Wissenschaft mit Auszeichnung: Herausragende Nachwuchsforscher auf der Jahrestagung der Deutschen…
Wund(er)heilung mit Amnion – DGFG erhält deutschen Wundpreis 2021
Ausschreibung DGNI-Pflege- und Therapiepreis 2022
Ausschreibung: Otsuka Team Award Psychiatry+ 2021
BGW-Gesundheitspreis 2022: Gute Praxis aus der Altenpflege gesucht!
5. Nürnberger Wundkongress vom 01. bis 02. Dezember 2022: „Wer…
2. Nationaler ITP Patiententag von Novartis am 10. September: Informationen…
20.-22.01.2022 online: ANIM: NeuroIntensivmediziner diskutieren neue Erkenntnisse zu COVID-19
8.-10. September 2021: Weimar Sepsis Update 2021 – Beyond the…
13.09. – 18.09.2021: Viszeralmedizin 2021
A new predictive model helps identify those at risk for severe COVID-19
- Buck scientists analyze data from 3 million people using a smartphone app in the United Kingdom
Novato, CA, USA (March 9, 2021) — Researchers at the Buck Institute analyzed data from the COVID-19 Symptom Tracker app used by 3 million people in the United Kingdom, adding the use of immunosuppressant medication, use of a mobility aid, shortness of breath, fever, and fatigue to the list of symptoms and comorbidities that increase the risk for severe COVID-19. Results are published in the Journal of Medical Internet Research.
„Even though there are established risk factors for severe COVID-19 there are no good predictors that enable healthcare providers, or even those who have tested positive, to assess who should seek advanced medical care,“ says Buck Institute Associate Professor David Furman, PhD, the senior scientist who led the study. „We are glad to add to the efforts underway around the world to determine how to best care for those infected by the coronavirus.“
Furman said that out of the three million people who used the app about 11,000 people tested positive for the virus and about 500 ended up in the hospital. The symptom-tracking app collects data from multiple angles, asking people to describe how they feel, symptoms they are experiencing, and medications they are using along with demographics and lifestyle factors such as nutrition and diet.
Results did not identify chronological age as a risk factor for severe COVID-19; Furman acknowledged that the fact that elderly people are less likely to use a smartphone app was a limitation of the study. „But our study,“ Furman says, „does emphasize that any population that expresses the features identified in our model could be susceptible to a more severe form of COVID-19.“
Adding that many of the factors identified in the study are related to aging, Furman says, „understanding vulnerable younger populations that are biologically older than their chronological age and exhibit features that are generally associated with the older population could help clinicians identify susceptible young populations.“
Furman says findings that identify the use of immunosuppressant medications as a major predictor of more serious disease warrant more investigation. „Are these patients doing worse because of an underlying auto-immune/auto-inflammatory disease or because the medications are suppressing their inflammatory response – we don’t know,“ he says. „Labs around the world are studying the overactive immune response that leads to the cytokine storm which is associated with severe COVID-19. Our findings highlight the need to understand the biology of what is at play in these cases.“
Furman and colleagues are using artificial intelligence and machine learning to pursue other COVID-related research. Efforts are underway to predict patients likely to become COVID „long haulers“ – those who experience ongoing debilitating symptoms long after they recover from acute disease. Researchers are also correlating earlier data that identified aging phenotypes within individual proteomes (the entire complement of proteins expressed within our cells and tissues) with the proteomes of those infected with the coronavirus. Furman says preliminary data suggests that there is a subgroup of COVID-19 patients who are aging faster in regards to their proteome. He says the hope is to identify interventions that would restore their protein expression to a younger state.
- Citation: COVID-19 Patients Seeking Treatment: Modeling Predictive Age-dependent and Independent Symptoms and Comorbidities
Other Buck Institute collaborators include Yingxiang Huang, Kevin Perez, and Eric Verdin. Other co-authors include Dina Radenkovic, Guys & St. Thomas‘ NHS Foundation Trust and King’s College London, UK; and Kari Nadeau, Stanford University, Palo Alto, CA
About the Buck Institute for Research on Aging
At the Buck, we aim to end the threat of age-related diseases for this and future generations. We bring together the most capable and passionate scientists from a broad range of disciplines to study mechanisms of aging and to identify therapeutics that slow down aging. Our goal is to increase human health span, or the healthy years of life. Located just north of San Francisco, we are globally recognized as the pioneer and leader in efforts to target aging, the number one risk factor for serious diseases including Alzheimer’s, Parkinson’s, cancer, macular degeneration, heart disease, and diabetes. The Buck wants to help people live better longer. Our success will ultimately change healthcare. Learn more at: https://buckinstitute.org
Buck Institute for Research on Aging, 09.03.2021 (tB).Schlagwörter: COVID-19, Immunsupressiva