David F Garway-Heath, Ana Quartilho, Philip Prah, David P Crabb, Qian Cheng, Haogang Zhu
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Growth curve models assessed the difference in VF and RNFLT rate of change between treatment groups. Incident progression was identified by 3 VF-based methods: Guided Progression Analysis (GPA), 'ANSWERS' and 'PoPLR', and one based on VFs and RNFLT: 'sANSWERS'. Sensitivity, specificity and discrimination between treatment groups were evaluated.</p><p><strong>Results: </strong>The rate of VF change was significantly faster in the placebo, compared to active treatment, group (-0.29 vs +0.03 dB/year, <i>P</i><.001); the rate of RNFLT change was not different (-1.7 vs -1.1 dB/year, <i>P</i>=.14). After 18 months and at 95% specificity, the sensitivity of ANSWERS and PoPLR was similar (35%); sANSWERS achieved a sensitivity of 70%. GPA, ANSWERS and PoPLR discriminated treatment groups with similar statistical significance; sANSWERS did not discriminate treatment groups.</p><p><strong>Conclusions: </strong>Although the VF progression-detection method including VF and RNFLT measurements is more sensitive, it does not improve discrimination between treatment arms.</p>","PeriodicalId":23166,"journal":{"name":"Transactions of the American Ophthalmological Society","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2017-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5652981/pdf/1545_6110-v115-t4.pdf","citationCount":"0","resultStr":"{\"title\":\"Evaluation of Visual Field and Imaging Outcomes for Glaucoma Clinical Trials (An American Ophthalomological Society Thesis).\",\"authors\":\"David F Garway-Heath, Ana Quartilho, Philip Prah, David P Crabb, Qian Cheng, Haogang Zhu\",\"doi\":\"\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>To evaluate the ability of various visual field (VF) analysis methods to discriminate treatment groups in glaucoma clinical trials and establish the value of time-domain optical coherence tomography (TD OCT) imaging as an additional outcome.</p><p><strong>Methods: </strong>VFs and retinal nerve fibre layer thickness (RNFLT) measurements (acquired by TD OCT) from 373 glaucoma patients in the UK Glaucoma Treatment Study (UKGTS) at up to 11 scheduled visits over a 2 year interval formed the cohort to assess the sensitivity of progression analysis methods. Specificity was assessed in 78 glaucoma patients with up to 11 repeated VF and OCT RNFLT measurements over a 3 month interval. Growth curve models assessed the difference in VF and RNFLT rate of change between treatment groups. Incident progression was identified by 3 VF-based methods: Guided Progression Analysis (GPA), 'ANSWERS' and 'PoPLR', and one based on VFs and RNFLT: 'sANSWERS'. Sensitivity, specificity and discrimination between treatment groups were evaluated.</p><p><strong>Results: </strong>The rate of VF change was significantly faster in the placebo, compared to active treatment, group (-0.29 vs +0.03 dB/year, <i>P</i><.001); the rate of RNFLT change was not different (-1.7 vs -1.1 dB/year, <i>P</i>=.14). After 18 months and at 95% specificity, the sensitivity of ANSWERS and PoPLR was similar (35%); sANSWERS achieved a sensitivity of 70%. GPA, ANSWERS and PoPLR discriminated treatment groups with similar statistical significance; sANSWERS did not discriminate treatment groups.</p><p><strong>Conclusions: </strong>Although the VF progression-detection method including VF and RNFLT measurements is more sensitive, it does not improve discrimination between treatment arms.</p>\",\"PeriodicalId\":23166,\"journal\":{\"name\":\"Transactions of the American Ophthalmological Society\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-08-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5652981/pdf/1545_6110-v115-t4.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Transactions of the American Ophthalmological Society\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2017/8/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transactions of the American Ophthalmological Society","FirstCategoryId":"1085","ListUrlMain":"","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2017/8/1 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
目的:评价各种视野(VF)分析方法在青光眼临床试验中区分治疗组的能力,并确定时域光学相干断层扫描(TD OCT)成像作为附加结果的价值。方法:英国青光眼治疗研究(UKGTS)中373名青光眼患者的VFs和视网膜神经纤维层厚度(RNFLT)测量(由TD OCT获得),间隔2年,最多11次定期就诊,形成队列,以评估进展分析方法的敏感性。我们对78名青光眼患者进行了特异性评估,在3个月的时间间隔内进行了多达11次的VF和OCT RNFLT测量。生长曲线模型评估各组间VF和RNFLT变化率的差异。通过3种基于vf的方法确定事件进展:导引进展分析(GPA)、“ANSWERS”和“PoPLR”,以及基于vf和RNFLT的“sANSWERS”。评估治疗组间的敏感性、特异性和区别性。结果:与积极治疗组相比,安慰剂组的VF变化率明显更快(-0.29 vs +0.03 dB/年,PP=.14)。18个月后,在95%的特异性下,ANSWERS和PoPLR的敏感性相似(35%);sANSWERS的灵敏度达到70%。GPA、ANSWERS、PoPLR三组差异有统计学意义;sANSWERS没有歧视治疗组。结论:虽然包括VF和RNFLT测量在内的VF进展检测方法更敏感,但它并不能改善治疗组之间的区分。
Evaluation of Visual Field and Imaging Outcomes for Glaucoma Clinical Trials (An American Ophthalomological Society Thesis).
Purpose: To evaluate the ability of various visual field (VF) analysis methods to discriminate treatment groups in glaucoma clinical trials and establish the value of time-domain optical coherence tomography (TD OCT) imaging as an additional outcome.
Methods: VFs and retinal nerve fibre layer thickness (RNFLT) measurements (acquired by TD OCT) from 373 glaucoma patients in the UK Glaucoma Treatment Study (UKGTS) at up to 11 scheduled visits over a 2 year interval formed the cohort to assess the sensitivity of progression analysis methods. Specificity was assessed in 78 glaucoma patients with up to 11 repeated VF and OCT RNFLT measurements over a 3 month interval. Growth curve models assessed the difference in VF and RNFLT rate of change between treatment groups. Incident progression was identified by 3 VF-based methods: Guided Progression Analysis (GPA), 'ANSWERS' and 'PoPLR', and one based on VFs and RNFLT: 'sANSWERS'. Sensitivity, specificity and discrimination between treatment groups were evaluated.
Results: The rate of VF change was significantly faster in the placebo, compared to active treatment, group (-0.29 vs +0.03 dB/year, P<.001); the rate of RNFLT change was not different (-1.7 vs -1.1 dB/year, P=.14). After 18 months and at 95% specificity, the sensitivity of ANSWERS and PoPLR was similar (35%); sANSWERS achieved a sensitivity of 70%. GPA, ANSWERS and PoPLR discriminated treatment groups with similar statistical significance; sANSWERS did not discriminate treatment groups.
Conclusions: Although the VF progression-detection method including VF and RNFLT measurements is more sensitive, it does not improve discrimination between treatment arms.