Mingfei Yang1, Chaonan Du2, Qiang Zhang3, Qingfang Ma4, Ruili Li5. 1. Soochow University, Suzhou, Jiangsu, China; Department of Neurosurgery, Shengli Oilfield Central Hospital, Dongying, Shandong. 2. Graduate School, Qinghai University, Xining, Qinghai. 3. Soochow University, Suzhou, Jiangsu, China; Department of Neurosurgery, Shengli Oilfield Central Hospital, Dongying, Shandong. Electronic address: zhangqiang691212@163.com. 4. Department of Neurosurgery, Xuzhou City Centre Hospital, Xuzhou, Jiangsu. 5. Department of Neurosurgery, Shengli Oilfield Central Hospital, Dongying, Shandong, China.
Abstract
OBJECTIVE: 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 patients with spontaneous intracerebral hemorrhage in 3 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 programming language. According to the related risk factors of nomogram, decision curve analysis and clinical impact curve were established. RESULTS: The numbers of the 3 cooperative units were 554, 582, and 202, respectively. Island sign, blend sign, swirl sign, intraventricular hemorrhage, history of diabetes, time to baseline computed tomography scan, 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 scan ≤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 >40%, the prediction probability of hematoma expansion was close to the actual probability. CONCLUSIONS: 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.
OBJECTIVE: 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 patients with spontaneous intracerebral hemorrhage in 3 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 programming language. According to the related risk factors of nomogram, decision curve analysis and clinical impact curve were established. RESULTS: The numbers of the 3 cooperative units were 554, 582, and 202, respectively. Island sign, blend sign, swirl sign, intraventricular hemorrhage, history of diabetes, time to baseline computed tomography scan, 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 scan ≤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 >40%, the prediction probability of hematoma expansion was close to the actual probability. CONCLUSIONS: 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.