Bowd Christopher, Zangwill Linda M, Medeiros Felipe A, Hao Jiucang, Chan Kwokleung, Lee Te-Won, Sejnowski Terrence J, Goldbaum Michael H, Sample Pamela A, Crowston Jonathan G, Weinreb Robert N
Hamilton Glaucoma Center, Department of Ophthalmology, University of California San Diego, La Jolla, 92093-0946, USA.
Invest Ophthalmol Vis Sci. 2004 Jul;45(7):2255-62. doi: 10.1167/iovs.03-1087.
To determine whether Heidelberg Retina Tomograph (HRT; Heidelberg Engineering, Dossenheim, Germany) classification techniques and investigational support vector machine (SVM) analyses can detect optic disc abnormalities in glaucoma-suspect eyes before the development of visual field abnormalities.
Glaucoma-suspect eyes (n = 226) were classified as converts or nonconverts based on the development of repeatable (either two or three consecutive) standard automated perimetry (SAP)-detected abnormalities over the course of the study (mean follow-up, approximately 4.5 years). Hazard ratios for development of SAP abnormalities were calculated based on baseline classification results, follow-up time, and end point status (convert, nonconvert). Classification techniques applied were HRT classification (HRTC), Moorfields Regression Analysis, forward-selection optimized SVM (SVM fwd) and backward elimination-optimized SVM (SVM back) analysis of HRT data, and stereophotograph assessment.
Univariate analyses indicated that all classification techniques were predictors of the development of two repeatable abnormal SAP results, with hazards ratios (95% confidence interval [CI]) ranging from 1.32 (1.00-1.75) for HRTC to 2.0 (1.48-2.76) for stereophotograph assessment (all P < or = 0.05). Only SVM (SVM fwd and SVM back) analysis of HRT data and stereophotograph assessment were univariate predictors of the development of three repeatable abnormal SAP results, with hazard ratios (95% CI) ranging from 1.73 (1.16-2.82) for SVM fwd to 1.82 (1.19-3.12) for SVM back (both P < 0.007). Multivariate analyses including each classification technique individually in a model with age, baseline SAP pattern standard deviation [PSD], and baseline IOP indicated that all classification techniques except HRTC (P = 0.06) were predictors of the development of two repeatable abnormal SAP results with hazards ratios ranging from 1.30 (0.99, 1.73) for HRTC to 1.90 (1.37, 2.69) for stereophotograph assessment. Only SVM (SVM fwd and SVM back) analysis of HRT data and stereophotograph assessment were significant predictors of the development of three repeatable abnormal SAP results in multivariate analyses; hazard ratios of 1.57 (1.03, 2.59) and 1.70 (1.18, 2.51), respectively. SAP PSD was a significant predictor of two repeatable abnormal SAP results in multivariate models with all classification techniques, with hazard ratios ranging from 3.31 (1.39, 7.89) to 4.70 (2.02, 10.93) per 1-dB increase.
HRT classifications techniques and stereophotograph assessment can detect optic disc topography abnormalities in glaucoma-suspect eyes before the development of SAP abnormalities. These data support strongly the importance of optic disc examination for early glaucoma diagnosis.
确定海德堡视网膜断层扫描仪(HRT;德国海德堡工程公司,多森海姆)分类技术及研究性支持向量机(SVM)分析能否在视野异常出现之前检测出可疑青光眼眼中的视盘异常。
根据在研究过程中(平均随访约4.5年)是否出现可重复(连续两次或三次)的标准自动视野计(SAP)检测到的异常,将可疑青光眼眼(n = 226)分为转变者或未转变者。基于基线分类结果、随访时间和终点状态(转变者、未转变者)计算SAP异常发生的风险比。应用的分类技术包括HRT分类(HRTC)、穆尔费尔德回归分析、对HRT数据进行前向选择优化的SVM(SVM fwd)和反向消除优化的SVM(SVM back)分析,以及立体照片评估。
单因素分析表明,所有分类技术都是两次可重复异常SAP结果发生的预测因素,风险比(95%置信区间[CI])范围从HRTC的1.32(1.00 - 1.75)到立体照片评估的2.0(1.48 - 2.76)(所有P≤0.05)。只有对HRT数据的SVM(SVM fwd和SVM back)分析以及立体照片评估是三次可重复异常SAP结果发生的单因素预测因素,风险比(95%CI)范围从SVM fwd的1.73(1.16 - 2.82)到SVM back的1.82(1.19 - 3.12)(两者P < 0.007)。多因素分析在包含年龄、基线SAP模式标准差[PSD]和基线眼压的模型中分别纳入每种分类技术,结果表明除HRTC(P = 0.06)外,所有分类技术都是两次可重复异常SAP结果发生的预测因素,风险比范围从HRTC的1.30(0.99,1.73)到立体照片评估的1.90(1.37,2.69)。在多因素分析中,只有对HRT数据的SVM(SVM fwd和SVM back)分析以及立体照片评估是三次可重复异常SAP结果发生的显著预测因素;风险比分别为1.57(1.03,2.59)和1.70(1.18,2.51)。在所有分类技术的多因素模型中,SAP PSD是两次可重复异常SAP结果发生的显著预测因素,每增加1 dB,风险比范围从3.31(1.39,7.89)到4.70(2.02,10.93)。
HRT分类技术和立体照片评估能够在SAP异常出现之前检测出可疑青光眼眼中的视盘地形图异常。这些数据有力地支持了视盘检查对早期青光眼诊断的重要性。