| 1 | Death after discharge:prognostic model of 1-year mortality in traumatic brain injury patients undergoing decompressive craniectomy显示文摘Background:Despite advances in decompressive craniectomy(DC)for the treatment of traumatic brain injury(TBI),these patients are at risk of having a poor long-term prognosis.The aim of this study was to predict 1-year mortality in TBI patients undergoing DC using logistic regression and random tree models.Methods:This was a retrospective analysis of TBI patients undergoing DC from January 1,2015,to April 25,2019.Patient demographic characteristics,biochemical tests,and intraoperative factors were collected.One-year mortality prognostic models were developed using multivariate logistic regression and random tree algorithms.The overall accuracy,sensitivity,specificity,and area under the receiver operating characteristic curves(AUCs)were used to evaluate model performance.Results:Of the 230 patients,70(30.4%)died within 1 year.Older age(OR,1.066;95%CI,1.045-1.087;P<0.001),higher Glasgow Coma Score(GCS)(OR,0.737;95%CI,0.660-0.824;P<0.001),higherD-dimer(OR,1.005;95%CI,1.001-1.009;P=0.015),coagulopathy(OR,2.965;95%CI,1.808-4.864;P<0.001),hypotension(OR,3.862;95%CI,2.176-6.855;P<0.001),and completely effaced basal cisterns(OR,3.766;95%CI,2.255-6.290;P<0.001)were independent predictors of 1-year mortality.Random forest demonstrated better performance for 1-year mortality prediction,which achieved an overall accuracy of 0.810,sensitivity of 0.833,specificity of 0.800,and AUC of 0.830 on the testing data compared to the logistic regression model.Conclusions:The random forest model showed relatively good predictive performance for 1-year mortality in TBI patients undergoing DC.Further external tests are required to verify our prognostic model. | Wenxing Cui Shunnan Ge Yingwu Shi Xun Wu Jianing Luo Haixiao Lui Gang Zhu Hao Guo Dayun Feng Yan Qu | 2022 | Chinese Neurosurgical Journal2022，8，1： | 0 |
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