Construction of risk prediction model for postoperative hydrocephalus after spontaneous intracerebral hemorrhage based on LASSO-Logistic regression

HAO Guangzhi; SUN Linlin; ZHANG Bingying; HUO Da; DONG Yushu

Abstract：Objective To construct and validate a clinical prediction model for postoperative hydrocephalus after spontaneous intracerebral hemorrhage (SICH) based on LASSO-Logistic regression. Methods A total of 724 patients with spontaneous cerebral hemorrhage admitted to the General Hospital of the Northern Theater Command from August 2023 to March 2024 were randomly divided into the training set (n=507) and the verification set (n=217) according to a ratio of 7∶3. In the training set,Lasso regression combined with univariate Logistic regression was used to jointly screen the risk factors,and then multivariate Logistic regression was used to determine the risk factors and establish the model. The model was visualized with a nomogram. The area under ROC curve and calibration chart were respectively used to evaluate the differentiation and calibration of the model . The clinical value of the model was evaluated by clinical decision curve (DCA). Results The occurrence of post-ICH hydrocephalus was correlated with age,diabetes,GCS score,bleeding range,CSF glucose quantitation and CSF protein content (P<0.05),but not with sex,alcohol consumption,blood pressure,smoking,coagulation index and lumbar puncture operation (P>0.05). The area under ROC curve of the model was 0.873 (95％CI:0.829-0.918). The calibration diagram showed that the model had a good calibration. Decision curve analysis showed that the predictive model had a good net benefit in the threshold probability between 0.02 and 0.85. Conclusions Old age,diabetes,low GCS score,bleeding into the ventricle,decreased glucose content in CSF and increased protein content in CSF are independent risk factors for hydrocephalus after SICH. The prediction model constructed by this method has a good degree of differentiation and calibration,and provides a reliable diagnosis and treatment basis for clinicians.