Machine learning improves prediction of delayed cerebral ischemia in patients with subarachnoid hemorrhage
Machine learning improves prediction of delayed cerebral ischemia in patients with subarachnoid hemorrhage
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DOI：
10.1136/neurintsurg-2018-014258
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发表时间：
2019-05-01
影响因子：
4.8
通讯作者：
Marquering, Henk A.
中科院分区：
文献类型：
--
作者：
Ramos, Lucas Alexandre;van der Steen, Wessel E.;Marquering, Henk A.
背景与目的迟发性脑缺血(DCI)是动脉瘤性蛛网膜下腔出血患者的严重并发症。之前已经确定了几个相关的预测因素。然而，它们的预测价值普遍较低。我们假设机器学习(ML)算法结合临床和图像数据预测DCI的准确性高于以前应用的Logistic回归。材料与方法收集317例动脉瘤性蛛网膜下腔出血患者的临床和基线CT图像资料。三种类型的分析被用于预测DCI。首先，用Logistic回归模型评估已知预测因素的预后价值。其次，ML模型是使用所有临床变量创建的。第三，使用自动编码器从CT图像中提取图像特征，并结合临床数据创建ML模型。以曲线下面积(AUC)、灵敏度和特异度(95%CI)评价其准确性。结果Logistic回归模型对已知预测因素的最佳AUC为0.63(95%CI为0.62~0.63)。对于有临床数据的ML算法，AUC有一个小的但统计上显着的改善，达到0.68(95%CI为0.65到0.69)。值得注意的是，许多ML模型中都包含了动脉瘤的宽度和高度。包含图像特征的ML模型的AUC最高，为0.74(95%CI为0.72~0.75)。结论ML算法显著提高了动脉瘤性蛛网膜下腔出血患者DCI的预测，尤其是在包含图像特征的情况下。我们的实验表明，动脉瘤的特征也与DCI的发生有关。
Background and purpose Delayed cerebral ischemia (DCI) is a severe complication in patients with aneurysmal subarachnoid hemorrhage. Several associated predictors have been previously identified. However, their predictive value is generally low. We hypothesize that Machine Learning (ML) algorithms for the prediction of DCI using a combination of clinical and image data lead to higher predictive accuracy than previously applied logistic regressions.Materials and methods Clinical and baseline CT image data from 317 patients with aneurysmal subarachnoid hemorrhage were included. Three types of analysis were performed to predict DCI. First, the prognostic value of known predictors was assessed with logistic regression models. Second, ML models were created using all clinical variables. Third, image features were extracted from the CT images using an auto-encoder and combined with clinical data to create ML models. Accuracy was evaluated based on the area under the curve (AUC), sensitivity and specificity with 95% CI.Results The best AUC of the logistic regression models for known predictors was 0.63 (95% CI 0.62 to 0.63). For the ML algorithms with clinical data there was a small but statistically significant improvement in the AUC to 0.68 (95% CI 0.65 to 0.69). Notably, aneurysm width and height were included in many of the ML models. The AUC was highest for ML models that also included image features: 0.74 (95% CI 0.72 to 0.75).Conclusion ML algorithms significantly improve the prediction of DCI in patients with aneurysmal subarachnoid hemorrhage, particularly when image features are also included. Our experiments suggest that aneurysm characteristics are also associated with the development of DCI.