Death Predictors in Patients with COVID-19 on Venovenous ECMO

Background: Patients with coronavirus disease 2019 (COVID-19) and severe acute respiratory distress syndrome (ARDS) need in 10.5 to 15% veno-venous ECMO (V-V ECMO) therapy. The worldwide mortality in COVID-19 patients on ECMO has been described as extremely high with a mortality rate of 40 to 70%. Method(s): We collected data from 56 patients with severe ARDS who received V-V ECMO in 2020 to January 2022 at the University Hospital Magdeburg due to COVID-19 infection. We recorded demographic, pre-, intra-, and posttreatment data retrospectively. We divided the patients into two groups (survivors and nonsurvivors) to build the final prediction model based on our statistic and to detect relevant mortality risk factors. Result(s): Only 39.3% of patients survived the intensive care unit. Compared groups didn't differ in associated diseases. Most of the non-survivors were male (14 [63.6%] vs. 28 [82.4%], p = 0.114). Nonsurvivors showed a higher incidence of bleeding complications (10 [45.5%] vs. 23 [67.6%], p = 0,099), especially hemothorax (1 [4.5%] vs. 7 [20.6%], p = 0.094) and endobronchial bleeding (0 vs. 5 [14.7%], p = 0.059) as well as a higher incidence of bacterial superinfection (9 [40.1%] vs. 22 [64.7%], p = 0.080). Moreover, groups differed concerning the incidence of acute kidney injury without dialysis (1 [4.5% vs. 9 [26.5%], p = 0.036), and acute liver failure (1 [4.5%] vs. 7 [20.6%], p = 0.094). According to the results of bivariate regression analysis, male sex (odd ratio [OR]: 2.66;95% confidence interval [CI]: 0.773-9.194;p = 0.120), major bleeding events (OR: 2.50;95% CI: 0.831-7.574;p = 0.103), bacterial superinfection (OR: 2.65;95% CI: 0.879-7.981;p = 0.084), acute kidney injury without dialysis (OR: 7.56;95% CI: 0.884-64.636;p = 0.065), and acute liver failure (OR: 5.44;95% CI: 0.621-47.756, p = 0.126) were tendentious significant predictors of death. Subsequently, according to the results of multivariate analysis, the most significant factors of mortality were major bleeding events (OR: 3.27;95% CI: 0.888-12.047, p = 0.075) and the bacterial superinfection (OR: 2.81;95% CI: 0.800-9.888, p = 0.107). The mortality prediction model explained 31.8% (Nagelkerke R2) of the variance in-hospital mortality and correctly classified 71.4% of the cases. Conclusion(s): Major bleeding events and bacterial superinfection might be relevant mortality factors in COVID-19 patients on V-V ECMO therapy. Especially prevention of superinfection and strictly anticoagulation management might result in lower mortality rates.

Public health and social measures during health emergencies such as the COVID-19 pandemic: an initial logic model to conceptualise and classify measures : Eva Rehfuess

Issue/problem In the context of the COVID-19 pandemic, public health and social measures (PHSM) are being implemented worldwide, but in very different ways. This is also due to a lacking common understanding of PHSM so far. As a result, there are limited insights regarding their components, implementation and effectiveness as well as impacts beyond health of PHSM. Description of the problem The project contributes to the WHO PHSM initiative. A logic model is being developed that offers a shared language and understanding of how PHSM are intended to achieve transmission-related outcomes, but also that non-intended consequences need to be considered. The overall approach is informed by a system-based logic model template and a staged approach to logic modeling. The development process is making use of (i) existing COVID PHSM taxonomies and related literature, (ii) existing theoretical frameworks related to complex interventions in complex systems, (iii) consultations with WHO staff and the according PHSM steering group, and (v) iterative brainstorming within the working group. Results The initial logic model is rooted in a complexity perspective which recognises that (i) all measures have both intended and unintended consequences for health and society and (ii) all elements within the logic model are interconnected and interact with each other. All PHSM operate through two basic mechanisms: reducing contacts and making contacts safer. Taken together, these two mechanisms work to reduce transmission-relevant contacts. Any specific PHSM is defined by a combination of the measure itself, the population and/or setting targeted and the mode of enactment. The central hub of the logic model connects and integrates all elements. The initial logic model was applied to school and travel measures as examples. Main messages The PHSM logic model is a conceptual basis to facilitate research on PHSM, monitoring and evaluation of PHSM, and evidence-informed decision-making.