PDF(Pubmed)
Abstract：
BACKGROUND: Ahmed valve implantation demonstrated an increasing proportion in glaucoma surgery, but predicting the successful maintenance of target intraocular pressure remains a challenging task. This study aimed to evaluate the performance of machine learning (ML) in predicting surgical outcomes after Ahmed valve implantation and to assess potential risk factors associated with surgical failure to contribute to improving the success rate.
METHODS: This study used preoperative data of patients who underwent Ahmed valve implantation from 2017 to 2021 at Ajou University Hospital. These datasets included demographic and ophthalmic parameters (dataset A), systemic medical records excluding psychiatric records (dataset B), and psychiatric medications (dataset C). Logistic regression, extreme gradient boosting (XGBoost), and support vector machines were first evaluated using only dataset A. The algorithm with the best performance was selected based on the area under the receiver operating characteristics curve (AUROC). Finally, three additional prediction models were developed using the best performance algorithm, incorporating combinations of multiple datasets to predict surgical outcomes at 1 year.
RESULTS: Among 153 eyes of 133 patients, 131 (85.6%) and 22 (14.4%) eyes were categorized as the success and failure groups, respectively. The XGBoost was shown as the best-performance model with an AUROC value of 0.684, using only dataset A. The final three further prediction models were developed based on the combination of multiple datasets using the XGBoost model. All datasets combinations demonstrated the best performances in terms of AUROC (dataset A + B: 0.782; A + C: 0.773; A + B + C: 0.801). Furthermore, advancing age was a risk factor associated with a higher surgical failure incidence.
CONCLUSIONS: ML provides some predictive value in predicting the outcomes of Ahmed valve implantation at 1 year. ML evaluation revealed advancing age as a common risk factor for surgical failure.
METHODS: This study used preoperative data of patients who underwent Ahmed valve implantation from 2017 to 2021 at Ajou University Hospital. These datasets included demographic and ophthalmic parameters (dataset A), systemic medical records excluding psychiatric records (dataset B), and psychiatric medications (dataset C). Logistic regression, extreme gradient boosting (XGBoost), and support vector machines were first evaluated using only dataset A. The algorithm with the best performance was selected based on the area under the receiver operating characteristics curve (AUROC). Finally, three additional prediction models were developed using the best performance algorithm, incorporating combinations of multiple datasets to predict surgical outcomes at 1 year.
RESULTS: Among 153 eyes of 133 patients, 131 (85.6%) and 22 (14.4%) eyes were categorized as the success and failure groups, respectively. The XGBoost was shown as the best-performance model with an AUROC value of 0.684, using only dataset A. The final three further prediction models were developed based on the combination of multiple datasets using the XGBoost model. All datasets combinations demonstrated the best performances in terms of AUROC (dataset A + B: 0.782; A + C: 0.773; A + B + C: 0.801). Furthermore, advancing age was a risk factor associated with a higher surgical failure incidence.
CONCLUSIONS: ML provides some predictive value in predicting the outcomes of Ahmed valve implantation at 1 year. ML evaluation revealed advancing age as a common risk factor for surgical failure.
摘要：
背景：Ahmed瓣膜植入在青光眼手术中的比例越来越高，但是预测目标眼压的成功维持仍然是一项具有挑战性的任务。这项研究旨在评估机器学习（ML）在预测Ahmed瓣膜植入后手术结果方面的表现，并评估与手术失败相关的潜在风险因素，以提高成功率。
方法：本研究使用了2017年至2021年在Ajou大学医院接受Ahmed瓣膜植入的患者的术前数据。这些数据集包括人口统计学和眼科参数(数据集A)，不包括精神病记录的系统医疗记录(数据集B)，和精神病药物（数据集C）。Logistic回归，极端梯度提升(XGBoost)，首先仅使用数据集A对支持向量机进行评估。根据受试者工作特征曲线下面积（AUROC）选择性能最佳的算法。最后,使用最佳性能算法开发了另外三个预测模型，合并多个数据集的组合以预测1年的手术结果。
结果：在133例患者的153只眼中，131只（85.6％）和22只（14.4％）眼睛被归类为成功和失败组，分别。XGBoost显示为具有0.684的AUROC值的最佳性能模型,仅使用数据集A。基于使用XGBoost模型的多个数据集的组合来开发最后三个进一步的预测模型。所有数据集组合在AUROC方面表现出最佳性能(数据集A+B:0.782;A+C:0.773;A+B+C:0.801)。此外，年龄增长是手术失败发生率较高的危险因素.
结论：ML在预测Ahmed瓣膜植入1年后的结果方面提供了一定的预测价值。ML评估显示，年龄增长是手术失败的常见风险因素。
方法：本研究使用了2017年至2021年在Ajou大学医院接受Ahmed瓣膜植入的患者的术前数据。这些数据集包括人口统计学和眼科参数(数据集A)，不包括精神病记录的系统医疗记录(数据集B)，和精神病药物（数据集C）。Logistic回归，极端梯度提升(XGBoost)，首先仅使用数据集A对支持向量机进行评估。根据受试者工作特征曲线下面积（AUROC）选择性能最佳的算法。最后,使用最佳性能算法开发了另外三个预测模型，合并多个数据集的组合以预测1年的手术结果。
结果：在133例患者的153只眼中，131只（85.6％）和22只（14.4％）眼睛被归类为成功和失败组，分别。XGBoost显示为具有0.684的AUROC值的最佳性能模型,仅使用数据集A。基于使用XGBoost模型的多个数据集的组合来开发最后三个进一步的预测模型。所有数据集组合在AUROC方面表现出最佳性能(数据集A+B:0.782;A+C:0.773;A+B+C:0.801)。此外，年龄增长是手术失败发生率较高的危险因素.
结论：ML在预测Ahmed瓣膜植入1年后的结果方面提供了一定的预测价值。ML评估显示，年龄增长是手术失败的常见风险因素。
