中国组织工程研究

中国组织工程研究 ›› 2026, Vol. 30 ›› Issue (10): 2466-2474.doi: 10.12307/2026.642

• 组织构建临床实践 clinical practice in tissue construction • 上一篇    下一篇

构建高血压脑出血后认知障碍风险预测列线图模型

黄凤琴，胡亚琳，杨伯银，罗兴梅   

  1. 贵州医科大学附属医院，贵州医科大学，贵州省贵阳市  550000

  • 收稿日期:2025-02-26 接受日期:2025-06-30 出版日期:2026-04-08 发布日期:2025-08-28
  • 通讯作者: Luo Xingmei, MD, Chief physician, Affiliated Hospital of Guizhou Medical University, Guiyang 550000, Guizhou Province, China
  • 作者简介:Huang Fengqin, MS candidate, Physician, Affiliated Hospital of Guizhou Medical University, Guiyang 550000, Guizhou Province, China
  • 基金资助:
    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

Constructing a risk prediction nomogram model for cognitive impairment in hypertensive intracerebral hemorrhage

Huang Fengqin, Hu Yalin, Yang Boyin, Luo Xingmei   

  1. Affiliated Hospital of Guizhou Medical University, Guiyang 550000, Guizhou Province, China
  • Received:2025-02-26 Accepted:2025-06-30 Online:2026-04-08 Published:2025-08-28
  • Contact: 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
  • About author: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。
  • Supported by:
     the National Natural Science Foundation of China (NSFC) Regional Fund Cultivation Program for the Affiliated Hospital of Guizhou Medical University, No. gyfynsfc[2023]-46 (to LXM); Science and Technology Fund of Guizhou Provincial Science and Technology Department, No. Qiankeheji-ZK[2024] General 225 (to LXM); Doctoral Research Foundation of the Affiliated Hospital of Guizhou Medical University, No. gyfybsky-2023-28 (to LXM)

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摘要：


文题释义：
卒中后认知功能障碍：中国卒中后认知障碍管理专家共识2021定义卒中后认知功能障碍为脑卒中事件后出现并持续到6个月时仍存在的以认知损害为特征的临床综合征，由于脑卒中后谵妄及一过性认知损害等可早期恢复，卒中后认知功能障碍诊断常常要在脑卒中后3-6个月进行认知评估来最终确定。
风险预测模型：是一种通过数学或统计方法，基于历史数据和已知风险因素，预测个体未来发生某种事件(如疾病、并发症、死亡等)概率的工具。在脑卒中管理中，风险预测模型可用于评估发生卒中后认知功能障碍的可能性，从而制定个性化康复计划。

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χ2 =12.710，P=0.122(P > 0.05)；验证集的χ2 =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。
http://orcid.org/0009-0008-3998-5093(黄凤琴)

中国组织工程研究杂志出版内容重点：干细胞；骨髓干细胞；造血干细胞；脂肪干细胞；肿瘤干细胞；胚胎干细胞；脐带脐血干细胞；干细胞诱导；干细胞分化；组织工程

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

Abstract: 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 a χ2 value of 12.710 and a P-value of 0.122 (P > 0.05) for the training set (χ2=12.170, P=0.122 > 0.05) and the validation set (χ2=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.


Key words: hypertensive intracerebral hemorrhage, cognitive impairment, novel inflammatory markers, nomogram prediction model, risk factors, lasso regression

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