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Identifying and classifying opioid-related overdoses: A validation study.

ABSTRACT: PURPOSE:The study aims to develop and validate algorithms to identify and classify opioid overdoses using claims and other coded data, and clinical text extracted from electronic health records using natural language processing (NLP). METHODS:Primary data were derived from Kaiser Permanente Northwest (2008-2014), an integrated health care system (~n > 475 000 unique individuals per year). Data included International Classification of Diseases, Ninth Revision (ICD-9) codes for nonfatal diagnoses, International Classification of Diseases, Tenth Revision (ICD-10) codes for fatal events, clinical notes, and prescription medication records. We assessed sensitivity, specificity, positive predictive value, and negative predictive value for algorithms relative to medical chart review and conducted assessments of algorithm portability in Kaiser Permanente Washington, Tennessee State Medicaid, and Optum. RESULTS:Code-based algorithm performance was excellent for opioid-related overdoses (sensitivity = 97.2%, specificity = 84.6%) and classification of heroin-involved overdoses (sensitivity = 91.8%, specificity = 99.0%). Performance was acceptable for code-based suicide/suicide attempt classifications (sensitivity = 70.7%, specificity = 90.5%); sensitivity improved with NLP (sensitivity = 78.7%, specificity = 91.0%). Performance was acceptable for the code-based substance abuse-involved classification (sensitivity = 75.3%, specificity = 79.5%); sensitivity improved with the NLP-enhanced algorithm (sensitivity = 80.5%, specificity = 76.3%). The opioid-related overdose algorithm performed well across portability assessment sites, with sensitivity greater than 96% and specificity greater than 84%. Cross-site sensitivity for heroin-involved overdose was greater than 87%, specificity greater than or equal to 99%. CONCLUSIONS:Code-based algorithms developed to detect opioid-related overdoses and classify them according to heroin involvement perform well. Algorithms for classifying suicides/attempts and abuse-related opioid overdoses perform adequately for use for research, particularly given the complexity of classifying such overdoses. The NLP-enhanced algorithms for suicides/suicide attempts and abuse-related overdoses perform significantly better than code-based algorithms and are appropriate for use in settings that have data and capacity to use NLP.

SUBMITTER: Green CA

PROVIDER: S-EPMC6767606 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Project description:AimTo understand the potential harmful effects of dose escalation among patients with chronic, non-cancer pain (CNCP) on chronic opioid therapy.DesignRetrospective cohort study.SettingUnited States Veterans Healthcare Administration.ParticipantsVeterans with CNCP and on chronic opioid therapy were identified using data from fiscal years 2008-15. The Veteran sample was approximately 90% male and 70% white.MeasurementsDose escalators [increase of > 20% average morphine milligram equivalent (MME) daily dose] were compared with dose maintainers (change of ±20% average MME daily dose). A composite measure of subsequent substance use disorders (SUDs: opioid, non-opioid and alcohol use disorders) and opioid-related adverse outcomes (AOs: accidents resulting in wounds/injuries, opioid-related and alcohol and non-opioid medication-related accidents and overdoses, self-inflicted injuries) as well as the individual SUDs and AOs was examined. The primary analyses were conducted among a 1 : 1 matched sample of escalators and maintainers matched on propensity score and index date. Propensity scores were generated using demographic characteristics, medical comorbidities, medication and health-care utilization characteristics. Subgroup analyses were conducted by quartile of the propensity score. Sensitivity analyses were conducted using adjusted logistic regression, logistic regression using stabilized inverse probability of treatment weighting (SIPTW) and instrumental variable (IV) models using geographic variation in opioid dose escalation as the IV.FindingsThere were 32 420 maintainers and 20 767 escalators resulting in 19 358 (93.2%) matched pairs. Composite AOs [odds ratio (OR) = 1.31, 95% confidence interval (CI) = 1.23, 1.40], composite SUDs (OR = 1.31, 95% CI = 1.22, 1.41) and individual SUD and AO subtypes were higher among dose escalators, except for opioid-related accidents and overdoses and violence-related injuries. Subgroup analyses within the propensity score quartiles found similar results. Sensitivity analyses with the adjusted and SIPTW logistic regressions found similar results to the primary analyses for all outcomes except for opioid-related accidents and overdoses, which were found to be significantly higher among escalators. Sensitivity analyses with IV models provided mixed results with SUDs and the individual types of AOs.ConclusionEscalating the opioid dose for those with chronic, non-cancer pain is associated with increased risks of substance use disorder and opioid-related adverse outcomes.

Project description:ImportancePrescription opioids are involved in 40% of all deaths from opioid overdose in the United States and are commonly the first opioids encountered by individuals with opioid use disorder. It is unclear whether the pharmaceutical industry marketing of opioids to physicians is associated with mortality from overdoses.ObjectiveTo identify the association between direct-to-physician marketing of opioid products by pharmaceutical companies and mortality from prescription opioid overdoses across US counties.Design, setting, and participantsThis population-based, county-level analysis of industry marketing information used data from the Centers for Medicare & Medicaid Services Open Payments database linked with data from the Centers for Disease Control and Prevention on opioid prescribing and mortality from overdoses. All US counties were included, with data on overdoses from August 1, 2014, to December 31, 2016, linked to marketing data from August 1, 2013, to December 31, 2015, using a 1-year lag. Statistical analyses were conducted between February 1 and June 1, 2018.Main outcomes and measuresCounty-level mortality from prescription opioid overdoses, total cost of marketing of opioid products to physicians, number of marketing interactions, opioid prescribing rates, and sociodemographic factors.ResultsBetween August 1, 2013, and December 31, 2015, there were 434 754 payments totaling $39.7 million in nonresearch-based opioid marketing distributed to 67 507 physicians across 2208 US counties. After adjustment for county-level sociodemographic factors, mortality from opioid overdoses increased with each 1-SD increase in marketing value in dollars per capita (adjusted relative risk, 1.09; 95% CI, 1.05-1.12), number of payments to physicians per capita (adjusted relative risk, 1.18; 95% CI, 1.14-1.21, and number of physicians receiving marketing per capita (adjusted relative risk, 1.12; 95% CI, 1.08-1.16). Opioid prescribing rates also increased with marketing and partially mediated the association between marketing and mortality.Conclusions and relevanceIn this study, across US counties, marketing of opioid products to physicians was associated with increased opioid prescribing and, subsequently, with elevated mortality from overdoses. Amid a national opioid overdose crisis, reexamining the influence of the pharmaceutical industry may be warranted.

Project description:BackgroundNon-fatal opioid-related overdoses have increased significantly over the past two decades and there have been increasing reports of brain injuries and/or neurocognitive impairments following overdose events. Limited preclinical research suggests that opioid overdoses may cause brain injury; however, little is known about such injuries in humans. The purpose this systematic review is to summarize existing studies on neurocognitive impairments and/or brain abnormalities associated with an opioid-related overdose in humans.MethodsPubMed, Web of Science, Ovid MEDLINE and PsyINFO were searched, without year restrictions, and identified 3099 articles. An additional 24 articles were identified by reviewing references. Articles were included if they were published in English, reported study findings in humans, included individuals 18 years of age or older, and reported an objective measure of neurocognitive impairments and/or brain abnormalities resulting from an opioid-related overdose. Six domains of bias (selection, performance, attrition, detection (two dimensions) and reporting were evaluated and themes were summarized.ResultsSeventy-nine journal articles, published between 1973-2020, were included in the review. More than half of the articles were case reports (n = 44) and there were 11 cohort studies, 18 case series, and 6 case-control studies. All of the studies were categorized as at-risk of bias, few controlled for confounding factors, and methodological differences made direct comparisons difficult. Less than half of the studies reported toxicology results confirming an opioid-related overdose; 64.6 % reported brain MRI results and 27.8 % reported results of neuropsychological testing. Only two studies had within subject comparative data to document changes in the brain possibly associated with an overdose. Despite these limitations, existing publications suggest that brain injuries and neurocognitive impairments are associated with opioid overdose. Additional research is needed to establish the incidence of overdose-related brain injuries and the potential impact on functioning, as well as engagement in treatment of substance use disorders.ConclusionsRespiratory depression is a defining characteristic of opioid overdose and prolonged cerebral hypoxia may cause brain injuries and/or neurocognitive impairments. The onset, characteristics, and duration of such injuries is variable and additional research is needed to understand their clinical implications.

Project description:BACKGROUND:North America is in the midst of an unabated opioid overdose epidemic due to the increasing non-medical use of fentanyl and ultra-potent opioids. Naloxone is an effective antidote to opioid toxicity, yet its optimal dosing in the context of fentanyl and ultra-potent opioid overdoses remains unknown. This review aims to determine the relationship between the first empiric dose of naloxone and reversal of toxicity, adverse events, and the total cumulative dose required among patients with undifferentiated opioid overdoses and those with suspected toxicity from ultra-potent opioids. Secondary objectives include evaluating the relationship between the cumulative naloxone dose and toxicity reversal and adverse events, among patients with undifferentiated opioid overdoses and those with suspected toxicity from ultra-potent opioids. METHODS:To identify studies, we will search MEDLINE, Embase, CENTRAL, DARE, CDAG, CINAHL, Science Citation Index, multiple trial registries, and the gray literature. Included studies will evaluate patients with suspected or confirmed opioid toxicity from undifferentiated opioids and ultra-potent opioids, who received an empiric and possibly additional doses of naloxone. The main outcomes of interest are the relationship between naloxone dose and toxicity reversal and adverse events. We will include controlled and non-controlled interventional studies, observational studies, case reports/series, and reports from poison control centers. We will extract data and assess study quality in duplicate with discrepancies resolved by consensus or a third party. We will use the Downs and Black and Cochrane risk of bias tools for observational and randomized controlled studies. If we find sufficient variation in dose, we will fit a random effects one-stage model to estimate a dose-response relationship. We will conduct multiple subgroup analyses, including by type of opioid used and by suspected high and low prevalence of ultra-potent opioid use based on geographic location and time of the original studies. DISCUSSION:Our review will include the most up-to-date available data including ultra-potent opioids to inform the current response to the opioid epidemic, addressing the limitations of recent reviews. We anticipate limitations relating to study heterogeneity. We will disseminate study results widely to update overdose treatment guidelines and naloxone dosing in Take Home Naloxone programs.

Project description:PurposeTo facilitate surveillance and evaluate interventions addressing opioid-related overdoses, algorithms are needed for use in large health care databases to identify and differentiate community-occurring opioid-related overdoses from inpatient-occurring opioid-related overdose/oversedation.MethodsData were from Kaiser Permanente Northwest (KPNW), a large integrated health plan. We iteratively developed and evaluated an algorithm for electronically identifying inpatient overdose/oversedation in KPNW hospitals from 1 January 2008 to 31 December 2014. Chart audits assessed accuracy; data sources included administrative and clinical records.ResultsThe best-performing algorithm used these rules: (1) Include events with opioids administered in an inpatient setting (including emergency department/urgent care) followed by naloxone administration within 275 hours of continuous inpatient stay; (2) exclude events with electroconvulsive therapy procedure codes; and (3) exclude events in which an opioid was administered prior to hospital discharge and followed by readmission with subsequent naloxone administration. Using this algorithm, we identified 870 suspect inpatient overdose/oversedation events and chart audited a random sample of 235. Of the random sample, 185 (78.7%) were deemed overdoses/oversedation, 37 (15.5%) were not, and 13 (5.5%) were possible cases. The number of hours between time of opioid and naloxone administration did not affect algorithm accuracy. When "possible" overdoses/oversedations were included with confirmed events, overall positive predictive value (PPV) was very good (PPV = 84.0%). Additionally, PPV was reasonable when evaluated specifically for hospital stays with emergency/urgent care admissions (PPV = 77.0%) and excellent for elective surgery admissions (PPV = 97.0%).ConclusionsAlgorithm performance was reasonable for identifying inpatient overdose/oversedation with best performance among elective surgery patients.

Project description:BackgroundIn response to the opioid epidemic, many states have enacted policies limiting opioid prescriptions. There is a paucity of evidence of the impact of opioid prescribing interventions in primary care populations, including whether unintended consequences arise from limiting the availability of prescribed opioids.ObjectiveOur aim was to compare changes in opioid overdose and related adverse effects rate among primary care patients following the implementation of state-level prescribing policies.DesignA cohort of primary care patients within an interrupted time series model.ParticipantsElectronic medical record data for 62,776 adult (18+ years) primary care patients from a major medical center in Vermont from January 1, 2016, to June 30, 2018.InterventionsState-level opioid prescription policy changes limiting dose and duration.Main measuresChanges in (1) opioid overdose rate and (2) opioid-related adverse effects rate per 100,000 person-months following the July 1, 2017, prescription policy change.Key resultsAmong primary care patients, there was no change in opioid overdose rate following implementation of the prescribing policy (incidence rate ratio; IRR: 0.64, 95% confidence interval; CI: 0.22-1.88). There was a 78% decrease in the opioid-related adverse effects rate following the prescribing policy (IRR: 0.22, 95%CI: 0.09-0.51). This association was moderated by opioid prescription history, with decreases observed among opioid-naïve patients (IRR: 0.18, 95%CI: 0.06-0.59) and among patients receiving chronic opioid prescriptions (IRR: 0.17, 95%CI: 0.03-0.99), but not among those with intermittent opioid prescriptions (IRR: 0.51, 95%CI: 0.09-2.82).ConclusionsLimiting prescription opioids did not change the opioid overdose rate among primary care patients, but it reduced the rate of opioid-related adverse effects in the year following the state-level policy change, particularly among patients with chronic opioid prescription history and opioid-naïve patients. Limiting the quantity and duration of opioid prescriptions may have beneficial effects among primary care patients.

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Identifying and classifying opioid-related overdoses: A validation study.

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