• World Neurosurg · Jul 2019

    Predicting recurrent hypertensive intracerebral hemorrhage: derivation and validation of a risk scoring model based on clinical characteristics.

    • Sheng Zhang, Xin Zhang, Ying Ling, and Aimin Li.
    • Nanjing Medical University, Nanjing, China.
    • World Neurosurg. 2019 Jul 1; 127: e162-e171.

    ObjectiveTo 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.MethodsPatient 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.ResultsOf 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).ConclusionsThis model will help identify high-risk groups for RHCH in order to facilitate and improve preemptive clinical intervention.Copyright © 2019. Published by Elsevier Inc.

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