Du Senlin, Wu Yanze, Tao Jiarong, Shu Lei, Yan Tengfeng, Xiao Bing, Lv Shigang, Ye Minhua, Gong Yanyan, Zhu Xingen, Hu Ping, Wu Miaojing
Department of Neurosurgery, The second Affiliated Hospital, Jiangxi Medical College of Nanchang University, Nanchang, 330006, People's Republic of China.
Jiangxi Key Laboratory of Neurological Tumors and Cerebrovascular Diseases, Nanchang, 330006, People's Republic of China.
Ther Clin Risk Manag. 2025 Mar 7;21:293-307. doi: 10.2147/TCRM.S504745. eCollection 2025.
Endovascular treatment (EVT) has been recommended as a superior modality for the treatment of intracranial aneurysm. However, there still exists a worse percentage of poor functional outcome in patients with poor-grade aneurysmal subarachnoid hemorrhage (aSAH) undergoing EVT. Therefore, it is urgently needed to investigate the risk factors and develop a critical decision model in the subtype of such patients.
We extracted the target variables from an ongoing registry cohort study, PROSAH-MPC, which was conducted in multiple centers in China. We randomly assigned these patients to training and validation cohorts with a ratio of 7:3. Univariate and multivariate logistic regressions were performed to find the potential factors, and then nine machine learning models and a stack ensemble model were developed with optimized variables. The performance of these models was evaluated through several indicators, including area under the receiver operating characteristic curve (AUC-ROC). We further use Shapley Additive Explanations (SHAP) methods for the distribution of feature visualization based on the optimal models.
A total of 226 eligible patients with poor-grade aSAH undergoing EVT were enrolled, while 89 (39.4%) has a poor 12-month outcome. Age (Adjusted OR [aOR], 1.08; 95% CI: 1.03-1.13; p = 0.002), subarachnoid hemorrhage volume (aOR, 1.02; 95% CI: 1.00-1.05; p = 0.033), World Federation of Neurosurgical Societies grade (WFNS) (aOR, 2.03; 95% CI: 1.05-3.93; p = 0.035), and Hunt-Hess grade (aOR, 2.36; 95% CI: 1.13-4.93; p = 0.022) were identified as the independent risk factors of the poor outcome. Then, the prediction models developed have revealed that LightGBM algorithm has a superior performance with an AUC-ROC value of 0.842 in the validation cohort, while the SHAP results showed that age is the most important risk factor affecting functional outcomes.
The LightGBM model holds immense potential in facilitating risk stratification for poor-grade aSAH patients undergoing endovascular treatment who are at risk of adverse outcomes, thereby enhancing clinical decision-making processes.
PROSAH-MPC. NCT05738083. Registered 16 November 2022 - Retrospectively registered, https://clinicaltrials.gov/study/NCT05738083.
血管内治疗（EVT）已被推荐为治疗颅内动脉瘤的一种更优方式。然而，在接受EVT的低级别动脉瘤性蛛网膜下腔出血（aSAH）患者中，功能预后不良的比例仍然较高。因此，迫切需要研究此类患者亚型的危险因素并建立关键决策模型。
我们从正在进行的一项在中国多个中心开展的注册队列研究PROSAH-MPC中提取目标变量。我们将这些患者以7:3的比例随机分配到训练队列和验证队列。进行单因素和多因素逻辑回归以寻找潜在因素，然后使用优化变量开发九个机器学习模型和一个堆叠集成模型。通过包括受试者操作特征曲线下面积（AUC-ROC）在内的多个指标评估这些模型的性能。我们进一步基于最优模型使用Shapley加性解释（SHAP）方法进行特征可视化分布。
共纳入226例接受EVT的低级别aSAH合格患者，其中89例（39.4%）12个月预后不良。年龄（调整后比值比[aOR]，1.08；95%置信区间：1.03 - 1.13；p = 0.002）、蛛网膜下腔出血量（aOR，1.02；95%置信区间：1.00 - 1.05；p = 0.033）、世界神经外科联合会分级（WFNS）（aOR，2.03；95%置信区间：1.05 - 3.93；p = 0.035）和Hunt-Hess分级（aOR，2.36；95%置信区间：1.13 - 4.93；p = 0.022）被确定为预后不良的独立危险因素。然后，所开发的预测模型显示，LightGBM算法在验证队列中具有优越性能，AUC-ROC值为0.842，而SHAP结果表明年龄是影响功能预后的最重要危险因素。
LightGBM模型在为接受血管内治疗且有不良结局风险的低级别aSAH患者进行风险分层方面具有巨大潜力，从而增强临床决策过程。
PROSAH-MPC。NCT05738083。2022年11月16日注册 - 回顾性注册，https://clinicaltrials.gov/study/NCT05738083。
