Prediction of Poor Outcome in Intracerebral Hemorrhage Based on Computed Tomography Markers.

ABSTRACT

INTRODUCTION: Intracerebral hemorrhage (ICH) is the most fatal type of stroke worldwide. Herein, we aim to develop a predictive model based on computed tomography (CT) markers in an ICH cohort and validate it in another cohort. METHODS: This retrospective observational cohort study was conducted in 3 medical centers in China. The values of CT markers, including hypodensities, hematoma density, blend sign, black hole sign, island sign, midline shift, baseline hematoma volume, and satellite sign, in predicting poor outcome were analyzed by logistic regression analysis. A nomogram was developed based on the results of multivariate logistic regression analysis in development cohort. Area under curve (AUC) and calibration plot were used to assess the accuracy of nomogram in this development cohort and validate in another cohort. RESULTS: A total of 1,498 patients were included in this study. Multivariate logistic regression analysis indicated that hypodensities, black hole sign, island sign, midline shift, and baseline hematoma volume were independently associated with poor outcome in development cohort. The AUC was 0.75 (95% confidence interval [CI] 0.73-0.76) in the internal validation with development cohort and 0.74 (95% CI 0.72-0.75) in the external validation with validation cohort. The calibration plot in development and validation cohort indicated that the nomogram was well calibrated. CONCLUSIONS: CT markers of hypodensities, black hole sign, and island sign might predict poor outcome of ICH patients within 90 days.