Nanjing Medical University, Nanjing, China.
Lianyungang Clinical College of Nanjing Medical University, Lianyungang, China.
World Neurosurg. 2019 Jul;127:e162-e171. doi: 10.1016/j.wneu.2019.03.024. Epub 2019 Mar 12.
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.
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.
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).
This model will help identify high-risk groups for RHCH in order to facilitate and improve preemptive clinical intervention.
建立并验证一个预测首发高血压性脑出血(HICH)后 1 年内复发性高血压性脑出血(RHCH)的风险评分模型,以便为预防继发性出血提供临床干预的参考。
采用逻辑回归分析患者的性别、年龄、血压、格拉斯哥昏迷评分(GCS)、脑出血部位、手术、既往病史、血液生化参数和格拉斯哥预后评分,以确定 RHCH 的独立预测因素。通过为每个预测因素分配系数,构建风险评分模型,并在另一独立队列中进行验证。通过受试者工作特征曲线(ROC)下面积(AUC)评估模型的准确性,通过 Hosmer-Lemeshow 检验评估模型的校准能力。
在 520 例来自推导队列的患者中,有 38 例在出院后 1 年内发生 RHCH。RHCH 的独立危险因素为年龄>60 岁;入院时为 3 期高血压;入院时 GCS 评分为 9-12 分;出院时 GCS 评分为 3-8 分;有脑缺血性卒中史、吸烟、酗酒;以及血浆同型半胱氨酸(Hcy)水平≥10μmol/L。低危(0-13 分)、中危(14-26 分)和高危(27-39 分)组的复发率分别为 1.73%、6.11%和 57.14%(P<0.001)。在验证队列中,107 例患者中有 10 例(9.35%)发生 RHCH,其相应的复发率分别为 3.45%、7.14%和 71.43%(P<0.001)。该风险评分模型在推导和验证队列中均具有良好的区分能力,AUC 分别为 0.802 和 0.863。该模型也具有良好的校准能力(两个队列的 Hosmer-Lemeshow P 值分别为 0.532 和 0.724)。
该模型有助于识别 RHCH 的高危人群,以便为临床干预提供便利并改善临床干预效果。