Skip directly to search Skip directly to A to Z list Skip directly to navigation Skip directly to page options Skip directly to site content


Hot Topics of the Day|PHGKB
Search PHGKB:

04/06/2020

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.

Sign up MyPHGKB to receive the daily hot topic email alert.

Search Archive:
Archived Hot Topics of the Day By Date

Coronavirus Disease 2019 in Children — United States, February 12–April 2, 2020
CDC MMWR, April 6, 2020 Brand

Among 345 pediatric cases, 80 (23%) had at least one underlying condition. The most common underlying conditions were chronic lung disease (including asthma) (40), cardiovascular disease (25), and immunosuppression (10). 77% of hospitalized patients, including all six patients admitted to an ICU, had one or more underlying medical condition.

Assessment of Specimen Pooling to Conserve SARS CoV-2 Testing Resources
B Abdalhamid et al, MEDRXIV, April 6, 2020

Parameters affecting the optimal pool size of 5 specimens were: prevalence rate of 5%, a lower limit of detection of 1 to 3 RNA copies per microliter, sensitivity and specificity of 100%, two-stage pooling algorithm, and a range of pool sizes of 3 to 10 samples.

Google Searches Can Help Us Find Emerging Covid-19 Outbreaks
NY Times, April 5, 2020

While there does seem to be important information about Covid-19 prevalence in search data, we have to use great care in building models based on this data and learn from past attempts that tried to use this data to measure the geographic spread of different diseases.

Forecasting the dynamics of COVID-19 Pandemic in Top 15 countries in April 2020: ARIMA Model with Machine Learning Approach
P Kumar e al, MEDRXIV, April 5, 2020

Systematic review and critical appraisal of prediction models for diagnosis and prognosis of COVID-19 infection
L Wynants, MEDRXIV, April 5, 2020

Most reported predictors of severe prognosis in infected patients included age, sex, features derived from CT, C-reactive protein, lactic dehydrogenase, and lymphocyte count. Estimated C-index estimates for the prediction models ranged from 0.73 to 0.81. All studies were rated at high risk of bias, mostly because of non-representative selection of control patients.

The mystery of why the coronavirus kills some young people
S Gupta, CNN, April 6, 2020

So, what could be behind it? Scientists and researchers wonder if the answer could lie in our genes and are beginning to try and understand what differentiates people who get mild cases from those who die. One possibility is a gene variation in the ACE2 gene. ACE2 is an enzyme that attaches to the outer surface of cells in the lungs, as well as the heart.

Quantifying the use of connected digital products in clinical research
C Marra et al, NPJ Digital Medicine, April 2020

Using 18 years of data from ClinicalTrials.gov, we document substantial growth in the use of connected digital products in clinical trials (~34% CAGR) and show that these products have been used across all phases of research and by a diverse group of trial sponsors.

Electronic health records and polygenic risk scores for predicting disease risk
R Li et al, Nat Rev Genetics, March 31, 2020

Increasingly, electronic health records (EHRs) are being linked to patient genetic data in biobanks, which provides new opportunities for developing and applying polygenic risk scores in the clinic, to systematically examine and evaluate patient susceptibilities to disease. However, the heterogeneous nature of EHR data brings forth many practical challenges.

Personalized predictions of patient outcomes during and after hospitalization using artificial intelligence
CB Hilton, et al. NPJ Digital Medicine, April 2020

Hospital systems, payers, and regulators have focused on reducing length of stay (LOS) and early readmission, with uncertain benefit. Interpretable machine learning (ML) may assist in transparently identifying the risk of important outcomes. We conducted a retrospective cohort study of hospitalizations at a tertiary academic medical center.

Moving towards personalized treatments of immune-related adverse events
K Esfahani et al, Nat Rev Clin Oncology, April 3, 2020

We provide an overview of key cellular and soluble immunological factors mediating immunotherapy related adverse effects, and propose a model integrating this knowledge with the immunohistopathological findings of the affected organs for a personalized decision-making process for each patient.


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.
TOP