Machine Learning Using Presentation CT Perfusion Imaging for Predicting Clinical Outcomes in Patients With Aneurysmal...

BACKGROUND. Prediction of outcomes in patients with aneurysmal subarachnoid hemorrhage (aSAH) is challenging using current clinical predictors. OBJECTIVE. The purpose of our study was to evaluate the utility of machine learning (ML) models incorporating presentation clinical and CT perfusion imaging (CTP) data in predicting delayed cerebral ischemia (DCI) and poor functional outcome in patients with aSAH. METHODS. This study entailed retrospective analysis of data from 242 patients (mean age, 60.9 ± 11.8 [SD] years; 165 women, 77 men) with aSAH who, as part of a prospective trial, underwent CTP followed by standardized evaluation for DCI during initial hospitalization and poor 3-month functional outcome (i.e., modified Rankin scale score ≥ 4). Patients were randomly divided into training (n = 194) and test (n = 48) sets. Five ML models (k-nearest neighbor [KNN], logistic regression [LR], support vector machine [SVM], random forest [RF], and category boosting [CatBoost]) were developed for predicting outcomes using presentation clinical and CTP data. The least absolute shrinkage and selection operator method was used for feature selection. Ten-fold cross-validation was performed in the training set. Traditional clinical models were developed using stepwise LR analysis of clinical, but not CTP, data. RESULTS. Qualitative CTP analysis was identified as the most impactful feature for both outcomes. In the test set, the traditional clinical model, KNN, LR, SVM, RF, and CatBoost showed AUC for predicting DCI of 0.771, 0.812, 0.824, 0.908, 0.930, and 0.949, respectively, and AUC for predicting poor 3-month functional outcome of 0.863, 0.858, 0.879, 0.908, 0.926, and 0.958. CatBoost was selected as the optimal model. In the test set, AUC was higher for CatBoost than for the traditional clinical model for predicting DCI (p = .004) and poor 3-month functional outcome (p = .04). In the test set, sensitivity and specificity for predicting DCI were 92.3% and 60.0% for the traditional clinical model versus 92.3% and 85.7% for CatBoost, and sensitivity and specificity for predicting poor 3-month functional outcome were 100.0% and 65.8% for the traditional clinical model versus 90.0% and 94.7% for CatBoost. A web-based prediction tool based on CatBoost was created. CONCLUSION. ML models incorporating presentation clinical and CTP data outperformed traditional clinical models in predicting DCI and poor 3-month functional outcome. CLINICAL IMPACT. ML models may help guide early management of patients with aSAH.