, comorbidities, and dozens more," said David Natanov, a fourth-year RWJMS student who is the study's lead author."We pumped that through a series of different machine-learning models tuned to slightly different parameters and generated an initial 77-variable model. That model performed well, but no one has time to enter 77 separate data points into anything."
The researchers dubbed the most accurate of their models PLABAC, an acronym of the first letter of each component variable: platelet count, lactate, age, blood urea nitrogen, aspartate aminotransferase and C-reactive protein. The strong results in patients hospitalized after vaccines show PLABAC can predict the prognosis of patients with COVID-19 variants beyond the original virus that infected the first patient group.
They also believe their model has another key benefit over others they have seen: ease of use. Most hospitals already collect all sixon COVID-19 patients. The only extra work is typing those six variables into the formula—and the study team hopes to make it easier still.