New model to predict chances of coronavirus infection

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Can we predict the possible infection of coronavirus? May be, we can, if the studies by the Cleveland Clinic researchers are any indication.

They have developed risk prediction model, first time in the world, to help healthcare providers forecast the likelihood of testing positive for COVID-19. The risk prediction model (called a nomogram) shows the relevance of age, race, gender, socioeconomic status, vaccination history and current medications in risk.

The nomogram, which has been deployed as a freely available online risk calculator at https://riskcalc.org/COVID19/, was developed using data from nearly 12,000 patients enrolled in Cleveland Clinic’s COVID-19 Registry, which includes all individuals tested at Cleveland Clinic for the disease, not just those that test positive.

The study also found that patients who have received the pneumococcal polysaccharide vaccine (PPSV23) and flu vaccine are less likely to test positive for COVID-19 than those who have not received the vaccinations.

Another finding was that patients actively taking melatonin (over-the-counter sleep aid), carvedilol (high blood pressure and heart failure treatment) or paroxetine (anti-depressant) are less likely to test positive than patients not taking the drugs.

They also found that those from low socioeconomic status (as measured in this study by zip code) are more likely to test positive than patients of greater economic means. Likewise, they said, the patients of Asian descent are less likely than Caucasian patients to test positive.

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