Long Erping, Xu Shuangjuan, Liu Zhenzhen, Wu Xiaohang, Zhang Xiayin, Wang Jinghui, Li Wangting, Liu Runzhong, Chen Zicong, Chen Kexin, Yu Tongyong, Wu Dongxuan, Zhao Xutu, Chen Jingjing, Lin Zhuoling, Cao Qianzhong, Lin Duoru, Li Xiaoyan, Cai Jingheng, Lin Haotian
State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, 510060, China.
School of Mathematics, Sun Yat-sen University, Guangzhou, 510275, China.
BMC Ophthalmol. 2017 May 19;17(1):74. doi: 10.1186/s12886-017-0468-5.
The majority of rare diseases are complex diseases caused by a combination of multiple morbigenous factors. However, uncovering the complex etiology and pathogenesis of rare diseases is difficult due to limited clinical resources and conventional statistical methods. This study aims to investigate the interrelationship and the effectiveness of potential factors of pediatric cataract, for the exploration of data mining strategy in the scenarios of rare diseases.
We established a pilot rare disease specialized care center to systematically record all information and the entire treatment process of pediatric cataract patients. These clinical records contain the medical history, multiple structural indices, and comprehensive functional metrics. A two-layer structural equation model network was applied, and eight potential factors were filtered and included in the final modeling.
Four risk factors (area, density, location, and abnormal pregnancy experience) and four beneficial factors (axis length, uncorrected visual acuity, intraocular pressure, and age at diagnosis) were identified. Quantifiable results suggested that abnormal pregnancy history may be the principle risk factor among medical history for pediatric cataracts. Moreover, axis length, density, uncorrected visual acuity and age at diagnosis served as the dominant factors and should be emphasized in regular clinical practice.
This study proposes a generalized evidence-based pattern for rare and complex disease data mining, provides new insights and clinical implications on pediatric cataract, and promotes rare-disease research and prevention to benefit patients.
大多数罕见病是由多种致病因素共同作用引起的复杂疾病。然而，由于临床资源有限和传统统计方法的限制，揭示罕见病复杂的病因和发病机制具有一定难度。本研究旨在探讨小儿白内障潜在因素之间的相互关系及其作用效果，以探索罕见病场景下的数据挖掘策略。
我们建立了一个罕见病专科试点护理中心，系统记录小儿白内障患者的所有信息及整个治疗过程。这些临床记录包含病史、多个结构指标和综合功能指标。应用了一个两层结构方程模型网络，筛选出八个潜在因素并纳入最终建模。
确定了四个危险因素（面积、密度、位置和异常妊娠史）和四个有益因素（眼轴长度、未矫正视力、眼压和诊断时年龄）。量化结果表明，异常妊娠史可能是小儿白内障病史中的主要危险因素。此外，眼轴长度、密度、未矫正视力和诊断时年龄是主要因素，在常规临床实践中应予以重视。
本研究提出了一种针对罕见复杂疾病数据挖掘的通用循证模式，为小儿白内障提供了新的见解和临床启示，并推动罕见病研究与预防工作，使患者受益。
