入院时 CXCL10 水平可预测 COVID-19 结局：在一项观察性研究中对 53 种潜在炎症生物标志物进行分层评估。

CXCL10 levels at hospital admission predict COVID-19 outcome: hierarchical assessment of 53 putative inflammatory biomarkers in an observational study.

机构信息

Division of Immunology, Transplantation and Infectious Diseases, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milano, Italy.

Emerging Bacterial Pathogens Unit, IRCCS San Raffaele Scientific Institute, Milano, Italy.

出版信息

Mol Med. 2021 Oct 18;27(1):129. doi: 10.1186/s10020-021-00390-4.

DOI:10.1186/s10020-021-00390-4

PMID:34663207

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8521494/

Abstract

BACKGROUND

Host inflammation contributes to determine whether SARS-CoV-2 infection causes mild or life-threatening disease. Tools are needed for early risk assessment.

METHODS

We studied in 111 COVID-19 patients prospectively followed at a single reference Hospital fifty-three potential biomarkers including alarmins, cytokines, adipocytokines and growth factors, humoral innate immune and neuroendocrine molecules and regulators of iron metabolism. Biomarkers at hospital admission together with age, degree of hypoxia, neutrophil to lymphocyte ratio (NLR), lactate dehydrogenase (LDH), C-reactive protein (CRP) and creatinine were analysed within a data-driven approach to classify patients with respect to survival and ICU outcomes. Classification and regression tree (CART) models were used to identify prognostic biomarkers.

RESULTS

Among the fifty-three potential biomarkers, the classification tree analysis selected CXCL10 at hospital admission, in combination with NLR and time from onset, as the best predictor of ICU transfer (AUC [95% CI] = 0.8374 [0.6233-0.8435]), while it was selected alone to predict death (AUC [95% CI] = 0.7334 [0.7547-0.9201]). CXCL10 concentration abated in COVID-19 survivors after healing and discharge from the hospital.

CONCLUSIONS

CXCL10 results from a data-driven analysis, that accounts for presence of confounding factors, as the most robust predictive biomarker of patient outcome in COVID-19.

摘要

背景

宿主炎症有助于确定 SARS-CoV-2 感染是导致轻症还是危及生命的疾病。需要有工具来进行早期风险评估。

方法

我们前瞻性地研究了 111 名在一家参考医院接受治疗的 COVID-19 患者，共研究了 53 种潜在的生物标志物，包括警报素、细胞因子、脂肪细胞因子和生长因子、体液先天免疫和神经内分泌分子以及铁代谢调节剂。在数据驱动的方法中，对入院时的生物标志物以及年龄、缺氧程度、中性粒细胞与淋巴细胞比值（NLR）、乳酸脱氢酶（LDH）、C 反应蛋白（CRP）和肌酐进行分析，以便根据生存和 ICU 结果对患者进行分类。使用分类和回归树（CART）模型来识别预后生物标志物。

结果

在 53 种潜在生物标志物中，分类树分析选择入院时的 CXCL10，与 NLR 和发病时间相结合，作为 ICU 转移的最佳预测因子（AUC [95%CI] = 0.8374 [0.6233-0.8435]），而单独选择 CXCL10 来预测死亡（AUC [95%CI] = 0.7334 [0.7547-0.9201]）。COVID-19 幸存者在治愈并出院后，其 CXCL10 浓度下降。