炎症性肠病数学模型研究进展

数学模型(MM)是指使用函数原理和概念刻画现实世界的模型，在医学中常表现为评分系统。MM的判断效能直接体现在特异性及敏感性方面。在炎症性肠病(IBD)研究领域，MM可辅助诊断、评价疗效及预后，包括Best CDAI、HBI、CDEIS、SES-CD、Lewis评分、Rutgeerts评分、MaRIA、Clermont评分等，然而上述模型较多依赖临床症状表现、内镜或影像学表现，也易受临床医生的主观影响，且还存在指标获取困难或费用昂贵等缺陷，而甚少把临床容易获取的外周血检验指标尤其是免疫学指标纳入变量进行建模，这在一定程度上影响了MM的效能。笔者在既往动物及人体的系列研究中发现，外周血CD8+CD28+/CD8+CD28-T细胞比值(平衡)在预测实验性结肠炎大鼠病情方面表现出良好的效能；临床研究发现该比值的降低可预测IBD患者由缓解期进展为活动期，并发现该比值可以敏感预测克罗恩病患者由无狭窄无穿透(B1型)进展为狭窄或穿透(B2或B3型)。由于免疫学机制是IBD发病的基本机制，因此笔者在综述当前常用的几大MM的基础上，结合既往研究基础，提出结合免疫学的实验室指标是改良IBD-MM特异性及敏感性的可行性思路。 

Advance on mathematical models in inflammatory bowel disease

A mathematical model (MM) is a tool that imitates reality by using the language of function. It is often represented as a scoring system in medicine. The efficiency of MM is directly reflected in its specificity and sensitivity. In the field of inflammatory bowel disease (IBD), MM, such as the Best CDAI, HBI, CDISS, SES-CD, Lewis score, Rutgeerts score, MaRIA, and Clermont score, can assist in the diagnosis, evaluation and prognosis. However, these models are dependent on clinical symptoms, endoscopy and imaging findings, which are also limited to subjective experience of clinicians. There are also defects such as difficulty in obtaining indicators or high expense. A few peripheral blood markers that are easy to obtain, especially immunologic markers, aren't included in the variables for modeling, which affects the efficiency of MM. It was found in the previous series of studies on animals and humans that peripheral blood CD8+CD28+/CD8+CD28- T cell ratio (equilibrium) showed a powerful efficiency in predicting the stage for experimental colitis rats. Clinical studies also found that the decrease of this ratio could accurately predict the active stage of IBD, and that this ratio could sensitively predict whether Crohn's disease progresses from non-stricturing and non-penetrating (type B1) to stricturing (B2) or penetrating (B3). Since the immunological mechanisms are vital for the pathogenesis of IBD, we summarize the current major MMs and combine our previous research foundations and thus propose that an immunology-combining MM could be more powerful in diagnosing and predicting outcomes for IBD.