急诊重度EPVSs急性脑梗死患者预后不良风险列线图预测模型的构建及临床意义

doi:10.3969/j.issn.1006-6233.2026.01.017

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摘要 目的: 通过分析影响急诊重度血管周围间隙扩大(EPVSs)急性脑梗死(ACI)患者预后不良的影响因素,构建风险列线图预测模型,并分析其临床指导意义。方法: 前瞻性选择2021年1月至2024年11月急诊收治的300例重度EPVSs ACI患者为研究对象,根据7∶3的比例分为建模集(n=210)和验证集(n=90),根据改良Rankin量表评分将建模集分为预后良好组(0~2分,n=127)和预后不良组(3~6分,n=83)。收集ACI患者入院时基本信息、临床病史等一般资料并检测相关实验室指标。多因素Logistic回归分析急诊重度EPVSs的ACI患者预后不良的影响因素,并应用rms程序包构建列线图预测模型;ROC曲线评估模型的预测效能;校准曲线评估模型预测概率与观察结果之间的一致性;决策曲线评估模型的净收益。结果: 建模集和验证集预后不良率为分别39.52%、35.56%;建模集预后不良组年龄≥65岁、梗死部位为关键脑区、高血压、脑白质疏松占比及美国国立卫生研究院卒中量表(NIHSS)、梗死面积、EPVSs数量、超敏C反应蛋白(hs-CRP)高于预后良好组(均P<0.05);年龄(≥65岁)、高血压(是)及NIHSS评分、梗死面积、EPVSs数量增加是急诊重度EPVSs的ACI患者预后不良的独立危险因素(P<0.05);基于多因素Logistic回归分析结果构建列线图预测模型,建模集和验证集ROC曲线的AUC分别为0.810(95%CI:0.750~0.860)、0.802(95%CI:0.741~0.853);两组预测曲线与实际曲线拟合良好;决策曲线分析阈值概率均大于0。结论: 年龄、高血压、NIHSS评分、梗死面积、EPVSs数量是急诊重度EPVSs的ACI患者预后不良的独立影响因素,基于该结果构建的列线图预测模型具有较高的预测效能,为临床预后评估及个性化治疗提供有效参考。

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	韩明明
	唐秀丽
	韩咪

关键词 ：急性脑梗死, 血管周围间隙扩大, 预测模型

Abstract：Objective: To analyze the influencing factors for the poor prognosis in emergency patients with acute cerebral infarction (ACI) complicated by severe enlarged perivascular spaces (EPVSs), construct a risk nomogram prediction model, and analyze its clinical guiding significance. Methods: A total of 300 emergency patients with severe EPVSs ACI admitted from January 2021 to November 2024 were prospectively selected as the research objects and divided into a modeling set (n=210) and a validation set (n=90) at a ratio of 7∶3. According to the modified Rankin scale scores, the modeling set was further divided into a good prognosis group (0~2 points, n=127) and a poor prognosis group (3~6 points, n=83). The basic admission information, clinical medical history and other general data of ACI patients were collected, and relevant laboratory indicators were detected. Multivariate Logistic regression was used to analyze the influencing factors for the poor prognosis in emergency patients with ACI complicated with severe EPVSs. Then the rms package was applied to construct a nomogram prediction model. The receiver operating characteristic (ROC) curve was used to evaluate the predictive efficacy of the model; the calibration curve was used to evaluate the consistency between the predicted probabilitiy of the model and the observed results; the decision curve was used to evaluate the net benefit of the model. Results: The poor prognosis rates in the modeling set and validation set were 39.52% and 35.56%, respectively. In the modeling set, the proportion of patients aged ≥65 years with infarctions in key cerebral regions and complicated with hypertension and leukoaraiosis, as well as the scores of the National Institutes of Health Stroke Scale (NIHSS),infarct size,the quantity of enlarged perivascular spaces (EPVSs), and levels of high-sensitivity C-reactive protein (hs-CRP) were all significantly higher in the poor prognosis group than those in the good prognosis group(all P<0.05). Age ≥65 years, hypertension (yes), increased NIHSS score, larger infarct size, and greater EPVSs quantity were independent risk factors for poor prognosis in emergency patients with acute cerebral infarction (ACI),complicated with severe EPVSs (P<0.05). A nomogram prediction model was constructed based on the results of multivariate Logistic regression analysis. The area under the ROC curve (AUC) of the modeling set and validation set was 0.810 (95%CI: 0.750~0.860) and 0.802 (95%CI: 0.741~0.853), respectively. The predicted curves in both sets showed good fit with the actual curves. The threshold probabilities in decision curve analysis were all greater than 0. Conclusion: Age, hypertension, NIHSS score, infarct area, and EPVSs quantity are independent influencing factors for the poor prognosis in emergency patients with ACI complicated with severe EPVSs. The nomogram prediction model constructed based on these results has high predictive efficacy which provides an effective reference for clinical prognosis assessment and personalized treatment.

Key words： Acute cerebral infarction Enlarged perivascular spaces Prediction model

基金资助:江苏省优势学科建设工程项目,(编号:YSHL2103-311)

通讯作者: 韩咪

引用本文:

韩明明, 唐秀丽, 韩咪. 急诊重度EPVSs急性脑梗死患者预后不良风险列线图预测模型的构建及临床意义[J]. 河北医学, 2026, 32(1): 105-112.
HAN Mingming, TANG Xiuli, HAN Mi. Construction and Clinical Significance of a Nomogram Prediction Model for Poor Prognosis Risk in Emergency Patients with ACI Complicated by Severe Enlarged Perivascular Spaces. HeBei Med, 2026, 32(1): 105-112.

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http://www.hbyxzzs.cn/CN/10.3969/j.issn.1006-6233.2026.01.017 或 http://www.hbyxzzs.cn/CN/Y2026/V32/I1/105

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