A comprehensive clinical and genetic model for predicting severe COVID-19 risk

The coronavirus disease (COVID-19) pandemic has infected over 35 million people worldwide and caused more than 1 million deaths so far, making it clear that it is an urgent threat to public health worldwide. While COVID-19 can cause mild disease in many people, with only cough and fever as reported symptoms, studies show that up to 30% of those infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may require hospitalization, and some patients will need intensive clinical intervention for acute respiratory distress syndrome.

Across the globe, public health responses have been focused on limiting new infections by preventing community transmission through social distancing, usage of masks, shutting down non-essential services, and enforcing travel restrictions. These interventions have had devastating social and economic impacts, along with a steep increase in reported mental health issues.

At a time when nations are experiencing increased pressure to re-open economies and return to normal

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Model Predicts Severe Disease in Those With COVID-19

Editor’s note: Find the latest COVID-19 news and guidance in Medscape’s Coronavirus Resource Center.

A new prediction model can help clinicians and hospitals discern which patients with COVID-19 are likely to progress to severe disease and how quickly, researchers say.

Brian Garibaldi, MD, associate professor of medicine at the Johns Hopkins University School of Medicine in Baltimore, Maryland, and colleagues developed the COVID-19 Inpatient Risk Calculator with 24 variables known to be linked with COVID-19, such as age, body mass index, underlying conditions, vital signs, and symptom severity at the time of admission.

Data were gathered from the care of 832 consecutive patients with COVID-19 between March 4 and April 24 at five Johns Hopkins hospitals in Maryland and Washington, DC.

Findings were published online September 22 in the Annals of Internal Medicine.

Model Shows Extremes in Risk

The authors say the model can predict likelihood of severe disease

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