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Abstract: Strategies are needed to identify at-risk patients for adverse events associated with prescription opioids. This study identified prescription opioid misuse in an integrated health system using electronic health record (EHR) data, and examined predictors of misuse and overdose. The sample included patients from an EHR-based registry of adults who used prescription opioids in 2011 in Kaiser Permanente Northern California, a large integrated health care system. Researchers characterized time-at-risk for opioid misuse and overdose, and used Cox proportional hazard models to model predictors of these events from 2011 to 2014. Results found that among 396,452 patients, 2.7% were identified with opioid misuse and 1044 had an overdose event. Older patients were less likely to meet misuse criteria or have an overdose. Whites were more likely to be identified with misuse, but not to have an overdose. Alcohol and drug disorders were related to higher risk of misuse and overdose, with the exception that marijuana disorder was not related to opioid misuse. Higher daily opioid dosages and benzodiazepine use increased the risk of both opioid misuse and overdose. Conclusions: EHR data can be used to identify several risk factors associated with misuse and overdose, including comorbidities and benzodiazepine use, critical information for identifying at-risk patients. Findings can inform targeted patient screening and intervention, prescribing of medications such as buprenorphine for opioid dependence or naloxone for opioid reversal, disease management approaches for this patient population, and guide appropriate opioid prescribing policies which are becoming increasingly conservative and risk being applied without an individualized approach. Related protocols: CTN-0061-Ot

Abstract:

The use of prescription opioids has increased dramatically in the past 2 decades, with associated increases in opioid misuse/abuse and opioid overdose. These are among the most commonly prescribed medications, with 259 million prescriptions written for opioid pain relievers in the U.S. in 2012.

This study aimed to establish a prescription opioid registry protocol in a large health system, Kaiser Permanente Northern California (KPNC), and to describe algorithms to characterize individuals using prescription opioids, opioid use episodes, and concurrent use of sedative/hypnotics.

Using KPNC electronic health record data, the investigators selected patients using prescription opioids in 2011. Opioid and sedative/hypnotic fills, and physical and psychiatric comorbidity diagnoses were extracted for years 2008 to 2014. Algorithms were developed to identify each patient’s daily opioid and sedative/hypnotic use, and morphine daily-dose equivalent. Opioid episodes were classified as long-term, episodic, or acute. Logistic regression was used to predict characteristics associated with becoming a long-term opioid user.

Results found that in 2011, 18% of KPNC adult members filled at least 1 opioid prescription. Among those patients, 25% used opioids long term and their average duration of use was more than 4 years. Sedative/hypnotics were used by 76% of long-term users. Being older, white, living in a more deprived neighborhood, having a chronic pain diagnosis, and use of sedative/hypnotics were predictors of initiating long-term opioid use.

Conclusions: This study established a population-based opioid registry that is flexible and can be used to address important questions of prescription opioid use. It will be used in future studies to answer a broad range of other critical public health issues relating to prescription opioid use.

Related protocols: CTN-0061-Ot