Omoto Takashi, Murata Hiroshi, Fujino Yuri, Matsuura Masato, Yamashita Takehiro, Miki Atsuya, Ikeda Yoko, Mori Kazuhiko, Tanito Masaki, Asaoka Ryo
Department of Ophthalmology, University of Tokyo Graduate School of Medicine, Tokyo, Japan.
Department of Ophthalmology, Seirei Hamamatsu General Hospital, Shizuoka, Hamamatsu, Japan.
Br J Ophthalmol. 2022 Apr;106(4):497-501. doi: 10.1136/bjophthalmol-2020-317391. Epub 2021 Jan 13.
To evaluate the usefulness of the application of the clustering method to the trend analysis (sectorwise regression) in comparison with the pointwise linear regression (PLR).
This study included 153 eyes of 101 patients with open-angle glaucoma. With PLR, the total deviation (TD) values of the 10th visual field (VF) were predicted using the shorter VF sequences (from first 3 to 9) by extrapolating TD values against time in a pointwise manner. Then, 68 test points were stratified into 29 sectors. In each sector, the mean of TD values was calculated and allocated to all test points belonging to the sector. Subsequently, the TD values of the 10th VF were predicted by extrapolating the allocated TD value against time in a pointwise manner. Similar analyses were conducted to predict the 11th-16th VFs using the first 10 VFs.
When predicting the 10th VF using the shorter sequences, the mean absolute error (MAE) values were significantly smaller in the sectorwise regression than in PLR. When predicting from the 11th and 16th VFs using the first 10 VFs, the MAE values were significantly larger in the sectorwise regression than in PLR when predicting the 11th VF; however, no significant difference was observed with other VF predictions.
Accurate prediction was achieved using the sectorwise regression, in particular when a small number of VFs were used in the prediction. The accuracy of the sectorwise regression was not hampered in longer follow-up compared with PLR.
与逐点线性回归（PLR）相比，评估聚类方法在趋势分析（按扇区回归）中的应用价值。
本研究纳入了101例开角型青光眼患者的153只眼。采用PLR，通过以逐点方式将总偏差（TD）值外推至时间，使用较短的视野（VF）序列（前3至9次）预测第10次视野检查的TD值。然后，将68个测试点分层为29个扇区。在每个扇区中，计算TD值的平均值并分配给属于该扇区的所有测试点。随后，通过以逐点方式将分配的TD值外推至时间来预测第10次VF的TD值。使用前10次VF进行类似分析以预测第11至16次VF。
使用较短序列预测第10次VF时，按扇区回归的平均绝对误差（MAE）值显著小于PLR。使用前10次VF预测第11至16次VF时，预测第11次VF时按扇区回归的MAE值显著大于PLR；然而，在其他VF预测中未观察到显著差异。
使用按扇区回归可实现准确预测，尤其是在预测中使用少量VF时。与PLR相比，在更长的随访中按扇区回归的准确性并未受到影响。
