A risk model to predict the in-hospital mortality of subarachnoid hemorrhage
Objective Subarachnoid hemorrhage is a severe disease with high mortality and disability rate.The aim of this study is to develop a model to predict the in-hospital mortality of subarachnoid hemorrhage.Methods Seven hundred and ninty-seven patients with subarachnoid hemorrhage are extracted from 10 hospitals affiliated to Peking University during a 5-year period(2014-2018).A univariate Logistic regression and a multivariate Logistic regression are used to find the predictive factors for subarachnoid hemorrhage.A nomogram was constructed to predict the mortality.Results Of the included patients,the mortality rate is 7.53%.The predictors are aneurysm,heart disease,brain herniation,intracerebral hematoma,coma,pulmonary infection,respiratory failure and pneumonia(P<0.05).The area under the curve of the nomogram is 0.860(95%CI:0.809-0.911).Conclusion An accurate nomogram is developed to predict the in-hospital mortality of patients with subarachnoid hemorrhage.It will help reduce the mortality rates.
subarachnoid hemorrhagenomogramhospital mortalityrisk model
NETL
NSTL
万方数据
维普
