Risk Estimation of Infectious and Inflammatory Disorders in Hospitalized Patients With Acute Ischemic Stroke Using Clinical-Lab Nomogram.
Risk Estimation of Infectious and Inflammatory Disorders in Hospitalized Patients With Acute Ischemic Stroke Using Clinical-Lab Nomogram.
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DOI：
10.3389/fneur.2021.710144
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发表时间：
2021
影响因子：
3.4
通讯作者：
Chen W
中科院分区：
文献类型：
--
作者：
Li J;Huang J;Pang T;Chen Z;Li J;Wu L;Hu Y;Chen W
背景：急性缺血性卒中后感染是常见的，可能会使临床病程复杂化，并对患者的预后产生负面影响。尽管卒中后感染性和炎症性疾病(IAID)的各种危险因素和预测模型已经发展，但更客观和更容易获得的预测指标仍然是必要的。这项研究涉及开发和验证一种可访问的、准确的诺模图，用于预测急性缺血性中风(AIS)患者的医院内IAID。方法：对2257例经神经学检查和放射学检查证实的AIS患者进行回顾性队列分析。Iaid使用国际疾病和健康相关问题统计分类的定义。数据是在2016年1月至2020年3月期间从两家医院获得的。结果：在派生和验证队列中，iAID的发生率分别为19.8%和20.8%。使用绝对收缩和选择算子(LASSO)算法，从55个特征中优化出4个生化血液预测指标和4个临床指标。通过多变量分析，发现年龄(调整后的优势比为1.038；95%可信区间为1.038-1.062；p<；0.001)、昏迷状态(28.033[4.706-0.649]，p=0.002)、糖尿病(0.417[4.706-0.649]，p<；0.001)和充血性心力衰竭(5.488[2.451-12.912]，p<；0.001)是Iaid的危险因素。此外，中性粒细胞、单核细胞、血红蛋白和高敏C反应蛋白也被发现与IAID独立相关。因此，在我们的研究中，构建了一个可靠的临床实验室诺模图来预测IAID(C-index值=0.83)。ROC分析结果与校正曲线分析结果一致。决策曲线显示，在区分IAID和AIS患者方面，临床-实验室模式比实验室评分或临床模式增加了更多的净收益。结论：临床实验室标准图可预测急性缺血性卒中患者的IAID。因此，这一诺模图可用于识别高危患者，并进一步指导临床决策。
Background: Infections after acute ischemic stroke are common and likely to complicate the clinical course and negatively affect patient outcomes. Despite the development of various risk factors and predictive models for infectious and inflammatory disorders (IAID) after stroke, more objective and easily obtainable predictors remain necessary. This study involves the development and validation of an accessible, accurate nomogram for predicting in-hospital IAID in patients with acute ischemic stroke (AIS). Methods: A retrospective cohort of 2,257 patients with AIS confirmed by neurological examination and radiography was assessed. The International Statistical Classification of Diseases and Health related Problem's definition was used for IAID. Data was obtained from two hospitals between January 2016 and March 2020. Results: The incidence of IAID was 19.8 and 20.8% in the derivation and validation cohorts, respectively. Using an absolute shrinkage and selection operator (LASSO) algorithm, four biochemical blood predictors and four clinical indicators were optimized from fifty-five features. Using a multivariable analysis, four predictors, namely age (adjusted odds ratio, 1.05; 95% confidence interval [CI], 1.038–1.062; p < 0.001), comatose state (28.033[4.706–536.403], p = 0.002), diabetes (0.417[0.27–0.649], p < 0.001), and congestive heart failure (CHF) (5.488[2.451–12.912], p < 0.001) were found to be risk factors for IAID. Furthermore, neutrophil, monocyte, hemoglobin, and high-sensitivity C-reactive protein were also found to be independently associated with IAID. Consequently, a reliable clinical-lab nomogram was constructed to predict IAID in our study (C-index value = 0.83). The results of the ROC analysis were consistent with the calibration curve analysis. The decision curve demonstrated that the clinical-lab model added more net benefit than either the lab-score or clinical models in differentiating IAID from AIS patients. Conclusions: The clinical-lab nomogram predicted IAID in patients with acute ischemic stroke. As a result, this nomogram can be used for identification of high-risk patients and to further guide clinical decisions.
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影响因子：
3.4
作者：
Lan Y;Sun W;Chen Y;Miao J;Li G;Qiu X;Song X;Zhao X;Zhu Z;Fan Y;Zhu S
通讯作者：
Zhu S
影响因子：
5.1
作者：
Harari, Yaar;O'Brien, Megan K.;Jayaraman, Arun
通讯作者：
Jayaraman, Arun
影响因子：
8.3
作者：
Nam, Ki-Woong;Kim, Tae Jung;Yoon, Byung-Woo
通讯作者：
Yoon, Byung-Woo
影响因子：
8.3
作者：
Hoffmann, Sarah;Malzahn, Uwe;Heuschmann, Peter Ulrich
通讯作者：
Heuschmann, Peter Ulrich
影响因子：
2.7
作者：
Ni, Yingmeng;Ding, Lin;Shi, Guochao
通讯作者：
Shi, Guochao