Prediction of hematoma expansion in spontaneous intracerebral hemorrhage using support vector machine

Background: Spontaneous intracerebral hemorrhage (ICH) is a devastating disease with high mortality rate. This study aimed to predict hematoma expansion in spontaneous ICH from routinely available variables by using support vector machine (SVM) method. Methods: We retrospectively reviewed 1157 patients with spontaneous ICH who underwent initial computed tomography (CT) scan within 6 h and follow-up CT scan within 72 h from symptom onset in our hospital between September 2013 and August 2018. Hematoma region was manually segmented at each slice to guarantee the measurement accuracy of hematoma volume. Hematoma expansion was defined as a proportional increase of hematoma volume > 33% or an absolute growth of hematoma volume > 6 mL from initial Cr scan to follow-up CT scan. Univariate and multivariate analyses were performed to assess the association between clinical variables and hematoma expansion. SVM machine learning model was developed to predict hematoma expansion. Findings: 246 of 1157 (21.3%) patients experienced hematoma expansion. Multivariate analyses revealed the following 6 independent factors associated with hematoma expansion: male patient (odds ratio [OR] = 1.82), time to initial CT scan (OR = 0.73), Glasgow Coma Scale (OR = 0.86), fibrinogen level (OR = 0.72), black hole sign (OR = 2.52), and blend sign (OR = 4.03). The SVM model achieved a mean sensitivity of 81.3%, specificity of 84.8%, overall accuracy of 83.3%, and area under receiver operating characteristic curve (AUC) of 0.89 in prediction of hematoma expansion. Interpretation: The designed SVM model presented good performance in predicting hematoma expansion from routinely available variables. (C) 2019 The Authors. Published by Elsevier B.V.