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.

Utilizing microbiome approaches to assist source tracking, treatment and prevention of COVID-19: Review and assessment.

COVID-19 has been one of the most serious infectious diseases since the end of 2019. However, the original source, as well as the treatment and prevention of causative agent of COVID-19 (namely SARS-CoV-2) are still unclear nearly a year after its publicly report. The microbiome approach, which has emerged in recent years focusing on human-related microbes, has become one of the promising avenues for source tracking, treatment, and prevention of a variety of infectious diseases including COVID-19. In this review, we summarized the microbiome approach as a supplementary approach for source tracking, treatment, and prevention of SARS-CoV-2 infection. We first provided background information on SARS-CoV-2 and microbiome approaches. Then we illustrated current strategies of microbiome methods to assist three aspects of COVID-19 research, namely source tracking, treatment, and prevention, respectively. Finally, we summarized the microbiome approaches and provided perspectives for future studies on faster and more effective SARS-CoV-2 epidemiology and pathogenesis based on microbiome approaches.