摘要
背景:近年来关于急性脑出血病理机制的研究显示,急性脑出血患者术后预后不良的发生与脑内出血导致的脑组织水肿直接相关,而脑组织水肿的严重情况又与炎症因子级联反应关系密切。目的:基于机器学习算法探讨老年急性脑出血患者血清炎症因子蛋白水平与术后脑水肿体积的相关性及对术后发生预后不良的影响。方法:选择2022年6月至2024年6月宜春市人民医院收治的老年急性脑出血手术患者250例,根据患者术后是否出现预后不良将其分为预后不良组及预后良好组。收集患者相关资料,分析患者术前血清基质金属蛋白酶9、NOD样受体蛋白3、血管生成素样蛋白2蛋白水平与术后脑水肿体积的相关性;以患者是否发生预后不良为因变量进行危险因素分析,基于机器学习算法中的Logistic回归、决策树、反向传播神经网络算法、支持向量机算法构建老年急性脑出血术后发生预后不良的风险预测模型,采用受试者工作曲线评价不同算法的预测效果。结果与结论:①250例患者中,预后不良组患者113例(45.2%),预后良好组患者137例(54.8%);②多因素分析显示,两组患者的术前基质金属蛋白酶9(OR=1.037,95%CI=1.010-1.064,P=0.007)、NOD样受体蛋白3(OR=64.050,95%CI=5.139-798.325,P=0.001)、血管生成素样蛋白2蛋白水平(OR=82.519,95%CI=6.961-978.225,P<0.001)及术后脑水肿体积(OR=6.859,95%CI=2.109-22.309,P=0.001)为老年急性脑出血术后发生预后不良的独立影响因素;③决策分类回归树算法显示患者的NOD样受体蛋白3、脑水肿体积及基质金属蛋白酶9为老年急性脑出血术后发生预后不良的影响因素;④反向传播神经网络算法显示,影响因素重要性排序:血管生成素样蛋白2>NOD样受体蛋白3>基质金属蛋白酶9>脑水肿体积>肿瘤坏死因子α>美国国立卫生研究院脑卒中量表评分>饮酒史>高血压病史>出血量>病程;⑤支持向量机算法显示影响因素重要性前5位排序为:NOD样受体蛋白3(预测变量重要性=0.25)、血管生成素样蛋白2(预测变量重要性=0.22)、出血量(预测变量重要性=0.14)、肿瘤坏死因子α(预测变量重要性=0.12)、脑水肿体积(预测变量重要性=0.10);⑥4种机器学习算法构建的模型中,支持向量机预测效能最佳;⑦结果提示老年急性脑出血术后患者血清基质金属蛋白酶9、NOD样受体蛋白3、血管生成素样蛋白2蛋白与其术后脑水肿体积相关,以此为基础使用机器学习算法构建的风险预测模型对老年急性脑出血术后预后情况具有较好预测效能,其中以支持向量机算法模型诊断效能最佳。
BACKGROUND:Recent studies on the pathological mechanisms of acute intracerebral hemorrhage have shown that the occurrence of poor postoperative prognosis in patients with acute intracerebral hemorrhage is directly related to brain tissue edema caused by intracerebral hemorrhage.The severity of brain tissue edema is closely associated with a cascade reaction of inflammatory factors.OBJECTIVE:To investigate the correlation between serum inflammatory factor protein levels and postoperative brain edema volume in older adult patients with acute intracerebral hemorrhage using machine learning algorithms to analyze their impact on the occurrence of poor postoperative prognosis.METHODS:A total of 250 older adult patients with acute cerebral hemorrhage who underwent surgery at Yichun People's Hospital between June 2022 and June 2024 were included in this study.They were divided into a poor prognosis group and a good prognosis group based on their postoperative outcomes.Relevant patient data were collected to analyze the correlation between serum levels of matrix metalloproteinase-9,NOD-like receptor protein 3,and angiopoietin-like protein 2 and postoperative brain edema volume.A risk factor analysis was conducted using the occurrence of poor postoperative outcomes as the dependent variable.Risk prediction models for poor postoperative outcomes in older adult patients with acute cerebral hemorrhage were constructed using machine learning algorithms,including Logistic regression,Classification and Regression Tree,Back Propagation Neural Network,and Support Vector Machine.The receiver operating characteristic curve was used to assess the predictive efficacy of the different algorithms.RESULTS AND CONCLUSION:(1)Among the 250 patients included in this study,113 patients(45.20%)were assigned to the poor prognosis group,while 137 patients(54.80%)to the good prognosis group.(2)Multivariate analysis revealed that matrix metalloproteinase-9(OR=1.037,95%CI=1.010-1.064,P=0.007),NOD-like receptor protein 3(OR=64.050,95%CI=5.139-798.325,P=0.001),angiopoietin-like protein 2(OR=82.519,95%CI=6.961-978.225,P<0.001),and brain edema volume(OR=6.859,95%CI=2.109-22.309,P=0.001)were independent factors associated with poor postoperative outcomes in older adult patients with acute cerebral hemorrhage.(3)The Classification and Regression Tree algorithm indicated that NOD-like receptor protein 3,brain edema volume,and matrix metalloproteinase-9 were risk factors associated with poor postoperative outcomes.(4)The Back Propagation Neural Network algorithm ranked the influential factors as follows:Angiopoietin-like protein 2>NOD-like receptor protein 3>matrix metalloproteinase-9>brain edema volume>tumor necrosis factor-alpha>National Institutes of Health Stroke Scale(NIHSS)score>history of alcohol consumption>history of hypertension>amount of blood loss>duration of illness.(5)The Support Vector Machine algorithm identified the top five influential factors as NOD-like receptor protein 3(predictor importance=0.25),angiopoietin-like protein 2(predictor importance=0.22),amount of blood loss(predictor importance=0.14),tumor necrosis factor-alpha(predictor importance=0.12),and brain edema volume(predictor importance=0.10).(6)Among the four machine learning algorithms evaluated,the Support Vector Machine algorithm demonstrated the best predictive performance.(7)Results from this study suggest that serum levels of matrix metalloproteinase-9,NODlike receptor protein 3,and angiopoietin-like protein 2 in older adult patients with acute cerebral hemorrhage are correlated with postoperative brain edema volume.These factors can be used to construct a risk prediction model for postoperative outcomes using machine learning algorithms,with the Support Vector Machine algorithm showing the best diagnostic efficacy.
作者
陈飞军
陈英果
李征阳
胡圆
李芳
Chen Feijun;Chen Yingguo;Li Zhengyang;Hu Yuan;Li Fang(Yichun People's Hospital,Yichun 336000,Jiangxi Province,China)
出处
《中国组织工程研究》
北大核心
2026年第16期4045-4053,共9页
Chinese Journal of Tissue Engineering Research
基金
江西省卫生计生委科技计划(20204762),项目负责人:陈飞军。
关键词
急性脑出血
脑水肿
预后
机器学习法
风险预测模型
支持向量机
acute cerebral hemorrhage
brain edema
prognosis
machine learning
risk prediction model
Support Vector Machine
