Background Microvascular, arterial and venous thrombotic events have been largely described during severe coronavirus disease 19 (COVID-19). However, mechanisms underlying hemostasis dysregulation remain unclear. Methods We explored two independent cross-sectional cohorts to identify soluble markers and gene-expression signatures that discriminated COVID-19 severity and outcomes. Results We found that elevated soluble (s)P-selectin at admission was associated with disease severity. Elevated sP-selectin was predictive of intubation and death (ROC AUC = 0.67, p = 0.028 and AUC = 0.74, p = 0.0047, respectively). An optimal cutoff value was predictive of intubation with 66% negative predictive value (NPV) and 61% positive predictive value (PPV), and of death with 90% NPV and 55% PPV. An unbiased gene set enrichment analysis revealed that critically ill patients had increased expression of genes related to platelet activation. Hierarchical clustering identified ITG2AB , GP1BB , PPBP and SELPLG to be upregulated in a grade-dependent manner. ROC curve analysis for the prediction of intubation was significant for SELPLG and PPBP (AUC = 0.8, p = 0.046 for both). An optimal cutoff value for PBPP was predictive of intubation with 100% NPV and 45% PPV, and for SELPLG with 100% NPV and 50% PPV. Conclusion We provide evidence that platelets contribute to COVID-19 severity. Plasma sP-selectin level was associated with severity and in-hospital mortality. Transcriptional analysis identified PPBP/CXCL7 and SELPLG as biomarkers for intubation. These findings provide additional evidence for platelet activation in driving critical COVID-19. Specific studies evaluating the performance of these biomarkers are required. Supplementary Information The online version contains supplementary material available at 10.1186/s13613-021-00899-1.
【저자키워드】 COVID-19, Platelets, Primary hemostasis, Thrombo-inflammation, 【초록키워드】 platelet activation, Biomarker, cross-sectional, severity, disease severity, COVID-19 severity, intubation, Hemostasis, outcomes, Cohort, Positive predictive value, ROC, death, Platelet, enrichment analysis, Admission, Critical, mechanism, in-hospital mortality, marker, Evidence, Analysis, Negative predictive value, ROC Curve, hierarchical clustering, dysregulation, Predictive, Critically ill patient, supplementary material, increased expression, severe coronavirus disease, cutoff value, Specific, gene-expression, driving, venous, independent, thrombotic event, Result, described, identify, required, elevated, contribute, upregulated, GP1BB, NPV, PPBP, PPV, SELPLG, 【제목키워드】 Critically ill, COVID-19 patient, Activation,