“Most of those people have interacted with the health system before their diagnosis, and that provides us with data that could allow us to predict and potentially prevent some of the cases”
Artificial intelligence could be used by clinicians and policy makers to predict opioid use disorder, new research from the University of Alberta shows.
Opioid use disorder occurs when patients’ regular use of opioids is more than wanted or intended, leading to harms such as addiction, overdose and death.
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By analyzing administrative health data — created every time a patient interacts with the heath-care system by visiting a doctor, or filling a prescription, for example — the team of researchers created and tested a machine learning model.
They say it reliably predicts the risk of developing opioid use disorder and could help lead to early detection and intervention.
In 2018, some 12.7 per cent of Canadians reported using opioid pain relief medications in the previous year, and among those, 9.6 per cent engaged in some form of problematic use. Between January 2016 and June 2022, there have been a total of 32,632 apparent opioid toxicity deaths in Canada.
In Alberta, as of August, there had been 976 opioid-related deaths this year.
According to the researchers, about one in four opioid users will develop opioid use disorder, and eight to 12 per cent of those prescribed opioids for chronic pain will develop the disorder.
“Most of those people have interacted with the health system before their diagnosis, and that provides us with data that could allow us to predict and potentially prevent some of the cases,” said principal investigator Bo Cao, Canada Research Chair in Computational Psychiatry and associate professor of psychiatry in a news release.
The machine learning model analyzed health data from nearly 700,000 patients in Alberta who received opioid prescriptions between 2014 and 2018, cross-referencing 62 factors such as the number of doctor and emergency room visits, diagnoses, and sociodemographic information.
Researchers found the top risk factors for opioid use disorder included frequency of opioid use, high dosage, and a history of other substance use disorders.
The model predicted high-risk patients with an accuracy of 86 per cent when it was validated against a new sample of 316,000 patients from 2019.
According to the study, the findings suggest early detection of opioid use disorder is possible with a data-driven approach and may provide timely clinical intervention and policy changes to help curb the current crisis.
“It’s important that the model’s prediction of whether someone will develop opioid use disorder is interpreted as a risk instead of a label,” said first author Yang Liu, a post-doctoral fellow in psychiatry, in the release.
“It is information to put into the hands of clinicians, who are actually making the diagnosis.”
Cao said the next stage of testing for the model will be in a clinical setting, involving clinicians and people with lived experience with the disorder.
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