Bergamaschi R, Berzuini C, Romani A, Cosi V
Neurological Institute Fondazione C. Mondino, via Palestro 3, 27100 Pavia, Italy.
J Neurol Sci. 2001 Aug 15;189(1-2):13-21. doi: 10.1016/s0022-510x(01)00572-x.
With the aid of a Bayesian statistical model of the natural course of relapsing remitting Multiple Sclerosis (MS), we identify short-term clinical predictors of long-term evolution of the disease, with particular focus on predicting onset of secondary progressive course (failure event) on the basis of patient information available at an early stage of disease. The model specifies the full joint probability distribution for a set of variables including early indicator variables (observed during the early stage of disease), intermediate indicator variables (observed throughout the course of disease, prefailure) and the time to failure. Our model treats the intermediate indicators as a surrogate response event, so that in right-censored patients, these indicators provide supplementary information pointing towards the unobserved failure times. Moreover, the full probability modelling approach allows the considerable uncertainty which affects certain early indicators, such as the early relapse rates, to be incorporated in the analysis. With such a model, the ability of early indicators to predict failure can be assessed more accurately and reliably, and explained in terms of the relationship between early and intermediate indicators. Moreover, a model with the aforementioned features allows us to characterize the pattern of disease course in high-risk patients, and to identify short-term manifestations which are strongly related to long-term evolution of disease, as potential surrogate responses in clinical trials. Our analysis is based on longitudinal data from 186 MS patients with a relapsing-remitting initial course. The following important early predictors of the time to progression emerged: age; number of neurological functional systems (FSs) involved; sphincter, or motor, or motor-sensory symptoms; presence of sequelae after onset. During the first 3 years of follow up, to reach EDSS> or =4 outside relapse, to have sphincter or motor relapses and to reach moderate pyramidal involvement were also found to be unfavourable prognostic factors.
借助复发缓解型多发性硬化症（MS）自然病程的贝叶斯统计模型，我们确定了该疾病长期演变的短期临床预测指标，特别关注基于疾病早期可获得的患者信息来预测继发进展型病程（失败事件）的发作。该模型指定了一组变量的完整联合概率分布，这些变量包括早期指标变量（在疾病早期观察到）、中间指标变量（在疾病整个病程中、失败前观察到）以及失败时间。我们的模型将中间指标视为替代反应事件，这样在截尾患者中，这些指标提供了指向未观察到的失败时间的补充信息。此外，完整概率建模方法允许将影响某些早期指标（如早期复发率）的相当大的不确定性纳入分析。有了这样一个模型，早期指标预测失败的能力可以更准确可靠地评估，并根据早期和中间指标之间的关系进行解释。此外，具有上述特征的模型使我们能够描述高危患者的疾病病程模式，并识别与疾病长期演变密切相关的短期表现，作为临床试验中的潜在替代反应。我们的分析基于186例初始病程为复发缓解型的MS患者的纵向数据。出现了以下重要的进展时间早期预测指标：年龄；涉及的神经功能系统（FSs）数量；括约肌、运动或运动感觉症状；发病后后遗症的存在。在随访的前3年中，发现复发外达到扩展残疾状态量表（EDSS）≥4、出现括约肌或运动性复发以及达到中度锥体束受累也是不良预后因素。
