切换至 "中华医学电子期刊资源库"
高级检索
中华危重症医学杂志(电子版)

中华危重症医学杂志(电子版) ›› 2026, Vol. 19 ›› Issue (02) : 115 -121. doi: 10.3877/cma.j.issn.1674-6880.2026.02.004

论著

综合炎症预后指数对接受再灌注治疗的急性缺血性脑卒中患者90 d预后的预测价值
胡琼丹, 陈霞†()   
  1. 230001　合肥，中国科学技术大学附属第一医院（安徽省立医院）护理部
  • 收稿日期:2026-04-10 出版日期:2026-04-30
  • 通信作者: 陈霞
  • 基金资助:
    2025年度国家卫生健康委能力建设和继续教育中心慢病管理研究课题(GWJJMB202510021104)

Predictive value of comprehensive inflammatory prognostic index for 90-day outcomes in acute ischemic stroke patients undergoing reperfusion therapy

Qiongdan Hu, Xia Chen†()   

  1. Department of Nursing, The First Affiliated Hospital of University of Science and Technology of China (Anhui Provincial Hospital), 230001 Hefei, China
  • Received:2026-04-10 Published:2026-04-30
  • Corresponding author: Xia Chen
全文 ( ) 下载PDF ( ) 导出引用
引用本文：

胡琼丹, 陈霞. 综合炎症预后指数对接受再灌注治疗的急性缺血性脑卒中患者90 d预后的预测价值[J/OL]. 中华危重症医学杂志(电子版), 2026, 19(02): 115-121.

Qiongdan Hu, Xia Chen. Predictive value of comprehensive inflammatory prognostic index for 90-day outcomes in acute ischemic stroke patients undergoing reperfusion therapy[J/OL]. Chinese Journal of Critical Care Medicine(Electronic Edition), 2026, 19(02): 115-121.

目的

探讨综合炎症预后指数（CIPI）对接受再灌注的急性缺血性脑卒中（AIS）患者90 d临床预后的预测效能，为优化该类患者的预后评估提供循证医学证据。

方法

回顾性纳入2022年1月至2024年12月于中国科学技术大学附属第一医院（安徽省立医院）接受再灌注治疗的1 327例AIS患者，收集基线临床资料及发病24 h内、再灌注治疗前的血常规检测指标，计算CIPI值；以发病后90 d改良Rankin量表（mRS）评分为结局指标，将患者分为预后良好组（mRS评分0 ~ 2分）和预后不良组（mRS评分3 ~ 6分）。比较两组患者基线资料差异，通过单因素及多模型逐步校正的logistic回归分析CIPI与患者90 d预后不良的关联性，采用限制性立方样条（RCS）模型分析CIPI与预后不良风险的剂量-反应关系。

结果

1 327例患者中，预后良好组773例（58.25%），预后不良组554例（41.75%）。预后不良组CIPI水平显著高于预后良好组[-0.302 （-2.147，2.720）比-1.944（-2.759，-0.061），Z = 9.078，P < 0.001]。单因素logistic回归显示，CIPI是AIS患者90 d预后不良的危险因素[比值比（OR）= 1.102，95%置信区间（CI）（1.071，1.135），P < 0.001]；逐步校正人口学特征、基础病史、基线美国国立卫生研究院卒中量表（NIHSS）评分后，CIPI仍为预后不良的独立预测指标[OR = 1.047，95%CI（1.014，1.082），P = 0.005]。RCS模型显示，CIPI与预后不良呈非线性关联（整体关联P < 0.001，非线性关联P < 0.001），当CIPI ≥ -0.162时，患者预后不良风险随CIPI升高呈加速上升趋势。

结论

CIPI与接受再灌注的AIS患者90 d预后密切相关，是该类患者预后不良的独立预测指标，可作为AIS再灌注治疗后早期风险分层的便捷评估工具。

关键词: 综合炎症预后指数, 急性缺血性脑卒中, 再灌注治疗, 预后
Objective

To investigate the predictive efficacy of the comprehensive inflammatory prognostic index (CIPI) for 90-day clinical prognosis in patients with acute ischemic stroke (AIS) undergoing reperfusion therapy, and to provide medical evidence for optimizing the prognostic assessment system in this patient population.

Methods

A total of 1 327 AIS patients who underwent reperfusion therapy at the First Affiliated Hospital of University of Science and Technology of China (Anhui Provincial Hospital) from January 2022 to December 2024 were retrospectively enrolled. Baseline clinical data and routine blood test parameters obtained within 24 hours of symptom onset and before reperfusion therapy were collected to calculate the CIPI value. The modified Rankin scale (mRS) score at 90 days after symptom onset was set as the outcome measure, and patients were divided into a favorable prognosis group (mRS score 0-2) and an unfavorable prognosis group (mRS score 3-6). The baseline data between the two groups were compared. Univariate and multiple-model stepwise-adjusted logistic regression analyses were performed to analyze the association between CIPI and 90-day unfavorable prognosis. A restricted cubic spline (RCS) model was applied to explore the dose-response relationship between CIPI and the risk of unfavorable prognosis.

Results

Among the 1 327 enrolled patients, 773 (58.25%) were assigned to the favorable prognosis group and 554 (41.75%) to the unfavorable prognosis group. The CIPI level in the unfavorable prognosis group was significantly higher than that in the favorable prognosis group [-0.302 (-2.147, 2.720) vs. -1.944 (-2.759, -0.061), Z = 9.078, P < 0.001]. Univariate logistic regression analysis showed that CIPI was a risk factor for 90-day unfavorable prognosis in AIS patients [odds ratio (OR) = 1.102, 95% confidence interval (CI) (1.071, 1.135), P < 0.001]. After stepwise adjustment for demographic characteristics, underlying medical history, and baseline National Institutes of Health Stroke Scale (NIHSS) score, CIPI remained an independent predictor of unfavorable prognosis [OR = 1.047, 95%CI (1.014, 1.082), P = 0.005]. The RCS model revealed a significant nonlinear association between CIPI and the risk of unfavorable prognosis (overall association P < 0.001, nonlinear association P < 0.001), with an accelerated upward trend in the risk of unfavorable prognosis as CIPI increased when CIPI ≥ -0.162.

Conclusions

CIPI is closely correlated with the 90-day prognosis of AIS patients undergoing reperfusion therapy. It is an independent predictor of unfavorable prognosis in this patient population, and can serve as a convenient assessment tool for early risk stratification of AIS after reperfusion therapy.

Key words: Comprehensive inflammatory prognostic index, Acute ischemic stroke, Reperfusion therapy, Prognosis
表1 两组AIS患者基线特征比较
项目 预后良好组（n = 773） 预后不良组（n = 554） χ2/Z值 P值
性别[例（%）]     2.541 0.111
男 525（67.92） 353（63.72）    
女 248（32.08） 201（36.28）    
年龄[岁，M（P25，P75）] 65（55，73） 71（61，79） 8.727 < 0.001
高血压[例（%）] 492（63.65） 357（64.44） 0.088 0.767
糖尿病[例（%）] 163（21.09） 132（23.83） 1.401 0.236
高血脂[例（%）] 100（12.94） 21（3.79） 32.575 < 0.001
心房颤动[例（%）] 129（16.69） 161（29.06） 28.930 < 0.001
基线NIHSS评分[分，M（P25，P75）] 6.000（3.000，12.000） 15.000（11.000，21.000） 19.170 < 0.001
WBC[× 109/L，M（P25，P75）] 7.620（6.150，9.320） 8.430（6.482，10.418） 4.950 < 0.001
ALC[× 109/L，M（P25，P75）] 1.550（1.090，2.160） 1.180（0.780，1.780） 7.737 < 0.001
AMC[× 109/L，M（P25，P75）] 0.400（0.310，0.510） 0.400（0.290，0.540） 0.512 0.609
ANC[× 109/L，M（P25，P75）] 5.100（3.810，6.880） 6.445（4.390，8.605） 7.069 < 0.001
PLT[× 109/L，M（P25，P75）] 190.000（161.000，225.000） 185.500（152.250，230.000） 1.192 0.233
NLR[M（P25，P75）] 3.090（1.961，5.800） 5.399（2.876，10.165） 9.245 < 0.001
SII[M（P25，P75）] 606.100（363.892，1 113.333） 1 016.812（507.107，1 923.221） 8.207 < 0.001
SIRI[M（P25，P75）] 1.211（0.760，2.261） 1.980（1.068，3.748） 8.155 < 0.001
CIPI[M（P25，P75）] -1.944（-2.759，-0.061） -0.302（-2.147，2.720） 9.078 < 0.001
表2 CIPI及各项指标与AIS患者90 d预后的单因素logistic回归分析
项目 回归系数 标准误 Z值 P值 OR值 95%CI
性别            
男         1.000 参考
女 0.187 0.117 1.593 0.111 1.205 0.958 ~ 1.517
年龄（岁） 0.039 0.005 8.304 < 0.001 1.040 1.031 ~ 1.050
高血压 0.034 0.116 0.296 0.767 1.035 0.824 ~ 1.299
糖尿病 0.158 0.133 1.183 0.237 1.171 0.902 ~ 1.520
高血脂 -1.327 0.247 -5.376 < 0.001 0.265 0.163 ~ 0.430
心房颤动 0.715 0.134 5.324 < 0.001 2.045 1.572 ~ 2.661
基线NIHSS评分（分） 0.161 0.010 15.580 < 0.001 1.174 1.151 ~ 1.198
WBC（× 109/L） 0.087 0.018 4.887 < 0.001 1.091 1.053 ~ 1.129
ALC（× 109/L） -0.365 0.070 -5.191 < 0.001 0.694 0.605 ~ 0.797
AMC（× 109/L） 0.234 0.226 1.037 0.300 1.264 0.812 ~ 1.969
ANC（× 109/L） 0.113 0.018 6.302 < 0.001 1.120 1.081 ~ 1.160
PLT（× 109/L） < 0.001 0.001 -0.437 0.662 1.000 0.998 ~ 1.001
NLR 0.078 0.011 7.153 < 0.001 1.082 1.059 ~ 1.105
SII 0.001 < 0.001 5.908 < 0.001 1.001 1.001 ~ 1.001
SIRI 0.094 0.019 4.976 < 0.001 1.099 1.059 ~ 1.141
CIPI 0.097 0.015 6.588 < 0.001 1.102 1.071 ~ 1.135
表3 CIPI与AIS患者90 d预后的多模型logistic回归分析
项目 模型1 模型2 模型3 模型4
OR值 95%CI P值 OR值 95%CI P值 OR值 95%CI P值 OR值 95%CI P值
CIPI 1.102 1.071 ~ 1.135 < 0.001 1.109 1.076 ~ 1.143 < 0.001 1.100 1.067 ~ 1.134 < 0.001 1.047 1.014 ~ 1.082 0.005
图1 CIPI与AIS患者90 d预后不良风险的RCS图注：CIPI.综合炎症预后指数；AIS.急性缺血性卒中；RCS.限制性立方样条曲线；OR.比值比；CI.置信区间；横轴为CIPI值，纵轴为OR值（95%CI），虚线为参考值（OR = 1.00）
1
GBD 2019 Stroke Collaborators. Global, regional, and national burden of stroke and its risk factors, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019[J]. Lancet Neurol, 2021, 20 (10): 795-820.
2
GBD 2021 Stroke Risk Factor Collaborators. Global, regional, and national burden of stroke and its risk factors, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021[J]. Lancet Neurol, 2024, 23 (10): 973-1003.
3
Feigin VL, Owolabi MO; World Stroke Organization-Lancet Neurology Commission Stroke Collaboration Group. Pragmatic solutions to reduce the global burden of stroke: a World Stroke Organization-Lancet Neurology Commission[J]. Lancet Neurol, 2023, 22 (12): 1160-1206.
4
Venketasubramanian N. Stroke epidemiology in Asia[J]. Cerebrovasc Dis Extra, 2025, 15 (1): 81-92.
5
中国卒中学会，中国卒中学会神经介入分会，中华预防医学会卒中预防与控制专业委员会介入学组.急性缺血性卒中血管内治疗中国指南2023[J].中国卒中杂志，2023，18（6）：684-711.
6
张军根，杨逢露，唐春福，等.脑卒中院前救治转送现状分析[J/OL].中华危重症医学杂志（电子版），2024，17（4）：318-322.
7
Quan K, Wang A, Zhang X, et al. Neutrophil to lymphocyte ratio and adverse clinical outcomes in patients with ischemic stroke[J]. Ann Transl Med, 2021, 9 (13): 1047.
8
Huang YW, Yin XS, Li ZP. Association of the systemic immune-inflammation index (SIII) and clinical outcomes in patients with stroke: a systematic review and meta-analysis[J]. Front Immunol, 2022, 13: 1090305.
9
Zhou Y, Zhang Y, Cui M, et al. Prognostic value of the systemic inflammation response index in patients with acute ischemic stroke[J]. Brain Behav, 2022, 12 (6): e2619.
10
Wu B, Liu F, Sun G, et al. Prognostic role of dynamic neutrophil-to-lymphocyte ratio in acute ischemic stroke after reperfusion therapy: a meta-analysis[J]. Front Neurol, 2023, 14: 1118563.
11
Huang L. Increased systemic immune-inflammation index predicts disease severity and functional outcome in acute ischemic stroke patients[J]. Neurologist, 2023, 28 (1): 32-38.
12
Wu S, Shi X, Zhou Q, et al. The association between systemic immune-inflammation index and all-cause mortality in acute ischemic stroke patients: analysis from the MIMIC-IV database[J]. Emerg Med Int, 2022, 2022: 4156489.
13
Chen X, Xie Y, Yu C, et al. Systemic inflammation response index as a predictor of 3-month functional outcomes in acute ischemic stroke patients following intravenous thrombolysis[J]. Neuroscience, 2025, 576: 234-240.
14
Dang H, Mao W, Wang S, et al. Systemic inflammation response index as a prognostic predictor in patients with acute ischemic stroke: a propensity score matching analysis[J]. Front Neurol, 2023, 13: 1049241.
15
Nam KW, Kim TJ, Lee JS, et al. Neutrophil-to-lymphocyte ratio predicts early worsening in stroke due to large vessel disease[J]. PLoS One, 2019, 14 (8): e0221597.
16
Shu C, Zheng C, Zhang G. Exploring the utility of a latent variable as comprehensive inflammatory prognostic index in critically ill patients with cerebral infarction[J]. Front Neurol, 2024, 15: 1287895.
17
中华医学会神经病学分会，中华医学会神经病学分会脑血管病学组.中国急性缺血性卒中诊治指南2023[J].中华神经科杂志，2024，57（6）：523-559.
18
Chen C, Gu L, Chen L, et al. Neutrophil-to-lymphoc yte ratio and platelet-to-lymphocyte ratio as potential predictors of prognosis in acute ischemic dtroke[J]. Front Neurol, 2021, 11: 525621.
19
Lattanzi S, Norata D, Broggi S, et al. Neutrophil-to-lymphocyte ratio predicts early neurological deterioration after endovascular treatment in patients with ischemic stroke[J]. Life (Basel), 2022, 12 (9): 1415.
20
Zou F, Wang J, Han B, et al. Early neutrophil-to-lymphocyte ratio is a prognostic marker in acute ischemic stroke after successful revascularization[J]. World Neurosurg, 2022, 157: e401-e409.
21
Karakayali M, Omar T, Artac I, et al. The relationship between the systemic immune-inflammation index and reverse-dipper circadian pattern in newly diagnosed hypertensive patients[J]. J Clin Hypertens (Greenwich), 2023, 25 (8): 700-707.
22
Luo Y, Dong W, Yuan L, et al. The role of thromboinflammation in ischemic stroke: focus on the manipulation and clinical application[J]. Mol Neurobiol, 2025, 62 (2): 2362-2375.
23
Sun Y, Langer HF. Platelets, thromboinflammation and neurovascular disease[J]. Front Immunol, 2022, 13: 843404.
24
DeLong JH, Ohashi SN, O'Connor KC, et al. Inflammatory responses after ischemic stroke[J]. Semin Immunopathol, 2022, 44 (5): 625-648.
25
Denorme F, Portier I, Rustad JL, et al. Neutrophil extracellular traps regulate ischemic stroke brain injury[J]. J Clin Invest, 2022, 132 (10): e154225.
26
Xie M, Hao Y, Feng L, et al. Neutrophil heterogeneity and its roles in the inflammatory network after ischemic stroke[J]. Curr Neuropharmacol, 2023, 21 (3): 621-650.
27
Cai W, Shi L, Zhao J, et al. Neuroprotection against ischemic stroke requires a specific class of early responder T cells in mice[J]. J Clin Invest, 2022, 132 (15): e157678.
28
Zhang W, Zhao J, Wang R, et al. Macrophages reprogram after ischemic stroke and promote efferocytosis and inflammation resolution in the mouse brain[J]. CNS Neurosci Ther, 2019, 25 (12): 1329-1342.
29
Li Y, Li J, Yu Q, et al. METTL14 regulates microglia/macrophage polarization and NLRP3 inflammasome activation after ischemic stroke by the KAT3B-STING axis[J]. Neurobiol Dis, 2023, 185: 106253.
30
Kollikowski AM, Pham M, Marz AG, et al. Platelet activation and chemokine release are related to local neutrophil-dominant inflammation during hyperacute human stroke[J]. Transl Stroke Res, 2022, 13 (3): 364-369.
31
Greinacher A, Warkentin TE. Platelet factor 4 triggers thrombo-inflammation by bridging innate and adaptive immunity[J]. Int J Lab Hematol, 2023, 45 (Suppl 2): 11-22.
32
申慧鑫，孙蔚，武霄，等.炎性反应相关指标对急性缺血性卒中患者血管内治疗临床预后的影响[J].中国脑血管病杂志，2023，20（6）：382-391.
33
Arslan K, Sahin AS. Prognostic value of systemic immune-inflammation index and systemic inflammatory response index on functional status and mortality in patients with critical acute ischemic stroke[J]. Tohoku J Exp Med, 2025, 265 (2): 91-97.
34
Pirson FAVA, Boodt N, Brouwer J, et al; MR CLEAN Registry Investigators. Etiology of large vessel occlusion posterior circulation stroke: results of the MR CLEAN Registry[J]. Stroke, 2022, 53 (8): 2468-2477.
35
Vinding NE, Kristensen SL, Rorth R, et al. Ischemic stroke severity and mortality in patients with and without atrial fibrillation[J]. J Am Heart Assoc, 2022, 11 (4): e022638.
36
Chen ZM, Mo JL, Yang KX, et al. Beyond low-density lipoprotein cholesterol levels: impact of prior statin treatment on ischemic stroke outcomes[J]. Innovation (Camb), 2024, 5 (6): 100713.
37
陈金花，李婷，姚梅琪，等.深度学习算法在急性缺血性脑卒中后出血转化预测中的应用进展[J/OL].中华危重症医学杂志（电子版），2024，17（3）：236-240.
[1] 丁彩霞, 曲景辉, 裴英宏, 李静娜, 郑晓瑜, 徐岭植, 李思思. 基于RNA结合蛋白基因表达特征的乳腺癌预后预测模型构建及评价分析[J/OL]. 中华乳腺病杂志(电子版), 2026, 20(03): 138-147.
[2] 黄家骏, 周奕延, 王斐然, 何志贤. 系统性红斑狼疮与乳腺癌关系的研究进展[J/OL]. 中华乳腺病杂志(电子版), 2026, 20(03): 179-183.
[3] 叶秀华, 邵莉滨, 李群星, 叶剑涛. 套筒冠义齿基牙5年存留率及其预后因素的回顾性研究[J/OL]. 中华口腔医学研究杂志(电子版), 2026, 20(02): 126-131.
[4] 付丽坤, 崔红梅, 高福来, 乔红, 冯钟煦. 腹腔镜下胆总管探查“T”管引流术与经胆囊管胆总管探查取石术治疗继发性胆总管结石的疗效对比[J/OL]. 中华普外科手术学杂志(电子版), 2026, 20(03): 226-230.
[5] 辛林璞, 杨敏, 杜峻峰. 腹腔镜结直肠癌根治术后常见并发症防治与管理[J/OL]. 中华普外科手术学杂志(电子版), 2026, 20(03): 248-251.
[6] 张鹏, 舒芳, 肖赟, 吴书清, 钟斌. 针式抓钳辅助单孔腹腔镜在治疗小儿腹股沟嵌顿斜疝中的应用[J/OL]. 中华疝和腹壁外科杂志(电子版), 2026, 20(03): 334-340.
[7] 李旭柯, 熊培尧, 杨子良, 敖玉凤, 徐立. 肿瘤包绕型血管在接受仑伐替尼治疗肝癌患者生存预后中的预测价值[J/OL]. 中华肝脏外科手术学电子杂志, 2026, 15(03): 346-354.
[8] 叶金宝, 梁声强, 马建新, 林小强, 张海森. 小野寺预后营养指数对肝癌复发预测价值[J/OL]. 中华肝脏外科手术学电子杂志, 2026, 15(03): 372-378.
[9] 杨卫红, 郭向飞. 糖尿病合并慢性肾脏病患者血清前白蛋白和白蛋白/球蛋白比值与肾功能及预后的相关性研究[J/OL]. 中华肾病研究电子杂志, 2026, 15(03): 151-157.
[10] 黄霞, 张和平, 何永成. 机器学习驱动的IgA肾病精准治疗：疗效评估、预后预测及临床转化[J/OL]. 中华肾病研究电子杂志, 2026, 15(03): 166-169.
[11] 中国研究型医院学会放射学专业委员会, 上海抗癌协会实体肿瘤聚焦诊疗专业委员会. 血管包裹肿瘤簇在肝细胞癌影像诊疗中的临床应用专家共识（2026年版）[J/OL]. 中华消化病与影像杂志(电子版), 2026, 16(03): 193-198.
[12] 齐洪武, 徐泽雨. 脑脓肿的临床诊治进展[J/OL]. 中华临床医师杂志(电子版), 2026, 20(04): 327-331.
[13] 柳云鹏, 贾建文, 黄菊梅, 汪阳. 早期血压变异性对大血管闭塞性急性缺血性脑卒中术后神经功能恢复的影响[J/OL]. 中华脑血管病杂志(电子版), 2026, 20(03): 274-279.
[14] 吴嶛, 蔡诗昆, 左鹏, 倪贵华, 倪小宇. 急性缺血性脑卒中机械取栓前血栓迁移与临床资料的相关性[J/OL]. 中华脑血管病杂志(电子版), 2026, 20(03): 287-293.
[15] 梁怡凡, 牟婧宇, 吴雅婷, 邳靖陶, 陈乐, 武剑. 不同人工智能模型预测脑卒中不良预后诊断效能的荟萃分析[J/OL]. 中华脑血管病杂志(电子版), 2026, 20(03): 308-319.
阅读次数
全文


摘要

版权所有 中华医学会 中华医学电子音像出版社有限责任公司  京ICP备14006079号-1  网络出版服务许可证：(署)网出证(京)字第075号

AI
小
编
AI小编
你好！我是《中华医学电子期刊资源库》AI小编，有什么可以帮您的吗？