Background: Coronavirus disease 2019 (COVID-19) has rapidly become a global pandemic. Because the severity of the disease is highly variable, predictive models to stratify patients according to their mortality risk are needed. Objective: Our aim was to develop a model able to predict the risk of fatal outcome in patients with COVID-19 that could be used easily at the time of patients' arrival at the hospital. Methods: We constructed a prospective cohort with 611 adult patients in whom COVID-19 was diagnosed between March 10 and April 12, 2020, in a tertiary hospital in Madrid, Spain. The analysis included 501 patients who had been discharged or had died by April 20, 2020. The capacity of several biomarkers, measured at the beginning of hospitalization, to predict mortality was assessed individually. Those biomarkers that independently contributed to improve mortality prediction were included in a multivariable risk model. Results: High IL-6 level, C-reactive protein level, lactate dehydrogenase (LDH) level, ferritin level, D-dimer level, neutrophil count, and neutrophil-to-lymphocyte ratio were all predictive of mortality (area under the curve >0.70), as were low albumin level, lymphocyte count, monocyte count, and ratio of peripheral blood oxygen saturation to fraction of inspired oxygen (SpO 2 /FiO 2). A multivariable mortality risk model including the SpO 2 /FiO 2 ratio, neutrophil-to-lymphocyte ratio, LDH level, IL-6 level, and age was developed and showed high accuracy for the prediction of fatal outcome (area under the curve 0.94). The optimal cutoff reliably classified patients (including patients with no initial respiratory distress) as survivors and nonsurvivors with 0.88 sensitivity and 0.89 specificity. Conclusion: This mortality risk model allows early risk stratification of hospitalized patients with COVID-19 before the appearance of obvious signs of clinical deterioration, and it can be used as a tool to guide clinical decision making. (J Allergy Clin Immunol 2020;146:799-807.)

Critical illness in COVID-19 is an extreme and clinically homogeneous disease phenotype that we have previously shown1 to be highly efficient for discovery of genetic associations2. Despite the advanced stage of illness at presentation, we have shown that host genetics in patients who are critically ill with COVID-19 can identify immunomodulatory therapies with strong beneficial effects in this group3. Here we analyse 24,202 cases of COVID-19 with critical illness comprising a combination of microarray genotype and whole-genome sequencing data from cases of critical illness in the international GenOMICC (11,440 cases) study, combined with other studies recruiting hospitalized patients with a strong focus on severe and critical disease: ISARIC4C (676 cases) and the SCOURGE consortium (5,934 cases). To put these results in the context of existing work, we conduct a meta-analysis of the new GenOMICC genome-wide association study (GWAS) results with previously published data. We find 49 genome-wide significant associations, of which 16 have not been reported previously. To investigate the therapeutic implications of these findings, we infer the structural consequences of protein-coding variants, and combine our GWAS results with gene expression data using a monocyte transcriptome-wide association study (TWAS) model, as well as gene and protein expression using Mendelian randomization. We identify potentially druggable targets in multiple systems, including inflammatory signalling (JAK1), monocyte–macrophage activation and endothelial permeability (PDE4A), immunometabolism (SLC2A5 and AK5), and host factors required for viral entry and replication (TMPRSS2 and RAB2A).

Summary Intestinal grafts carry large donor lymphoid load that is replaced by recipient cells. The dynamics of this process may influence the tolerance, rejection or graft‐versus‐host disease. We analysed distribution and turnover of T and B (Lin+) lymphocytes, natural killer (NK) and helper innate lymphoid cells (hILC) in intestinal epithelium (IEp) and lamina propia (LP) from a long‐term cohort of eight intestinal recipients and from a single patient monitored deeply during the first 8 months post‐transplant (posTx). Long‐term intestinal grafts showed significantly higher %hILC than native bowels in IEp and LP until 10 years posTx and recovery to normal levels was observed afterwards. We also observed an imbalance between hILC subsets in IEp [increase of type 1 (ILC1) and decrease in type 3 (ILC3) innate lymphoid cells] that persisted along posTx time even when %hILC was similar to native bowels. Regarding hILC origin, we still detected the presence of donor cells at 13 years posTx. However, this chimerism was significantly lower than in Lin+ and NK populations. According to these findings, observation from the patient monitored in early posTx period showed that recipient hILC repopulate earlier and faster than Lin+ cells, with increase in ILC1 related to rejection and infection episodes.

In the version of this article initially published, the name of Ana Margarita Baldión-Elorza, of the SCOURGE Consortium, appeared incorrectly (as Ana María Baldion) and has now been amended in the HTML and PDF versions of the article.

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