Aim This evidence-based guideline aims to present current and comprehensive recommendations for the diagnosis and management of spontaneous intracerebral haemorrhage (ICH). Methods A formal literature search was conducted on MEDLINE (1 January 1990 to 30 June 2019). Data were synthesised using evidence tables. The members of the working group met by teleconference to update and formulate data-based recommendations. The recommendations are graded according to levels of evidence grading algorithm of the Chinese Stroke Association. The guideline draft has been reviewed by Chinese Stroke Association Stroke Council Guideline Writing Committee. Results Evidence-based guideline is proposed for the management of patients with ICH. The focus of the guideline is divided into the diagnosis and aetiology of ICH, management of ICH in emergency department, surgical treatment for removal of hematoma, management of complications and prevention of secondary ICH. Conclusions This guideline provides a framework for ICH management. Early active and reasonable treatment may improve the clinical outcome of patients.

Multidrug-resistant tuberculosis (MDR-TB) has become a major public health problem. We tried to apply the classification tree model in building and evaluating a risk prediction model for MDR-TB. In this case–control study, 74 newly diagnosed MDR-TB patients served as the case group, and 95 patients without TB from the same medical institution served as the control group. The classification tree model was built using Chi-square Automatic Interaction Detectormethod and evaluated by income diagram, index map, risk statistic, and the area under receiver operating characteristic (ROC) curve. Four explanatory variables (history of exposure to TB patients, family with financial difficulties, history of other chronic respiratory diseases, and history of smoking) were included in the prediction model. The risk statistic of misclassification probability of the model was 0.160, and the area under ROC curve was 0.838 (P < 0.01). These suggest that the classification tree model works well for predicting MDR-TB. Classification tree model can not only predict the risk of MDR-TB effectively but also can reveal the interactions among variables.