Patrick Czorlich1, Thomas Sauvigny2, Franz Ricklefs2, Stefan Kluge3, Eik Vettorazzi4, Jan Regelsberger2, Manfred Westphal2, Nils Ole Schmidt2. 1. Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246, Hamburg, Germany. p.czorlich@uke.de. 2. Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246, Hamburg, Germany. 3. Department of Intensive Care, University Medical Center Hamburg-Eppendorf, Hamburg, Germany. 4. Department of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Abstract
BACKGROUND: Early prediction of increased morbidity and mortality in aneurysmal subarachnoid hemorrhage (aSAH) remains crucial to improving patient management. Most prediction models lack external validation and focus on disease-specific items without considering physiological parameters and the past medical history. The aim was to assess the validity of the established Simplified Acute Physiology Score II (SAPS-II) in an aSAH cohort for the prediction of hospital mortality and to identify additional physiological and clinical predictors. METHODS: The predictive value of SAPS-II for hospital mortality was assessed in a retrospective analysis of 263 consecutive patients with aSAH. Additional physiological and clinical parameters including the past medical history were analyzed by forward selection multivariate analysis to identify independent predictors of hospital mortality and to improve the prediction model. RESULTS: The SAPS-II predicted hospital mortality with an area under the curve (AUC) of 0.834 with an odds ratio (OR) of 1.097 [95 % confidence interval 1.067-1.128) for each additional point. Forward selection multivariate analysis identified the Glasgow Coma Scale score (P < 0.001), history of chronic headache (P = 0.01) and medication with anticoagulants (P = 0.04) as independent predictors of hospital mortality. Adding these parameters to the SAPS-II, the AUC increased to 0.86. CONCLUSION: This study validates the predictive accuracy of SAPS-II for hospital mortality in aSAH patients. Additional parameters from the past medical history increase its predictive power. From a practical viewpoint, SAPS-II alone already represents a sufficient and powerful score to predict hospital mortality at an early time point and may help to improve patient management.
BACKGROUND: Early prediction of increased morbidity and mortality in aneurysmal subarachnoid hemorrhage (aSAH) remains crucial to improving patient management. Most prediction models lack external validation and focus on disease-specific items without considering physiological parameters and the past medical history. The aim was to assess the validity of the established Simplified Acute Physiology Score II (SAPS-II) in an aSAH cohort for the prediction of hospital mortality and to identify additional physiological and clinical predictors. METHODS: The predictive value of SAPS-II for hospital mortality was assessed in a retrospective analysis of 263 consecutive patients with aSAH. Additional physiological and clinical parameters including the past medical history were analyzed by forward selection multivariate analysis to identify independent predictors of hospital mortality and to improve the prediction model. RESULTS: The SAPS-II predicted hospital mortality with an area under the curve (AUC) of 0.834 with an odds ratio (OR) of 1.097 [95 % confidence interval 1.067-1.128) for each additional point. Forward selection multivariate analysis identified the Glasgow Coma Scale score (P < 0.001), history of chronic headache (P = 0.01) and medication with anticoagulants (P = 0.04) as independent predictors of hospital mortality. Adding these parameters to the SAPS-II, the AUC increased to 0.86. CONCLUSION: This study validates the predictive accuracy of SAPS-II for hospital mortality in aSAH patients. Additional parameters from the past medical history increase its predictive power. From a practical viewpoint, SAPS-II alone already represents a sufficient and powerful score to predict hospital mortality at an early time point and may help to improve patient management.