Diagnosis of Dry Eye Disease Using Principal Component Analysis: A Study in Animal Models of the Disease
Diagnosis of Dry Eye Disease Using Principal Component Analysis: A Study in Animal Models of the Disease
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DOI：
10.1080/02713683.2020.1830115
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发表时间：
2020-11-29
影响因子：
2
通讯作者：
Rigas, Basil
中科院分区：
文献类型：
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作者：
Honkanen, Robert;Nemesure, Barbara;Rigas, Basil
PurposeTo评估主成分分析（PCA）是否可以评估干眼症（DED）的各种诊断测试，提供一个简化的，更有信息量的疾病状态的措施比个别临床测试parameters（ICTP）。材料和方法ICTP使用PCA分析两组正常兔（组1和2）。第3组，不真正正常，也进行了评估。第1组通过完全泪腺切除术诱导DED，第2组和第3组通过注射刀豆球蛋白A诱导DED。泪膜破裂时间、泪液渗透压、Schirmer泪液试验和玫瑰红染色是所有组中测量的ICTP。统计分析包括描述性统计、t检验、相关系数和主成分分析。PCA使用ICTP数据从组1产生的轴;组2和3绘制在这些axis.ResultsAll组诱导DED。所有ICTP的相关性都在正确的方向上，第1组最强，第3组最弱。PCA明确区分DED和正常眼。主成分（PC）1由四项临床试验几乎相等的贡献组成，解释了73%的变异，并提供了一种将正常与DED分开的方法。低于0.52的PC 1值可以在数学上定义为DED。所有成对比较，PC 1与PC 2和PC 1与PC 3是最翔实的提供良好的空间分离和额外的信息DED status.ConclusionsPCA证明是有用的评估DED提供一个更简单，更全面的评估比ICTP。与ICTP相比，PC 1是DED状态和严重程度的有价值、临床相关和信息性指标，具有上级诊断价值和统计强度。PC 1与PC 3的双标图上的空间信息也是有用的。PCA，特别是PC 1，有可能作为DED的生物标志物。
PurposeTo evaluate whether principal component analysis (PCA) can assess various diagnostic tests of dry eye disease (DED), providing a simplified, more informative measure of disease status than individual clinical test parameters (ICTP).Materials and MethodsICTP were analyzed using PCA in two groups of normal rabbits (Groups 1 and 2). Group 3, not truly normal, was also assessed. DED was induced in Group 1 by complete dacryoadenectomy; in Groups 2 and 3 by injection of concanavalin A. Tear break up time, tear osmolarity, Schirmer's tear test and rose bengal staining were the ICTP measured in all groups. Statistical analysis including descriptive statistics, t test, correlation coefficients and PCA was done. PCA using ICTP data from Group 1 generated axes; Group 2 and 3 were plotted over these axes.ResultsAll groups had induction of DED. Correlations for all ICTP were in the correct direction and were strongest for Group 1 and weakest in Group 3. PCA clearly separated DED and normal eyes. Principal component (PC) 1, made up of nearly equal contributions from the four clinical tests, explained 73% of the variation and provided a means to separate normal from DED. PC 1 values under 0.52 can be mathematically defined as DED. Of all pairwise comparisons, PC 1 vs PC 2 and PC 1 vs PC 3 were the most informative providing excellent spatial separation and additional information regarding DED status.ConclusionsPCA proved useful for evaluating DED providing a simpler, more comprehensive assessment than ICTP. PC 1 is a valuable, clinically relevant, and informative metric for DED status and severity having superior diagnostic value and statistical strength compared to ICTP. Spatial information on biplots of PC 1 vs PC 3 is also informative. PCA, and specifically PC 1, has the potential to serve as a biomarker for DED.