Department of Neurology, the Second Affiliated Hospital of Nanchang University, Nanchang, JiangXi, China; Department of Neurology, ShangRao people's Hospital, ShangRao, JiangXi, China.
Medical Big-Data Center, the Second Affiliated Hospital of Nanchang University, Nanchang, JiangXi, China.
Clin Neurol Neurosurg. 2024 Jul;242:108308. doi: 10.1016/j.clineuro.2024.108308. Epub 2024 Apr 29.
The aim of this study was at building an effective machine learning model to contribute to the prediction of stroke recurrence in adult stroke patients subjected to moyamoya disease (MMD), while at analyzing the factors for stroke recurrence.
The data of this retrospective study originated from the database of JiangXi Province Medical Big Data Engineering & Technology Research Center. Moreover, the information of MMD patients admitted to the second affiliated hospital of Nanchang university from January 1st, 2007 to December 31st, 2019 was acquired. A total of 661 patients from January 1st, 2007 to February 28th, 2017 were covered in the training set, while the external validation set comprised 284 patients that fell into a scope from March 1st, 2017 to December 31st, 2019. First, the information regarding all the subjects was compared between the training set and the external validation set. The key influencing variables were screened out using the Lasso Regression Algorithm. Furthermore, the models for predicting stroke recurrence in 1, 2, and 3 years after the initial stroke were built based on five different machine learning algorithms, and all models were externally validated and then compared. Lastly, the CatBoost model with the optimal performance was explained using the SHapley Additive exPlanations (SHAP) interpretation model.
In general, 945 patients suffering from MMD were recruited, and the recurrence rate of acute stroke in 1, 2, and 3 years after the initial stroke reached 11.43%(108/945), 18.94%(179/945), and 23.17%(219/945), respectively. The CatBoost models exhibited the optimal prediction performance among all models; the area under the curve (AUC) of these models for predicting stroke recurrence in 1, 2, and 3 years was determined as 0.794 (0.787, 0.801), 0.813 (0.807, 0.818), and 0.789 (0.783, 0.795), respectively. As indicated by the results of the SHAP interpretation model, the high Suzuki stage, young adults (aged 18-44), no surgical treatment, and the presence of an aneurysm were likely to show significant correlations with the recurrence of stroke in adult stroke patients subjected to MMD.
In adult stroke patients suffering from MMD, the CatBoost model was confirmed to be effective in stroke recurrence prediction, yielding accurate and reliable prediction outcomes. High Suzuki stage, young adults (aged 18-44 years), no surgical treatment, and the presence of an aneurysm are likely to be significantly correlated with the recurrence of stroke in adult stroke patients subjected to MMD.
本研究旨在构建有效的机器学习模型，以预测成人烟雾病患者中风复发的情况，并分析中风复发的相关因素。
本回顾性研究的数据来源于江西省医学大数据工程技术研究中心数据库。此外，还收集了南昌大学第二附属医院 2007 年 1 月 1 日至 2019 年 12 月 31 日收治的 MMD 患者的信息。2007 年 1 月 1 日至 2017 年 2 月 28 日的 661 例患者纳入训练集，2017 年 3 月 1 日至 2019 年 12 月 31 日的 284 例患者纳入外部验证集。首先，比较了训练集和外部验证集的所有受试者的信息。然后，使用 Lasso 回归算法筛选关键影响变量。进一步，基于五种不同的机器学习算法构建了预测初次中风后 1、2、3 年中风复发的模型，并对所有模型进行了外部验证和比较。最后，使用 SHapley Additive exPlanations (SHAP) 解释模型解释表现最佳的 CatBoost 模型。
共纳入 945 例 MMD 患者，初次中风后 1、2、3 年的急性中风复发率分别为 11.43%（108/945）、18.94%（179/945）和 23.17%（219/945）。CatBoost 模型在所有模型中的预测性能最佳；其预测初次中风后 1、2、3 年中风复发的曲线下面积（AUC）分别为 0.794（0.787，0.801）、0.813（0.807，0.818）和 0.789（0.783，0.795）。SHAP 解释模型的结果表明，高 Suzuki 分期、青年（18-44 岁）、未手术治疗和存在动脉瘤与成人烟雾病患者中风复发显著相关。
在成人烟雾病患者中，CatBoost 模型在中风复发预测中表现出良好的效果，能提供准确可靠的预测结果。高 Suzuki 分期、青年（18-44 岁）、未手术治疗和存在动脉瘤与成人烟雾病患者中风复发显著相关。
