Taghavi Reza M, Zhu Guangming, Wintermark Max, Kuraitis Gabriella M, Sussman Eric S, Pulli Benjamin, Biniam Brook, Ostmeier Sophie, Steinberg Gary K, Heit Jeremy J
Department of Medicine, University of California at Davis Medical School, Sacramento, CA, USA.
Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
Interv Neuroradiol. 2023 Apr 17:15910199231170411. doi: 10.1177/15910199231170411.
Aneurysmal subarachnoid hemorrhage results in significant mortality and disability, which is worsened by the development of delayed cerebral ischemia. Tests to identify patients with delayed cerebral ischemia prospectively are of high interest.
We created a machine learning system based on clinical variables to predict delayed cerebral ischemia in aneurysmal subarachnoid hemorrhage patients. We also determined which variables have the most impact on delayed cerebral ischemia prediction using SHapley Additive exPlanations method.
500 aneurysmal subarachnoid hemorrhage patients were identified and 369 met inclusion criteria: 70 patients developed delayed cerebral ischemia (delayed cerebral ischemia+) and 299 did not (delayed cerebral ischemia-). The algorithm was trained based upon age, sex, hypertension (HTN), diabetes, hyperlipidemia, congestive heart failure, coronary artery disease, smoking history, family history of aneurysm, Fisher Grade, Hunt and Hess score, and external ventricular drain placement. Random Forest was selected for this project, and prediction outcome of the algorithm was delayed cerebral ischemia+. SHapley Additive exPlanations was used to visualize each feature's contribution to the model prediction.
The Random Forest machine learning algorithm predicted delayed cerebral ischemia: accuracy 80.65% (95% CI: 72.62-88.68), area under the curve 0.780 (95% CI: 0.696-0.864), sensitivity 12.5% (95% CI: -3.7 to 28.7), specificity 94.81% (95% CI: 89.85-99.77), PPV 33.3% (95% CI: -4.39 to 71.05), and NPV 84.1% (95% CI: 76.38-91.82). SHapley Additive exPlanations value demonstrated Age, external ventricular drain placement, Fisher Grade, and Hunt and Hess score, and HTN had the highest predictive values for delayed cerebral ischemia. Lower age, absence of hypertension, higher Hunt and Hess score, higher Fisher Grade, and external ventricular drain placement increased risk of delayed cerebral ischemia.
Machine learning models based upon clinical variables predict delayed cerebral ischemia with high specificity and good accuracy.
动脉瘤性蛛网膜下腔出血导致显著的死亡率和残疾率，而迟发性脑缺血的发生会使其进一步恶化。前瞻性识别迟发性脑缺血患者的检测方法备受关注。
我们基于临床变量创建了一个机器学习系统，以预测动脉瘤性蛛网膜下腔出血患者的迟发性脑缺血。我们还使用夏普利值法确定了哪些变量对迟发性脑缺血预测影响最大。
确定了500例动脉瘤性蛛网膜下腔出血患者，其中369例符合纳入标准：70例发生迟发性脑缺血（迟发性脑缺血阳性），299例未发生（迟发性脑缺血阴性）。该算法基于年龄、性别、高血压（HTN）、糖尿病、高脂血症、充血性心力衰竭、冠状动脉疾病、吸烟史、动脉瘤家族史、Fisher分级、Hunt和Hess评分以及外置脑室引流管置入情况进行训练。本项目选择随机森林算法，算法的预测结果为迟发性脑缺血阳性。使用夏普利值法来可视化每个特征对模型预测的贡献。
随机森林机器学习算法预测迟发性脑缺血的结果为：准确率80.65%（95%可信区间：72.62 - 88.68），曲线下面积0.780（95%可信区间：0.696 - 0.864），灵敏度12.5%（95%可信区间：-3.7至28.7），特异度94.81%（95%可信区间：89.85 - 99.77），阳性预测值33.3%（95%可信区间：-4.39至71.05），阴性预测值84.1%（95%可信区间：76.38 - 91.82）。夏普利值显示年龄、外置脑室引流管置入情况、Fisher分级、Hunt和Hess评分以及高血压对迟发性脑缺血具有最高的预测价值。年龄较小、无高血压、Hunt和Hess评分较高、Fisher分级较高以及外置脑室引流管置入会增加迟发性脑缺血的风险。
基于临床变量的机器学习模型能够以较高的特异度和良好的准确率预测迟发性脑缺血。
