What You Should Know:
– Mount Sinai researchers have developed machine learning models that predict the
likelihood of critical events and mortality in COVID-19 patients within
clinically relevant time windows.
– The new machine learning models were outlined in a recent study published in the Journal of Medical Internet Research—could aid clinical practitioners at Mount Sinai and across the world in the care and management of COVID-19 patients.
– In the retrospective study using EHRs from more than
4,000 adult patients admitted to five Mount Sinai Health System hospitals from
March to May, researchers and clinicians from the MSCIC analyzed
characteristics of COVID-19
patients, including past medical history, comorbidities, vital signs, and
laboratory test results at admission, to predict critical events such as
intubation and mortality within various clinically relevant time windows that
can forecast short and medium-term risks of patients over the hospitalization.
used the machine learning models to predict a critical event or mortality at time
windows of 3, 5, 7, and 10 days from admission.
– At the
one-week mark—which performed best overall, correctly flagging the most critical events while returning the fewest false positives—acute
kidney injury, fast breathing, high blood sugar, and elevated lactate
dehydrogenase (LDH) indicating tissue damage or disease were the strongest
drivers in predicting critical illness. Older age, blood level imbalance, and C-reactive protein
levels indicating inflammation, were the strongest drivers in predicting mortality.
“From the initial outburst of COVID-19 in New York City, we saw that COVID-19 presentation and disease course are heterogeneous and we have built machine learning models using patient data to predict outcomes,” said Benjamin Glicksberg, PhD, Assistant Professor of Genetics and Genomic Sciences at the Icahn School of Medicine at Mount Sinai, member of the Hasso Plattner Institute for Digital Health at Mount Sinai and Mount Sinai Clinical Intelligence Center (MSCIC), and one of the study’s principal investigators. “Now in the early stages of a second wave, we are much better prepared than before. We are currently assessing how these models can aid clinical practitioners in managing care of their patients in practice.”