Researchers at Vanderbilt University Medical Center and the Tennessee Department of Health (TDOH) have developed 30-day predictive models for fatal and non-fatal opioid-related overdose among patients receiving opioid prescriptions in the state.
The team applied machine learning techniques to statewide data sources that included details on 2,574 fatal and 8,455 non-fatal opioid-related overdoses occurring within 30 days of an opioid prescription. In all, the data involved just over 3 million patients and more than 71 million prescriptions for controlled substances.
The team’s report appeared Oct. 19 in the Journal of the American Medical Informatics Association.
According to TDOH, there were 3,032 overdose deaths in Tennessee in 2020, a 45% increase from 2019. Opioids, both illicit and prescribed, were involved in 79% of the state’s overdose deaths in 2020, and 19% of Tennesseans who died of a drug overdose in 2020 had an opioid prescription in the 60 days before death.
To assess and engage the opioid overdose crisis, public health authorities in Tennessee have relied solely on current and retrospective descriptive data, without prognostication.
According to the report’s senior author, Colin Walsh, MD, MA, associate professor of Biomedical Informatics, Medicine, and Psychiatry and Behavioral Sciences, TDOH will continue to study the predictive models with an eye to their potential deployment in the public heath response to the ongoing crisis.