis.

中文翻译：

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准确分类多发性硬化的继发性进展

从复发缓解型转变为继发进行性表型是多发性硬化症临床发展中的重要里程碑。在缺乏可靠的表型转变的影像学或生物标记物的情况下，当前表型状态的分配取决于对个体病史的回顾性评估。在这里，我们试图确定来自多发性硬化症患者的人口统计学和临床​​信息是否可用于准确分配当前的疾病表型：复发缓解型或继发性进行性状态。来自瑞典多发性硬化症登记处的14387名多发性硬化症患者的最新临床访视数据。基于性别，症状发作年龄，扩展残疾量表状态得分以及年龄和年龄的决策树 最近一次门诊就诊的疾病持续时间进行了检查，以建立分类器来确定疾病表型。使用来自加拿大不列颠哥伦比亚省的多发性硬化症患者的独立队列进行验证，并且还测试了先前发表的分类器以分配表型。100名随机选择的患者的临床记录用于由三位独立的神经科医生对表型进行手动分类。仅包含最近可用的残疾评分和年龄的决策树（分类器）获得89.3％（95％置信区间（CI）：88.8％至89.8％）的分类准确性，定义为与注册表中最新报告的状态一致。在不列颠哥伦比亚省的一个独立队列中进行复制得到了82.0％（95％CI：81.0％至83.1％）的准确性。分配疾病表型时，稍加修改的先前发布的分类算法可达到77.8％（95％CI：77.1％至78.4％）的准确性。有了完整的患者历史记录数据，三名神经科医生平均获得了84.7％的准确性，而使用相同数据的分类器则为85％。该模型易于解释，并且可以进行研究和随机临床试验，以评估尚未追溯分配这种状态的患者已经进入继发性进展阶段的可能性，并使不同人群中疾病表型的定义标准化。临床上，该模型可以通过提供有关继发进行性疾病可能性的其他信息来帮助神经科医生。