Department of Neurosurgery, Charité Universitaetsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany.
CLAIM - Charité Lab for AI in Medicine, Charité Universitaetsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany.
Neurosurg Rev. 2021 Oct;44(5):2837-2846. doi: 10.1007/s10143-020-01453-6. Epub 2021 Jan 20.
Reliable prediction of outcomes of aneurysmal subarachnoid hemorrhage (aSAH) based on factors available at patient admission may support responsible allocation of resources as well as treatment decisions. Radiographic and clinical scoring systems may help clinicians estimate disease severity, but their predictive value is limited, especially in devising treatment strategies. In this study, we aimed to examine whether a machine learning (ML) approach using variables available on admission may improve outcome prediction in aSAH compared to established scoring systems. Combined clinical and radiographic features as well as standard scores (Hunt & Hess, WFNS, BNI, Fisher, and VASOGRADE) available on patient admission were analyzed using a consecutive single-center database of patients that presented with aSAH (n = 388). Different ML models (seven algorithms including three types of traditional generalized linear models, as well as a tree bosting algorithm, a support vector machine classifier (SVMC), a Naive Bayes (NB) classifier, and a multilayer perceptron (MLP) artificial neural net) were trained for single features, scores, and combined features with a random split into training and test sets (4:1 ratio), ten-fold cross-validation, and 50 shuffles. For combined features, feature importance was calculated. There was no difference in performance between traditional and other ML applications using traditional clinico-radiographic features. Also, no relevant difference was identified between a combined set of clinico-radiological features available on admission (highest AUC 0.78, tree boosting) and the best performing clinical score GCS (highest AUC 0.76, tree boosting). GCS and age were the most important variables for the feature combination. In this cohort of patients with aSAH, the performance of functional outcome prediction by machine learning techniques was comparable to traditional methods and established clinical scores. Future work is necessary to examine input variables other than traditional clinico-radiographic features and to evaluate whether a higher performance for outcome prediction in aSAH can be achieved.
可靠的预测动脉瘤性蛛网膜下腔出血（aSAH）的结局基于患者入院时的可用因素，这可能有助于合理分配资源以及治疗决策。影像学和临床评分系统可以帮助临床医生评估疾病严重程度，但它们的预测价值有限，特别是在制定治疗策略时。在这项研究中，我们旨在检验使用入院时可用的变量的机器学习（ML）方法是否可以改善 aSAH 的结局预测，与既定的评分系统相比。使用来自单个中心的连续数据库分析了入院时的综合临床和影像学特征以及标准评分（Hunt & Hess、WFNS、BNI、Fisher 和 VASOGRADE）（n = 388）。不同的 ML 模型（七种算法，包括三种传统的广义线性模型，以及树提升算法、支持向量机分类器（SVMC）、朴素贝叶斯（NB）分类器和多层感知器（MLP）人工神经网络）针对单一特征、评分和组合特征进行训练，使用随机分为训练集和测试集（4:1 比例）、十折交叉验证和 50 次洗牌。对于组合特征，计算了特征重要性。传统和其他 ML 应用在使用传统临床影像学特征时，性能没有差异。另外，在入院时可用的综合临床影像学特征（最高 AUC 0.78，树提升）和最佳表现的临床评分 GCS（最高 AUC 0.76，树提升）之间，也没有发现相关差异。GCS 和年龄是特征组合中最重要的变量。在这个 aSAH 患者队列中，机器学习技术的功能结局预测性能与传统方法和既定的临床评分相当。未来的工作需要检查除传统临床影像学特征之外的输入变量，并评估是否可以实现 aSAH 结局预测的更高性能。
