摘要
目的:评估全身免疫炎症指数(systemic immune inflammation index,SII)对低中危社区获得性肺炎发生脓毒症的预测价值。方法:纳入2020年1月~2023年1月海南医学院第二附属医院急诊科收治的589例低中危社区获得性肺炎老年患者为研究对象,收集患者一般资料、实验室检测结果,通过绘制受试者工作特征(ROC)曲线确定连续变量预测低中危社区获得性肺炎老年患者并发脓毒症的最佳界值,转化为二分类变量,采用单因素和多因素Logistic回归分析低中危社区获得性肺炎老年患者发生脓毒症的影响因素。并以此构建预测脓毒症发生风险的列线图模型。分别通过校准曲线和ROC曲线验证模型的区分度、一致性和准确性,并采用决策曲线分析法确定模型的临床实用性。结果:本研究共纳入589例低中危社区获得性肺炎老年患者,其中发生脓毒症者96例(16.30%)。脓毒症组和非脓毒症组的年龄、合并糖尿病及慢性阻塞性肺疾病、Lac、PCT、SII等指标的差异有统计学意义(P<0.05)。Logistics回归分析显示:年龄、合并糖尿病及慢性阻塞性肺疾病、Lac、SII为低中危社区获得性肺炎老年患者发生脓毒症的独立危险因素。使用列线图预测模型进行验证,结果显示AUC为0.826(95%CI:0.780~0.872),校准曲线趋于理想曲线,具有较好的准确性。决策曲线表明,该模型的阈值在0.10~0.78之间时,该模型有临床获益优势。结论:基于SII构建的预测低中危社区获得性肺炎老年患者发生脓毒症的列线图预测模型具有较好的准确性,可以早期预测脓毒症的发生,有助于早期识别高危人群和及时干预,从而改善患者预后。
Objective:To assess the predictive value of systemic immune inflammation index(SII)for sepsis in low-medium risk community-acquired pneumonia.Methods:A total of 589 elderly patients with low-medium risk community-acquired pneumonia admitted to the Emergency Department of the Second Affiliated Hospital of Hainan Medical University from January 2020 to January 2023 were included as the research subjects,and the general information and laboratory test results of the patients were collected,and the optimal cut-off value of continuous variables for predicting sepsis in elderly patients with low-medium risk com-munity-acquired pneumonia was determined by plotting the receiver work characteristic(ROC)curve,which was converted into dichotomous variables and univariate and multivariate logistic regression analysis of the influencing factors of sepsis in elderly pa-tients with low-medium risk community-acquired pneumonia.Based on this,a nomogram model is constructed to predict the risk of sepsis.The differentiation,consistency and accuracy of the model were verified by calibration curve and subject operating characteristic curve,and the clinical utility of the model was determined by decision curve analysis.Results:A total of 589 elderly patients with low-medium risk community-acquired pneumonia were included in this study,of which 96(16.30%)developed sepsis.There were significant differences in age,diabetes mellitus and chronic obstructive pulmonary disease,Lac,PCT,SII and other indexes between sepsis and non-sepsis groups(P<0.05).Logistics regression analysis showed that age,diabetes mellitus and chronic obstructive pulmonary disease,Lac,and SII were independent risk factors for sepsis in elderly patients with low-medium risk community-acquired pneumonia.The nomogram prediction model was used to verify the results,and the AUC was 0.826(95%CI:0.780-0.872),and the calibration curve tended to the ideal curve with good accuracy.The decision curve shows that when the threshold of the model is between 0.10~0.78,the model has the advantage of clinical benefit.Conclusion:The nomogram prediction model constructed based on SII to predict sepsis in elderly patients with low-medium risk community-acquired pneumonia has good accuracy,which can predict the occurrence of sepsis early,help early identification of high-risk groups and timely intervention,and thus improve the prognosis of patients.
作者
柴豆豆
王晓苗
邢柏
CHAI Dou-dou;WANG Xiao-miao;XING Bo(School of Emergency Traumatology,Hainan Medical University,Haikou,571199,China;Emergency department of the Second Affiliated Hospital of Hainan Medical University,Haikou,570311,China)
出处
《海南医学院学报》
CAS
2024年第2期113-119,共7页
Journal of Hainan Medical University
基金
海南省自然科学基金资助项目(819MS128)。
关键词
老年人
全身免疫炎症指数
社区获得性肺炎
脓毒症
列线图模型
Senior citizen
Systemic immunoinflammation index
Community-acquired pneumonia
Sepsis
Nomogram model
