持续性严重急性呼吸综合征冠状病毒2感染危险因素深入分析及预测模型构建探索性研究

IF 3.4 3区 医学 Q2 INFECTIOUS DISEASES
Jia Zhang, Weihua Zhu, Piping Jiang, Feng Ma, Yulin Li, Yuwei Cao, Jiaxin Li, Zhe Zhang, Xin Zhang, Wailong Zou, Jichao Chen
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引用次数: 0

摘要

背景:持续性严重急性呼吸综合征冠状病毒2型(SARS-CoV-2)感染不同于长冠状病毒病(COVID-19)(急性症状≥清除后12周)。与Delta变异相比,Omicron BA.5变异的中位清除时间(10-14天)更短,这表明传统的20天诊断阈值可能会延迟对高危人群的干预。本研究结合多阈值分析(14/20/30天)、全基因组测序和机器学习来研究持续SARS-CoV-2感染的诊断阈值,并建立了一个可推广的风险预测模型。方法:回顾性分析2021年1月至2024年10月在航空航天中心医院住院的1216例新冠肺炎患者的资料。我们使用全基因组测序对所有COVID-19病例进行基因分型,并确定主要变异(如Omicron BA)。5,δ)。“持续性SARS-CoV-2感染”定义为病毒核酸阳性≥14天。通过多重逻辑回归(调整年龄、合并症、疫苗接种状况和病毒株)和机器学习模型(70%训练,30%测试数据集)的亚组分析,确定与持续感染相关的危险因素。结果:在住院的COVID-19患者中,15.5%(188/ 1216)存在持续的SARS-CoV-2感染。关键的预测因素包括合并症——高血压、糖尿病和活动性恶性肿瘤——和免疫功能障碍,以b细胞和CD4 + t细胞计数减少为标志。未接种疫苗的患者持续感染的风险高出82%。计算机断层扫描显示炎症标志物(c反应蛋白和白细胞介素-6)升高和双侧肺浸润进一步区分了持续性病例。在外部验证中,该预测模型的曲线下面积(AUC)为0.847(95%置信区间:0.815-0.879),外部AUC为0.81,具有很强的鉴别能力,强调了其在风险分层中的临床应用。结论:高血压、糖尿病、恶性肿瘤、免疫抑制(低B/CD4 +细胞)和未接种疫苗是持续感染SARS-CoV-2的独立危险因素。将这些因素纳入临床风险分层可以优化高危人群的管理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
In-depth analysis of the risk factors for persistent severe acute respiratory syndrome coronavirus 2 infection and construction of predictive models: an exploratory research study.

Background: Persistent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection differs from long coronavirus disease (COVID-19) (acute symptoms ≥ 12 weeks post-clearance). The Omicron BA.5 variant has a shorter median clearance time (10-14 days) than the Delta variant, suggesting that the traditional 20-day diagnostic threshold may delay interventions in high-risk populations. This study integrated multi-threshold analysis (14/20/30 days), whole-genome sequencing, and machine learning to investigate diagnostic thresholds for persistent SARS-CoV-2 infection and developed a generalizable risk prediction model.

Methods: This retrospective study analyzed data from 1,216 patients with COVID-19 hospitalized at Aerospace Center Hospital between January 2021 and October 2024. We used whole-genome sequencing to genotype all COVID-19 cases and to identify major variants (such as Omicron BA. 5, Delta). The outcome, "persistent SARS-CoV-2 infection," was defined as viral nucleic acid positivity ≥ 14 days. Risk factors associated with persistent infection were identified through subgroup analysis with multiple logistic regression (adjusted for age, comorbidities, vaccination status, and virus strain) and machine learning models (70% training, 30% testing dataset).

Results: Persistent SARS-CoV-2 infection was identified in 15.5% (188/1,216) of hospitalized COVID-19 patients. Key predictors included comorbidities-hypertension, diabetes, and active malignancy-and immune dysfunction, marked by reduced B-cell and CD4 + T-cell counts. Unvaccinated patients exhibited an 82% higher risk of persistent infection. Elevated inflammatory markers (C-reactive protein and interleukin-6) and bilateral lung infiltrates on computed tomography further distinguished persistent cases. The predictive model demonstrated strong discrimination with an area under the curve (AUC) of 0.847 (95% confidence interval: 0.815-0.879) and an AUC of 0.81 externally in external validation, underscoring its clinical utility for risk stratification.

Conclusions: Hypertension, diabetes, malignancy, immunosuppression (low B/CD4 + cells), and non-vaccination are independent risk factors for persistent SARS-CoV-2 infection. Integrating these factors into clinical risk stratification may optimize management of high-risk populations.

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来源期刊
BMC Infectious Diseases
BMC Infectious Diseases 医学-传染病学
CiteScore
6.50
自引率
0.00%
发文量
860
审稿时长
3.3 months
期刊介绍: BMC Infectious Diseases is an open access, peer-reviewed journal that considers articles on all aspects of the prevention, diagnosis and management of infectious and sexually transmitted diseases in humans, as well as related molecular genetics, pathophysiology, and epidemiology.
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