■动脉瘤性蛛网膜下腔出血(aSAH)是一种危及生命的疾病,死亡率和发病率高。aSAH的改良Fisher等级与神经功能缺损之间存在实质性联系。本研究旨在使用机器学习方法分析与aSAH的修改Fisher等级相关的因素。
■进行了多中心观察性研究。从中国五家三级医院招募aSAH患者。使用改良的Fisher分级量表测量aSAH的出血量。分析了aSAH改良Fisher分级的危险因素,其中包括社会人口因素,临床因素,血液指数,动脉瘤破裂的特点。我们构建了几个基于树的机器学习模型(XGBoost,CatBoost,LightGBM)用于预测,并使用网格搜索来优化模型参数。综合评价模型,我们使用了准确性,Precision,接收器工作特性曲线下面积(AUROC),精确召回曲线下的面积(AUPRC),和Brier作为评价指标,评估模型性能,选择最优模型。
■共招募了888例aSAH患者,其中305人的Fisher改良等级为3级和4级。结果表明,XGBoost模型的AUROC最高,为0.772,各项指标优于CatBoost和LightGBM。特征重要性图显示顶部特征变量包括血小板,凝血酶时间,纤维蛋白原,入院前收缩压,活化部分凝血活酶时间,以及aSAH发作与首次CT检查之间的时间间隔。
■确定了导致aSAH改良Fisher等级的因素,这为未来的研究和临床干预提供了有价值的见解。在未破裂动脉瘤的治疗中应控制这些危险因素,如有必要,可以给予适当的治疗,以降低动脉瘤破裂后严重出血的风险。
UNASSIGNED: Aneurysmal subarachnoid hemorrhage (aSAH) is a life-threatening medical condition with a high fatality and morbidity rate. There was a substantial link between the modified Fisher grade of aSAH and the neurological function deficit. This study aimed to analyze the factors associated with the modified Fisher grade of aSAH using a machine learning approach.
UNASSIGNED: A multi-center observational study was conducted. The patients with aSAH were recruited from five tertiary hospitals in China. The volume of hemorrhage in aSAH was measured using the modified Fisher grade scale. The risk factors responsible for the modified Fisher grade of aSAH were analyzed, which include sociodemographic factors, clinical factors, blood index, and ruptured aneurysm characteristics. We built several tree-based machine learning models (XGBoost, CatBoost, LightGBM) for prediction and used grid search to optimize model parameters. To comprehensively evaluate the model, we used Accuracy, Precision, Area Under the Receiver Operating Characteristic Curve (AUROC), Area Under the Precision-Recall Curve (AUPRC), and Brier as evaluation indicators to assess the model performance and select the best model.
UNASSIGNED: A total of 888 patients with aSAH were recruited, of whom 305 with modified Fisher grade of 3 and 4. The results show that the XGBoost model has the highest AUROC of 0.772, and the indicators are better than CatBoost and LightGBM. The feature importance graph shows that the top feature variables include platelet, thrombin time, fibrinogen, preadmission systolic blood pressure, activated partial thromboplastin time, and the time interval between the onset of aSAH and the first-time CT examination.
UNASSIGNED: The factors responsible for the modified Fisher grade of aSAH were identified, which offered valuable insights for future research and clinical intervention. These risk factors should be controlled in the treatment of unruptured aneurysms, and appropriate treatment can be given if necessary to reduce the risk of severe hemorrhage after aneurysm rupture.