Intestinal inflammation modulates the expression of ACE2 and TMPRSS2 and potentially overlaps with the pathogenesis of SARS-CoV-2 related disease (preprint)

Immune dysregulation and cytokine release syndrome have emerged as pathological hallmarks of severe Coronavirus Disease 2019 (COVID-19), leading to the evaluation of cytokine antagonists as therapeutic agents. A number of immune-directed therapies being considered for COVID-19 patients are already in clinical use in chronic inflammatory conditions like inflammatory bowel disease (IBD). These considerations led us to systematically examine the intersections between COVID-19 and the GI tract during health and intestinal inflammation. We have observed that IBD medications, both biologic and non-biologic, do not significantly impact ACE2 and TMPRSS2 expression in the uninflamed intestines. Additionally, by comparing SARS CoV2-induced epithelial gene signatures with IBD-associated genes, we have identified a shared molecular subnetwork between COVID-19 and IBD. These data generate a novel appreciation of the confluence of COVID-19- and IBD-associated inflammation and provide mechanistic insights supporting further investigation of specific IBD drugs in the treatment of COVID-19.

Acute Kidney Injury in Hospitalized Patients with COVID-19 (preprint)

Importance: Preliminary reports indicate that acute kidney injury (AKI) is common in coronavirus disease (COVID)-19 patients and is associated with worse outcomes. AKI in hospitalized COVID-19 patients in the United States is not well-described. Objective: To provide information about frequency, outcomes and recovery associated with AKI and dialysis in hospitalized COVID-19 patients. Design: Observational, retrospective study. Setting: Admitted to hospital between February 27 and April 15, 2020. Participants: Patients aged [≥]18 years with laboratory confirmed COVID-19 Exposures: AKI (peak serum creatinine increase of 0.3 mg/dL or 50% above baseline). Main Outcomes and Measures: Frequency of AKI and dialysis requirement, AKI recovery, and adjusted odds ratios (aOR) with mortality. We also trained and tested a machine learning model for predicting dialysis requirement with independent validation. Results: A total of 3,235 hospitalized patients were diagnosed with COVID-19. AKI occurred in 1406 (46%) patients overall and 280 (20%) with AKI required renal replacement therapy. The incidence of AKI (admission plus new cases) in patients admitted to the intensive care unit was 68% (553 of 815). In the entire cohort, the proportion with stages 1, 2, and 3 AKI were 35%, 20%, 45%, respectively. In those needing intensive care, the respective proportions were 20%, 17%, 63%, and 34% received acute renal replacement therapy. Independent predictors of severe AKI were chronic kidney disease, systolic blood pressure, and potassium at baseline. In-hospital mortality in patients with AKI was 41% overall and 52% in intensive care. The aOR for mortality associated with AKI was 9.6 (95% CI 7.4-12.3) overall and 20.9 (95% CI 11.7-37.3) in patients receiving intensive care. 56% of patients with AKI who were discharged alive recovered kidney function back to baseline. The area under the curve (AUC) for the machine learned predictive model using baseline features for dialysis requirement was 0.79 in a validation test. Conclusions and Relevance: AKI is common in patients hospitalized with COVID-19, associated with worse mortality, and the majority of patients that survive do not recover kidney function. A machine-learned model using admission features had good performance for dialysis prediction and could be used for resource allocation.