Predicting Relapsing-Remitting Dynamics in Multiple Sclerosis Using Discrete Distribution Models: A Population Approach
Predicting Relapsing-Remitting Dynamics in Multiple Sclerosis Using Discrete Distribution Models: A Population Approach
复制标题
DOI：
10.1371/journal.pone.0073361
复制
发表时间：
2013-09-05
期刊：
影响因子：
3.7
通讯作者：
Bies, Robert R.
中科院分区：
文献类型：
--
作者：
de Mendizabal, Nieves Velez;Hutmacher, Matthew M.;Bies, Robert R.
背景：复发缓解动力学是自身免疫性疾病如多发性硬化症（MS）的标志。MS的临床复发反映了中枢神经系统中的急性局灶性炎症事件，其通过损伤有髓轴突影响信号传导。这些事件在T1加权增强后磁共振成像（MRI）中表现为对比增强病变（CEL）。CEL动态被认为是不可预测的，其特征在于患者内和患者间的高变异性。在这里，人口的方法（非线性混合效应模型）被应用到分析的CEL进展，旨在提出一个模型，充分捕捉CEL dynamics.Methods和调查结果：我们探讨了几个离散分布模型观察到的9个MS患者进行每月MRI 48个月的CEL计数。所有患者入组研究时均未使用免疫抑制药物，除了静脉注射甲泼尼龙或口服泼尼松以减少临床复发。使用非线性混合效应建模软件NONMEM 7.2进行分析。虽然几个模型能够充分表征观察到的CEL动力学，负二项分布模型具有最佳的预测能力。当将前几个月的CEL计数合并以预测当前月份的CEL计数时，观察到拟合的显著改善。该模型的预测能力进行了验证，使用第二个队列的14名患者在6个月内每月进行MRI。该分析还确定和量化的类固醇的复发treatment.Conclusions的效果：该模型能够表征观察到的复发缓解CEL动态和量化患者间的变异性。此外，类固醇治疗效果的性质表明，这种疗法有助于解决旧的CEL，但不影响当月新出现的活动性病变。该模型可用于未来纵向研究和临床试验的设计，以及新疗法的评估。
Background: Relapsing-remitting dynamics are a hallmark of autoimmune diseases such as Multiple Sclerosis (MS). A clinical relapse in MS reflects an acute focal inflammatory event in the central nervous system that affects signal conduction by damaging myelinated axons. Those events are evident in T1-weighted post-contrast magnetic resonance imaging (MRI) as contrast enhancing lesions (CEL). CEL dynamics are considered unpredictable and are characterized by high intra- and interpatient variability. Here, a population approach (nonlinear mixed-effects models) was applied to analyse of CEL progression, aiming to propose a model that adequately captures CEL dynamics.Methods and Findings: We explored several discrete distribution models to CEL counts observed in nine MS patients undergoing a monthly MRI for 48 months. All patients were enrolled in the study free of immunosuppressive drugs, except for intravenous methylprednisolone or oral prednisone taper for a clinical relapse. Analyses were performed with the nonlinear mixed-effect modelling software NONMEM 7.2. Although several models were able to adequately characterize the observed CEL dynamics, the negative binomial distribution model had the best predictive ability. Significant improvements in fitting were observed when the CEL counts from previous months were incorporated to predict the current month's CEL count. The predictive capacity of the model was validated using a second cohort of fourteen patients who underwent monthly MRIs during 6-months. This analysis also identified and quantified the effect of steroids for the relapse treatment.Conclusions: The model was able to characterize the observed relapsing-remitting CEL dynamic and to quantify the interpatient variability. Moreover, the nature of the effect of steroid treatment suggested that this therapy helps resolve older CELs yet does not affect newly appearing active lesions in that month. This model could be used for design of future longitudinal studies and clinical trials, as well as for the evaluation of new therapies.