Objectives: Cerebrovascular disease can be roughly divided into 2 subtypes: Cerebral ischemia (CI) and cerebral hemorrhage (CH). No scale currently exist that can predict the subtypes of cerebrovascular diseases. This study aims to establish a prediction scale for the subtypes of cerebrovascular diseases.
Methods: A total of 1 200 cerebrovascular disease patients were included in this study, data from 1 081 (90%) patients were used to establish the CI-CH risk scale, and data from 119 (10%) patients were used to test it. Risk factors for the CI-CH risk scale were identified by 2 screens, with two-tailed student's t-test and two-tailed Fisher's exact test preliminarily and with logistic regression analysis further. The scores of each risk factor for CI-CH risk scale were determined according to the odds rate, and the cut-off point was determined by Youden index.
Results: Nine risk factors were ultimately selected for score system, including age (≥75 years old was -1, <75 years old was 0), BMI (<24 kg/m2 was 0, 24-28 kg/m2 was -1, >28 kg/m2 was -2), hypertension grade (grade 1 was 1, grade 2 was 2, and grade 3 was 3), diabetes status (no was 0, yes was -1), antihypertensive drug use (no was 0, yes was -2), alcohol consumption (<60 g/d was 1, ≥60 g/d was 2), uric acid (less than normal was 0, normal was -1, high than normal was -2), LDL cholesterol (<2 mmol/L was 0, 2-4 mmol/L was -1, and >4 mmol/L was -2), and HDL cholesterol (<1.55 mmol/L was 0, ≥1.55 mmol/L was 2). Patients with a score more than 0 were classified as the CH group, Conversely, they were assigned to the CI group; its sensitivity, specificity, and accuracy were 74.5%, 77.9%, and 76.4%, respectively.
Conclusions: The CI-CH risk scale can help the clinician predict the subtypes of cerebrovascular diseases.
目的: 脑血管病大致可分为2个亚型，即脑缺血(cerebral ischemia，CI)和脑出血(cerebral hemorrhage，CH)，目前还没有可以预测脑血管病亚型的模型。本研究旨在建立一个脑血管疾病亚型的预测模型。方法: 本研究共纳入1 200名脑血管病患者，其中1 081名(90%)患者的数据用于建立CI-CH量表，119名(10%)患者的数据对CI-CH量表进行测试。通过t检验和Fisher’s检验对预测因子进行第1次筛选，利用logistic回归对预测因子进行第2次筛选，以确定CI-CH量表的预测因子；参考OR值确定CI-CH量表的各因子的分值，计算约登指数作为CI-CH量表的分界点。结果: 最终选择了9个危险因素作为评分系统，包括年龄(≥75岁为-1；<75岁为0)，BMI(24-28 kg/m2为-1， >28 kg/m2为-2)，高血压等级(1级为1，2级为2，3级为3)，糖尿病(无为0，有为-1)，使用降压药物(否为0，是为 -2)，饮酒量(<60 g/d为1，≥60 g/d为2)，尿酸(低于正常为0，正常为-1，高于正常为-2)，低密度脂蛋白胆固醇 (<2 mmol/L为0，2-4 mmol/L为-1，>4 mmol/L为-2)，高密度脂蛋白胆固醇(<1.55 mmol/L为0，≥1.55 mmol/L为2)。得分大于0的患者被归入CH组，反之，被归入CI组；其敏感性、特异性和准确性分别为74.5%、77.9%和76.4%。结论: CI-CH量表可以帮助临床医生预测脑血管疾病的亚型。.
目的: 脑血管病大致可分为2个亚型，即脑缺血(cerebral ischemia，CI)和脑出血(cerebral hemorrhage，CH)，目前还没有可以预测脑血管病亚型的模型。本研究旨在建立一个脑血管疾病亚型的预测模型。
方法: 本研究共纳入1 200名脑血管病患者，其中1 081名(90%)患者的数据用于建立CI-CH量表，119名(10%)患者的数据对CI-CH量表进行测试。通过t检验和Fisher’s检验对预测因子进行第1次筛选，利用logistic回归对预测因子进行第2次筛选，以确定CI-CH量表的预测因子；参考OR值确定CI-CH量表的各因子的分值，计算约登指数作为CI-CH量表的分界点。
结果: 最终选择了9个危险因素作为评分系统，包括年龄(≥75岁为-1；<75岁为0)，BMI(24-28 kg/m2为-1， >28 kg/m2为-2)，高血压等级(1级为1，2级为2，3级为3)，糖尿病(无为0，有为-1)，使用降压药物(否为0，是为 -2)，饮酒量(<60 g/d为1，≥60 g/d为2)，尿酸(低于正常为0，正常为-1，高于正常为-2)，低密度脂蛋白胆固醇 (<2 mmol/L为0，2-4 mmol/L为-1，>4 mmol/L为-2)，高密度脂蛋白胆固醇(<1.55 mmol/L为0，≥1.55 mmol/L为2)。得分大于0的患者被归入CH组，反之，被归入CI组；其敏感性、特异性和准确性分别为74.5%、77.9%和76.4%。
结论: CI-CH量表可以帮助临床医生预测脑血管疾病的亚型。
Keywords: cerebral hemorrhage; cerebral ischemia; logistic regression analysis; prediction scale.