Gut microbiome alterations in patients with COVID-19-related coagulopathy.

COVID-19 is characterized by a predominantly prothrombotic state, which underlies severe disease and poor outcomes. Imbalances of the gut microbiome have been linked with abnormal hemostatic processes. Understanding the relationship between the gut microbiome and abnormal coagulation parameters in COVID-19 could provide a novel framework for the diagnosis and management of COVID-related coagulopathies (CRC). This cross-sectional study used shotgun metagenomic sequencing to examine the gut microbiota of patients with CRC (n = 66) and compared it to COVID control (CCs) (n = 27) and non-COVID control (NCs) (n = 22) groups. Three, 1, and 3 taxa were found enriched in CRCs, CCs, and NCs. Next, random forest models using 7 microbial biomarkers and differential clinical characteristics were constructed and achieved strong diagnostic potential in distinguishing CRC. Specifically, the most promising biomarker species for CRC were Streptococcus thermophilus, Enterococcus faecium, and Citrobacter portucalensis. Conversely, Enterobacteriaceae family and Fusicatenibacter genus are potentially protective against CRC in COVID patients. We further identified 4 species contributing to 20 MetaCyc pathways that were differentially abundant among groups, with S. thermophilus as the main coding species in CRCs. Our findings suggest that the alterations of gut microbiota compositional and functional profiles may influence the pathogenesis of CRC and that microbiota-based diagnosis and treatment could potentially benefit COVID patients in preventing and alleviating thrombosis-related clinical outcomes.

Reliable and Interpretable Mortality Prediction With Strong Foresight in COVID-19 Patients: An International Study From China and Germany.

Cohort-independent robust mortality prediction model in patients with COVID-19 infection is not yet established. To build up a reliable, interpretable mortality prediction model with strong foresight, we have performed an international, bi-institutional study from China (Wuhan cohort, collected from January to March) and Germany (Würzburg cohort, collected from March to September). A Random Forest-based machine learning approach was applied to 1,352 patients from the Wuhan cohort, generating a mortality prediction model based on their clinical features. The results showed that five clinical features at admission, including lymphocyte (%), neutrophil count, C-reactive protein, lactate dehydrogenase, and α-hydroxybutyrate dehydrogenase, could be used for mortality prediction of COVID-19 patients with more than 91% accuracy and 99% AUC. Additionally, the time-series analysis revealed that the predictive model based on these clinical features is very robust over time when patients are in the hospital, indicating the strong association of these five clinical features with the progression of treatment as well. Moreover, for different preexisting diseases, this model also demonstrated high predictive power. Finally, the mortality prediction model has been applied to the independent Würzburg cohort, resulting in high prediction accuracy (with above 90% accuracy and 85% AUC) as well, indicating the robustness of the model in different cohorts. In summary, this study has established the mortality prediction model that allowed early classification of COVID-19 patients, not only at admission but also along the treatment timeline, not only cohort-independent but also highly interpretable. This model represents a valuable tool for triaging and optimizing the resources in COVID-19 patients.