Pascal Salazar1, Mario Di Napoli2, Mostafa Jafari3, Alibay Jafarli3, Wendy Ziai4, Alexander Petersen5, Stephan A Mayer6, Eric M Bershad7, Rahul Damani7, Afshin A Divani8,9. 1. Vital Images, Minnetonka, MN, USA. 2. Department of Neurology, San Camillo de' Lellis District General Hospital, Rieti, Italy. 3. Department of Neurology, University of Minnesota, MMC 295, 420 Delaware Street S.E., Minneapolis, MN, 55455, USA. 4. Department of Neurology, Neurosurgery, and Anesthesia/Critical Care Medicine, Johns Hopkins, Baltimore, USA. 5. Department of Statistics and Applied Probability, University of California, Santa Barbara, CA, USA. 6. Department of Neurology, Henry Ford Health System, Detroit, MI, USA. 7. Department of Neurology, Baylor College of Medicine, Houston, TX, USA. 8. Department of Neurology, University of Minnesota, MMC 295, 420 Delaware Street S.E., Minneapolis, MN, 55455, USA. adivani@gmail.com. 9. Department of Neurosurgery, University of Minnesota, Minneapolis, MN, USA. adivani@gmail.com.
Abstract
BACKGROUND: Rapid diagnosis and proper management of intracerebral hemorrhage (ICH) play a crucial role in the outcome. Prediction of the outcome with a high degree of accuracy based on admission data including imaging information can potentially influence clinical decision-making practice. METHODS: We conducted a retrospective multicenter study of consecutive ICH patients admitted between 2012-2017. Medical history, admission data, and initial head computed tomography (CT) scan were collected. CT scans were semiautomatically segmented for hematoma volume, hematoma density histograms, and sphericity index (SI). Discharge unfavorable outcomes were defined as death or severe disability (modified Rankin Scores 4-6). We compared (1) hematoma volume alone; (2) multiparameter imaging data including hematoma volume, location, density heterogeneity, SI, and midline shift; and (3) multiparameter imaging data with clinical information available on admission for ICH outcome prediction. Multivariate analysis and predictive modeling were used to determine the significance of hematoma characteristics on the outcome. RESULTS: We included 430 subjects in this analysis. Models using automated hematoma segmentation showed incremental predictive accuracies for in-hospital mortality using hematoma volume only: area under the curve (AUC): 0.85 [0.76-0.93], multiparameter imaging data (hematoma volume, location, CT density, SI, and midline shift): AUC: 0.91 [0.86-0.97], and multiparameter imaging data plus clinical information on admission (Glasgow Coma Scale (GCS) score and age): AUC: 0.94 [0.89-0.99]. Similarly, severe disability predictive accuracy varied from AUC: 0.84 [0.76-0.93] for volume-only model to AUC: 0.88 [0.80-0.95] for imaging data models and AUC: 0.92 [0.86-0.98] for imaging plus clinical predictors. CONCLUSIONS: Multiparameter models combining imaging and admission clinical data show high accuracy for predicting discharge unfavorable outcome after ICH.
BACKGROUND: Rapid diagnosis and proper management of intracerebral hemorrhage (ICH) play a crucial role in the outcome. Prediction of the outcome with a high degree of accuracy based on admission data including imaging information can potentially influence clinical decision-making practice. METHODS: We conducted a retrospective multicenter study of consecutive ICH patients admitted between 2012-2017. Medical history, admission data, and initial head computed tomography (CT) scan were collected. CT scans were semiautomatically segmented for hematoma volume, hematoma density histograms, and sphericity index (SI). Discharge unfavorable outcomes were defined as death or severe disability (modified Rankin Scores 4-6). We compared (1) hematoma volume alone; (2) multiparameter imaging data including hematoma volume, location, density heterogeneity, SI, and midline shift; and (3) multiparameter imaging data with clinical information available on admission for ICH outcome prediction. Multivariate analysis and predictive modeling were used to determine the significance of hematoma characteristics on the outcome. RESULTS: We included 430 subjects in this analysis. Models using automated hematoma segmentation showed incremental predictive accuracies for in-hospital mortality using hematoma volume only: area under the curve (AUC): 0.85 [0.76-0.93], multiparameter imaging data (hematoma volume, location, CT density, SI, and midline shift): AUC: 0.91 [0.86-0.97], and multiparameter imaging data plus clinical information on admission (Glasgow Coma Scale (GCS) score and age): AUC: 0.94 [0.89-0.99]. Similarly, severe disability predictive accuracy varied from AUC: 0.84 [0.76-0.93] for volume-only model to AUC: 0.88 [0.80-0.95] for imaging data models and AUC: 0.92 [0.86-0.98] for imaging plus clinical predictors. CONCLUSIONS: Multiparameter models combining imaging and admission clinical data show high accuracy for predicting discharge unfavorable outcome after ICH.
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