Department of Ophthalmology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.
J Glaucoma. 2012 Dec;21(9):579-85. doi: 10.1097/IJG.0b013e31822543e0.
To identify risk factors for visual field progression in glaucoma and to compare different statistical approaches with this risk factor analysis.
We included 221 eyes of 221 patients. Progression was analyzed using Nonparametric Progression Analysis applied to Humphrey Field Analyzer data. Risk factors were analyzed using the statistical approaches from the Advanced Glaucoma Intervention Study, the Early Manifest Glaucoma Trial, and the Canadian Glaucoma Study. Four intraocular pressure (IOP) variables (baseline IOP, mean IOP during follow-up, IOP fluctuation, and pretreatment IOP) and 8 other risk factors were investigated.
On average, 7.1 reliable fields were available after a mean follow-up of 5.3 years; 89 eyes progressed. With the Advanced Glaucoma Intervention Study approach, age [odds ratio (OR) 1.03/y; 95% confidence interval (CI), 1.00-1.06; P = 0.044] predicted progression. With an additional stepwise selection procedure, mean IOP during follow-up (1.16 per mm Hg; 1.05-1.29; P=0.003),baseline HFA mean deviation (MD; 2.72 for worse versus better than --6 dB; 1.50-4.95; P=0.001) and age (1.03; 1.01-1.06;P=0.010) predicted progression [corrected]. With the Early Manifest Glaucoma Trial approach, baseline IOP [hazard ratio (HR) 1.07; 95% CI, 1.02-1.11; P = 0.010], baseline Frequency Doubling Perimeter MD (HR = 1.75; 95% CI, 1.14-2.70; P = 0.013), and age (HR = 1.03; 95% CI, 1.01-1.05; P = 0.006) predicted progression. Finally, with the Canadian Glaucoma Study approach, baseline IOP (HR = 1.07; 95% CI, 1.02-1.11; P = 0.010), baseline Frequency Doubling Perimeter MD (HR = 1.75; 95% CI, 1.14-2.70; P = 0.013), and age (HR = 1.03; 95% CI, 1.01-1.05; P = 0.006) predicted progression.
IOP, disease stage, and age seemed to be robust independent risk factors for visual field progression in glaucoma. The IOP variable that was significant depended on the statistical approach applied.
确定青光眼视野进展的风险因素，并比较不同的统计方法与这种风险因素分析。
我们纳入了 221 例 221 只眼。使用非参数进展分析（应用于 Humphrey 视野分析仪数据）来分析进展情况。使用来自高级青光眼干预研究、早期显性青光眼试验和加拿大青光眼研究的统计方法来分析风险因素。研究了 4 个眼压（IOP）变量（基线 IOP、随访期间的平均 IOP、IOP 波动和治疗前 IOP）和 8 个其他风险因素。
平均随访 5.3 年后，7.1 个可靠视野可用；89 只眼进展。采用高级青光眼干预研究方法，年龄（每增加 1 岁的比值比（OR）为 1.03/y；95%置信区间（CI）为 1.00-1.06；P = 0.044）预测进展。通过额外的逐步选择程序，随访期间的平均 IOP（每增加 1mmHg 为 1.16；1.05-1.29；P=0.003）、基线 HFA 平均偏差（MD；恶化者为 2.72 对好于 -6dB；1.50-4.95；P=0.001）和年龄（1.03；1.01-1.06；P=0.010）预测进展[校正]。采用早期显性青光眼试验方法，基线 IOP（风险比（HR）为 1.07；95%CI，1.02-1.11；P = 0.010）、基线频域加倍周边 MD（HR = 1.75；95%CI，1.14-2.70；P = 0.013）和年龄（HR = 1.03；95%CI，1.01-1.05；P = 0.006）预测进展。最后，采用加拿大青光眼研究方法，基线 IOP（HR = 1.07；95%CI，1.02-1.11；P = 0.010）、基线频域加倍周边 MD（HR = 1.75；95%CI，1.14-2.70；P = 0.013）和年龄（HR = 1.03；95%CI，1.01-1.05；P = 0.006）预测进展。
IOP、疾病阶段和年龄似乎是青光眼视野进展的可靠独立风险因素。具有统计学意义的 IOP 变量取决于所应用的统计方法。
