Drug, and specifically opioid-related, overdoses remain a major public health problem
in the United States. Multiple studies have examined individual risk factors associated
with overdose risk, but research developing clinical risk prediction tools for overdose
has only emerged in the last few years. We conducted a comprehensive review of the
literature on patient-level factors associated with opioid-related overdose risk,
with an emphasis on clinical risk prediction models for opioid-related overdose in
the United States. Studies that developed and/or validated clinical prediction models
were closely reviewed and evaluated to determine the state of the field. We identified
12 studies that reported risk prediction models for opioid-related overdose risk.
Published models were developed from a variety of data sources, including Veterans
Health Administration data, Medicare data, commercial insurance data, and statewide
linked datasets. Studies reported model performance using measures of discrimination,
usually at good-to-excellent levels, though they did not always assess calibration.
C-statistics were better for models that included clinical predictors (c-statistics:
0.75–0.95) compared to models without them (c-statistics: 0.69–0.82). External validation
of models was rare, and we found no studies evaluating implementation of models or
risk prediction tools into clinical practice. A common feature of these models was
a high rate of false positives, largely because opioid-related overdose is rare in
the general population. Thus, efforts to implement prediction models into practice
should take into account that published models overestimate overdose risk for many
low-risk patients. Future prediction models assessing overdose risk should employ
external validation and address model calibration. In order to translate findings
from prediction models into clinical public health benefit, future studies should
focus on developing clinical prediction tools based on prediction models, implementing
these tools into clinical practice, and evaluating the impact of these models on treatment
decisions, patient outcomes, and, ultimately, opioid overdose rates.
Abbreviations:
CDC (Centers for Disease Control and Prevention), CMS (Center for Medicare and Medicaid Services), ICD-9 (International Classification of Diseases, 9th edition), ICD-10 (International Classification of Diseases, 10th edition), LASSO (least absolute shrinkage and selection operator), PDMP (prescription drug monitoring program), STORM (Stratification Tool for Opioid Risk Management), VHA (Veterans’ Health Administration)To read this article in full you will need to make a payment
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Article info
Publication history
Published online: March 21, 2021
Accepted:
March 16,
2021
Received in revised form:
February 24,
2021
Received:
December 18,
2020
Identification
Copyright
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