Circulating adipokine levels and COVID-19 severity in hospitalized patients.

BACKGROUND: Obesity is a risk factor for adverse outcomes in COVID-19, potentially driven by chronic inflammatory state due to dysregulated secretion of adipokines and cytokines. We investigated the association between plasma adipokines and COVID-19 severity, systemic inflammation, clinical parameters, and outcome of COVID-19 patients. METHODS: In this multi-centre prospective cross-sectional study, we collected blood samples and clinical data from COVID-19 patients. The severity of COVID-19 was classified as mild (no hospital admission), severe (ward admission), and critical (ICU admission). ICU non-COVID-19 patients were also included and plasma from healthy age, sex, and BMI-matched individuals obtained from Lifelines. Multi-analyte profiling of plasma adipokines (Leptin, Adiponectin, Resistin, Visfatin) and inflammatory markers (IL-6, TNFα, IL-10) were determined using Luminex multiplex assays. RESULTS: Between March and December 2020, 260 SARS-CoV-2 infected individuals (age: 65 [56-74] BMI 27.0 [24.4-30.6]) were included: 30 mild, 159 severe, and 71 critical patients. Circulating leptin levels were reduced in critically ill patients with a high BMI yet this decrease was absent in patients that were administered dexamethasone. Visfatin levels were higher in critical COVID-19 patients compared to non-COVID-ICU, mild and severe patients (4.7 vs 3.4, 3.0, and 3.72 ng/mL respectively, p < 0.05). Lower Adiponectin levels, but higher Resistin levels were found in severe and critical patients, compared to those that did not require hospitalization (3.65, 2.7 vs 7.9 µg/mL, p < 0.001, and 18.2, 22.0 vs 11.0 ng/mL p < 0.001). CONCLUSION: Circulating adipokine levels are associated with COVID-19 hospitalization, i.e., the need for oxygen support (general ward), or the need for mechanical ventilation and other organ support in the ICU, but not mortality.

Prospective Multicenter Observational Cohort Study on Time to Death in Potential Controlled Donation After Circulatory Death Donors-Development and External Validation of Prediction Models: The DCD III Study.

BACKGROUND: Acceptance of organs from controlled donation after circulatory death (cDCD) donors depends on the time to circulatory death. Here we aimed to develop and externally validate prediction models for circulatory death within 1 or 2 h after withdrawal of life-sustaining treatment. METHODS: In a multicenter, observational, prospective cohort study, we enrolled 409 potential cDCD donors. For model development, we applied the least absolute shrinkage and selection operator (LASSO) regression and machine learning-artificial intelligence analyses. Our LASSO models were validated using a previously published cDCD cohort. Additionally, we validated 3 existing prediction models using our data set. RESULTS: For death within 1 and 2 h, the area under the curves (AUCs) of the LASSO models were 0.77 and 0.79, respectively, whereas for the artificial intelligence models, these were 0.79 and 0.81, respectively. We were able to identify 4% to 16% of the patients who would not die within these time frames with 100% accuracy. External validation showed that the discrimination of our models was good (AUCs 0.80 and 0.82, respectively), but they were not able to identify a subgroup with certain death after 1 to 2 h. Using our cohort to validate 3 previously published models showed AUCs ranging between 0.63 and 0.74. Calibration demonstrated that the models over- and underestimated the predicted probability of death. CONCLUSIONS: Our models showed a reasonable ability to predict circulatory death. External validation of our and 3 existing models illustrated that their predictive ability remained relatively stable. We accurately predicted a subset of patients who died after 1 to 2 h, preventing starting unnecessary donation preparations, which, however, need external validation in a prospective cohort.

Developing the First Generally-Available openEHR Archetypes and Templates for Physiotherapy: An Example of Building Clinical Models and Modelling Capacity via Student-Led Academic-Industrial Collaboration.

We present the first public openEHR archetypes and templates for physiotherapy, and the context of multidisciplinary academic-industry partnership that has enabled their production by a team led by a clinically trained student on the UCL health informatics MSc programme.