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炎症和肾功能状态对接受静脉溶栓治疗的急性缺血性卒中患者院内结局的联合影响。

The joint effects of inflammation and renal function status on in-hospital outcomes in patients with acute ischemic stroke treated with intravenous thrombolysis.

作者信息

Huang Zhichao, Zhu Xiaoyue, Xu Xiuman, Wang Yi, Zhu Yafang, Chen Dongqin, Cao Yongjun, Zhang Xia

机构信息

Department of Neurology and Clinical Research Center of Neurological Disease, the Second Affiliated Hospital of Soochow University, Sanxiang Road, Suzhou, Jiangsu Province, 1055, China.

Department of Clinical Nutrition, Suzhou Municipal Hospital, Suzhou, China.

出版信息

BMC Neurol. 2024 Dec 31;24(1):493. doi: 10.1186/s12883-024-04002-6.

DOI:10.1186/s12883-024-04002-6
PMID:39736651
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11686970/
Abstract

OBJECTIVE

We aimed to determine the predictive value of renal function status [estimating glomerular filtration rate (eGFR)] in conjunction with inflammatory biomarkers [white blood cell(WBC) and C-reactive protein (CRP)] for in-hospital outcomes in acute ischemic stroke (AIS) patients treated with intravenous thrombolysis (IVT).

METHODS

We retrospectively screened a total of 409 AIS patients treated with IVT. The study participants were classified into two groups according to post-stroke pneumonia or functional outcome. They were divided into four groups according to the cut-offs of inflammatory biomarkers and eGFR by receiver operating characteristics(ROC) curves for two outcomes of post-stroke pneumonia and functional status: WBC↓/eGFR↑, WBC↓/eGFR↓, WBC↑/eGFR↑, and WBC↑/eGFR↓for post-stroke pneumonia; and CRP↓/eGFR↑, CRP↓/eGFR↓, CRP↑/eGFR↑, and CRP↑/eGFR↓for functional outcome. Logistic regression models were used to calculate the odds ratios (ORs) and 95% confidence intervals (CIs) of post-stroke pneumonia or at-discharge functional outcome, using the WBC↓/eGFR↑group or CRP↓/eGFR↑group as the reference. The Net Reclassification Index (NRI) and the Integrated Discrimination Improvement (IDI) were calculated to analyze the combined predictive value.

RESULTS

Compared with patients in WBC↓/eGFR↑group, those in WBC↑/eGFR↑group had increased risk of post-stroke pneumonia (OR 5.15, 95% CI 1.67-15.87) and poor functional outcome (OR 5.95, 95% CI 2.25-15.74). Furthermore, patients in WBC↑/ eGFR↓group had the highest risk of clinical outcomes (all P value for trend < 0.001), the multivariable-adjusted ORs (95% CIs) were 7.04 (2.42-20.46) for post-stroke pneumonia and 8.64 (3.30-22.65) for poor functional outcome. The addition of WBC and eGFR to the basic model significantly improved risk prediction for post-stroke pneumonia (category-free NRI 69.0%, 95% CI 47.3%-90.7%; IDI 5.4%, 95% CI 2.6%-8.3%) and functional outcome (category-free NRI 59.4%, 95% CI 39.2%-79.9%; IDI 5.3%, 95% CI 2.9%-7.8%). Similarly, when we added CRP and eGFR to the basic model with conventional risk factors, the risk discrimination and prediction for post-stroke pneumonia and functional outcome was also significantly improved.

CONCLUSION

Combining renal function status and inflammatory biomarkers within 4.5 h after onset could better predict in-hospital outcomes of AIS patients with IVT.

摘要

目的

我们旨在确定肾功能状态[估算肾小球滤过率(eGFR)]联合炎症生物标志物[白细胞(WBC)和C反应蛋白(CRP)]对接受静脉溶栓(IVT)治疗的急性缺血性卒中(AIS)患者院内结局的预测价值。

方法

我们回顾性筛查了总共409例接受IVT治疗的AIS患者。根据卒中后肺炎或功能结局将研究参与者分为两组。根据炎症生物标志物和eGFR的截断值,通过受试者工作特征(ROC)曲线将他们分为四组,以评估卒中后肺炎和功能状态的两个结局:卒中后肺炎的WBC↓/eGFR↑、WBC↓/eGFR↓、WBC↑/eGFR↑和WBC↑/eGFR↓;功能结局的CRP↓/eGFR↑、CRP↓/eGFR↓、CRP↑/eGFR↑和CRP↑/eGFR↓。采用逻辑回归模型计算卒中后肺炎或出院时功能结局的比值比(OR)和95%置信区间(CI),以WBC↓/eGFR↑组或CRP↓/eGFR↑组作为对照。计算净重新分类指数(NRI)和综合判别改善(IDI)以分析联合预测价值。

结果

与WBC↓/eGFR↑组患者相比,WBC↑/eGFR↑组患者发生卒中后肺炎的风险增加(OR 5.15,95%CI 1.67 - 15.87),功能结局较差(OR 5.95,95%CI 2.25 - 15.74)。此外,WBC↑/eGFR↓组患者临床结局风险最高(所有趋势P值<0.001),多变量调整后的OR(95%CI)对于卒中后肺炎为7.04(2.42 - 20.46),对于功能结局较差为8.64(3.30 - 22.65)。在基本模型中加入WBC和eGFR显著改善了对卒中后肺炎的风险预测(无类别NRI 69.0%,95%CI 47.3% - 90.7%;IDI 5.4%,95%CI 2.6% - 8.3%)和功能结局(无类别NRI 59.4%,95%CI 39.2% - 79.9%;IDI 5.3%,95%CI 2.9% - 7.8%)。同样,当我们在具有传统危险因素的基本模型中加入CRP和eGFR时,对卒中后肺炎和功能结局的风险判别和预测也显著改善。

结论

在发病后4.5小时内结合肾功能状态和炎症生物标志物可以更好地预测接受IVT治疗的AIS患者的院内结局。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f5f/11686970/ccee4e85d31d/12883_2024_4002_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f5f/11686970/ed3f54f5f40e/12883_2024_4002_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f5f/11686970/dbb412c6ab82/12883_2024_4002_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f5f/11686970/ccee4e85d31d/12883_2024_4002_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f5f/11686970/ed3f54f5f40e/12883_2024_4002_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f5f/11686970/dbb412c6ab82/12883_2024_4002_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f5f/11686970/ccee4e85d31d/12883_2024_4002_Fig3_HTML.jpg

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