Nomogram model for predicting hematoma expansion in spontaneous intracerebral hemorrhage - multicenter retrospective study

Abstract Purpose To establish a new nomogram model and provide a new theoretical basis for the diagnosis and treatment of spontaneous intracerebral hemorrhage. Methods The clinical data and noncontrast computed tomography images of spontaneous intracerebral hemorrhage patients in three tertiary medical centers were collected continuously. Univariate and binary logistic regression analysis were performed to screen out the independent predictors that were significantly associated with hematoma expansion. The nomogram model was drawn by R software. According to the related risk factors of nomogram, decision curve analysis and clinical impact curve were established. Outcome The number of the three cooperative units mentioned above were 554, 582 and 202, respectively. Island sign, blend sign, swirl sign, intraventricular hemorrhage, history of diabetes, time to baseline computed tomography and baseline hematoma volume were independent predictors of hematoma expansion. Baseline hematoma volume ＞ 20 ml (odds ratio: 4.088, 95% confidence interval: 2.802-5.964, P ﹤ 0.0001) was the most dangerous factor for predicting hematoma expansion, followed by the time to baseline computed tomography ≤ 1 hour (odds ratio: 4.188, 95% confidence interval: 2.598-6.750, P ﹤ 0.0001). Decision curve analysis showed that the net benefit of patients was the highest when nomogram score existed. When the threshold probability was more than 40%, the prediction probability of hematoma expansion was close to the actual probability. Conclusion This nomogram model could accurately predict hematoma expansion of spontaneous intracerebral hemorrhage, which provided a theoretical basis for clinicians to intervene in the early stage.