LAUSR

BACKGROUND Hematoma expansion (HE) is an important risk factor for poor prognosis in patients with hypertensive intracerebral hemorrhage. This study aimed to establish a nomogram model for predicting HE, and evaluate the model. METHODS The clinical data and plain computed tomography (CT) scan signs of 341 patients with hypertensive intracerebral hemorrhage were retrospectively analyzed. According to the development of HE, the patients were divided into an HE group (100 cases) and a non-HE group (241 cases). The clinical data and CT scan signs of the patients in these two groups were compared. Variables that had statistically significant differences were included in the multivariate logistic regression analysis to screen for independent predictors of HE and establish a nomogram model. The discrimination, calibration, and clinical practicability of this model were evaluated using the receiver operating characteristic (ROC) curve, calibration curve, and a decision curve analysis (DCA), respectively. Finally, the internal validation of this model was performed using the bootstrap method. RESULTS The time interval from disease onset to the first CT [odds ratio (OR) =0.807, 95% confidence interval (CI): 0.665-0.979], volume of the hematoma at the first CT (OR =1.017, 95% CI: 1.001-1.033), irregular shape of the hematoma (OR =2.458, 95% CI: 1.355-4.456), swirl sign (OR =2.308, 95% CI: 1.239-4.298), and blend sign (OR =2.509, 95% CI: 1.304-4.830) were independent predictors of HE (all P<0.05). These factors were used to establish a nomogram model. The area under the ROC curve of the model was 0.762 (95% CI: 0.703-0.821). The results of the Hosmer-Lemeshow test and calibration curves showed that the predictive probabilities of the model fit the actual probabilities well. The DCA results showed that the domain probability range of the model was wide. The internal validation results showed that the C-index was 0.751, and the model's discrimination was good. CONCLUSIONS The nomogram model established in this study had good discrimination, calibration, and clinical practicability. The model could serve as an intuitive and reliable guiding tool for the clinical identification of HE risk of hypertensive intracerebral hemorrhage.