Readmissions, Postdischarge Mortality, and Sustained Recovery Among Patients Admitted to Hospital With Coronavirus Disease 2019 (COVID-19).

BACKGROUND: Many interventional in-patient coronavirus disease 2019 (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 18 March 2020-12 January 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 3386 patients included in the study, 2796 (82.6%) reached recovery and 2600 (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 2 days of admission. CONCLUSIONS: Postdischarge 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 for the critically ill.

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.

Chest x-ray imaging score is associated with severity of COVID-19 pneumonia: the MBrixia score.

Spatial resolution in existing chest x-ray (CXR)-based scoring systems for coronavirus disease 2019 (COVID-19) pneumonia is low, and should be increased for better representation of anatomy, and severity of lung involvement. An existing CXR-based system, the Brixia score, was modified to increase the spatial resolution, creating the MBrixia score. The MBrixia score is the sum, of a rule-based quantification of CXR severity on a scale of 0 to 3 in 12 anatomical zones in the lungs. The MBrixia score was applied to CXR images from COVID-19 patients at a single tertiary hospital in the period May 4th-June 5th, 2020. The relationship between MBrixia score, and level of respiratory support at the time of performed CXR imaging was investigated. 37 hospitalized COVID-19 patients with 290 CXRs were identified, 22 (59.5%) were admitted to the intensive care unit and 10 (27%) died during follow-up. In a Poisson regression using all 290 MBrixia scored CXRs, a higher MBrixia score was associated with a higher level of respiratory support at the time of performed CXR. The MBrixia score could potentially be valuable as a quantitative surrogate measurement of COVID-19 pneumonia severity, and future studies should investigate the score's validity and capabilities of predicting clinical outcomes.

Isolation and In Vitro Characterization of Epidermal Stem Cells.

Colony-forming assays represent prospective methods, where cells isolated from enzymatically dissociated tissues or from tissue cultures are assessed for their proliferative capacity in vitro. Complex tissues such as the epithelial component of the skin (the epidermis) are characterized by a substantial cellular heterogeneity. Analysis of bulk populations of cells by colony-forming assays can consequently be convoluted by a number of factors that are not controlled for in population wide studies. It is therefore advantageous to refine in vitro growth assays by sub-fractionation of cells using flow cytometry. Using markers that define the spatial origin of epidermal cells, it is possible to interrogate the specific characteristics of subpopulations of cells based on their in vivo credentials. Here, we describe how to isolate, culture, and characterize keratinocytes from murine back and tail skin sorted by surface antigens associated with adult stem cell characteristics.