Cardiovascular Disease Management in the Context of Global Crisis.

The outbreak of coronavirus disease 2019 (COVID-19) initiated a pandemic that has deteriorated health care access and thus disadvantaged vulnerable populations [...].

A Sneak-Peek into the Physician's Brain: A Retrospective Machine Learning-Driven Investigation of Decision-Making in TAVR versus SAVR for Young High-Risk Patients with Severe Symptomatic Aortic Stenosis.

Transcatheter aortic valve replacement (TAVR) has rapidly become a viable alternative to the conventional isolated surgical aortic valve replacement (iSAVR) for treating severe symptomatic aortic stenosis. However, data on younger patients is scarce and a gap exists between data-based recommendations and the clinical use of TAVR. In our study, we utilized a machine learning (ML) driven approach to model the complex decision-making process of Heart Teams when treating young patients with severe symptomatic aortic stenosis with either TAVR or iSAVR and to identify the relevant considerations. Out of the considered factors, the variables most prominently featured in our ML model were congestive heart failure, established risk assessment scores, previous cardiac surgeries, a reduced left ventricular ejection fraction and peripheral vascular disease. Our study demonstrates a viable application of ML-based approaches for studying and understanding complex clinical decision-making processes.

Interim estimates in null models of COVID-19 vaccine effectiveness.

Recently released interim numbers from advanced vaccine candidate clinical trials suggest that a COVID-19 vaccine effectiveness (VE) of >90% is achievable. However, SARS-CoV-2 transmission dynamics are highly heterogeneous and exhibit localized bursts of transmission, which may lead to sharp localized peaks in the number of new cases, often followed by longer periods of low incidence. Here we show that, for interim estimates of VE, these characteristic bursts in SARS-CoV-2 infection may introduce a strong positive bias in VE. Specifically, we generate null models of vaccine effectiveness, i.e., random models with bursts that over longer periods converge to zero VE but that for interim periods frequently produce apparent VE near 100%. As an example, by following the relevant clinical trial protocol, we can reproduce recently reported interim outcomes from an ongoing phase 3 clinical trial of an RNA-based vaccine candidate. Thus, to avoid potential random biases in VE, it is suggested that interim estimates on COVID-19 VE should control for the intrinsic inhomogeneity in both SARS-CoV-2 infection dynamics and reported cases.

Interim Estimates in Null Models of COVID-19 Vaccine Effectiveness (preprint)

Recently released interim numbers from several ongoing vaccine candidate clinical trials suggest that a COVID-19 vaccine effectiveness (VE) above 90% is achievable. However, SARS-CoV-2 transmission dynamics is highly heterogeneous and exhibits localized bursts of transmission, which may lead to sharp localized peaks in the number of new cases, often followed by longer periods of zero incidence. Here we show that, for interim estimates of VE, this characteristic burstiness in SARS-CoV-2 infection dynamics may introduce a strong positive bias in VE. Specifically, one can generate null models of vaccine effectiveness, i.e., random models with burstiness that over longer times converge to exactly zero VE, but that for interim times frequently produce apparent VE near 100%. For example, by following the relevant clinical trial protocol, one can reproduce recently reported interim outcomes (VE > 90%, for 94 COVID-19 cases) from an ongoing phase 3 clinical trial of a RNA based vaccine candidate. Thus, to avoid potential random biases in VE, it is suggested that interim estimates on COVID-19 vaccine effectiveness should control for the intrinsic inhomogeneity in both SARS-CoV-2 infection dynamics and in reported cases.

Actual and Perceived Knowledge about COVID-19: The Role of Information Behavior in Media (preprint)

The COVID-19 pandemic poses a health threat that has dominated media coverage. However, not much is known about individual media use to acquire knowledge about COVID-19. To address this, this study investigated how perceived threat is linked to media use, and how media use is associated with perceived and actual knowledge about COVID-19. In a German online survey, N=952 participants provided information on their perceived threat and media use to inform themselves about COVID-19. They indicated how well they are informed about COVID-19 (perceived knowledge) and completed a COVID-19 knowledge test (actual knowledge). Results indicated that individuals who felt more threatened by COVID-19 used media more often to inform themselves but focused on fewer media channels. Frequent media use was associated with higher perceived knowledge, but not with higher actual knowledge about COVID-19 (i.e., illusion of knowledge). Finally, using fewer media channels was linked to higher perceived and actual knowledge.

Association between Influenza Vaccination Rates and SARS-CoV-2 Outbreak Infection Rates in OECD Countries (preprint)

From December 2019 to early March 2020, the local outbreak of novel corona virus disease (COVID-19) in central China’s Hubei region has grown into a worldwide pandemic. This rapid and catastrophic escalation makes the search for and understanding of the underlying mechanisms of infection and disease, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), as well as of their associated risk factors an urgent priority. In particular, strong variations in COVID-19 infection rates as seen internationally require a better understanding. Here we show that reported influenza vaccination coverage rates for 29 OECD countries are associated significantly with recently observed SARS-CoV-2 infection rates in these countries. This early observation, which merits further investigation, suggests that during the current coronavirus outbreak an influenza vaccination background might be a relevant factor for SARS-CoV-2 infection. The observed phenomenon is discussed in the context of vaccine associated virus interference and antibody-dependent enhancement of viral infectivity.