The Cleveland Clinic announced June 15 that its researchers have developed the world's first model to predict the likelihood of patients testing positive for COVID-19 and their outcomes from the disease.
The model was based on data from nearly 12,000 patients in the Cleveland Clinic's COVID-19 Registry, including those who tested positive and negative. It also isolated 400 variables and included the data in statistical algorithms to develop the risk calculator.
The model has been shown to be 85% accurate. It takes only minutes to complete the questions in the risk calculator, and the percentage risk of developing COVID-19 is immediately available.
Among the findings are that patients are less likely to test positive for COVID-19 if they:
- received the pneumococcal polysaccharide vaccine and the flu vaccine
- are taking melatonin (sleep aid), carvedilol (high blood pressure and heart failure medication), or paroxetine (anti-depressent)
- are of Asian descent.
This nomogram will bring precision medicine to the COVID-19 pandemic, enabling researchers and physicians to predict a patient's risk of testing positive, the researchers say.