An overdose prediction tool for cocaine and other stimulants
To treat stimulant use disorder more like other chronic diseases, researchers at Penn Medicine have created a prediction tool to identify who may be at risk and what they have in common.
2 min. read
A new tool designed to help people who use stimulants like cocaine or methamphetamine may serve as a predictor for overdoses, using demographics and other available information to identify who is at risk. The tool’s developers at the Perelman School of Medicine hope that it can be used to proactively offer resources and treatment to save lives. Their research is published in JAMA Health Forum.
“Substance use disorder, like other relapsing, chronic, or remitting disorders, will have ‘flares,’ so our work is meant to proactively offer resources and needed care to patients,” says lead author Tuhina Srivastava, a former epidemiology graduate student at Penn who is now a research scientist at the Institute for Health Metrics and Evaluation. “Too often, the response to people with substance use disorder is reactive or even punitive, so we believe this provides a potential step toward minimizing or eliminating that. It’s classified as a chronic disease and should be treated as such.”
The predictor tool was trained with de-identified data from the Medicaid program, which covers low-income people and other disadvantaged groups. In tests, the researchers found the model to be extremely accurate at identifying those at risk of stimulant-involved overdose, effectively scoring above a nine out of 10 on a common statistical accuracy scale. The tests also helped identify some common risk factors that are especially useful in identifying people who are at risk for future overdose, such as instances of previous overdoses, higher poverty levels in the area where they live, and factors in their living arrangement, such as how many people lived in one home.
“I think it’s important to realize that the most predictive element is past history, and that combined with some of the other predictors may help providers identify who might especially benefit from extra resources, just as we would address a patient’s history of heart attack,” says co-author Cheryl Bettigole, a professor of clinical family medicine and community health, as well as medical ethics and health policy.
The team is hopeful their model will be used in population health settings soon to better direct resources such as cognitive behavioral therapy, the provision of naloxone, or offering incentive-based programs that reward individualized recovery goals.
“This is a transparent model,” says senior author Sean Hennessy, director and professor of epidemiology. “It’s an open algorithm. You can see exactly what’s going on here, and that should build trust among clinicians and public health officials.”
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