Sheng Zhang1, Xin Zhang2, Ying Ling3, Aimin Li4. 1. Nanjing Medical University, Nanjing, China. 2. Lianyungang Clinical College of Nanjing Medical University, Lianyungang, China. 3. Shanghai Yangpu District Central Hospital, Shanghai, China. 4. Department of Neurosurgery, Lianyungang Clinical College of Nanjing Medical University (The First People's Hospital of Lianyungang), Lianyungang, China. Electronic address: 1127595015@qq.com.
Abstract
OBJECTIVE: To develop and validate a risk-scoring model for predicting recurrent hypertensive cerebral hemorrhage (RHCH) occurring within 1 year after initial hypertensive cerebral hemorrhage and to facilitate preemptive clinical intervention for the prevention of secondary hemorrhage. METHODS: Patient gender, age, blood pressure, Glasgow Coma Scale (GCS) score, location of cerebral hemorrhage, surgery, past medical history, blood biochemical parameters, and Glasgow Outcome Scale score were analyzed using logistic regression analysis to determine independent predictors of RHCH. A risk-scoring model was constructed by assigning coefficients to each predictor and validating it in another independent cohort. The accuracy of the model was then assessed by the area under the receiver operating characteristic curve (AUC), and the calibration ability of the model was assessed by the Hosmer-Lemeshow test. RESULTS: Of 520 patients in the derivation cohort, 38 developed RHCH within 1 year after discharge. Independent risk factors of RHCH were age >60 years; stage 3 hypertension at admission; GCS score 9-12 (admission); GCS score 3-8 (discharge); history of cerebral ischemic stroke, smoking, alcoholism; and plasma homocysteine (Hcy) level ≥10 μmol/L. The recurrence rates for the low-risk (0-13 points), intermediate-risk (14-26 points), and high-risk (27-39 points) groups were 1.73%, 6.11%, and 57.14%, respectively (P < 0.001). The corresponding rates in the validation cohort, of whom 10/107 (9.35%) developed RHCH, were 3.45%, 7.14%, and 71.43%, respectively (P < 0.001). The risk-scoring model showed good discrimination in both the derivation and validation cohorts, with an AUC of 0.802 versus 0.863. The model also showed good calibration ability (the Hosmer-Lemeshow P values of the two cohorts were 0.532 vs. 0.724). CONCLUSIONS: This model will help identify high-risk groups for RHCH in order to facilitate and improve preemptive clinical intervention.
OBJECTIVE: To develop and validate a risk-scoring model for predicting recurrent hypertensive cerebral hemorrhage (RHCH) occurring within 1 year after initial hypertensive cerebral hemorrhage and to facilitate preemptive clinical intervention for the prevention of secondary hemorrhage. METHODS:Patient gender, age, blood pressure, Glasgow Coma Scale (GCS) score, location of cerebral hemorrhage, surgery, past medical history, blood biochemical parameters, and Glasgow Outcome Scale score were analyzed using logistic regression analysis to determine independent predictors of RHCH. A risk-scoring model was constructed by assigning coefficients to each predictor and validating it in another independent cohort. The accuracy of the model was then assessed by the area under the receiver operating characteristic curve (AUC), and the calibration ability of the model was assessed by the Hosmer-Lemeshow test. RESULTS: Of 520 patients in the derivation cohort, 38 developed RHCH within 1 year after discharge. Independent risk factors of RHCH were age >60 years; stage 3 hypertension at admission; GCS score 9-12 (admission); GCS score 3-8 (discharge); history of cerebral ischemic stroke, smoking, alcoholism; and plasma homocysteine (Hcy) level ≥10 μmol/L. The recurrence rates for the low-risk (0-13 points), intermediate-risk (14-26 points), and high-risk (27-39 points) groups were 1.73%, 6.11%, and 57.14%, respectively (P < 0.001). The corresponding rates in the validation cohort, of whom 10/107 (9.35%) developed RHCH, were 3.45%, 7.14%, and 71.43%, respectively (P < 0.001). The risk-scoring model showed good discrimination in both the derivation and validation cohorts, with an AUC of 0.802 versus 0.863. The model also showed good calibration ability (the Hosmer-Lemeshow P values of the two cohorts were 0.532 vs. 0.724). CONCLUSIONS: This model will help identify high-risk groups for RHCH in order to facilitate and improve preemptive clinical intervention.