A blood microRNA classifier for the prediction of ICU mortality in COVID-19 patients: a multicenter validation study

Abstract

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.

Enhancing sepsis biomarker development: key considerations from public and private perspectives

“…As genes underlying host susceptibility for sepsis are identified in broad genome studies [ 5 , 6 ], the functionality of the genes (transcriptomic) and their protein expression (proteomic) is now being interrogated [ 7 – 11 ]. Notably, gene expression candidate biomarkers were shown to outperform traditional, advocated protein biomarkers, particularly distinguishing between differing infectious aetiologies [ 12 , 13 ], and COVID-19 [ 14 ]. A more recent investigation has also highlighted the potential diagnostic value of integrating host transcriptomics and plasma metagenomics for sepsis diagnosis [ 15 ].…”

A Risk Model for 28-Day in-Hospital Mortality in 173 COVID-19 Patients Admission to ICU: A Retrospective Study

“…Finally, they identified age and derived neutrophils/(leukocytes minus neutrophils) ratio (dNLR) as valuable predictors of mortality in ICU hospitalized COVID-19 patients. David, et al 20 used samples from a large multicenter cohort of COVID-19 critically ill patients to construct a blood miRNA classifier that could be used to predict all-cause mortality in the ICU. The clinical outcome predictors for the mortality of ICU COVID-19 patients were still unclear.…”