Lei Tang1,2, Shixin Liu1,2, Yanhe Xiao2, Thi My Linh Tran3, Ji Whae Choi3, Jing Wu4, Kasey Halsey3, Raymond Y Huang5, Jerrold Boxerman3, Sohil H Patel6, David Kung7, Renyu Liu8, Michael D Feldman9, Daniel D Danoski9, Wei-Hua Liao10, Scott E Kasner11, Tao Liu12, Bo Xiao1, Paul J Zhang9, Michael Reznik13, Harrison X Bai3, Li Yang14. 1. Department of Neurology, Xiangya Hospital, Central South University, Changsha, China. 2. Xiangya School of Medicine, Central South University, Changsha, China. 3. Department of Diagnostic Imaging, Warren Alpert Medical School of Brown University, Providence, RI, USA. 4. Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, China. 5. Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA. 6. Department of Radiology, University of Virginia, Charlottesville, VA, USA. 7. Department of Neurosurgery, Hospital of the University of Pennsylvania, Philadelphia, PA, USA. 8. Department of Anaesthesiology and critical care medicine, Hospital of the University of Pennsylvania, Philadelphia, PA, USA. 9. Department of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA, USA. 10. Department of Radiology, Xiangya Hospital, Central South University, Changsha, China. 11. Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA. 12. Department of Biostatistics and Public Health, Brown University, Providence, RI, USA. 13. Department of Neurology, Warren Alpert Medical School of Brown University, Providence, RI, USA. 14. Department of Neurology, The Second Xiangya Hospital, Central South University, Changsha, China.
Abstract
AIMS: To determine if neurologic symptoms at admission can predict adverse outcomes in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). METHODS: Electronic medical records of 1053 consecutively hospitalized patients with laboratory-confirmed infection of SARS-CoV-2 from one large medical center in the USA were retrospectively analyzed. Univariable and multivariable Cox regression analyses were performed with the calculation of areas under the curve (AUC) and concordance index (C-index). Patients were stratified into subgroups based on the presence of encephalopathy and its severity using survival statistics. In sensitivity analyses, patients with mild/moderate and severe encephalopathy (defined as coma) were separately considered. RESULTS: Of 1053 patients (mean age 52.4 years, 48.0% men [n = 505]), 35.1% (n = 370) had neurologic manifestations at admission, including 10.3% (n = 108) with encephalopathy. Encephalopathy was an independent predictor for death (hazard ratio [HR] 2.617, 95% confidence interval [CI] 1.481-4.625) in multivariable Cox regression. The addition of encephalopathy to multivariable models comprising other predictors for adverse outcomes increased AUCs (mortality: 0.84-0.86, ventilation/ intensive care unit [ICU]: 0.76-0.78) and C-index (mortality: 0.78 to 0.81, ventilation/ICU: 0.85-0.86). In sensitivity analyses, risk stratification survival curves for mortality and ventilation/ICU based on severe encephalopathy (n = 15) versus mild/moderate encephalopathy (n = 93) versus no encephalopathy (n = 945) at admission were discriminative (p < 0.001). CONCLUSIONS: Encephalopathy at admission predicts later progression to death in SARS-CoV-2 infection, which may have important implications for risk stratification in clinical practice.
AIMS: To determine if neurologic symptoms at admission can predict adverse outcomes in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). METHODS: Electronic medical records of 1053 consecutively hospitalized patients with laboratory-confirmed infection of SARS-CoV-2 from one large medical center in the USA were retrospectively analyzed. Univariable and multivariable Cox regression analyses were performed with the calculation of areas under the curve (AUC) and concordance index (C-index). Patients were stratified into subgroups based on the presence of encephalopathy and its severity using survival statistics. In sensitivity analyses, patients with mild/moderate and severe encephalopathy (defined as coma) were separately considered. RESULTS: Of 1053 patients (mean age 52.4 years, 48.0% men [n = 505]), 35.1% (n = 370) had neurologic manifestations at admission, including 10.3% (n = 108) with encephalopathy. Encephalopathy was an independent predictor for death (hazard ratio [HR] 2.617, 95% confidence interval [CI] 1.481-4.625) in multivariable Cox regression. The addition of encephalopathy to multivariable models comprising other predictors for adverse outcomes increased AUCs (mortality: 0.84-0.86, ventilation/ intensive care unit [ICU]: 0.76-0.78) and C-index (mortality: 0.78 to 0.81, ventilation/ICU: 0.85-0.86). In sensitivity analyses, risk stratification survival curves for mortality and ventilation/ICU based on severe encephalopathy (n = 15) versus mild/moderate encephalopathy (n = 93) versus no encephalopathy (n = 945) at admission were discriminative (p < 0.001). CONCLUSIONS:Encephalopathy at admission predicts later progression to death in SARS-CoV-2 infection, which may have important implications for risk stratification in clinical practice.