作者：王君，周莉莉，罗书引

单位：

223002江苏省淮安市第二人民医院神经内科

Units：

Department of Neurology, Huaian Second People's Hospital, Huaian 223002, China

关键词：

脑出血；原发性脑出血；脑微出血；危险因素；列线图模型

Keywords：

Cerebral hemorrhage; Primary intracerebral hemorrhage; Cerebral microbleeds; Risk factors;Nomogram model

CLC：

R 743.34

DOI：

10.12114/j.issn.1008-5971.2022.00.223

Funds：

摘要：

目的　构建原发性脑出血患者脑微出血（CMBs）发生风险预测列线图模型，并验证其有效性。方法　选取2018年6月至2021年6月淮安市第二人民医院收治的原发性脑出血患者140例为研究对象，根据是否发生CMBs将其分为CMBs组（46例）和无CMBs组（94例）。收集患者临床资料，采用多因素Logistic回归分析探讨原发性脑出血患者发生CMBs的影响因素；基于多因素Logistic回归分析结果，采用R 3.6.3软件及rms程序包构建原发性脑出血患者CMBs发生风险预测列线图模型；绘制ROC曲线以评估该列线图模型预测原发性脑出血患者发生CMBs的区分度；采用Hosmer-Lemeshow拟合优度检验评估该列线图模型预测原发性脑出血患者发生CMBs的一致性。结果　CMBs组年龄大于无CMBs组，有高血压史、脑出血史者占比及超敏C反应蛋白（hs-CRP）、白蛋白高于无CMBs组（P ＜0.05）。多因素Logistic回归分析结果显示，年龄、高血压史、脑出血史、hs-CRP、白蛋白是原发性脑出血患者发生CMBs的影响因素（P ＜0.05）。基于多因素Logistic回归分析结果，将年龄、高血压史、脑出血史、hs-CRP、白蛋白引入R3.6.3软件，建立原发性脑出血患者CMBs发生风险预测列线图模型。ROC曲线分析结果显示，该列线图模型预测原发性脑出血患者发生CMBs的曲线下面积为0.798〔95%CI （0.717，0.879）〕。Hosmer-Lemeshow拟合优度检验结果显示，该列线图模型预测原发性脑出血患者CMBs发生率与原发性脑出血患者CMBs实际发生率比较，差异无统计学意义（P ＞0.05）。结论　年龄增长、高血压史、脑出血史、hs-CRP升高、白蛋白升高是原发性脑出血患者发生CMBs的危险因素，基于上述危险因素构建的原发性脑出血患者CMBs发生风险预测列线图模型，对原发性脑出血患者发生CMBs具有一定预测价值和较好的准确性。

Abstract：

Objective　To construct a nomogram model for predicting the risk of cerebral microbleeds (CMBs) inpatients with primary cerebral hemorrhage, and to verify its validity. Methods　A total of 140 patients with primary intracerebralhemorrhage who were admitted to Huaian Second People's Hospital from June 2018 to June 2021 were selected as the researchobjects, and they were divided into CMBs group (46 cases) and non-CMBs group (94 cases) according to whether CMBs occurredor not. The clinical data of the patients were collected, and multivariate Logistic regression analysis was used to explore theinfluencing factors of CMBs in patients with primary cerebral hemorrhage. Based on the results of multivariate Logistic regressionanalysis, R 3.6.3 software and rms program package were used to construct a nomogram model for predicting the risk of CMBsin patients with primary cerebral hemorrhage. The ROC curve was drawn to evaluate the discrimination of this nomogram modelin predicting CMBs in patients with primary intracerebral hemorrhage. The Hosmer-Lemeshow goodness-of-fit test was used toevaluate the consistency of the nomogram model in predicting CMBs in patients with primary intracerebral hemorrhage. Results　The age of the CMBs group was older than that of the non-CMBs group, and the proportion of patients with history of hypertensionand cerebral hemorrhage, hypersensitive C-reactive protein (hs-CRP) and albumin were higher than those of the non-CMBsgroup (P < 0.05) . Multivariate Logistic regression analysis results showed that age, history of hypertension, history of cerebralhemorrhage, hs-CRP and albumin were the influencing factors of CMBs in patients with primary cerebral hemorrhage (P < 0.05) .Based on the results of multivariate Logistic regression analysis, age, history of hypertension, history of cerebral hemorrhage, hs-CRP, and albumin were introduced into R 3.6.3 software, and the nomogram model for predicting the risk of CMBs in patientswith primary cerebral hemorrhage was established. The results of ROC curve analysis showed that the area under curve ofnomogram model for predicting CMBs in patients with primary intracerebral hemorrhage was 0.798 [95%CI (0.717, 0.879) ] . Theresults of the Hosmer-Lemeshow goodness of fit test showed that there was no significant difference between the incidence ofCMBs predicted by nomogram model and the actual incidence of CMBs in patients with primary cerebral hemorrhage (P > 0.05) .Conclusion　Increased age, history of hypertension, history of cerebral hemorrhage, elevated hs-CRP, and elevated albuminare risk factors for CMBs in patients with primary cerebral hemorrhage. The nomogram model for predicting the risk of CMBsin patients with primary cerebral hemorrhage constructed based on the above risk factors has certain predictive value and goodaccuracy for CMBs in patients with primary cerebral hemorrhage.

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