Department of Ophthalmology, Ajou University Medical Center, Ajou University School of Medicine, 164, World Cup-ro, Yeongtong-gu, Suwon, 16499, Republic of Korea.
Department of Biomedical Informatics, Ajou University School of Medicine, 154, Word Cup-ro, Yeongtong-gu, Suwon, 16499, Republic of Korea.
BMC Ophthalmol. 2024 Jun 11;24(1):248. doi: 10.1186/s12886-024-03510-w.
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.
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.
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.
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.
Ahmed 阀植入术在青光眼手术中的应用比例不断增加，但预测目标眼内压维持成功仍然是一项具有挑战性的任务。本研究旨在评估机器学习（ML）在预测 Ahmed 阀植入术后手术结果中的性能，并评估与手术失败相关的潜在风险因素，以提高成功率。
本研究使用了 2017 年至 2021 年在 Ajou 大学医院接受 Ahmed 阀植入术的患者的术前数据。这些数据集包括人口统计学和眼科参数（数据集 A）、排除精神病史的系统病历（数据集 B）和精神科药物（数据集 C）。首先仅使用数据集 A 评估逻辑回归、极端梯度提升（XGBoost）和支持向量机。根据接受者操作特征曲线下的面积（AUROC）选择性能最佳的算法。最后，使用最佳性能算法开发了三个额外的预测模型，结合多个数据集预测 1 年的手术结果。
在 133 名患者的 153 只眼中，131 只（85.6%）和 22 只（14.4%）眼分别归入成功组和失败组。XGBoost 表现为使用数据集 A 的最佳性能模型，AUROC 值为 0.684。最终基于 XGBoost 模型结合多个数据集开发了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，年龄增长是与较高手术失败发生率相关的风险因素。
ML 在预测 Ahmed 阀植入术后 1 年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。
