一种用于早期识别2019冠状病毒病老年患者合并感染的新型列线图。

A novel nomogram for the early identification of coinfections in elderly patients with coronavirus disease 2019.

作者信息

Zou Ju, Wang Xiaoxu, Li Jie, Liu Min, Zhao Xiaoting, Wang Ling, Kuang Xuyuan, Huang Yan, Quan Jun, Chen Ruochan

机构信息

Hunan Key Laboratory of Viral Hepatitis, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China.

Department of Infectious Diseases, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China.

出版信息

Virol J. 2025 Jul 3;22(1):219. doi: 10.1186/s12985-025-02854-z.

DOI:10.1186/s12985-025-02854-z

PMID:40611144

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12231678/

Abstract

OBJECTIVES

This study aimed to establish a novel and practical nomogram for use upon hospital admission to identify coinfections among elderly patients with coronavirus disease 2019 (COVID-19) to provide timely intervention, limit antimicrobial agent overuse, and finally reduce unfavourable outcomes.

METHODS

This prospective cohort study included COVID-19 patients consecutively admitted at multicenter medical facilities in a two-stage process. The nomogram was built on the multivariable logistic regression analysis. The performance of the nomogram was assessed for discrimination and calibration using receiver operating characteristic curves, calibration plots, and decision curve analysis (DCA) in rigorous internal and external validation settings. Two different cutoff values were determined to stratify coinfection risk in elderly patients with COVID-19.

RESULTS

The coinfection rates in elderly patients determined to be and 26.61%. The nomogram was developed with the parameters of diabetes comorbidity, previous invasive procedure, and procalcitonin (PCT) level, which together showed areas under the curve of 0.86, 0.82, and 0.83 in the training, internal validation, and external validation cohorts, respectively. The nomogram outperformed both PCT or C-reactive protein level alone in detecting coinfections in elderly patients with COVID-19; in addition, we found the nomogram was specific for the elderly compared to non-elderly group. To facilitate clinical decision-making among elderly patients with COVID-19, we defined two cutoff values of prediction probability: a low cutoff of 6.65% to rule out coinfections and a high cutoff of 27.79% to confidently confirm coinfections.

CONCLUSIONS

This novel nomogram will assist in the early identification of coinfections in elderly patients with COVID-19.

摘要

目的

本研究旨在建立一种新颖实用的列线图，用于在老年2019冠状病毒病（COVID-19）患者入院时识别合并感染，以便及时进行干预，限制抗菌药物的过度使用，并最终减少不良结局。

方法

这项前瞻性队列研究分两个阶段纳入了在多中心医疗机构连续入院的COVID-19患者。列线图基于多变量逻辑回归分析构建。在严格的内部和外部验证环境中，使用受试者操作特征曲线、校准图和决策曲线分析（DCA）对列线图的区分度和校准度进行评估。确定了两个不同的临界值来分层COVID-19老年患者的合并感染风险。

结果

确定的老年患者合并感染率为26.61%。列线图是根据糖尿病合并症、既往侵入性操作和降钙素原（PCT）水平等参数开发的，在训练队列、内部验证队列和外部验证队列中，这些参数共同显示的曲线下面积分别为0.86、0.82和0.83。在检测COVID-19老年患者的合并感染方面，列线图的表现优于单独的PCT或C反应蛋白水平；此外，我们发现与非老年组相比，列线图对老年患者具有特异性。为便于COVID-19老年患者的临床决策，我们定义了两个预测概率临界值：低临界值为6.65%以排除合并感染，高临界值为27.79%以可靠地确认合并感染。