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A Combined Biomechanical and Tomographic Model for Identifying Cases of Subclinical Keratoconus

机译：一种鉴定亚临床角质管病例的组合生物力学和断层模型

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Purpose: To develop a combined biomechanical and tomographic model for identifying eyes with subclinical keratoconus (SKC) that are categorized as normal or borderline in the Pentacam Belin/Ambrosio Enhanced Ectasia Display. Methods: This case-control study comprised 62 eyes with SKC and randomly selected eyes of 186 age-matched healthy controls. SKC was defined as the presence of the following: 1) normal topography, topometric indices, and slit lamp; 2) normal or borderline Belin/Ambrosio Enhanced Ectasia Display D index, back and front elevation difference; and 3) keratoconus in the fellow eye. Stepwise logistic regression analysis was performed to identify the best variable combination for detecting SKC cases from Ocular Response Analyzer and Pentacam parameters. Receiver operating characteristic curve analysis was used to determine the predictive accuracy [area under the curve (AUC)] of the model. Based on the predictors in the final logistic regression model, a linear equation was derived using the discriminant function analysis. Results: The final model (AUC: 0.948, sensitivity: 87.1%, and specificity: 91.4%) chose corneal hysteresis (CH) and D index from a total of 63 candidate variables. The final model had a higher AUC compared with D (0.933, P = 0.053) and CH (0.80, P < 0.001) alone. According to the discriminant function analysis, a higher CH was required with increasing D index to classify an eye as normal. Conclusions: The proposed combined model provided varying cutoffs for CH and D as a function of the other. The probability plot as a function of CH and D index may be used for identifying eyes with SKC.
机译：目的：开发一种用于鉴定亚临床角蛋白酶（SKC）的眼睛的组合生物力学和断层扫描模型，该模型被分类为偏见的Belin / Ambrosio增强的异构展示中正常或边界线。方法：这种情况对照研究包括62只眼睛，SKC和186年龄匹配的健康对照的随机选择的眼睛。 SKC被定义为以下内容：1）正常地形，压芯指数和粘接灯; 2）正常或边缘线Belin / Ambrosio增强的异构型显示D指数，返回和前高程差;和3）同胞中的角蛋白。执行逐步逻辑回归分析以识别用于检测来自眼睛响应分析仪和五萨克坦参数的SKC案例的最佳变量组合。接收器操作特征曲线分析用于确定模型的预测精度[区域下的曲线（AUC）区域。基于最终逻辑回归模型中的预测器，使用判别函数分析来导出线性方程。结果：最终模型（AUC：0.948，敏感性：87.1％，特异性：91.4％）选择角膜滞后（CH）和D指数，总共63个候选变量。最终模型与D（0.933，P = 0.053）和CH（0.80，P <0.001）相比具有更高的AUC。根据判别函数分析，需要增加一个较高的CH，增加D指数以将眼视为正常。结论：所提出的组合模型为CH和D提供了不同的截止值。作为CH和D索引的函数的概率曲线可用于用SKC识别眼睛。

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