Machine learning algorithms utilizing blood parameters enable early detection of immunethrombotic dysregulation in COVID‐19

From a more pragmatic perspective, the early detection of patients who may experience rapid clinical deterioration will enable prompt interventions and avert disease progression.1 T cell exhaustion, immunothrombotic dysregulation, as well as complement-associated microvascular injury are considered as the hallmarks of disease severity in COVID-19.2–5 It is generally accepted that the identification of useful surrogates, for example, IL-6, TNFα, MIP1α, LDH, ferritin, D-dimer, CK, etc., to represent as immune response to COVID-19 infection is crucial.3,4,6 Nevertheless, no individual parameter was so far predictive of immune-thrombotic dysregulation fueled by a maladaptive host inflammatory response in severe infection with SARS-CoV-2.7–9 We, therefore, consider to develop potential solutions for forecasting thrombotic complications prior to clinicopathological exacerbation. Circus plots revealed that the differentially expressed genes (DEGs) were enriched into the key processes, that is, neutrophil activation, platelet activation, blood coagulation, complement receptor-mediated signaling pathway, leukocyte activation, and cytokines production. The key molecules associating with NET formation (NETosis), including PAD4, FCGR2A (FcγRIIa), PLCG2 (PLCγ2), CFP, F8, and F12, were considerably upregulated, facilitating platelets–neutrophils conjugates and highly procoagulant microcirculation disturbances via intrinsic pathways.10 Those transcriptional signatures were also partially evidenced in the proteomics level by Tian et al.11 Intriguingly, neutrophil effector molecules, such as ELANE (neutrophil elastase), MPO (myeloperoxidase), CTSG (Cathepsin G), as well as vascular inflammation mediator PTX3 and neutrophil-derived lactoferrin, were significantly upregulated in severe compared to critical illness (Figure 1D). AVAILABILITY OF DATA AND MATERIALS The transcriptome sequencing data was deposited at the Gene Expression Omnibus under the accession number GSE167930.