Abstract：
OBJECTIVE: Aimed to create a nomogram using clinical and eye-specific metrics to predict the efficacy of intravenous glucocorticoid (IVGC) therapy in patients with active and moderate-to-severe Thyroid-Associated Ophthalmopathy (TAO).
METHODS: This study was conducted on 84 eyes from 42 moderate-to-severe TAO patients who received systemic IVGC therapy, and 42 eyes from 21 controls. Data were collected retrospectively from June 2020 to December 2021. The least absolute shrinkage and selection operator (LASSO) method was used to identify predictive factors for \"unresponsiveness\" to IVGC therapy. These factors were then analyzed using logistic regression to create a nomogram. The model\'s discriminative ability was robustly assessed using a Bootstrap resampling method with 1000 iterations for receiver operating characteristic (ROC) curve analysis.
RESULTS: The LASSO analysis identified six factors with non-zero coefficients as significant, including Schirmer I test values, Meibomian gland (MG) diameter, MG length, disease duration, whole capillary vessel density (VD) in the radial peripapillary capillary (RPC), and whole macular VD for the superficial retinal capillary plexus (SRCP). The subsequent logistic regression model highlighted MG length, whole macular VD for SRCP, and disease duration as independent predictors of IVGC therapy response. The constructed nomogram demonstrated an area under the curve (AUC) of 0.82 (95% CI: 0.73-0.91), affirming the model\'s consistent and reliable ability to distinguish between responsive and non-responsive TAO patients.
CONCLUSIONS: Our nomogram, combining MG length (<4.875 mm), SRCP VD (<50.25%), and disease duration (>5.5 months), reliably predicts lower IVGC therapy effectiveness in active, moderate-to-severe TAO patients.
METHODS: This study was conducted on 84 eyes from 42 moderate-to-severe TAO patients who received systemic IVGC therapy, and 42 eyes from 21 controls. Data were collected retrospectively from June 2020 to December 2021. The least absolute shrinkage and selection operator (LASSO) method was used to identify predictive factors for \"unresponsiveness\" to IVGC therapy. These factors were then analyzed using logistic regression to create a nomogram. The model\'s discriminative ability was robustly assessed using a Bootstrap resampling method with 1000 iterations for receiver operating characteristic (ROC) curve analysis.
RESULTS: The LASSO analysis identified six factors with non-zero coefficients as significant, including Schirmer I test values, Meibomian gland (MG) diameter, MG length, disease duration, whole capillary vessel density (VD) in the radial peripapillary capillary (RPC), and whole macular VD for the superficial retinal capillary plexus (SRCP). The subsequent logistic regression model highlighted MG length, whole macular VD for SRCP, and disease duration as independent predictors of IVGC therapy response. The constructed nomogram demonstrated an area under the curve (AUC) of 0.82 (95% CI: 0.73-0.91), affirming the model\'s consistent and reliable ability to distinguish between responsive and non-responsive TAO patients.
CONCLUSIONS: Our nomogram, combining MG length (<4.875 mm), SRCP VD (<50.25%), and disease duration (>5.5 months), reliably predicts lower IVGC therapy effectiveness in active, moderate-to-severe TAO patients.
摘要：
目的：旨在使用临床和眼部特异性指标创建列线图，以预测活动性和中度至重度甲状腺相关眼病（TAO）患者静脉内糖皮质激素（IVGC）治疗的疗效。
方法：这项研究是对42例接受全身IVGC治疗的中重度TAO患者的84只眼进行的，和21只对照的42只眼睛。数据是2020年6月至2021年12月回顾性收集的。使用最小绝对收缩和选择算子（LASSO）方法来确定IVGC治疗“无应答”的预测因素。然后使用逻辑回归分析这些因素以创建列线图。使用Bootstrap重采样方法对模型的判别能力进行了稳健评估，该方法进行了1000次迭代，以进行接收器工作特性（ROC）曲线分析。
结果：LASSO分析确定了非零系数为显着的六个因素，包括SchirmerI测试值,睑板腺(MG)直径,MG长度,疾病持续时间,径向乳头状周围毛细血管（RPC）中的整个毛细血管密度（VD），和整个黄斑VD为浅表视网膜毛细血管丛（SRCP）。随后的逻辑回归模型突出了MG长度，SRCP的全黄斑VD，和疾病持续时间是IVGC治疗反应的独立预测因子。构建的列线图显示曲线下面积（AUC）为0.82（95％CI：0.73-0.91），确认该模型在区分有反应和无反应的TAO患者方面具有一致和可靠的能力。
结论：我们的列线图，组合MG长度（<4.875mm），SRCPVD(<50.25%),和疾病持续时间（>5.5个月），可靠地预测活跃的IVGC治疗有效性较低，中重度TAO患者。
方法：这项研究是对42例接受全身IVGC治疗的中重度TAO患者的84只眼进行的，和21只对照的42只眼睛。数据是2020年6月至2021年12月回顾性收集的。使用最小绝对收缩和选择算子（LASSO）方法来确定IVGC治疗“无应答”的预测因素。然后使用逻辑回归分析这些因素以创建列线图。使用Bootstrap重采样方法对模型的判别能力进行了稳健评估，该方法进行了1000次迭代，以进行接收器工作特性（ROC）曲线分析。
结果：LASSO分析确定了非零系数为显着的六个因素，包括SchirmerI测试值,睑板腺(MG)直径,MG长度,疾病持续时间,径向乳头状周围毛细血管（RPC）中的整个毛细血管密度（VD），和整个黄斑VD为浅表视网膜毛细血管丛（SRCP）。随后的逻辑回归模型突出了MG长度，SRCP的全黄斑VD，和疾病持续时间是IVGC治疗反应的独立预测因子。构建的列线图显示曲线下面积（AUC）为0.82（95％CI：0.73-0.91），确认该模型在区分有反应和无反应的TAO患者方面具有一致和可靠的能力。
结论：我们的列线图，组合MG长度（<4.875mm），SRCPVD(<50.25%),和疾病持续时间（>5.5个月），可靠地预测活跃的IVGC治疗有效性较低，中重度TAO患者。
