Distinguishing Highly Asymmetric Keratoconus Eyes Using Combined Scheimpflug and Spectral-Domain OCT Analysis.
Distinguishing Highly Asymmetric Keratoconus Eyes Using Combined Scheimpflug and Spectral-Domain OCT Analysis.
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DOI：
10.1016/j.ophtha.2018.06.020
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发表时间：
2018-12
期刊：
影响因子：
13.7
通讯作者：
Randleman JB
中科院分区：
文献类型：
--
作者：
Hwang ES;Perez-Straziota CE;Kim SW;Santhiago MR;Randleman JB
确定Scheimpflug和光谱域眼部相干断层扫描（SD-OCT）成像的最佳目标、机器衍生变量和变量组合，以区分不对称圆锥角膜患者与正常对照人群中临床未受影响的眼睛。回顾性病例对照研究：30例高度不对称圆锥角膜（AKC）患者的30只临床未受影响的眼睛，裂隙灯检查无体格检查结果，角膜成像无明确异常，CDVA为20/20或更好，60例正常对照患者的60只眼睛，这些患者接受了至少两年稳定随访的无事件LASIK（对照）。在所有眼睛中获得Scheimpflug和SD-OCT成像，并生成受试者工作特征（ROC）曲线以确定每个机器衍生变量和变量组合的曲线下面积（AUC）、灵敏度和特异性。通过AUC、灵敏度和特异性将AKC眼与对照眼区分开。来自Scheimpflug或SD-OCT技术的单个机器衍生指标均未产生高于0.75的AUC。结合5个Scheimpflug指标（指数高度偏心（IHD）、指数垂直不对称（IVA）、心尖测厚、上前（IS）值、ARTmax），获得了最佳Scheimpflug结果（AUC 0.86，灵敏度83%，特异性83%）。结合11个SD-OCT厚度指标（最小值-中位数、颞外侧、上级鼻外侧、最小值、上皮最小值-最大值、上皮标准差、上级内侧、上级外侧、上级颞外侧、上级鼻内侧、中央），得到最佳SD-OCT结果（AUC 0.96，灵敏度89%，特异性89%）。结合13个总Scheimpflug/SD-OCT指标，总体上产生了最佳结果（AUC 1.0，灵敏度100%，特异性100%）。组合模型中最具影响力的变量包括来自SD-OCT的上皮厚度变异性和总局灶性角膜厚度变异性以及来自Scheimpflug技术的前曲率和拓扑指数。没有后角膜指标对建模有影响。来自Scheimpflug和SD-OCT成像的个体机器导出的指标很难区分正常眼与高度不对称圆锥角膜患者的最小受影响眼。组合SD-OCT指标的表现优于组合Scheimpflug指标。结合Scheimpflug的前曲率和不对称指数与SD-OCT的区域总厚度和上皮厚度变异性指标，可清楚地区分两个人群。后角膜指数在区分人群中没有用。
To determine optimal objective, machine derived variables and variable combinations from Scheimpflug and spectral domain ocular coherence tomography (SD-OCT) imaging to distinguish the clinically unaffected eye in patients with asymmetric keratoconus from a normal control population. Retrospective case-control study Thirty clinically unaffected eyes with no physical findings on slit lamp examination, no definitive abnormalities on corneal imaging, and CDVA of 20/20 or better from 30 patients with highly asymmetric keratoconus (AKC) and 60 eyes from 60 normal control patients who had undergone uneventful LASIK with at least two years of stable follow-up (Controls). Scheimpflug and SD-OCT imaging was obtained in all eyes, and receiver operating characteristic (ROC) curves were generated to determine area under the curve (AUC), sensitivity, and specificity for each machine derived variable and variable combination. Distinguishing AKC eyes from Controls as determined by AUC, sensitivity, and specificity. No individual machine derived metric from Scheimpflug or SD-OCT technology yielded an AUC higher than 0.75. Combining 5 Scheimpflug metrics (index height decentration (IHD), index vertical asymmetry (IVA), pachymetry apex, inferior-superior (IS) value, ARTmax) yielded the best Scheimpflug results (AUC 0.86, Sensitivity 83%, Specificity 83%). Combining 11 SD-OCT thickness metrics (minimum-median, temporal outer, superior nasal outer, minimum, epithelium Min-Max, epithelial standard deviation, superior inner, superior outer, superior temporal outer, superior nasal inner, central) yielded the best SD-OCT results (AUC 0.96, Sensitivity 89%, Specificity 89%). Combining 13 total Scheimpflug/SD-OCT metrics yielded the best results overall (AUC 1.0, Sensitivity 100%, Specificity 100%). The most impactful variables in combined models included epithelial thickness variability and total focal corneal thickness variability from SD-OCT and anterior curvature and topometric indices from Scheimpflug technology. No posterior corneal metrics were impactful in modeling. Individual machine derived metrics from Scheimpflug and SD-OCT imaging poorly distinguished normal eyes from minimally affected eyes from patients with highly asymmetric keratoconus. Combined SD-OCT metrics performed better than combined Scheimpflug metrics. Combining anterior curvature and asymmetry indices from Scheimpflug with regional total thickness and epithelial thickness variability metrics from SD-OCT clearly distinguished the two populations. Posterior corneal indices were not useful in distinguishing populations.
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