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Prediction Accuracy of a Novel Dynamic Structure-Function Model for Glaucoma Progression

机译：青光眼进展的新型动态结构函数模型的预测精度

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?Purpose?To present a pilot evaluation of the prediction accuracy of a novel dynamic structure-function (DSF) model for monitoring the progression of glaucoma. ?Methods?Longitudinal series of paired mean sensitivity (MS) and neuroretinal rim area (RA) from 164 eyes of 114 patients with suspected or diagnosed primary open-angle glaucoma enrolled in the Diagnostics Innovations in Glaucoma Study or the African Descent and Glaucoma Evaluation Study were included. MS and RA were expressed as percent normal based on mean values derived from an independent dataset of 91 eyes from 91 healthy controls (Racette et al, J Glaucoma, 2007; 16:676-84). The DSF model uses centroids and velocity vectors to assess glaucomatous progression. The centroids are the central location for the cloud of paired structural and functional data points and estimate the current state of the disease in each patient. The velocity vectors represent the trend at which structure and function are jointly changing. The fourth, fifth, and sixth longitudinal pairs of MS and RA were predicted from the first three, four, and five pairs, respectively. The prediction accuracy of the DSF model was compared to that of the ordinary least squares linear regression (OLSLR) using Wilcoxon signed-rank test on root-mean-square prediction error (RMSPE). ?Results?For the prediction of the fourth and fifth pairs, the DSF model was on average more accurate than OLSLR (median RMSPE was lower for the DSF model by 3.2% and 2.0%, respectively, p 0.0001). For the prediction of the sixth pair, median RMSPE was only slightly lower for the DSF model than OLSLR (by 0.5%, p = 0.33). The DSF model was more accurate than OLSLR for 74%, 64%, and 55% of the subjects on the predictions for the fourth, fifth, and sixth pairs, respectively. The absolute prediction differences between the two models were lower than 10% for about 80% of the subjects. ?Conclusions?Overall, the two models have the similar prediction accuracy and the DSF model performs better than OLSLR in shorter time series. The results of this work are in agreement with those of Russell et al (Invest Ophthalmol Vis Sci, 2012;53:2760-9) in that OLSLR can be improved upon, particularly, when limited follow-up is available. ?View OriginalDownload SlideView OriginalDownload Slide? Keywords: 473 computational modeling ?.
机译：目的：提供对用于监测青光眼进展的新型动态结构功能（DSF）模型的预测准确性的初步评估。方法：对青光眼诊断创新或非洲人后裔和青光眼评估研究中114名可疑或确诊的原发性开角型青光眼患者的164眼的配对平均敏感性（MS）和神经视网膜边缘区域（RA）的纵向序列进行了研究被包括在内。 MS和RA表示为正常百分比，基于平均值的平均值来自91个健康对照的91只眼睛的独立数据集（Racette等，J Glaucoma，2007; 16：676-84）。 DSF模型使用质心和速度矢量来评估青光眼的进展。质心是成对的结构和功能数据点云的中心位置，并估计每个患者的疾病当前状态。速度矢量代表结构和功能共同变化的趋势。分别从前三对，四对和五对预测MS和RA的第四，第五和第六纵向对。使用Wilcoxon符号秩检验对均方根预测误差（RMSPE）进行比较，将DSF模型的预测准确性与普通最小二乘线性回归（OLSLR）进行比较。结果对于第四和第五对预测，平均而言，DSF模型比OLSLR更为准确（DSF模型的中位数RMSPE分别低3.2％和2.0％，p <0.0001）。对于第六对的预测，DSF模型的中位数RMSPE仅略低于OLSLR（降低0.5％，p = 0.33）。对于第四对，第五对和第六对的预测，分别有74％，64％和55％的受试者，DSF模型比OLSLR更为准确。对于约80％的受试者，两个模型之间的绝对预测差异低于10％。总结：总体而言，两个模型的预测精度相似，并且DSF模型在较短的时间序列中比OLSLR表现更好。这项工作的结果与Russell等人（Invest Ophthalmol Vis Sci，2012; 53：2760-9）的结果一致，特别是在可获得有限随访的情况下，可以改善OLSLR。查看原始下载幻灯片查看原始下载幻灯片？关键字：473计算建模。

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