Department of Intensive Care Unit, Peking University Third Hospital, Beijing, China.
Department of Pulmonary and Critical Care Medicine, Peking University Third Hospital, Beijing, China.
Front Immunol. 2024 Mar 27;15:1372539. doi: 10.3389/fimmu.2024.1372539. eCollection 2024.
The coronavirus disease 2019 (COVID-19) pandemic has affected billions of people worldwide, and the lessons learned need to be concluded to get better prepared for the next pandemic. Early identification of high-risk patients is important for appropriate treatment and distribution of medical resources. A generalizable and easy-to-use COVID-19 severity stratification model is vital and may provide references for clinicians.
Three COVID-19 cohorts (one discovery cohort and two validation cohorts) were included. Longitudinal peripheral blood mononuclear cells were collected from the discovery cohort (n = 39, mild = 15, critical = 24). The immune characteristics of COVID-19 and critical COVID-19 were analyzed by comparison with those of healthy volunteers (n = 16) and patients with mild COVID-19 using mass cytometry by time of flight (CyTOF). Subsequently, machine learning models were developed based on immune signatures and the most valuable laboratory parameters that performed well in distinguishing mild from critical cases. Finally, single-cell RNA sequencing data from a published study (n = 43) and electronic health records from a prospective cohort study (n = 840) were used to verify the role of crucial clinical laboratory and immune signature parameters in the stratification of COVID-19 severity.
Patients with COVID-19 were determined with disturbed glucose and tryptophan metabolism in two major innate immune clusters. Critical patients were further characterized by significant depletion of classical dendritic cells (cDCs), regulatory T cells (Tregs), and CD4 central memory T cells (Tcm), along with increased systemic interleukin-6 (IL-6), interleukin-12 (IL-12), and lactate dehydrogenase (LDH). The machine learning models based on the level of cDCs and LDH showed great potential for predicting critical cases. The model performances in severity stratification were validated in two cohorts (AUC = 0.77 and 0.88, respectively) infected with different strains in different periods. The reference limits of cDCs and LDH as biomarkers for predicting critical COVID-19 were 1.2% and 270.5 U/L, respectively.
Overall, we developed and validated a generalizable and easy-to-use COVID-19 severity stratification model using machine learning algorithms. The level of cDCs and LDH will assist clinicians in making quick decisions during future pandemics.
2019 年冠状病毒病（COVID-19）大流行影响了全球数十亿人，需要总结经验教训，为下一次大流行做好更好的准备。早期识别高危患者对于适当的治疗和医疗资源的分配非常重要。一个可推广且易于使用的 COVID-19 严重程度分层模型至关重要，可为临床医生提供参考。
纳入了三个 COVID-19 队列（一个发现队列和两个验证队列）。从发现队列（n = 39，轻症 = 15，重症 = 24）中收集纵向外周血单核细胞。使用时间飞行（CyTOF）的质谱流式细胞术，通过与健康志愿者（n = 16）和轻症 COVID-19 患者比较，分析 COVID-19 和重症 COVID-19 的免疫特征。随后，基于免疫特征和在区分轻症和重症方面表现良好的最有价值的实验室参数，开发机器学习模型。最后，使用来自已发表研究的单细胞 RNA 测序数据（n = 43）和前瞻性队列研究的电子健康记录（n = 840）验证关键临床实验室和免疫特征参数在 COVID-19 严重程度分层中的作用。
COVID-19 患者在两个主要的固有免疫群中表现出葡萄糖和色氨酸代谢紊乱。重症患者进一步表现为经典树突状细胞（cDCs）、调节性 T 细胞（Tregs）和 CD4 中央记忆 T 细胞（Tcm）明显耗竭，同时全身白细胞介素-6（IL-6）、白细胞介素-12（IL-12）和乳酸脱氢酶（LDH）水平升高。基于 cDCs 和 LDH 水平的机器学习模型在预测重症病例方面具有很大潜力。该模型在两个队列中的严重程度分层中的表现均得到验证（AUC = 0.77 和 0.88），两个队列感染的病毒株不同，处于不同时期。cDCs 和 LDH 作为预测重症 COVID-19 的生物标志物的参考限值分别为 1.2%和 270.5 U/L。
总体而言，我们使用机器学习算法开发并验证了一个可推广且易于使用的 COVID-19 严重程度分层模型。cDCs 和 LDH 水平将有助于临床医生在未来的大流行中做出快速决策。
