Researchers at Mount Sinai in New York see promise in new machine learning models they’ve developed that can assess – within key windows of time – the risk of certain adverse clinical events in some COVID-19 patients.
WHY IT MATTERS
Research published earlier this month in the Journal of Medical Internet Research describes how the algorithms are enabling better insights into potential risks for a diverse group of COVID-19 patients.
Researchers at Mount Sinai’s Icahn School of Medicine and Hasso Plattner Institute for Digital Health gathered electronic health record data from more than 4,000 adult patients admitted to five Mount Sinai Health System hospitals from this spring, during the pandemic’s first wave.
Clinicians from the Mount Sinai Covid Informatics Center 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.
The researchers used the 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 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.
THE LARGER TREND
Some experts have made the case that artificial intelligence had a somewhat disappointing showing in the early days of the pandemic’s spread. And it’s true that bias in certain algorithms might have an adverse effect on some healthcare disparities.
But AI and machine learning have a big role to play in diagnosis and decision support as the COVID-19 emergency reaches its newest peak. So far, an array of promising models, many pushed out to clinicians via EHR modules, have emerged to help detect the disease and assess risk on a population level.
Mount Sinai, meanwhile, has been innovating its research into COVID-19 over the eight months since it was inundated with patients during the pandemic’s early peak. It’s created an AI model diagnose COVID-19 in patients with otherwise normal lung scans, for instance. And has also pioneered the use of Apple Watch to study COVID-19 stress and burnout among healthcare workers.
ON THE RECORD
“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, assistant professor of genetics and genomic sciences at the Icahn School of Medicine at Mount Sinai.
“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.”
“We have created high-performing predictive models using machine learning to improve the care of our patients at Mount Sinai,” said Girish Nadkarni, MD, Assistant Professor of Medicine (Nephrology) at the Icahn School of Medicine.
“More importantly, we have created a method that identifies important health markers that drive likelihood estimates for acute care prognosis and can be used by health institutions across the world to improve care decisions, at both the physician and hospital level, and more effectively manage patients with COVID-19.”
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