A risk model to predict the in-hospital mortality of subarachnoid hemorrhage

Abstract

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.

Keywords

subarachnoid hemorrhage / nomogram / hospital mortality / risk model