摘要
背景:目前多基于Logistic回归构建卒中后认知功能障碍预测模型,联合Lasso回归筛选变量来避免共线性及过拟合的相关研究较少。目的:探索高血压脑出血后认知功能障碍发生的相关因素,基于LASSO回归构建列线图预测模型并进行验证。方法:选择2022年8月至2024年8月贵州医科大学附属医院急诊神经内科收治的高血压脑出血患者260例,其中卒中后认知功能障碍组127例,卒中后非认知功能障碍组133例。采用Lasso-logistic回归优化模型的特征选择,基于R studio软件按照7∶3随机将所有队列分为训练集182例和验证集78例,根据独立危险因素建立训练集风险预测列线图模型,并用受试者工作特征曲线评估模型区分度,Hosmer-Lemeshow拟合优度检验和校准曲线评估模型校准度,决策分析曲线评估模型临床获益。结果与结论:①Lasso-logistic回归分析显示,年龄(OR=1.112,95%CI=1.068-1.157,P=0.000)、脑血肿直径(OR=2.021,95%CI=1.025-3.983,P=0.042)、血肿破入脑室(OR=2.398,95%CI=1.149-5.006,P=0.020)、手术(OR=2.542,95%CI=1.278-5.056,P=0.008)、血肌酐值(OR=1.017,95%CI=1.004-1.031,P=0.010)是高血压脑出血患者发生认知功能障碍的独立危险因素;②受试者工作特征曲线分析显示训练集和验证集的列线图预测模型曲线下面积分别为0.826(95%CI 0.765-0.885)和0.795(95%CI 0.693-0.898);③Hosmer-Lemeshow拟合优度检验和校准曲线分析显示列线图模型拟合良好,训练集的χ^(2)=12.710,P=0.122(P>0.05);验证集的χ^(2)=4.328,P=0.826(P>0.05);④临床决策曲线显示模型有较好的临床净获益;⑤结果表明:基于年龄、脑血肿直径>3 cm、血肿破入脑室、手术以及血肌酐值等预测因素建立的列线图模型对高血压脑出血3个月后发生认知功能障碍具有一定预测价值。
BACKGROUND:Currently,constructing a risk predictive model for post-stroke cognitive impairment mostly depends on Logistic regression,with relatively few studies incorporating Lasso regression for variable selection to address collinearity and overfitting.OBJECTIVE:To explore the factors associated with post-stroke cognitive impairment following hypertensive intracerebral hemorrhage and to construct a nomogram prediction model using LASSO regression,followed by model validation.METHODS:A total of 260 intracerebral hemorrhage patients admitted to the Neurology Emergency Department of the Affiliated Hospital of Guizhou Medical University from August 2022 to August 2024 were initially selected,of whom 127 were classified into the post-stroke cognitive impairment group and 133 into the post-stroke non-cognitive impairment group.Feature selection was optimized using Lasso-logistic regression,and all cohorts were randomly divided into a training set(182 cases)and a validation set(78 cases)in a 7:3 ratio using R Studio software.A risk prediction nomogram model was constructed based on independent risk factors identified from the training set.The model’s discriminative ability was evaluated using the receiver operating characteristic curve,calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test and calibration curve,and clinical benefits were evaluated using a decision analysis curve.RESULTS AND CONCLUSION:(1)Lasso-logistic regression analysis identified the following independent risk factors for post-stroke cognitive impairment after hypertensive intracerebral hemorrhage:age[odds ratio(OR)=1.112,95%confidence interval(CI)=1.068-1.157,P=0.000],hematoma diameter(OR=2.021,95%CI=1.025-3.983,P=0.042),intraventricular rupture(OR=2.398,95%CI=1.149-5.006,P=0.020),surgery(OR=2.542,95%CI=1.278-5.056,P=0.008),and serum creatinine levels(OR=1.017,95%CI=1.004-1.031,P=0.010).(2)A nomogram prediction model was constructed accordingly.The receiver operating characteristic curve analysis revealed an area under the curve for the training and validation sets to be 0.826(95%CI=0.765-0.885)and 0.795(95%CI=0.693-0.898),respectively.(3)The Hosmer-Lemeshow goodness-of-fit test and calibration curve analysis showed a good fit of the nomogram model,with aχ^(2)value of 12.710 and a P-value of 0.122(P>0.05)for the training set(χ^(2)=12.170,P=0.122>0.05)and the validation set(χ^(2)=4.328,P=0.826>0.05).(4)The clinical decision curve demonstrated considerable clinical net benefit of the model.In conclusion,the nomogram model based on predictive factors such as age,hematoma diameter>3 cm,intraventricular rupture,surgery,and serum creatinine levels has a significant predictive value for cognitive impairment within 3 months after hypertensive intracerebral hemorrhage.
作者
黄凤琴
胡亚琳
杨伯银
罗兴梅
Huang Fengqin;Hu Yalin;Yang Boyin;Luo Xingmei(Affiliated Hospital of Guizhou Medical University,Guiyang 550000,Guizhou Province,China)
出处
《中国组织工程研究》
北大核心
2026年第10期2466-2474,共9页
Chinese Journal of Tissue Engineering Research
基金
贵州医科大学附属医院国家自然科学基金(NSFC)地区基金培育计划项目(gyfynsfc[2023]-46),项目负责人:罗兴梅
贵州省科学技术厅科学技术基金(黔科合基础-ZK[2024]一般225),项目负责人:罗兴梅
贵州医科大学附属医院博士科研启动基金(gyfybsky-2023-28),项目负责人:罗兴梅。
关键词
高血压脑出血
认知功能障碍
新型炎症指标
列线图预测模型
影响因素
Lasso回归
hypertensive intracerebral hemorrhage
cognitive impairment
novel inflammatory markers
nomogram prediction model
risk factors
Lasso regression
作者简介
第一作者:黄凤琴,女,1998年生,贵州省毕节市人,穿青族,贵州医科大学在读硕士,医师,主要从事老年神经方面的研究。http://orcid.org/0009-0008-3998-5093;通讯作者:罗兴梅,博士,主任医师,贵州医科大学附属医院,贵州省贵阳市550000。
