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Accurately predicting delirium in intensive care

More than one-third of all people admitted to hospital, and as many as 80% of all patients in an intensive care unit (ICU), develop delirium, it is reported.

This is a type of brain dysfunction marked by sudden bouts of confusion, inattention, paranoia, or even agitation and hallucinations.

A team from Johns Hopkins Medicine has now developed artificial intelligence (AI) algorithms that can detect the early warning signs of delirium and can predict – at any time during an ICU stay – a high risk of delirium for a significant number of patients.

“Being able to differentiate between patients at low and high risk of delirium is incredibly important in the ICU because it enables us to devote more resources toward interventions in the high-risk population,” says Robert Stevens, senior author.

The team applied AI algorithms to a publicly available dataset covering more than 200,000 ICU stays at 208 hospitals around the country.

Once the researchers developed the AI models, they tested them on two other sets of data from a Boston hospital, collectively covering more than 100,000 ICU stays. The area under the receiver operating characteristic curve (95% CI) for the first 24-hour model was 0.785, meaning that it was able to predict which patients would get delirium 78.5% of the time. The dynamic model performed even better, predicting delirium-prone patients up to 90% of the time.

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Image credit | Getty

 

 

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