■要比较使用高斯的线性混合模型(LMM),学生t,和log-gamma(LG)随机效应分布使用OCT估计青光眼种群的结构损失率,并将模型性能与普通最小二乘(OLS)回归进行比较。
■回顾性队列研究。
■BascomPalmer青光眼储存库(BPGR)的患者。
■从BPGR中识别出具有≥2年可靠的乳头周围视网膜神经纤维层(RNFL)OCT测试的眼睛。收集来自每个可靠测试(信号强度≥7/10)和相关时间点的视网膜神经纤维层厚度值。使用OLS回归以及使用不同随机效应分布的LMM对数据进行建模。预测建模涉及构建具有(n-1)个测试的LMM以预测后续测试的RNFL厚度。总共开发了1200只具有不同基线RNFL厚度值和进展速率的模拟眼睛,以评估宣布的进展和预测速率的可能性。
■在预测未来RNFL厚度值时,通过Watanabe-Akaike信息标准(WAIC)和平均绝对误差(MAE)评估模型拟合;模拟眼睛的对数秩检验和中位进展时间。
■共包括3491名受试者的5766只眼的35862次OCT扫描。平均随访时间为7.0±2.3年,每眼平均6.2±1.4次。Studentt模型产生了最低的WAIC。在预测模型中,与OLS相比,在估算未来RNFL厚度值时,所有LMM均显示MAE显著降低(P<0.001).高斯和Studentt模型相似,在估计未来RNFL厚度值方面明显优于LG模型(P<0.001)。模拟眼睛证实,在中度和快速进展者中,LMM在宣布进展方面的表现比OLS回归更快(P<0.01)。
■LMM优于传统方法来估计青光眼人群中OCTRNFL厚度损失率。Studentt模型为估计RNFL厚度的变化率提供了最佳模型拟合,尽管在模型中使用高斯或学生t分布在准确估计RNFL损失方面带来了类似的改进。
■专有或商业披露可在本文末尾的脚注和披露中找到。
UNASSIGNED: To compare how linear mixed models (LMMs) using Gaussian, Student t, and log-gamma (LG) random effect distributions estimate rates of structural loss in a glaucomatous population using OCT and to compare model performance to ordinary least squares (OLS) regression.
UNASSIGNED: Retrospective cohort study.
UNASSIGNED: Patients in the Bascom Palmer Glaucoma Repository (BPGR).
UNASSIGNED: Eyes with ≥ 5 reliable peripapillary retinal nerve fiber layer (RNFL) OCT tests over ≥ 2 years were identified from the BPGR. Retinal nerve fiber layer thickness values from each reliable test (signal strength ≥ 7/10) and associated time points were collected. Data were modeled using OLS regression as well as LMMs using different random effect distributions. Predictive modeling involved constructing LMMs with (n - 1) tests to predict the RNFL thickness of subsequent tests. A total of 1200 simulated eyes of different baseline RNFL thickness values and progression rates were developed to evaluate the likelihood of declared progression and predicted rates.
UNASSIGNED: Model fit assessed by Watanabe-Akaike information criterion (WAIC) and mean absolute error (MAE) when predicting future RNFL thickness values; log-rank test and median time to progression with simulated eyes.
UNASSIGNED: A total of 35 862 OCT scans from 5766 eyes of 3491 subjects were included. The mean follow-up period was 7.0 ± 2.3 years, with an average of 6.2 ± 1.4 tests per eye. The Student t model produced the lowest WAIC. In predictive models, all LMMs demonstrated a significant reduction in MAE when estimating future RNFL thickness values compared with OLS (P < 0.001). Gaussian and Student t models were similar and significantly better than the LG model in estimating future RNFL thickness values (P < 0.001). Simulated eyes confirmed LMM performance in declaring progression sooner than OLS regression among moderate and fast progressors (P < 0.01).
UNASSIGNED: LMMs outperformed conventional approaches for estimating rates of OCT RNFL thickness loss in a glaucomatous population. The Student t model provides the best model fit for estimating rates of change in RNFL thickness, although the use of the Gaussian or Student t distribution in models led to similar improvements in accurately estimating RNFL loss.
UNASSIGNED: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.