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Hot Topics of the Day|PHGKB
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04/24/2020

Hot Topics of the Day are picked by experts to capture the latest information and publications on public health genomics and precision health for various diseases and health topics. Sources include published scientific literature, reviews, blogs and popular press articles.

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Doctors are using AI to triage covid-19 patients. The tools may be here to stay: Faced with staff shortages and overwhelming patient loads, a growing number of hospitals are turning to automated tools to help them manage the pandemic.
K Hao, Tech Review, April 23, 2020

Hospitals are using AI to predict the decline of Covid-19 patients — before knowing it works
C Ross, StatNews, April 24, 2020

Dozens of hospitals are using an artificial intelligence system to predict which COVID-19 patients will become critically ill, even as many are struggling to validate the tool’s effectiveness on those with the new disease.

Can cell therapies halt cytokine storm in severe COVID-19 patients?
J Ankrum, Sci Trans Med, April 22, 2020

Renin-Angiotensin-Aldosterone System Inhibitors in Patients with Covid-19.
Vaduganathan Muthiah et al. The New England journal of medicine 2020 04 (17) 1653-1659

Nearly All Patients Hospitalized With Covid-19 Had Chronic Health Issues, Study Finds- Only 6 percent of patients at one New York area health system had no chronic conditions. Hypertension, obesity and diabetes were common.
RC Rabin, NY Times, April 23, 2020

Why Are Some Young, Healthy People Getting Severe COVID-19?
K Rogers, FiveThirtyEight, April 23, 2020

Some are looking into genetic differences. Our genes can affect the way our immune system responds to an infection, which could explain why some people fare worse than others. Another factor being investigated is the infectious dose, or the amount of virus a patient was exposed to when they first became ill.

Detection of Nucleocapsid Antibody to SARS-CoV-2 is More Sensitive than Antibody to Spike Protein in COVID-19 Patients
PD Burbelo et al, MEDRXIV, APril 24, 2020

A cell phone data driven time use analysis of the COVID-19 epidemic
EP Fenichel et al, MEDRXIV, April 243 2020

We simulate epidemics in almost every county in the United States. The model suggests that Americans' behavioral shifts have reduced cases in 55%-86% of counties and for 71%-91% of the population, depending on modeling assumptions. Resuming pre-epidemic behavior would lead to a rapid rise in cases in most counties.

Higher mortality in men from COVID19 infection-understanding the factors that drive the differences between the biological sexes.
SP Lyer et al, MEDRXIV, April 24, 2020

Association of Nongenetic Factors With Breast Cancer Risk in Genetically Predisposed Groups of Women in the UK Biobank Cohort
K Alajmi et al, JAMA Network Open, April 24, 2020

This cohort study evaluated 2728 women with breast cancer and 88?489 controls and noted lower risks of breast cancer among women who practice a healthy lifestyle (exercise, healthy weight, low alcohol intake, and no oral contraceptive use, as well as avoiding or limiting use of hormonal replacement therapy) among low, intermediate, and high genetic risk groups.

Genetic evidence for protective effects of smoking and drinking behavior on Parkinson's disease: A Mendelian Randomization study
CD Baleon et al, MEDRXIV, April 24, 2020

We use a two-sample Mendelian randomization (MR) experimental design to infer causal relationships between smoking (initiation, age of initiation, heaviness, and cessation) and alcohol (drinks per week) consumption as exposure variables and PD as the health outcome.

The evidence landscape in precision medicine.
Hey Spencer Phillips et al. Science translational medicine 2020 Apr (540)

Precision medicine is beginning to make an impact on the treatment of different diseases, but there are still challenges that must be overcome, such as the complexity of interventions, the need for marker validation, and the level of evidence necessary to demonstrate effectiveness. We describe how evidence landscapes can help to address these challenges.

Prediction Models - Development, Evaluation, and Clinical Application.
Pencina Michael J et al. The New England journal of medicine 2020 Apr (17) 1583-1586

Prediction models’ newfound importance and the emergence of model development based on machine learning raise questions about how to ensure their safety and efficacy, given their growing role in risk stratification, care pathways, and clinical outcomes.


Disclaimer: Articles listed in Hot Topics of the Day are selected by Public Health Genomics Branch to provide current awareness of the scientific literature and news. Inclusion in the update does not necessarily represent the views of the Centers for Disease Control and Prevention nor does it imply endorsement of the article's methods or findings. CDC and DHHS assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by CDC or DHHS. Opinion, findings and conclusions expressed by the original authors of items included in the Clips, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of CDC or DHHS. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by CDC or DHHS.
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