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用于预测自发性脑出血患者肺炎的炎症反应生物标志物列线图

Inflammatory response biomarkers nomogram for predicting pneumonia in patients with spontaneous intracerebral hemorrhage.

作者信息

Yu Tingting, Liu Haimei, Liu Ying, Jiang Jianxin

机构信息

Graduate School of Dalian Medical University, Dalian, China.

Department of Neurology, The Affiliated Taizhou People's Hospital of Nanjing Medical University, Taizhou, China.

出版信息

Front Neurol. 2023 Jan 12;13:1084616. doi: 10.3389/fneur.2022.1084616. eCollection 2022.

Abstract

OBJECTIVES

Inflammatory response biomarkers are promising prognostic factors to improve the prognosis of stroke-associated pneumonia (SAP) after ischemic stroke. This study aimed to investigate the prognostic significance of inflammatory response biomarkers on admission in SAP after spontaneous intracerebral hemorrhage (SICH) and establish a corresponding nomogram.

METHODS

The data of 378 patients with SICH receiving conservative treatment from January 2019 to December 2021 at Taizhou People's Hospital were selected. All eligible patients were randomized into the training (70%, 265) and validation cohorts (30%, 113). In the training cohort, multivariate logistic regression analysis was used to establish an optimal nomogram, including inflammatory response biomarkers and clinical risk factors. The area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, and decision curve analysis (DCA) were used to evaluate the nomogram's discrimination, calibration, and performance, respectively. Moreover, this model was further validated in a validation cohort.

RESULTS

A logistic regression analysis showed that intraventricular hemorrhage (IVH), hypertension, dysphagia, Glasgow Coma Scale (GCS), National Institute of Health Stroke Scale (NIHSS), systemic inflammation response index (SIRI), and platelet/lymphocyte ratio (PLR) were correlated with SAP after SICH ( < 0.05). The nomogram was composed of all these statistically significant factors. The inflammatory marker-based nomogram showed strong prognostic power compared with the conventional factors, with an AUC of 0.886 (95% CI: 0.841-0.921) and 0.848 (95% CI: 0.799-0.899). The calibration curves demonstrated good homogeneity between the predicted risks and the observed outcomes. In addition, the model has a significant net benefit for SAP, according to DCA. Also, internal validation demonstrated the reliability of the prediction nomogram. The length of hospital stay was shorter in the non-SAP group than in the SAP group. At the 3-month follow-up, clinical outcomes were worse in the SAP group ( < 0.001).

CONCLUSION

SIRI and PLR at admission can be utilized as prognostic inflammatory biomarkers in patients with SICH in the upper brain treated with SAP. A nomogram covering SIRI and PLR can more accurately predict SAP in patients' supratentorial SICH. SAP can influence the length of hospital stay and the clinical outcome.

摘要

目的

炎症反应生物标志物是改善缺血性中风后中风相关性肺炎(SAP)预后的有前景的预后因素。本研究旨在探讨自发性脑出血(SICH)后SAP患者入院时炎症反应生物标志物的预后意义,并建立相应的列线图。

方法

选取2019年1月至2021年12月在泰州市人民医院接受保守治疗的378例SICH患者的数据。所有符合条件的患者被随机分为训练队列(70%,265例)和验证队列(30%,113例)。在训练队列中,采用多因素逻辑回归分析建立最佳列线图,包括炎症反应生物标志物和临床危险因素。分别采用受试者操作特征(ROC)曲线下面积(AUC)、校准曲线和决策曲线分析(DCA)来评估列线图的区分度、校准度和性能。此外,该模型在验证队列中进一步验证。

结果

逻辑回归分析显示,脑室内出血(IVH)、高血压、吞咽困难、格拉斯哥昏迷量表(GCS)、美国国立卫生研究院卒中量表(NIHSS)、全身炎症反应指数(SIRI)和血小板/淋巴细胞比值(PLR)与SICH后SAP相关(<0.05)。列线图由所有这些具有统计学意义的因素组成。与传统因素相比,基于炎症标志物的列线图显示出较强的预后能力,AUC分别为0.886(95%CI:0.841-0.921)和0.848(95%CI:0.799-0.899)。校准曲线显示预测风险与观察结果之间具有良好的一致性。此外,根据DCA,该模型对SAP具有显著的净效益。内部验证也证明了预测列线图的可靠性。非SAP组的住院时间比SAP组短。在3个月随访时,SAP组的临床结局更差(<0.001)。

结论

入院时的SIRI和PLR可作为接受SAP治疗的幕上SICH患者的预后炎症生物标志物。涵盖SIRI和PLR的列线图可以更准确地预测幕上SICH患者的SAP。SAP会影响住院时间和临床结局。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/06d1/9879054/bf1a0a273c00/fneur-13-1084616-g0001.jpg

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