Mónica Mourelo-Fariña1, Sonia Pértega2, Rita Galeiras3. 1. Critical Care Unit, Complejo Hospitalario Universitario de A Coruña (CHUAC), Instituto de Investigación Biomédica de A Coruña (INIBIC), SERGAS, Universidade de A Coruña (UDC), As Xubias, 15006, A Coruña, Spain. monica.mourelo@gmail.com. 2. Clinical Epidemiology and Biostatistics Unit, Complejo Hospitalario Universitario de A Coruña (CHUAC), Instituto de Investigación Biomédica de A Coruña (INIBIC), SERGAS, Universidade de A Coruña (UDC), As Xubias, 15006, A Coruña, Spain. 3. Critical Care Unit, Complejo Hospitalario Universitario de A Coruña (CHUAC), Instituto de Investigación Biomédica de A Coruña (INIBIC), SERGAS, Universidade de A Coruña (UDC), As Xubias, 15006, A Coruña, Spain.
Abstract
BACKGROUND: Despite being a rare cause of stroke, spontaneous subarachnoid hemorrhage (SAH) is associated with high mortality rates. The prediction models that are currently being used on SAH patients are heterogeneous, and few address premature mortality. The aim of this study was to develop a mortality risk stratification score for SAH. METHODS: A retrospective study was carried out with 536 patients diagnosed with SAH who had been admitted to the intensive care unit (ICU) at the University Hospital Complex of A Coruña (Spain) between 2003 and 2013. A multivariate logistic regression model was developed to predict the likelihood of in-hospital mortality, adjusting it exclusively for variables present on admission. A predictive equation of in-hospital mortality was then computed based on the model's coefficients, along with a points-based risk-scoring system. Its discrimination ability was also tested based on the area under the receiver operating characteristics curve and compared with previously developed scores. RESULTS: The mean age of the patients included in this study was 56.9 ± 14.1 years. Most of these patients (73.9%) had been diagnosed with aneurysmal SAH. Their median length of stay was 7 days in the ICU and 20 days in the general hospital ward, with an overall in-hospital mortality rate of 28.5%. The developed scales included the following admission variables independently associated with in-hospital mortality: coma at onset [odds ratio (OR) = 1.87; p = 0.028], Fisher scale score of 3-4 (OR = 2.27; p = 0.032), Acute Physiology and Chronic Health Evaluation II (APACHE II) score within the first 24 h (OR = 1.10; p < 0.001), and total Sequential Organ Failure Assessment (SOFA) score on day 0 (OR = 1.19; p = 0.004). Our predictive equation demonstrated better discrimination [area under the curve (AUC) = 0.835] (bootstrap-corrected AUC = 0.831) and calibration properties than those of the HAIR scale (AUC = 0.771; p ≤ 0.001) and the Functional Recovery Expected after Subarachnoid Hemorrhage scale (AUC = 0.814; p = 0.154). CONCLUSIONS: In addition to the conventional risk factors for in-hospital mortality, in our study, mortality was associated with the presence of coma at onset of the condition, the physiological variables assessed by means of the APACHE II scale within the first 24 h, and the total SOFA score on day 0. A simple prediction model of mortality was developed with novel parameters assessed on admission, which also assessed organ failure and did not require a previous etiological diagnosis.
BACKGROUND: Despite being a rare cause of stroke, spontaneous subarachnoid hemorrhage (SAH) is associated with high mortality rates. The prediction models that are currently being used on SAHpatients are heterogeneous, and few address premature mortality. The aim of this study was to develop a mortality risk stratification score for SAH. METHODS: A retrospective study was carried out with 536 patients diagnosed with SAH who had been admitted to the intensive care unit (ICU) at the University Hospital Complex of A Coruña (Spain) between 2003 and 2013. A multivariate logistic regression model was developed to predict the likelihood of in-hospital mortality, adjusting it exclusively for variables present on admission. A predictive equation of in-hospital mortality was then computed based on the model's coefficients, along with a points-based risk-scoring system. Its discrimination ability was also tested based on the area under the receiver operating characteristics curve and compared with previously developed scores. RESULTS: The mean age of the patients included in this study was 56.9 ± 14.1 years. Most of these patients (73.9%) had been diagnosed with aneurysmal SAH. Their median length of stay was 7 days in the ICU and 20 days in the general hospital ward, with an overall in-hospital mortality rate of 28.5%. The developed scales included the following admission variables independently associated with in-hospital mortality: coma at onset [odds ratio (OR) = 1.87; p = 0.028], Fisher scale score of 3-4 (OR = 2.27; p = 0.032), Acute Physiology and Chronic Health Evaluation II (APACHE II) score within the first 24 h (OR = 1.10; p < 0.001), and total Sequential Organ Failure Assessment (SOFA) score on day 0 (OR = 1.19; p = 0.004). Our predictive equation demonstrated better discrimination [area under the curve (AUC) = 0.835] (bootstrap-corrected AUC = 0.831) and calibration properties than those of the HAIR scale (AUC = 0.771; p ≤ 0.001) and the Functional Recovery Expected after Subarachnoid Hemorrhage scale (AUC = 0.814; p = 0.154). CONCLUSIONS: In addition to the conventional risk factors for in-hospital mortality, in our study, mortality was associated with the presence of coma at onset of the condition, the physiological variables assessed by means of the APACHE II scale within the first 24 h, and the total SOFA score on day 0. A simple prediction model of mortality was developed with novel parameters assessed on admission, which also assessed organ failure and did not require a previous etiological diagnosis.
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Authors: G J Rinkel; E F Wijdicks; M Vermeulen; L M Ramos; H L Tanghe; D Hasan; L C Meiners; J van Gijn Journal: AJNR Am J Neuroradiol Date: 1991 Sep-Oct Impact factor: 3.825
Authors: Ross T Prager; Michael T Pratte; Laura H Thompson; Kylie E McNeill; Christina Milani; David M Maslove; Shannon M Fernando; Kwadwo Kyeremanteng Journal: Crit Care Explor Date: 2021-12-09