Runfa Tian1, Weiming Liu2, Jinqian Dong2, Ji Zhang3, Long Xu2, Bin Zhang2, Xiaogang Tao2, Jingsheng Li2, Baiyun Liu4. 1. Department of Neurosurgery, Beijing Tian Tan Hospital, Capital Medical University, Beijing, People's Republic of China; China National Clinical Research Center for Neurological Diseases, Beijing, People's Republic of China; Beijing Key Laboratory of Central Nervous System Injury, Beijing, People's Republic of China; Neurotrauma Laboratory, Beijing Neurosurgical Institute, Capital Medical University, Beijing, People's Republic of China. 2. Department of Neurosurgery, Beijing Tian Tan Hospital, Capital Medical University, Beijing, People's Republic of China; China National Clinical Research Center for Neurological Diseases, Beijing, People's Republic of China; Beijing Key Laboratory of Central Nervous System Injury, Beijing, People's Republic of China. 3. Department of Neurosurgery, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, People's Republic of China. 4. Department of Neurosurgery, Beijing Tian Tan Hospital, Capital Medical University, Beijing, People's Republic of China; China National Clinical Research Center for Neurological Diseases, Beijing, People's Republic of China; Beijing Key Laboratory of Central Nervous System Injury, Beijing, People's Republic of China; Neurotrauma Laboratory, Beijing Neurosurgical Institute, Capital Medical University, Beijing, People's Republic of China; Nerve Injury and Repair Center of Beijing Institute for Brain Disorders, Beijing, People's Republic of China. Electronic address: liubaiyun1212@163.com.
Abstract
OBJECTIVE: Although several prognostic factors for traumatic brain injury (TBI) have been evaluated, a useful predictive scoring model for the outcomes has not been developed for patients with severe TBI who undergo decompressive craniectomy (DC). The aim of the present study was to determine independent predictors and develop a multivariate logistic regression equation to predict the early outcome and discharge status for patients with severe TBI who have undergone DC. METHODS: A total of 13 different variables were evaluated. The data from all 278 patients with severe TBI who had undergone DC in the present study were retrospectively evaluated from July 2011 to June 2017. Using univariate, multiple logistic regression and prognostic regression scoring equations it was possible to draw receiver operating characteristic curves to predict the early outcomes and discharge status after TBI. RESULTS: We found that younger age (P = 0.012), no significant medical history (P = 0.044), diameter of both pupils <4 mm (P = 0.032), higher admission Glasgow coma scale score (P = 0.004), no tracheotomy (P < 0.001), and DC for severe TBI were associated with a favorable early outcome and discharge status. Using receiver operating characteristic curves to predict the probability of a favorable outcome, the sensitivity was 80.0% and the specificity was 79.5%. CONCLUSIONS: Our preliminary findings have shown that 5 variables can be used as independent predictors in assessing the early outcome and discharge status for patients with severe TBI after DC.
OBJECTIVE: Although several prognostic factors for traumatic brain injury (TBI) have been evaluated, a useful predictive scoring model for the outcomes has not been developed for patients with severe TBI who undergo decompressive craniectomy (DC). The aim of the present study was to determine independent predictors and develop a multivariate logistic regression equation to predict the early outcome and discharge status for patients with severe TBI who have undergone DC. METHODS: A total of 13 different variables were evaluated. The data from all 278 patients with severe TBI who had undergone DC in the present study were retrospectively evaluated from July 2011 to June 2017. Using univariate, multiple logistic regression and prognostic regression scoring equations it was possible to draw receiver operating characteristic curves to predict the early outcomes and discharge status after TBI. RESULTS: We found that younger age (P = 0.012), no significant medical history (P = 0.044), diameter of both pupils <4 mm (P = 0.032), higher admission Glasgow coma scale score (P = 0.004), no tracheotomy (P < 0.001), and DC for severe TBI were associated with a favorable early outcome and discharge status. Using receiver operating characteristic curves to predict the probability of a favorable outcome, the sensitivity was 80.0% and the specificity was 79.5%. CONCLUSIONS: Our preliminary findings have shown that 5 variables can be used as independent predictors in assessing the early outcome and discharge status for patients with severe TBI after DC.
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