A Comparison of LASSO Regression and Tree-Based Models for Delayed Cerebral Ischemia in Elderly Patients With Subarachnoid Hemorrhage - Hu, Ping; Liu, Yangfan; Li, Yuntao; Guo, Geng; Su, Zhongzhou; Gao, Xu; Chen, Junhui; Qi, Yangzhi; Xu, Yang; Yan, Tengfeng; Ye, Liguo; Sun, Qian; et al. (Frontiers in Neurology, 2022)
Abstract Backgrounds: As a most widely used machine learning method, tree-based algorithms have not been applied to predict delayed cerebral ischemia (DCI) in elderly patients with aneurysmal subarachnoid hemorrhage (aSAH). Hence, this study aims to develop conventional regression and tree-based models and determine which model had better prediction performance for DCI development in hospitalized elderly patients after aSAH. Methods: This was a multicenter, retrospective, observational cohort study analyzing elderly patients with aSAH aged 60 years and older. We randomly divided the multicentral data into model training and validation cohort in a ratio of 70% to 30%. One conventional regression and tree-based model, including least absolute shrinkage and selection operator (LASSO), decision tree (DT), random forest (RF), and eXtreme Gradient Boosting (XGBoost), were developed. Accuracy, sensitivity, specificity, area under the precision-recall curve (AUC-PR), and area under the receiver operating characteristic curve (AUC-ROC) with 95% confidence interval (CI) were employed to evaluate model prediction performance. DeLong test was conducted to calculate the statistical differences among models. Finally, we figured the importance weight of each feature to visualize the contribution on DCI. Results: There were 111 and 42 patients in the model training and validation cohorts, and 53 cases developed DCI. According to AUC-ROC value in model internal validation, DT of 0.836 (95%CI: 0.747-0.926, p=0.15), RF of 1 (95%CI:1-1, p<0.05), and XGBoost of 0.931 (95%CI:0.885-0.978, p=0.01) outperformed LASSO of 0.793 (95%CI: 0.692-0.893). However, the LASSO scored a highest AUC-ROC value of 0.894 (95%CI: 0.8-0.989) than DT of 0.764 (95%CI:0.6-0.928, p=0.05), RF of 0.821 (95%CI: 0.683-0.959, p=0.27), and XGBoost of 0.865 (95%CI: 0.751-0.979, p=0.69) in independent external validation. Moreover, the LASSO also had a highest AUC-PR value of 0.681 than DT of 0.615, RF of 0.667, and XGBoost of 0.622 in external validation. In addition, we found that CT value of subarachnoid clots, aneurysm therapy, and white blood cell counts were the most important features for DCI in elderly patients with aSAH. Conclusions The LASSO had a superior prediction power than tree-based models in external validation. As a result, we recommend the conventional LASSO regression model to predict DCI in elderly patients with aSAH.