Zhang Jia, Zhu Weihua, Jiang Piping, Ma Feng, Li Yulin, Cao Yuwei, Li Jiaxin, Zhang Zhe, Zhang Xin, Zou Wailong, Chen Jichao
Department of Respiratory and Critical Care Medicine, Aerospace Center Hospital, Beijing, 100049, China.
BMC Infect Dis. 2025 May 14;25(1):699. doi: 10.1186/s12879-025-11083-2.
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.
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).
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.
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.
持续性严重急性呼吸综合征冠状病毒2(SARS-CoV-2)感染与长期冠状病毒病(COVID-19)(清除后急性症状持续≥12周)不同。奥密克戎BA.5变体的中位清除时间(10 - 14天)比德尔塔变体短,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。本研究综合多阈值分析(14/20/30天)、全基因组测序和机器学习,以研究持续性SARS-CoV-2感染的诊断阈值,并开发了一种可推广的风险预测模型。
这项回顾性研究分析了2021年1月至2024年10月期间在航天中心医院住院的1216例COVID-19患者的数据。我们使用全基因组测序对所有COVID-19病例进行基因分型,并确定主要变体(如奥密克戎BA.5、德尔塔)。结果“持续性SARS-CoV-2感染”定义为病毒核酸阳性≥14天。通过多因素逻辑回归(根据年龄、合并症、疫苗接种状况和病毒株进行调整)和机器学习模型(70%训练集,30%测试集)的亚组分析,确定与持续性感染相关的危险因素。
在住院的COVID-19患者中,15.5%(188/1216)被确定为持续性SARS-CoV-2感染。关键预测因素包括合并症——高血压、糖尿病和活动性恶性肿瘤——以及免疫功能障碍,表现为B细胞和CD4 + T细胞计数减少。未接种疫苗的患者持续性感染风险高82%。炎症标志物(C反应蛋白和白细胞介素-6)升高以及计算机断层扫描显示双侧肺部浸润进一步区分了持续性病例。预测模型显示出很强的区分能力,曲线下面积(AUC)为0.847(95%置信区间:0.815 - 0.879),外部验证时的AUC为0.81,强调了其在风险分层中的临床实用性。
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性SARS-CoV-2感染的独立危险因素。将这些因素纳入临床风险分层可能会优化高危人群的管理。