AI tool predicts serious disease risk in COVID-19 patients
An artificial intelligence tool has accurately predicted which patients newly infected with COVID-19 would go on to develop severe respiratory disease, according to research led by New York University (NYU).
The aim of the study was to determine whether AI techniques could help to accurately predict which patients with the virus would go on to develop acute respiratory distress syndrome (ARDS), the fluid build-up in the lungs that can be fatal in the elderly. The results were published in the journal Computers, Materials & Continua.
“Our goal was to design and deploy a decision-support tool using AI capabilities — mostly predictive analytics — to flag future clinical coronavirus severity,” explained study co-author Anasse Bari, from NYU’s Courant Institute of Mathematical Sciences. “We hope that the tool, when fully developed, will be useful to physicians as they assess which moderately ill patients really need beds and who can safely go home, with hospital resources stretched thin.”
For the study, demographic, laboratory and radiological findings were collected from 53 patients as each tested positive in January 2020 for the SARS-CoV2 virus at the Wenzhou Central Hospital and Cangnan People’s Hospital, both in China. Symptoms were typically mild to begin with, including cough, fever and stomach upset. In a minority of patients, however, severe symptoms developed within a week, including pneumonia.
The researchers designed computer models that make decisions based on the data fed into them, with programs getting ‘smarter’ the more data they consider. Specifically, the current study used decision trees that track series of decisions between options and that model the potential consequences of choices at each step in a pathway.
The researchers were surprised to find that characteristics considered hallmarks of COVID-19 — like certain patterns seen in lung images (eg, ground glass opacities), fever and strong immune responses — were not useful in predicting which of the many patients with initial, mild symptoms would go on to develop severe lung disease. Neither age nor gender was helpful in predicting serious disease, although past studies had found men over 60 to be at higher risk.
Instead, the new AI tool found that changes in three particular features were most accurately predictive of subsequent, severe disease:
- Levels of the liver enzyme alanine aminotransferase (ALT), which rise dramatically as diseases like hepatitis damage the liver, were only a bit higher in patients with COVID-19 but still featured prominently in prediction of severity.
- Deep muscle aches (myalgia) were also more commonplace and have been linked by past research to higher general inflammation in the body.
- Higher levels of haemoglobin, the iron-containing protein that enables blood cells to carry oxygen to bodily tissues, were also linked to later respiratory distress. The researchers noted that this could be explained by other factors, like unreported smoking of tobacco, which has long been linked to increased haemoglobin levels.
Together with other factors, the team reported being able to predict risk of ARDS with up to 80% accuracy.
Limitations of the study included the relatively small dataset and the limited clinical severity of disease in the population studied. Nevertheless, corresponding author Megan Coffee from the NYU Grossman School of Medicine said, “While work remains to further validate our model, it holds promise as another tool to predict the patients most vulnerable to the virus, but only in support of physicians’ hard-won clinical experience in treating viral infections.”
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