用于预测 COVID-19 患者 ICU 死亡率的血液 microRNA 分类器：一项多中心验证研究。
A blood microRNA classifier for the prediction of ICU mortality in COVID-19 patients: a multicenter validation study.
机构信息
Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain.
CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain.
出版信息
Respir Res. 2023 Jun 17;24(1):159. doi: 10.1186/s12931-023-02462-x.
BACKGROUND
The identification of critically ill COVID-19 patients at risk of fatal outcomes remains a challenge. Here, we first validated candidate microRNAs (miRNAs) as biomarkers for clinical decision-making in critically ill patients. Second, we constructed a blood miRNA classifier for the early prediction of adverse outcomes in the ICU.
METHODS
This was a multicenter, observational and retrospective/prospective study including 503 critically ill patients admitted to the ICU from 19 hospitals. qPCR assays were performed in plasma samples collected within the first 48 h upon admission. A 16-miRNA panel was designed based on recently published data from our group.
RESULTS
Nine miRNAs were validated as biomarkers of all-cause in-ICU mortality in the independent cohort of critically ill patients (FDR < 0.05). Cox regression analysis revealed that low expression levels of eight miRNAs were associated with a higher risk of death (HR from 1.56 to 2.61). LASSO regression for variable selection was used to construct a miRNA classifier. A 4-blood miRNA signature composed of miR-16-5p, miR-192-5p, miR-323a-3p and miR-451a predicts the risk of all-cause in-ICU mortality (HR 2.5). Kaplan‒Meier analysis confirmed these findings. The miRNA signature provides a significant increase in the prognostic capacity of conventional scores, APACHE-II (C-index 0.71, DeLong test p-value 0.055) and SOFA (C-index 0.67, DeLong test p-value 0.001), and a risk model based on clinical predictors (C-index 0.74, DeLong test-p-value 0.035). For 28-day and 90-day mortality, the classifier also improved the prognostic value of APACHE-II, SOFA and the clinical model. The association between the classifier and mortality persisted even after multivariable adjustment. The functional analysis reported biological pathways involved in SARS-CoV infection and inflammatory, fibrotic and transcriptional pathways.
CONCLUSIONS
A blood miRNA classifier improves the early prediction of fatal outcomes in critically ill COVID-19 patients.
背景
识别有致命风险的 COVID-19 危重症患者仍然具有挑战性。在这里，我们首先验证了候选 microRNA（miRNA）作为危重症患者临床决策的生物标志物。其次，我们构建了一个血液 miRNA 分类器，用于 ICU 中不良预后的早期预测。
方法
这是一项多中心、观察性和回顾性/前瞻性研究，纳入了来自 19 家医院的 503 名 ICU 收治的危重症患者。在入院后 48 小时内采集的血浆样本中进行 qPCR 检测。根据我们小组最近发表的数据设计了一个包含 16 个 miRNA 的面板。
结果
在独立的危重症患者队列中，有 9 个 miRNA 被验证为全因 ICU 死亡率的生物标志物（FDR<0.05）。Cox 回归分析显示，8 个 miRNA 的低表达与死亡风险较高相关（HR 为 1.56 至 2.61）。用于变量选择的 LASSO 回归用于构建 miRNA 分类器。由 miR-16-5p、miR-192-5p、miR-323a-3p 和 miR-451a 组成的 4 个血液 miRNA 特征谱预测全因 ICU 死亡率的风险（HR 为 2.5）。Kaplan-Meier 分析证实了这些发现。miRNA 特征谱显著提高了传统评分、APACHE-II（C 指数 0.71，DeLong 检验 p 值 0.055）和 SOFA（C 指数 0.67，DeLong 检验 p 值 0.001）的预后能力，以及基于临床预测因子的风险模型（C 指数 0.74，DeLong 检验 p 值 0.035）。对于 28 天和 90 天死亡率，分类器也提高了 APACHE-II、SOFA 和临床模型的预后价值。即使在多变量调整后，分类器与死亡率之间的关联仍然存在。功能分析报告了与 SARS-CoV 感染以及炎症、纤维化和转录途径相关的生物学途径。
结论
血液 miRNA 分类器可改善 COVID-19 危重症患者致命结局的早期预测。
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