TY - JOUR
T1 - High-risk prescribing and opioid overdose
T2 - Prospects for prescription drug monitoring program-based proactive alerts
AU - Geissert, Peter
AU - Hallvik, Sara
AU - Van Otterloo, Joshua
AU - O'Kane, Nicole
AU - Alley, Lindsey
AU - Carson, Jody
AU - Leichtling, Gillian
AU - Hildebran, Christi
AU - Wakeland, Wayne
AU - Deyo, Richard A.
N1 - Publisher Copyright:
© 2017 International Association for the Study of Pain.
PY - 2018/1/1
Y1 - 2018/1/1
N2 - To develop a simple, valid model to identify patients at high risk of opioid overdose-related hospitalization and mortality, Oregon prescription drug monitoring program, Vital Records, and Hospital Discharge data were linked to estimate 2 logistic models; a first model that included a broad range of risk factors from the literature and a second simplified model. Receiver operating characteristic curves, sensitivity, and specificity of the models were analyzed. Variables retained in the final model were categories such as older than 35 years, number of prescribers, number of pharmacies, and prescriptions for long-acting opioids, benzodiazepines or sedatives, or carisoprodol. The ability of the model to discriminate between patients who did and did not overdose was reasonably good (area under the receiver operating characteristic curve = 0.82, Nagelkerke R 2 = 0.11). The positive predictive value of the model was low. Computationally simple models can identify high-risk patients based on prescription history alone, but improvement of the predictive value of models may require information from outside the prescription drug monitoring program. Patient or prescription features that predict opioid overdose may differ from those that predict diversion.
AB - To develop a simple, valid model to identify patients at high risk of opioid overdose-related hospitalization and mortality, Oregon prescription drug monitoring program, Vital Records, and Hospital Discharge data were linked to estimate 2 logistic models; a first model that included a broad range of risk factors from the literature and a second simplified model. Receiver operating characteristic curves, sensitivity, and specificity of the models were analyzed. Variables retained in the final model were categories such as older than 35 years, number of prescribers, number of pharmacies, and prescriptions for long-acting opioids, benzodiazepines or sedatives, or carisoprodol. The ability of the model to discriminate between patients who did and did not overdose was reasonably good (area under the receiver operating characteristic curve = 0.82, Nagelkerke R 2 = 0.11). The positive predictive value of the model was low. Computationally simple models can identify high-risk patients based on prescription history alone, but improvement of the predictive value of models may require information from outside the prescription drug monitoring program. Patient or prescription features that predict opioid overdose may differ from those that predict diversion.
KW - Addiction medicine
KW - Chronic pain management
KW - Opioid overdose
KW - Opioid prescription
KW - Predictive modeling
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UR - http://www.scopus.com/inward/citedby.url?scp=85044204452&partnerID=8YFLogxK
U2 - 10.1097/j.pain.0000000000001078
DO - 10.1097/j.pain.0000000000001078
M3 - Article
C2 - 28976421
AN - SCOPUS:85044204452
SN - 0304-3959
VL - 159
SP - 150
EP - 156
JO - Pain
JF - Pain
IS - 1
ER -