Subramanian Devika, Vittala Aadith, Chen Xinpu, Julien Christopher, Acosta Sebastian, Rusin Craig, Allen Carl, Rider Nicholas, Starosolski Zbigniew, Annapragada Ananth, Devaraj Sridevi
Department of Computer Science, Rice University, 6100 Main St. MS 132, Houston, TX 77005, USA.
Texas Children's Hospital/Baylor College of Medicine, 6621 Fannin Street, WB110.06, Houston, TX 77030, USA.
J Clin Med. 2023 Aug 22;12(17):5435. doi: 10.3390/jcm12175435.
While pediatric COVID-19 is rarely severe, a small fraction of children infected with SARS-CoV-2 go on to develop multisystem inflammatory syndrome (MIS-C), with substantial morbidity. An objective method with high specificity and high sensitivity to identify current or imminent MIS-C in children infected with SARS-CoV-2 is highly desirable. The aim was to learn about an interpretable novel cytokine/chemokine assay panel providing such an objective classification. This retrospective study was conducted on four groups of pediatric patients seen at multiple sites of Texas Children's Hospital, Houston, TX who consented to provide blood samples to our COVID-19 Biorepository. Standard laboratory markers of inflammation and a novel cytokine/chemokine array were measured in blood samples of all patients. Group 1 consisted of 72 COVID-19, 70 MIS-C and 63 uninfected control patients seen between May 2020 and January 2021 and predominantly infected with pre-alpha variants. Group 2 consisted of 29 COVID-19 and 43 MIS-C patients seen between January and May 2021 infected predominantly with the alpha variant. Group 3 consisted of 30 COVID-19 and 32 MIS-C patients seen between August and October 2021 infected with alpha and/or delta variants. Group 4 consisted of 20 COVID-19 and 46 MIS-C patients seen between October 2021 andJanuary 2022 infected with delta and/or omicron variants. Group 1 was used to train an L1-regularized logistic regression model which was tested using five-fold cross validation, and then separately validated against the remaining naïve groups. The area under receiver operating curve (AUROC) and F1-score were used to quantify the performance of the cytokine/chemokine assay-based classifier. Standard laboratory markers predict MIS-C with a five-fold cross-validated AUROC of 0.86 ± 0.05 and an F1 score of 0.78 ± 0.07, while the cytokine/chemokine panel predicted MIS-C with a five-fold cross-validated AUROC of 0.95 ± 0.02 and an F1 score of 0.91 ± 0.04, with only sixteen of the forty-five cytokines/chemokines sufficient to achieve this performance. Tested on Group 2 the cytokine/chemokine panel yielded AUROC = 0.98 and F1 = 0.93, on Group 3 it yielded AUROC = 0.89 and F1 = 0.89, and on Group 4 AUROC = 0.99 and F1 = 0.97. Adding standard laboratory markers to the cytokine/chemokine panel did not improve performance. A top-10 subset of these 16 cytokines achieves equivalent performance on the validation data sets. Our findings demonstrate that a sixteen-cytokine/chemokine panel as well as the top ten subset provides a highly sensitive, and specific method to identify MIS-C in patients infected with SARS-CoV-2 of all the major variants identified to date.
虽然儿童感染新冠病毒(COVID-19)很少会发展为重症,但一小部分感染严重急性呼吸综合征冠状病毒2(SARS-CoV-2)的儿童会继而患上多系统炎症综合征(MIS-C),并伴有较高的发病率。因此,非常需要一种具有高特异性和高灵敏度的客观方法,来识别感染SARS-CoV-2的儿童当前或即将出现的MIS-C。本研究旨在了解一种可解释的新型细胞因子/趋化因子检测组合,以提供这样一种客观分类。这项回顾性研究针对在德克萨斯州休斯顿市德克萨斯儿童医院多个地点就诊的四组儿科患者开展,这些患者均同意向我们的COVID-19生物样本库提供血样。对所有患者的血样进行了炎症的标准实验室指标检测以及新型细胞因子/趋化因子检测。第一组包括2020年5月至2021年1月期间就诊的72例COVID-19患者、70例MIS-C患者和63例未感染的对照患者,主要感染的是前α变异株。第二组包括2021年1月至5月期间就诊的29例COVID-19患者和43例MIS-C患者,主要感染的是α变异株。第三组包括2021年8月至10月期间就诊的30例COVID-19患者和32例MIS-C患者,感染的是α和/或δ变异株。第四组包括2021年10月至2022年1月期间就诊的20例COVID-19患者和46例MIS-C患者,感染的是δ和/或奥密克戎变异株。第一组用于训练L1正则化逻辑回归模型,该模型通过五折交叉验证进行测试,然后分别针对其余未参与训练的组进行验证。采用受试者工作特征曲线下面积(AUROC)和F1分数来量化基于细胞因子/趋化因子检测的分类器的性能。标准实验室指标预测MIS-C的五折交叉验证AUROC为0.86±0.05,F1分数为0.78±0.07,而细胞因子/趋化因子检测组合预测MIS-C的五折交叉验证AUROC为0.95±0.02,F1分数为0.91±0.04,45种细胞因子/趋化因子中仅16种就足以实现这一性能。在第二组上进行测试时,细胞因子/趋化因子检测组合的AUROC = 0.98,F1 = 0.93;在第三组上,AUROC = 0.89,F1 = 0.89;在第四组上,AUROC = 0.99,F1 = 0.97。将标准实验室指标添加到细胞因子/趋化因子检测组合中并未提高性能。这16种细胞因子的前10个子集在验证数据集上具有同等性能。我们的研究结果表明,一个包含16种细胞因子/趋化因子的检测组合以及前10个子集,为识别感染了迄今为止发现的所有主要变异株的SARS-CoV-2患者中的MIS-C提供了一种高度灵敏且特异的方法。