Johns Hopkins Medicine scientists have designed a prediction model, named the COVID Inpatient Risk Calculator (CIRC), capable of helping healthcare systems effectively “care for COVID-19 patients and make important decisions about planning and resource allocations.” As outlined in a Hopkins Medicine news release, the model was created using “demographic and clinical data gathered from seven weeks of COVID-19 patient care early in the coronavirus pandemic.” Based on an analysis of the data, which was published in the Annals of Internal Medicine, the researchers say a combination of certain risk factors can “quantify the probability of progression to severe disease or death among patients hospitalized with COVID-19.” These parameters include age, sex, body mass index, Charlson score, presence of respiratory or gastro-intestinal symptoms, loss of taste or smell, fever, white blood cell count and more.
Brian Garibaldi, of Johns Hopkins University School of Medicine, says the tool is especially important because, based on rapid progression of the disease following hospital admission, there is “a narrow window to intervene.” His colleague, Amita Gupta, explains that CIRC “can predict if someone has a 5% or a 90% risk of developing severe disease or dying” from the virus, which “is incredibly useful information to have when communicating with patients and their families, as well as for informing resource allocation in the hospital.”
MLMIC encourages policyholders to review the CIRC, which can be accessed without charge, on the Johns Hopkins Medicine website. Additionally, clinicians are advised to monitor guidance for the management and treatment of COVID-19 on our resources page and blog:
- New Risk Prediction Model Can Help Inform Clinical Care Decisions Related to COVID-19, a blog post on a new risk prediction model to help physicians forecast a patient’s likelihood of testing positive for COVID-19;
- CDC Expands Criteria for Those at Higher Risk for COVID-19, a blog post on the agency’s expanded list of who is at an increased risk for getting severely ill from COVID-19; and
- Strategies for Reducing Diagnostic Errors Related to COVID-19, a blog post with guidance on preventing COVID-19-related diagnostic error.
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