The nomogram model based on non-contrast computed tomography signs and clinical factors can effectively predict hematoma expansion in patients with spontaneous intracerebral hemorrhage

JIANG Mingkuan; MA Yichuan; CHEN Yan; XU Jiali; ZHU Yun; DONG Xiaohui

doi:10.12122/j.issn.1674-4500.2025.03.11

Abstract：Objective To construct and evaluate a new model for predicting hematoma expansion(HE)in patients with spontaneous intracerebral hemorrhage(sICH)using non-contrast computed tomography(NCCT)signs and clinical factors.Methods A retrospective analysis was performed on the patients with sICH admitted to the First Affiliated Hospital of Bengbu Medical University from January 2023 to September 2024.HE and non-HE group were divided according to whether the baseline hematoma volume increase was>6 mL or 33%.Univariate and binary logistic regression analysis were performed to screen out the independent predictors significantly related to the occurrence of HE.On this basis,R language was used to construct a nomogram prediction model,and the relevant clinical model and imaging model were constructed.The predictive performance of the model was evaluated by ROC curve,calibration curve and clinical decision curve.Results The time from onset to first CT examination,diabetes,platelet,hypodensity sign,blend sign,and satellite sign were independent predictors of HE occurrence(P<0.05).The above factors were used to establish a nomogram prediction model,the area under the ROC curve of the nomogram model was 0.928,and the clinical model and imaging model were 0.832 and 0.829,respectively.The Youden index,sensitivity and specificity of the nomogram model were also better than those of the above two models.The calibration curve results showed that the predicted probability of the nomogram model fitted well with the actual probability,and the decision curve results showed that the threshold probability range of the model was wide.Conclusion The nomogram model based on NCCT signs and clinical factors has a good performance in predicting the occurrence of HE in sICH patients,which is superior to the clinical model and imaging model alone,thus providing an effective and non-invasive tool for clinical risk assessment of HE in patients.