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Improved estimates of visual field progression using bayesian linear regression to integrate structural information in patients with ocular hypertension

机译:使用贝叶斯线性回归整合高眼压患者的结构信息,改善视野进展的估计

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PURPOSE. To assess whether neuroretinal rim area (RA) measurements of the optic disc could be used to improve the estimate of the rate of change in visual field (VF) mean sensitivity in patients with ocular hypertension (OHT) using a Bayesian linear regression (BLR), compared to a standard ordinary least squares linear regression (OLSLR) of mean sensitivity (MS) measurements alone. METHODS. MS and RA measurements were analyzed from a longitudinal series of 179 patients with OHT visiting Moorfields Eye Hospital between 1992 and 2000. For each patient, linear regression of RA was computed after an appropriate transformation to "scale" RA with MS measurements, and the slope coefficient from this regression was used as a prior for BLR of MS. The BLR then was compared with the OLSLR approach by evaluating how accurately each regression technique predicted future MS measurements. RESULTS. On average, BLR was significantly more accurate than OLSLR for series up to 8 measurements long (root-mean-square prediction error [RMSPE] was 0.14 decibels [dB] smaller with BLR than OLSLR; P < 0.001, Wilcoxon signed-rank test), with OLSLR of VF data alone being more accurate for longer series (RMSPE was 0.06 dB smaller with OLSLR than BLR). CONCLUSIONS. BLR provides a significantly more accurate estimate of the rate of change in MS than the standard OLSLR approach, especially in short time series, suggesting that structural measurements can be used successfully in statistical models to assist clinicians monitoring VF progression in patients with OHT. Further studies are necessary to validate the method in glaucoma patients.
机译:目的。为了评估视盘的神经视网膜边缘区域(RA)测量是否可用于通过贝叶斯线性回归(BLR)改善高眼压症患者(OHT)视野变化率(VF)平均敏感性的估计与单独的平均灵敏度(MS)测量值的标准普通最小二乘线性回归(OLSLR)进行比较。方法。对1992年至2000年间在Moorfields眼科医院就诊的179例OHT患者的纵向和纵向的MS和RA测量进行了分析。在对每位患者进行适当的转换后,通过对MS测量和“斜率”的“比例” RA进行线性回归,计算出RA的线性回归来自该回归的系数被用作MS的BLR的先验。然后,通过评估每种回归技术预测未来MS测量的准确性如何,将BLR与OLSLR方法进行比较。结果。平均而言,对于长达8个测量值的系列,BLR的准确性明显高于OLSLR(BLR的均方根预测误差[RMSPE]比OLSLR小0.14分贝[dB]; P <0.001,Wilcoxon符号秩检验) ,仅VF数据的OLSLR对于更长的序列而言更为准确(使用OLSLR的RMSPE比BLR小0.06 dB)。结论。与标准的OLSLR方法相比,BLR提供了MS变化率的准确得多的估计,尤其是在短时间序列中,这表明结构测量可以成功地用于统计模型中,以协助临床医生监测OHT患者的VF进展。有必要进行进一步的研究以验证青光眼患者的治疗方法。

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