From the Imaging, Biomechanics and Mathematical Modeling Solutions (Francis, Sinha Roy), Narayana Nethralaya Foundation, the Corneal and Refractive Surgery Division (Pahuja, Shroff, Gowda, Matalia, Shetty), Narayana Nethralaya, Bangalore, and the School of Biosciences and Technology (Nelson), Vellore Institute of Technology, Vellore, India.
From the Imaging, Biomechanics and Mathematical Modeling Solutions (Francis, Sinha Roy), Narayana Nethralaya Foundation, the Corneal and Refractive Surgery Division (Pahuja, Shroff, Gowda, Matalia, Shetty), Narayana Nethralaya, Bangalore, and the School of Biosciences and Technology (Nelson), Vellore Institute of Technology, Vellore, India.
J Cataract Refract Surg. 2017 Oct;43(10):1271-1280. doi: 10.1016/j.jcrs.2017.10.012.
To evaluate the performance of waveform-derived variables in distinguishing normal, suspect, and keratoconic eyes.
Narayana Nethralaya Eye Hospital, Bangalore, India.
Retrospective case series.
Scheimpflug tomography (Pentacam) and dynamic Scheimpflug analysis (Corvis ST) of 253 normal (253 patients) eyes and 205 keratoconic eyes (205 patients) were evaluated. Among the 205 patients, 62 had keratoconus in 1 eye, while the unaffected eye was suspect. From deformation amplitude, deflection amplitude and whole-eye movement were extracted. A biomechanical model was used to derive a linear (kc [constant]) and nonlinear measure (kc [mean]) of corneal stiffness. Multivariate logistic regression was performed to determine sensitivity and specificity. The analysis was validated in another dataset of 59 normal, 45 suspect, and 160 keratoconic eyes.
Deformation amplitude maximum, applanation 1 time and deformation amplitude, applanation 2 time, kc (constant), kc (mean), and deflection amplitude maximum were significantly different between normal and keratoconic eyes (P < .001). The deformation characteristics of the suspect eyes were similar to those of the keratoconic eyes, particularly grade 1 (P > .05). The kc (constant) and kc (mean) had the highest area under curve (>0.98), sensitivity, and specificity greater than 90% and 91%, respectively. Logistic regression using kc (constant) and kc (mean) improved the area to 1.0, with a sensitivity and specificity equal to 99.6% and 100%, respectively. In the validation dataset, the same cutoff yielded a sensitivity, specificity, and accuracy of 99.5%, 100%, and 99.6%, respectively.
Corneal stiffness and waveform analyses could be reliable differentiators of suspect and keratoconic eyes from normal eyes.
评估波形衍生变量在区分正常、疑似和圆锥角膜眼中的性能。
印度班加罗尔的 Narayana Nethralaya 眼科医院。
回顾性病例系列。
评估了 253 只正常眼(253 例患者)和 205 只圆锥角膜眼(205 例患者)的 Scheimpflug 断层扫描(Pentacam)和动态 Scheimpflug 分析。在 205 例患者中,62 例患者的一只眼患有圆锥角膜,而未受影响的眼为疑似。从变形幅度、挠度幅度和整个眼球运动中提取出来。使用生物力学模型推导出角膜刚度的线性(kc[常数])和非线性测量值(kc[平均值])。进行多变量逻辑回归以确定灵敏度和特异性。该分析在另一组 59 只正常眼、45 只疑似眼和 160 只圆锥角膜眼中得到验证。
正常眼和圆锥角膜眼之间的变形幅度最大值、压平 1 次时间和变形幅度、压平 2 次时间、kc[常数]、kc[平均值]和挠度幅度最大值有显著差异(P<0.001)。疑似眼的变形特征与圆锥角膜眼相似,尤其是 1 级(P>0.05)。kc[常数]和 kc[平均值]的曲线下面积(AUC)最高(>0.98),灵敏度和特异性均大于 90%和 91%。使用 kc[常数]和 kc[平均值]的逻辑回归将 AUC 提高到 1.0,灵敏度和特异性分别为 99.6%和 100%。在验证数据集,相同的截断值产生了 99.5%、100%和 99.6%的灵敏度、特异性和准确性。
角膜硬度和波形分析可以可靠地区分正常眼、疑似眼和圆锥角膜眼。