Readmissions, post-discharge mortality and sustained recovery among patients admitted to hospital with COVID-19.

BACKGROUND: Many interventional in-patient COVID-19 trials assess primary outcomes through day 28 post-randomization. Since a proportion of patients experience protracted disease or relapse, such follow-up period may not fully capture the course of the disease, even when randomization occurs a few days after hospitalization. METHODS: Among adults hospitalized with COVID-19 in Eastern Denmark from March 18, 2020 - January 12, 2021 we assessed: all-cause mortality, recovery and sustained recovery 90 days after admission, and readmission and all-cause mortality 90 days after discharge. Recovery was defined as hospital discharge and sustained recovery as recovery and alive without readmissions for 14 consecutive days. RESULTS: Among 3,386 patients included in the study 2,796 (82.6%) reached recovery and 2,600 (77.0%) achieved sustained recovery. Of those discharged from hospital, 556 (19.9%) were readmitted, and 289 (10.3%) died. Overall, the median time to recovery was 6 days (Interquartile range (IQR), 3-10), and 19 days (IQR, 11-33) among patients in intensive care in the first two days of admission. CONCLUSIONS: Post-discharge readmission and mortality rates were substantial. Therefore, sustained recovery should be favored to recovery outcomes in clinical COVID-19 trials. A 28-day follow-up period may be too short the critically ill.

Early stimulated immune responses predict clinical disease severity in hospitalized COVID-19 patients.

Background: The immune pathogenesis underlying the diverse clinical course of COVID-19 is poorly understood. Currently, there is an unmet need in daily clinical practice for early biomarkers and improved risk stratification tools to help identify and monitor COVID-19 patients at risk of severe disease. Methods: We performed longitudinal assessment of stimulated immune responses in 30 patients hospitalized with COVID-19. We used the TruCulture whole-blood ligand-stimulation assay applying standardized stimuli to activate distinct immune pathways, allowing quantification of cytokine responses. We further characterized immune cell subsets by flow cytometry and used this deep immunophenotyping data to map the course of clinical disease within and between patients. Results: Here we demonstrate impairments in innate immune response pathways at time of COVID-19 hospitalization that are associated with the development of severe disease. We show that these impairments are transient in those discharged from hospital, as illustrated by functional and cellular immune reconstitution. Specifically, we identify lower levels of LPS-stimulated IL-1ß, and R848-stimulated IL-12 and IL-17A, at hospital admission to be significantly associated with increasing COVID-19 disease severity during hospitalization. Furthermore, we propose a stimulated immune response signature for predicting risk of developing severe or critical COVID-19 disease at time of hospitalization, to validate in larger cohorts. Conclusions: We identify early impairments in innate immune responses that are associated with subsequent COVID-19 disease severity. Our findings provide basis for early identification of patients at risk of severe disease which may have significant implications for the early management of patients hospitalized with COVID-19.

Personalized survival probabilities for SARS-CoV-2 positive patients by explainable machine learning.

Interpretable risk assessment of SARS-CoV-2 positive patients can aid clinicians to implement precision medicine. Here we trained a machine learning model to predict mortality within 12 weeks of a first positive SARS-CoV-2 test. By leveraging data on 33,938 confirmed SARS-CoV-2 cases in eastern Denmark, we considered 2723 variables extracted from electronic health records (EHR) including demographics, diagnoses, medications, laboratory test results and vital parameters. A discrete-time framework for survival modelling enabled us to predict personalized survival curves and explain individual risk factors. Performance on the test set was measured with a weighted concordance index of 0.95 and an area under the curve for precision-recall of 0.71. Age, sex, number of medications, previous hospitalizations and lymphocyte counts were identified as top mortality risk factors. Our explainable survival model developed on EHR data also revealed temporal dynamics of the 22 selected risk factors. Upon further validation, this model may allow direct reporting of personalized survival probabilities in routine care.