Risk and protective factors for repeated overdose after opioid overdose survival.
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ABSTRACT: Background
Nonfatal opioid overdose (OD) is an opportunity to identify patients who may benefit from interventions to reduce repeated overdose (rOD). In this study, we sought to determine risk and protective factors associated with rOD.Methods
In this retrospective cohort study of 4,155 patients aged 18-64 who presented to one of 16 emergency departments in a single Western Pennsylvania health system between July 2015 and January 2018 for index opioid overdose (iOD) and survived to discharge, we identified demographic and clinical factors association with rOD within one-year. Relative risk of repeated opioid overdose was estimated using adjusted Cox proportional hazard ratios (aHRs).Results
14.9 % of patients (95 % CI 13.9-16.1) had a rOD, with 29 % occurring within 30 days from iOD. The adjusted hazard of opioid overdose was increased for male patients (aHR = 1.19; 95 % CI 1.01, 1.41), those with pre-iOD diagnoses of anxiety (aHR = 1.41; 95 % CI1.13, 1.77), depression (aHR = 1.44; 95 % CI 1.17, 1.78), substance use disorders (aHR = 1.30; 95 % CI 1.09, 1.55), and alcohol use disorder (aHR = 1.52; 95 % CI 1.02, 2.25). The hazard was lower for individuals prescribed an opioid in the 90 days prior to iOD (aHR = 0.59; 95 % CI 0.37, 0.97) and those admitted to the hospital for iOD (aHR = 0.56; 95 % CI 0.37, 0.86).Conclusion
We found that, among ED patients who survive an initial OD, mental health and substance use diagnoses are associated with a higher hazard of repeated overdoses whereas opioids prescriptions and admission are associated with lower hazards.
SUBMITTER: Suffoletto B
PROVIDER: S-EPMC7127977 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundNaloxone is a life-saving opioid antagonist. Chronic pain guidelines recommend that physicians co-prescribe naloxone to patients at high risk for opioid overdose. However, clinical tools to efficiently identify patients who could benefit from naloxone are lacking.ObjectiveTo develop and validate an overdose predictive model which could be used in primary care settings to assess the need for naloxone.DesignRetrospective cohort.SettingDerivation site was an integrated health system in Colorado; validation site was a safety-net health system in Colorado.ParticipantsWe developed a predictive model in a cohort of 42,828 patients taking chronic opioid therapy and externally validated the model in 10,708 patients.Main measuresPotential predictors and outcomes (nonfatal pharmaceutical and heroin overdoses) were extracted from electronic health records. Fatal overdose outcomes were identified from state vital records. To match the approximate shelf-life of naloxone, we used Cox proportional hazards regression to model the 2-year risk of overdose. Calibration and discrimination were assessed.Key resultsA five-variable predictive model showed good calibration and discrimination (bootstrap-corrected c-statistic?=?0.73, 95% confidence interval [CI] 0.69-0.78) in the derivation site, with sensitivity of 66.1% and specificity of 66.6%. In the validation site, the model showed good discrimination (c-statistic?=?0.75, 95% CI 0.70-0.80) and less than ideal calibration, with sensitivity and specificity of 82.2% and 49.5%, respectively.ConclusionsAmong patients on chronic opioid therapy, the predictive model identified 66-82% of all subsequent opioid overdoses. This model is an efficient screening tool to identify patients who could benefit from naloxone to prevent overdose deaths. Population differences across the two sites limited calibration in the validation site.
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