Timely initiation of effective therapy is crucial for preventing disability in multiple sclerosis; however, treatment response varies greatly among patients. Comprehensive predictive models of individual treatment response are lacking. Our aims were: (i) to develop predictive algorithms for individual treatment response using demographic, clinical and paraclinical predictors in patients with multiple sclerosis; and (ii) to evaluate accuracy, and internal and external validity of these algorithms. This study evaluated 27 demographic, clinical and paraclinical predictors of individual response to seven disease-modifying therapies in MSBase, a large global cohort study. Treatment response was analysed separately for disability progression, disability regression, relapse frequency, conversion to secondary progressive disease, change in the cumulative disease burden, and the probability of treatment discontinuation. Multivariable survival and generalized linear models were used, together with the principal component analysis to reduce model dimensionality and prevent overparameterization. Accuracy of the individual prediction was tested and its internal validity was evaluated in a separate, non-overlapping cohort. External validity was evaluated in a geographically distinct cohort, the Swedish Multiple Sclerosis Registry. In the training cohort (n = 8513), the most prominent modifiers of treatment response comprised age, disease duration, disease course, previous relapse activity, disability, predominant relapse phenotype and previous therapy. Importantly, the magnitude and direction of the associations varied among therapies and disease outcomes. Higher probability of disability progression during treatment with injectable therapies was predominantly associated with a greater disability at treatment start and the previous therapy. For fingolimod, natalizumab or mitoxantrone, it was mainly associated with lower pretreatment relapse activity. The probability of disability regression was predominantly associated with pre-baseline disability, therapy and relapse activity. Relapse incidence was associated with pretreatment relapse activity, age and relapsing disease course, with the strength of these associations varying among therapies. Accuracy and internal validity (n = 1196) of the resulting predictive models was high (>80%) for relapse incidence during the first year and for disability outcomes, moderate for relapse incidence in Years 2–4 and for the change in the cumulative disease burden, and low for conversion to secondary progressive disease and treatment discontinuation. External validation showed similar results, demonstrating high external validity for disability and relapse outcomes, moderate external validity for cumulative disease burden and low external validity for conversion to secondary progressive disease and treatment discontinuation. We conclude that demographic, clinical and paraclinical information helps predict individual response to disease-modifying therapies at the time of their commencement.

中文翻译：

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迈向多发性硬化症的个性化治疗：预测个体治疗反应

及时开始有效的治疗对于预防多发性硬化症的残疾至关重要。但是，患者之间的治疗反应差异很大。缺乏个体治疗反应的综合预测模型。我们的目标是：（i）为多发性硬化症患者使用人口统计学，临床和临床前预测因子开发针对个体治疗反应的预测算法；（ii）评估这些算法的准确性以及内部和外部有效性。这项研究评估了一项针对全球大型队列研究MSBase中对七种疾病改变疗法的个体反应的27种人口统计学，临床和临床前预测指标。分别分析了残障进展，残障消退，复发频率，继发性进行性疾病，累积疾病负担的变化，以及停药的可能性。使用多变量生存和广义线性模型，以及主成分分析，以减少模型维数并防止过度参数化。测试了单个预测的准确性，并在一个单独的，不重叠的队列中评估了其内部有效性。外部有效性在地理上不同的队列瑞典多发性硬化症登记系统中进行了评估。在培训队列中（测试了单个预测的准确性，并在一个单独的，不重叠的队列中评估了其内部有效性。外部有效性在地理上不同的队列瑞典多发性硬化症登记系统中进行了评估。在培训队列中（测试了单个预测的准确性，并在一个单独的，不重叠的队列中评估了其内部有效性。外部有效性在地理上不同的队列瑞典多发性硬化症登记系统中进行了评估。在培训队列中（ñ= 8513），最显着的治疗反应调节因素包括年龄，疾病持续时间，病程，先前的复发活动，残疾，主要的复发表型和先前的治疗。重要的是，在治疗和疾病结局之间，关联的程度和方向各不相同。注射疗法治疗期间残疾发展的更高可能性主要与治疗开始时和先前治疗中更大的残疾有关。对于芬戈莫德，那他珠单抗或米托蒽醌，它主要与较低的治疗前复发活性有关。残疾消退的可能性主要与基线前的残疾，治疗和复发活动有关。复发率与治疗前的复发活动，年龄和复发性疾病病程有关，这些关联的强度因疗法而异。准确性和内部有效性（n = 1196）的预测模型在第一年的复发率和致残率方面较高（> 80％），在2-4年的复发率和累积疾病负担变化中为中等，而在1-4年中则为低转变为继发性进行性疾病并终止治疗。外部验证显示出相似的结果，表明对于残疾和复发结局具有较高的外部有效性，对于累积的疾病负担具有中等的外部有效性，对于转化为继发性进行性疾病和治疗中断的外部有效性较低。我们得出的结论是，人口统计学，临床和临床前信息有助于预测个体在开始治疗疾病时对疾病改变疗法的反应。