On Jun. 15, 2020, Cleveland Clinic researchers announced have developed the world’s first risk prediction model for healthcare providers to forecast an individual patient’s likelihood of testing positive for COVID-19 as well as their outcomes from the disease.

According a new study published in CHEST, the risk prediction model (called a nomogram) shows the relevance of age, race, gender, socioeconomic status, vaccination history and current medications in COVID-19 risk. The risk calculator is a new tool for healthcare providers to aid them in predicting patient risk and tailoring decision-making about care. It provides a more scientific approach to testing which is important for the healthcare community which has faced increased demand for testing and limited resources.

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.

Data scientists, including co-author on the study Michael Kattan, Ph.D., chair of Lerner Research Institute’s Department of Quantitative Health Sciences, used statistical algorithms to transform data from registry patients’ electronic medical records into the first-of-its-kind nomogram.

This study revealed several novel insights into disease risk, including:

• 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.
• 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.
• Patients of low socioeconomic status (as measured in this study by zip code) are more likely to test positive than patients of greater economic means.
• Patients of Asian descent are less likely than Caucasian patients to test positive.